Climate Change, Community Response and Resilience: Insight for Socio-Ecological Sustainability [1 ed.] 044318707X, 9780443187070

Table of contents :
Contents
List of contributors
Foreword
Preface
Acknowledgments
Section 1: Introduction
1. Evaluation of community response and resilience on climate change: a bibliometric analysis • Suddhasil Bose, Subhra Halder and Snehamanju Basu
Section 2: Climate change, social response and resilience
2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh • Mst. Shifat Rumana, Ummey Kulsum, Md. Rayhan Ali, Hasan Mahmud, Dalce Shete Baroi, Nafia Muntakim, Zihad Ahmed, Md. Mizanoor Rahman and Md. Zahidul Hassan
3. Socio-economic and livelihood vulnerability in view of climate resilience: A case study of selected blocks of Sundarban, India • Semanti Das
4. Building resilient city in coastal urban areas: case study of community adaptation and response toward climate change and tidal floods in Semarang, Indonesia • Henny Warsilah
5. Climate change indicator, impact, adaptation, and innovation at the local level: learn from the peoples’ experience of the coastal plain of Probolinggo, East Java, Indonesia • Suyarso Suyarso, Martiwi Diah Setiawati, Indarto Happy Supriyadi and Bayu Prayudha
6. Climate change and geopolitical risks: cases of riverine communities of Teesta and Brahmaputra rivers of India • Parama Bannerji and Radhika Bhanja
7. Vulnerable countries, resilient communities: climate change governance in the coastal communities in Indonesia • Andi Luhur Prianto and Abdillah Abdillah
8. Solar-powered drip irrigation managed by women farmer groups as climate change adaptation for gender equality and social inclusion in East Lombok, Indonesia • Ayu Siantoro, Endang C. Purba, Anak Agung Ngurah Agung, Bayu Tumewu, Elvi Tambunan, Krishna Silalahi and Fransisca Novita
9. Climate change, local vulnerabilities, and involuntary migration indrought-prone Bundelkh and region ofcentral India • Debarghya Chakraborty, N. Savitha and Kunaljeet Roy
10. Climate change resilience by community involvement: a case study in Indian base stations for the well-known Himalayan trekroutes of Darjeeling and West Sikkim • Sanjoy Kumar Sadhukhan and Premangshu Chakrabarty
11. Indonesia’s engagement in the climate change negotiations: building national resilience • R.R. Emilia Yustiningrum, Athiqah Nur Alami, Ganewati Wuryandari and Nanto Sriyanto
12. The green economy to support women’s empowerment: social work approach for climate change adaptation toward sustainability development • Hari Harjanto Setiawan and Yanuar Farida Wismayanti
13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala • Jayarajan K and Dhanya Punnoli
14. Livelihood constraints and socio-ecological loops: household drought coping survival strategies in rural plateau tracks of eastern India • Susmita Sengupta and Sanat Kumar Guchhait
Section 3: Climate change, ecological impacts and resilience
15. Ground water depletion and climate change: role of geospatial technology for a mitigation strategy • Gouri Sankar Bhunia and Uday Chatterjee
16. Developing methods for building sustainable communities in flooded industrial complex areas • Tadashi Nakasu, Sutpratana Duangkaew and Chutaporn Amrapala
17. Climate change and agroecosystem: impacts, adaption, and mitigation in South Asia • Shobha Poudel, Bhogendra Mishra, Sujan Ghimire, Nirajan Luintel, Praseed Thapa and Regan Sapkota
18. Climate change and flood: vulnerability and community resilience • Henny Warsilah and Choerunisa Noor Syahid
19. Space technology in solving water crisis-rethinking research collaborative • Gouri Sankar Bhunia and Uday Chatterjee
20. Community resilience to climate change-induced disasters: the narratives of the cyclone affected communities of Sundarban biosphere reserve • Parama Bannerji and Uma Chatterjee
21. Climate change, urban flooding, and community perceptions of vulnerability and resilience: lessons from Diamond Harbour region • Sudarshana Sinha
22. Climate protection in spatial policy instruments, opportunities and barriers: the case study of Poland • Maciej Nowak and Przemyslaw Śleszński
23. Coastal vulnerability assessment fo rthe megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence • Abd. Rahman As-syakur, Herlambang Aulia Rachman, Muhammad Rizki Nandika, Martiwi Diah Setiawati, Masita Dwi Mandini Manessa, Atika Kumala Dewi and Rinaldy Terra Pratama
24. Integrated ecosystem-based riskr eduction into environmental-economic accounting in Gujarat coastal zones • Dhivya Narayanan, Karthi N., Balamurugan S. and Devaraj Asir Ramesh
25. Geospatial approach for reducing water stress: case study of Delhi • Ishita Singh and Vibhore Bakshi
26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates in the Dooars region of West Bengal • Piyashee Mallik and Tuhin Ghosh
Index

Citation preview

CLIMATE CHANGE, COMMUNITY RESPONSE, AND RESILIENCE

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Developments in Weather and Climate Science

CLIMATE CHANGE, COMMUNITY RESPONSE, AND RESILIENCE Insight for Socio-Ecological Sustainability Edited by

UDAY CHATTERJEE Department of Geography, Bhatter College, Dantan (Vidyasagar University), Paschim Medinipur, West Bengal, India

RAJIB SHAW Graduate School of Media and Governance, Keio University, Japan

GOURI SANKAR BHUNIA TPF Gentisa Euroestudio SL, India

MARTIWI DIAH SETIAWATI Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia

SOUMITA BANERJEE Department of Geography, Faculty Council of Science, Jadavpur University, India Series Editor

PAUL D. WILLIAMS

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-443-18707-0 For Information on all Elsevier publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Candice Janco Acquisitions Editor: Jennette McClain Editorial Project Manager: Sara Valentino Production Project Manager: Kumar Anbazhagan Cover Designer: Matthew Limbert Typeset by MPS Limited, Chennai, India

Contents 2

List of contributors xv Foreword xix Preface xxi Acknowledgments xxiii

Climate change, social response and resilience 2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh 27

1 Introduction

Mst. Shifat Rumana, Ummey Kulsum, Md. Rayhan Ali, Hasan Mahmud, Dalce Shete Baroi, Nafia Muntakim, Zihad Ahmed, Md. Mizanoor Rahman and Md. Zahidul Hassan

1. Evaluation of community response and resilience on climate change: a bibliometric analysis 3

2.1 2.2 2.3 2.4

Introduction 27 Objectives of the study 29 Materials and methods 29 Results and discussion 32 2.4.1 Discharge pattern of the river Teesta in the study area 32 2.4.2 Flood Intensity in the study area 36 2.4.3 Recurrence trend of flood in the study area 36 2.4.4 Inundation area in different flooding years 39 2.4.5 Impacts of flood in the study area 41 2.4.6 Flood risk assessment in Teesta floodprone area 42 2.4.7 Indigenous coping strategies 48 2.5 Conclusion 51 References 52

Suddhasil Bose, Subhra Halder and Snehamanju Basu

1.1 Introduction 3 1.2 Methodology for bibliometric analysis 4 1.3 Data collection 7 1.4 Result 9 1.4.1 Most relevant sources 9 1.4.2 Source dynamics 9 1.4.3 Most relevant authors 10 1.4.4 Country wise scientific production 10 1.4.5 Most globally cited documents 10 1.4.6 Most frequent words 15 1.4.7 Word growth 15 1.4.8 Co-occurrence network 15 1.4.9 Thematic evolution 17 1.4.10 Country collaboration map 19 1.5 Discussion 20 1.6 Conclusion 21 References 22

3. Socio-economic and livelihood vulnerability in view of climate resilience: A case study of selected blocks of Sundarban, India 57 Semanti Das

3.1 Introduction

v

57

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3.2 Materials and methods 59 3.2.1 Study area 59 3.2.2 Methodology 61 3.3 Results 67 3.3.1 Implementation of LAST tool, SLSI, ECVI 67 3.3.2 Relationship between adaptive capacity and adaptation in the light of SLSI and LAST matrix and SLSI and ECVI 67 3.4 Discussion 69 3.4.1 Livelihood status of economically marginalized people 69 3.4.2 Management strategy 70 3.5 Conclusion 71 Acknowledgments 71 References 72

4. Building resilient city in coastal urban areas: case study of community adaptation and response toward climate change and tidal floods in Semarang, Indonesia 75 Henny Warsilah

4.1 Introduction: problems of community adaptation and response toward climate change 75 4.2 Materials and methods 76 4.3 Disaster risk reduction (DRR) in Indonesia 77 4.3.1 Impacts of climate change on coastal urban areas in Indonesia 77 4.3.2 Indonesia’s climate change mitigation and adaptation strategies 78 4.3.3 Disaster risk reduction (DRR) and resilient city campaign in Indonesia 79 4.4 Case study: social cultural adaptation process of urban coastal community in Tambak Lorok toward rob flood 81 4.4.1 Rob flood and land subsidence in Tambak Lorok Kampong 81 4.4.2 Overview of Semarang city: urbanization and development of slum areas in coastal cities of Semarang 81

4.4.3 Current mitigation and adaptation to climate change strategies in the coastal city of Tambak Lorok Kampong 83 4.4.4 Identified impacts of mitigation and adaptation 86 4.5 Community perception toward tidal floods 86 4.5.1 Adaptation and strategies adopted by the community toward tidal floods 86 4.6 Recommendation 91 4.7 Conclusion 91 References 92

5. Climate change indicator, impact, adaptation, and innovation at the local level: learn from the peoples’ experience of the coastal plain of Probolinggo, East Java, Indonesia 93 Suyarso Suyarso, Martiwi Diah Setiawati, Indarto Happy Supriyadi and Bayu Prayudha

5.1 Introduction 93 5.1.1 Climate change and tidal flood in Indonesia 94 5.1.2 Tidal flood on the Northern Coast of Java 94 5.2 Materials and methods 96 5.2.1 Materials 96 5.2.2 Methods 97 5.3 Result and discussion 99 5.3.1 Geological formation of the Probolinggo coastal plain 99 5.3.2 Land cover and land use of the Probolinggo coastal area 100 5.3.3 Shoreline dynamic of the coast of Probolinggo 101 5.3.4 Climate change indicator in Probolinggo 105 5.3.5 The impacts of climate change in Probolinggo 106 5.3.6 Adaptation and resilient of climate change on the local peoples in Probolinggo 109 5.3.7 Discussion 112 5.4 Conclusion 114 Author contributions 115 References 115

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6. Climate change and geopolitical risks: cases of riverine communities of Teesta and Brahmaputra rivers of India 119

8. Solar-powered drip irrigation managed by women farmer groups as climate change adaptation for gender equality and social inclusion in East Lombok, Indonesia 153

Parama Bannerji and Radhika Bhanja

Ayu Siantoro, Endang C. Purba, Anak Agung Ngurah Agung, Bayu Tumewu, Elvi Tambunan, Krishna Silalahi and Fransisca Novita

6.1 Introduction 119 6.2 Literature review 120 6.2.1 Research gap 122 6.3 Rationale of the study 122 6.3.1 Objectives 123 6.4 Material and methodology 123 6.4.1 Profile of the case sites 124 6.5 Results 126 6.5.1 Climate-induced changes of Teesta basin and its Impact on geopolitics 126 6.6 Discussion 129 6.6.1 Macrolevel impact 129 6.6.2 Microlevel impact 129 6.7 Limitation of the study 130 6.8 Recommendation 130 6.9 Conclusion 131 References 131

7. Vulnerable countries, resilient communities: climate change governance in the coastal communities in Indonesia 135 Andi Luhur Prianto and Abdillah Abdillah

7.1 Introduction 135 7.2 Methods 138 7.3 Results and discussions 139 7.3.1 Evidence of coastal communities vulnerability in Pangkep Regency, East Lombok Regency, & Rembang Regency, Indonesia 139 7.3.2 Level of welfare of coastal communities and challenges of vulnerability due to climate change in Indonesia 142 7.3.3 A model of resilience in facing the vulnerability of coastal communities due to climate change vulnerable to disasters in Indonesia 145 7.4 Conclusions 148 References 148

8.1 Introduction 153 8.2 Limitations of the study 155 8.3 Materials and methods 156 8.3.1 Research questions 156 8.3.2 Sample 156 8.3.3 Research procedure 157 8.4 Results and discussion 158 8.4.1 Community resilience: increased agricultural productivity 158 8.4.2 Community resilience: sustainable management of natural resources 161 8.4.3 Gender equality and social inclusion (GESI): improved access, participation, decision-making, system and well-being for women 164 8.5 Recommendations 170 8.6 Conclusion 170 8.6.1 Related to the technical approach in agriculture and natural resource management (community resilience) 170 8.6.2 Related to gender equality and social inclusion (GESI) 171 Acknowledgments 171 References 172

9. Climate change, local vulnerabilities, and involuntary migration in drought-prone Bundelkhand region of central India 175 Debarghya Chakraborty, N. Savitha and Kunaljeet Roy

9.1 Introduction 175 9.2 Rationale and significance of the present study 177 9.3 Research objectives 179 9.4 Methods and materials 179 9.4.1 Data sources 180 9.4.2 Standard precipitation index 180

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9.4.3 Temporary migration and local vulnerabilities 182 9.4.4 Study area 182 9.4.5 Software 183 9.5 Result and discussion 184 9.5.1 Multiscale pattern of rainfall 184 9.5.2 SPI evaluation and characteristics of drought 184 9.5.3 Drought induced temporal migration and other vulnerabilities 186 9.6 Conclusion 189 9.7 Limitations of the study 190 Acknowledgment 190 References 190

11.4.2 Institutional arrangements and diplomacy toward climate change 214 11.4.3 State agencies and engagement in the climate change 219 11.5 Recommendations 220 11.6 Conclusions 220 References 221

12. The green economy to support women’s empowerment: social work approach for climate change adaptation toward sustainability development 225 Hari Harjanto Setiawan and Yanuar Farida Wismayanti

10. Climate change resilience by community involvement: a case study in Indian base stations for the well-known Himalayan trek routes of Darjeeling and West Sikkim 193 Sanjoy Kumar Sadhukhan and Premangshu Chakrabarty

10.1 Introduction 193 10.2 Study area 194 10.3 Materials and methods 196 10.4 Results 197 10.5 Discussion 203 10.6 Conclusion 206 References 206

11. Indonesia’s engagement in the climate change negotiations: building national resilience 209 R.R. Emilia Yustiningrum, Athiqah Nur Alami, Ganewati Wuryandari and Nanto Sriyanto

11.1 Introduction 209 11.2 Limitations of the study 210 11.3 Materials and methods 211 11.3.1 Two-level game of foreign policy making 211 11.3.2 Methodology 212 11.4 Results and discussion 212 11.4.1 Environment as international concern 212

12.1 Introduction 225 12.1.1 Human activities cause global warming 227 12.1.2 The impact of climate change on women as a vulnerable group 228 12.1.3 Adaptation of women’s groups to climate change 229 12.2 Material and methods 229 12.2.1 Population and sample 230 12.2.2 Data collection 230 12.2.3 Data processing and analysis 231 12.3 Results and discussion 231 12.3.1 Climate change case studies 231 12.3.2 Women are a vulnerable group 232 12.3.3 Adaptation through the green economy 233 12.3.4 Social entrepreneurship program for women 233 12.3.5 Improved economic welfare 234 12.3.6 Green social work approach 235 12.4 Limitation of the study 236 12.5 Recommendations 236 12.6 Conclusion 237 Acknowledgments 237 References 238

13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala 241 Jayarajan K and Dhanya Punnoli

13.1 Introduction

241

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13.2 Study area 242 13.2.1 Climate profile of the study area 245 13.2.2 Administrative divisions of the district 245 13.3 Methods 246 13.4 Results and discussions 248 13.4.1 Extraction method: principal component analysis 248 13.4.2 Major factors and their variable loadings 249 13.5 The spatial attributes of the multidimensional characteristics of Navara cultivation Integrated Approach for the Sustainable Agriculture Planning 254 13.5.1 Spatial pattern of multidimensional factors with Navara cultivators 254 13.5.2 Composite index: spatial pattern of resilience to natural hazards among Navara farming communities 259 13.6 Discussion 259 13.6.1 Way forward 261 13.7 Conclusion 262 Conflict of Interest 262 Acknowledgment 262 References 262

14. Livelihood constraints and socio-ecological loops: household drought coping survival strategies in rural plateau tracks of eastern India 265 Susmita Sengupta and Sanat Kumar Guchhait

14.1 14.2 14.3 14.4

Introduction 265 Relevance of the study 266 Description of study area 267 Materials and methods 269 14.4.1 Approaches and techniques 269 14.4.2 Selecting target groups and survey procedures 269 14.4.3 Household recall survey 270 14.5 Analysis and results 270 14.5.1 Land use: shadow of coarse physiography 270 14.5.2 Climatic hindrance, drought, and crop calendar 271 14.5.3 Economy: reflection of harsh physical setup 273

14.6 Livelihood constraints and socio-ecological loops 275 14.6.1 Seasonality of household economy— agricultural production: influence of rainfall and lack of ‘technical knowhow’ 275 14.6.2 Alternative livelihood strategy: low diversification 276 14.6.3 Assessment of poverty and food selfsufficiency 279 14.7 Resilience traps 281 14.8 Discussion 281 14.8.1 Production-consumption traps 281 14.8.2 Variability traps 282 14.8.3 Risk traps 283 14.8.4 Policy traps 284 14.9 Conclusion 284 References 285

3 Climate change, ecological impacts and resilience 15. Ground water depletion and climate change: role of geospatial technology for a mitigation strategy 291 Gouri Sankar Bhunia and Uday Chatterjee

15.1 Introduction 291 15.2 Impact of climate system on groundwater 292 15.3 Role of geospatial technology in groundwater depletion assessment 295 15.4 Role of geospatial technology in climate change assessment 299 15.5 Conclusion 301 References 302

16. Developing methods for building sustainable communities in flooded industrial complex areas 305 Tadashi Nakasu, Sutpratana Duangkaew and Chutaporn Amrapala

16.1 Introduction 305 16.2 Research methodology

306

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16.2.1 Scope: contribution to sustainable development from a social science perspective 306 16.2.2 Literature 307 16.2.3 Approaches to identifying and changing pre- and post-disaster social vulnerability at the district (amp-) level 308 16.2.4 Approach to disaster coping capacity at the sub-district (Tambon) level 310 16.2.5 Identification of social vulnerability and risk information and collection of experiences in areas surrounding industrial parks 312 16.3 Research results 312 16.3.1 Ayutthaya province, district (amp-) level 312 16.3.2 Sub-district level 313 16.3.3 Social vulnerability and risk information in areas surrounding industrial parks 315 16.4 Analysis and discussion 318 16.4.1 Comparison with before the 2011 flood disaster 318 16.4.2 Disaster coping capacity 322 16.5 Applying lessons learned for practical use 323 16.6 Challenges and responses 324 16.7 Conclusions for a sustainable future 325 Acknowledgments 326 References 326

17. Climate change and agroecosystem: impacts, adaption, and mitigation in South Asia 329 Shobha Poudel, Bhogendra Mishra, Sujan Ghimire, Nirajan Luintel, Praseed Thapa and Regan Sapkota

17.1 Introduction 329 17.2 Method 331 17.3 Climate change impacts, adaptation and mitigation measures in South Asian countries 332 17.3.1 Case 01: Afghanistan 332 17.3.2 Case 02: Bangladesh 333 17.3.3 Case 03: Bhutan 333 17.3.4 Case 04: India 334 17.3.5 Case 05: Maldives 335

17.3.6 Case 06: Nepal 335 17.3.7 Case 07: Pakistan 336 17.3.8 Case 08: Sri Lanka 337 17.4 Discussion 337 17.4.1 Climate change adaptation measures on agriculture in South Asia 337 17.4.2 Climate change mitigation measures on agroecosystem in South Asia 338 17.5 Enabling institutional and policy support 339 17.6 Conclusion and recommendation 340 References 340

18. Climate change and flood: vulnerability and community resilience 345 Henny Warsilah and Choerunisa Noor Syahid

18.1 Introduction: problems about vulnerability and community resilience 345 18.2 Climate change and social-ecological crisis on the Island of Java 346 18.3 Initiatives for developing smart and resilient city-based city management 347 18.4 The concept of social resilience 348 18.4.1 Buffer capacity 349 18.4.2 Social self organization 350 18.5 Methodology 351 18.6 Research findings: potential and condition of social resilience of the Kemijen community, Semarang 352 18.6.1 Buffer capacity 352 18.6.2 Capacity for learning 354 18.7 The city government’s response to climate change and the social-ecological crisis 358 18.8 Recommendations 358 18.9 Conclusion 359 References 359

19. Space technology in solving water crisis-rethinking research collaborative 361 Gouri Sankar Bhunia and Uday Chatterjee

19.1 19.2 19.3 19.4

Introduction 361 Methods 363 Virtual water and water footprint 363 The intensive use of groundwater: a silent revolution 364

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19.5 Desalination: potential and limitations 365 19.6 Increasing transparency and participation— role of geospatial technology 366 19.7 Data science: potentials and prospects 370 19.8 Innovation of emerging technology 372 19.9 Conclusion 373 References 373

20. Community resilience to climate change-induced disasters: the narratives of the cyclone affected communities of Sundarban biosphere reserve 377 Parama Bannerji and Uma Chatterjee

20.1 Introduction 377 20.2 Background 378 20.3 Rationale of the study 379 20.3.1 Objectives 380 20.4 Material and methodology 380 20.4.1 Data collection 380 20.4.2 Study area 380 20.5 Results 382 20.5.1 Sundarban as a climate hotspot 382 20.5.2 History of cyclone 382 20.5.3 Sundarban ecosystem and cyclone 383 20.5.4 Thematic narratives 383 20.5.5 Specific impact and coping techniques 384 20.6 Discussion 384 20.6.1 Community readiness model 384 20.6.2 Revisiting theories, conventions, and agreements 385 20.6.3 Socioeconomic background and vulnerability to disasters 385 20.7 Limitations of the study 386 20.8 Recommendation 387 20.9 Conclusion 387 References 388

21. Climate change, urban flooding, and community perceptions of vulnerability and resilience: lessons from Diamond Harbour region 391 Sudarshana Sinha

21.1 Introduction 391 21.2 Theoretical orientation 392

21.3 Objectives 394 21.4 Materials and methods 394 21.4.1 Justification for the selection of the indicators 394 21.4.2 Data sources 395 21.4.3 Statistical analysis 396 21.4.4 Software 401 21.5 Study area 401 21.6 Discussion and results 402 21.6.1 Demographic details of the respondents 402 21.6.2 Area vulnerability index 402 21.6.3 Individual vulnerability index 405 21.6.4 Livelihood vulnerability index 409 21.6.5 Adaptability vulnerability index 409 21.7 Conclusion 410 References 411

22. Climate protection in spatial policy instruments, opportunities and barriers: the case study of Poland 419 ´ ´ Maciej Nowak and Przemyslaw Sleszy nski

22.1 Introduction 419 22.1.1 Justification of the study 420 22.1.2 Limitations of the study 422 22.2 Material and methods 422 22.3 Results 423 22.4 Discussion 427 22.5 Recommendations 429 22.6 Conclusions 430 References 430

23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence 433 Abd. Rahman As-syakur, Herlambang Aulia Rachman, Muhammad Rizki Nandika, Martiwi Diah Setiawati, Masita Dwi Mandini Manessa, Atika Kumala Dewi and Rinaldy Terra Pratama

23.1 Introduction 433 23.2 Study area 435 23.3 Data and method 437 23.3.1 Significant wave height 437 23.3.2 Shoreline changes 437 23.3.3 Sea level rise 438 23.3.4 Tidal range 438 23.3.5 Coastal geomorphology 438

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23.3.6 23.3.7 23.3.8 23.4 Results 23.4.1

Coastal slope 438 Vertical land motion 438 CVI calculation 439 440 Physical drivers of coastal vulnerability 440 23.4.2 Coastal vulnerability status in Jakarta 443 23.5 Discussions 444 23.6 Conclusions 446 Author contributorship 447 References 447

24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones 451 Dhivya Narayanan, Karthi N., Balamurugan S. and Devaraj Asir Ramesh

24.1 Introduction 451 24.2 Materials and methods 453 24.2.1 Study area 453 24.2.2 Approach 456 24.2.3 Quantifying risk 457 24.3 Results and discussion 459 24.4 Recommendations 462 24.5 Rational of the study 463 24.6 Limitations 463 24.7 Conclusion 464 Acknowledgment 464 References 465

25. Geospatial approach for reducing water stress: case study of Delhi 467 Ishita Singh and Vibhore Bakshi

25.1 Introduction 467 25.1.1 Water as a sensitive issue 467 25.1.2 Need identification and global approaches 467 25.1.3 Water stress in Indian cities 469 25.2 Research process 470 25.2.1 Selection of site for research 470 25.2.2 Geospatial assessment of national capital territory, Delhi 470 25.2.3 Applicability of stream flow: D8 method 470 25.2.4 Applicability of stream ordering 472

25.2.5 Process of watershed delineation 473 25.3 Spatial detection of change in blue and green areas (1993
2020) 474 25.3.1 Normalized Difference Water Index assessment 475 25.3.2 Normalized Difference Vegetation Index assessment 477 25.3.3 Overall assessment of NDWI and NDVI for NCT (1993
2020) 478 25.3.4 Weighted overlay analysis: potential zones of recharge 479 25.4 Micro study area selection: pilot project 483 25.4.1 Physiographic setting of micro study area 485 25.4.2 Strategies: decentralized cleansing mechanism 487 25.4.3 Water detention area microanalysis 489 25.5 Implementable neighborhood water sensitive plan 491 25.5.1 Interventions: social, hydrological, and ecological 493 25.5.2 Integrating stakeholders in implementation process 495 25.6 Conclusion 495 References 496

26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates in the Dooars region of West Bengal 499 Piyashee Mallik and Tuhin Ghosh

26.1 Introduction 499 26.2 Materials and methods 501 26.2.1 Study area 501 26.2.2 Data compilation 501 26.2.3 Linear regression analyses for estimating the holistic effects of land and atmospheric variables on tea yield 503 26.2.4 Determining the order of polynomial for estimating the nonlinear effects of land and atmospheric variables 504 26.2.5 Multivariate polynomial regression analyses for estimating the nonlinear effects of land and atmospheric variables on tea yield 505

Contents

26.3 Results and discussion 505 26.3.1 Holistic effects of land and atmospheric covariates on tea production 505 26.3.2 Order of polynomial for estimating the nonlinear effects of land and atmospheric variables 509

26.3.3 Nonlinear effects of land and atmospheric covariates on tea production 509 26.4 Conclusion 517 References 517

Index 519

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List of contributors Abdillah Abdillah Department of Government Sciences, Universitas Muhammadiyah Makassar, Indonesia

Premangshu Chakrabarty Department of Geography, Visva-Bharati, Bolpur Santiniketan, West Bengal, India

Zihad Ahmed Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh

Debarghya Chakraborty Department of Social Sciences, School of Social Science and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Athiqah Nur Alami Research Centre for Politics, National Research and Innovation Agency (BRIN), Jakarta, Indonesia

Uday Chatterjee Department of Geography, Bhatter College, Dantan (Vidyasagar University), Paschim Medinipur, West Bengal, India

Md. Rayhan Ali Institute of Environmental Science, University of Rajshahi, Bangladesh

Uma Chatterjee Bengal, India

Chutaporn Amrapala College of Population Studies, Chulalongkorn University, Bangkok, Thailand

Sanjog India, Kolkata, West

Semanti Das Department of Geography, Chandrakona Vidyasagar Mahavidyalaya, Paschim Medinipuir, India

Abd. Rahman As-syakur Marine Science Department, Faculty of Marine and Fisheries, Udayana University, Bali, Indonesia

Atika Kumala Dewi Marine and Coastal Environment Mapping Center, Geospatial Information Agency, Bogor, Indonesia

Vibhore Bakshi School of Planning and Architecture, Bhopal, Madhya Pradesh, India

Sutpratana Duangkaew Faculty of Liberal Arts, Mahidol University, Nakhon Pathom, Thailand

Parama Bannerji Department of Geography, Nababarrackpore Prafulla Chandra Mahavidyalaya, Kolkata, West Bengal, India

Sujan Ghimire Science Hub, Kathmandu, Nepal

Dalce Shete Baroi Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh

Tuhin Ghosh School of Oceanographic Studies, Jadavpur University, Kolkata, West Bengal, India

Snehamanju Basu Lady Brabourne College, Kolkata, West Bengal, India

Sanat Kumar Guchhait Department of Geography, University of Burdwan, Bardhaman, West Bengal, India

Radhika Bhanja Department of Geography, Presidency University, Kolkata, West Bengal, India

Subhra Halder School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India

Gouri Sankar Bhunia Independent Researcher, Paschim Medinipore, West Bengal, India

Md. Zahidul Hassan Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh

Suddhasil Bose School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India

Jayarajan K Department of Geography, Govt College Chittur, Palakkad, Kerala, India

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Ummey Kulsum Department of Geography, University of Bonn, Germany Nirajan Luintel Science Hub, Kathmandu, Nepal

Shobha Poudel Science Hub, Kathmandu, Nepal; Policy Research Institute, Narayanhiti, Kathmandu, Nepal

Hasan Mahmud Institute of Environmental Science, University of Rajshahi, Bangladesh

Rinaldy Terra Pratama Marine Science Department, Faculty of Marine and Fisheries, Udayana University, Bali, Indonesia

Piyashee Mallik School of Oceanographic Studies, Jadavpur University, Kolkata, West Bengal, India

Bayu Prayudha Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia

Masita Dwi Mandini Manessa Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia

Andi Luhur Prianto Department of Government Sciences, Universitas Muhammadiyah Makassar, Indonesia

Bhogendra Mishra Science Hub, Kathmandu, Nepal; Policy Research Institute, Narayanhiti, Kathmandu, Nepal

Dhanya Punnoli Department of Geography, Govt College Chittur, Palakkad, Kerala, India Endang C. Purba Wahana Visi Indonesia, Jakarta, Indonesia

Nafia Muntakim Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh

Herlambang Aulia Rachman Department of Marine Science, Trunojoyo Madura University, Bangkalan, East Java, Indonesia

Karthi N. National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change (MoEF&CC), Integrated Social Sciences and Economics, Chennai, Tamil Nadu, India

Md. Mizanoor Rahman Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh

Tadashi Nakasu College of Population Studies, Chulalongkorn University, Bangkok, Thailand Muhammad Rizki Nandika Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia Dhivya Narayanan National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change (MoEF&CC), Integrated Social Sciences and Economics, Chennai, Tamil Nadu, India Anak Agung Ngurah Agung Indonesia, Jakarta, Indonesia Fransisca Novita Wahana Jakarta, Indonesia

Wahana Visi

Visi

Indonesia,

Maciej Nowak Faculty of Economics, Department of Real Estate, West Pomeranian University of Technology in Szczecin, Szczecin, Poland

Devaraj Asir Ramesh National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change (MoEF&CC), Integrated Social Sciences and Economics, Chennai, Tamil Nadu, India Kunaljeet Roy School of Social Sciences and Languages, Vellore Institute of Technology, Chennai, Tamil Nadu, India Mst. Shifat Rumana Department of Geography and Environmental Science, Begum Rokeya University, Rangpur, Bangladesh Balamurugan S. National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change (MoEF&CC), Integrated Social Sciences and Economics, Chennai, Tamil Nadu, India Sanjoy Kumar Sadhukhan Department of Geography, Visva-Bharati, Bolpur Santiniketan, West Bengal, India Regan Sapkota Policy Lalitpur, Nepal

Initiatives

Nepal,

xvii

List of contributors

N. Savitha Department of Social Sciences, School of Social Science and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Suyarso Suyarso Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia

Susmita Sengupta Department of Geography, Rabindra Mahavidyalaya, Champadanga, Hooghly, West Bengal, India

Choerunisa Noor Syahid Research Center for Area Studies, National Research and Innovation Agency (PRW-BRIN), Jakarta, Indonesia

Hari Harjanto Setiawan National Research and Innovation Agency, Jakarta, Indonesia

Elvi Tambunan Wahana Jakarta, Indonesia

Martiwi Diah Setiawati Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia

Praseed Thapa Agriculture University, Chitwan, Nepal

Ayu Siantoro Wahana Visi Indonesia, Jakarta, Indonesia Krishna Silalahi Wahana Jakarta, Indonesia

Visi

Indonesia,

Ishita Singh Pursuing MTech in Geomatics, CEPT, Ahmedabad, Gujarat, India Sudarshana Sinha Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Nanto Sriyanto Research Centre for Politics, National Research and Innovation Agency (BRIN), Jakarta, Indonesia Indarto Happy Supriyadi Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia

Bayu Tumewu Wahana Jakarta, Indonesia

Visi and Visi

Indonesia, Forestry Indonesia,

Henny Warsilah Rural and Urban Studies, Center for Community and Cultural Research, National Research and Innovation Agency (PMB BRIN), Jakarta, Indonesia Yanuar Farida Wismayanti National Research and Innovation Agency, Jakarta, Indonesia Ganewati Wuryandari Research Centre for Politics, National Research and Innovation Agency (BRIN), Jakarta, Indonesia R.R. Emilia Yustiningrum Research Centre for Politics, National Research and Innovation Agency (BRIN), Jakarta, Indonesia Przemyslaw S´leszyn´ski Polish Academy of Sciences, Institute of Geography and Spatial Organization, Warsaw, Poland

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Foreword It is a pleasure to write a foreword for the book "Climate Change, Community Response, and Resilience" that covers emerging science on environmental issues. The book is edited by Dr. Uday Chatterjee, Prof. Rajib Shaw, Dr. Gouri Sankar Bhunia, Dr. Martiwi Diah Setiawati and Soumita Banerjee, from the fields of geography, environment, engineering, and policy. The book is dedicated to environmental scientists and policymakers. Since 1950s, human population experienced more than a tripling, reaching to about 7.8 billion in 2020. While population growth greatly impacts supporting economic activities, it also increases the responsibility for environmental degradation of our planet. These conditions trigger more global emissions of carbon dioxide across the globe. If global warming surpasses a few tenths of a degree, some regions, including small islands, may become uninhabitable. However, inevitably, some areas will get too hot for individuals to work outside, posing a challenge for crop production. Therefore less developed countries and small islands will become very vulnerable to global warming, and they will need to redesign the economic growth with a focus on environmental sustainability, population stabilization, and poverty alleviation for a peaceful coexistence. Global warming and climate change are already causing severe weather patterns

xix

and these are expected to worsen further. Also, climate change has indeed exacerbated mortality, morbidity, and human suffering worldwide. It also has various effects on public health, agriculture, fisheries, biodiversity; has induced mass migration; and has resulted in an increase in the poverty rate. Therefore assessing climate change in an integrated manner becomes a crucial issue, starting from a theoretical framework; impact assessment from a global and local perspective; community response; linkages with policies and programs; and planned adaptation action by the government. Adaptation is a key to strengthen climate resilience in a region. According to project experiences, adapting to climate change requires an integrated approach. It includes a science based on climate change impact assessment and its future projection, socio-economic development, environmental conservation, disaster risk reduction, public policy, and law enforcement. The current volume is a collection and compilation of 26 research papers that cover topics from a theoretical framework, climate change impact assessment, social response, ecological impact, and resilience. Emerging issues that have been captured in the book include critical elements of climate change and coping mechanisms that are essential for community resilience.

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Foreword

I congratulate the editors for their worthy initiative in producing this valuable volume for readers from various academic disciplines. The approach of case studies used in the chapters with references from all over the world covering a wide range of economies reveals the authors’ and editors’ insights into the regional pattern of climate resilience and related issues under current conditions. I sincerely wish that the book will be widely acclaimed by academia from allied disciplines including environmentalists and policymakers particularly for highlighting the new emerging issues of climate resilience.

Anuradha Banerjee Centre for the Study of Regional Development, School of Social Sciences, Jawaharlal Nehru University, New Delhi, India

Preface The word "resilience" stands out mostly in all the discussions and debates about climate change mitigation. Though definitions differ, resilience is defined as a system’s ability to cope with a hazard by responding in ways that keep the system’s critical functions alive while also expanding its ability to learn and develop. Resilience in the case of climate change refers to the ability of humans (including social and economic) and environmental systems to tolerate the effects of climate change, including weather extremes and their consequences. In industrialized countries, climate change resilience strategies are being actively implemented at all levels of government. Rather than systematic, coordinated attempts to increase resilience, poor rural populations in developing nations generally adapt their lives in response to climate change. Therefore governments, aid groups, and researchers must comprehend how the people of the developing nations respond to climate change and how their efforts might be improved within the context of resilience thinking. Climate change adaptation studies frequently focus on the effects of tangible and measurable resources, such as infrastructure and income. This asset-based approach frequently overlooks the sociocognitive processes that influence adaptation intentions. Furthermore, the community’s ability to initiate and support its members’ adaptive behavior has been underappreciated. The benefits of collaborative efficacy are becoming more apparent, even though the influence of risk analysis is still being contested.

Climate change is no longer an ambiguous and indefinite future issue. It is an unavoidable event that is wreaking havoc on the planet at an alarming rate, an outcome of more than 200 years of excessive greenhouse gas emissions from fossil fuel combustion in the industries, transportation, energy generation, intensive agriculture, and deforestation. Moreover, the average world temperature has risen by around 0.8˚C since 1880. Despite the fact that it might only seem like a slight alteration, global warming has caused major climate change, offering several dangers to both nature and human beings, including unpredictable and excessive precipitation, coastal flooding, cyclones, wildfires, and heat waves. Additionally, among the consequences of weather events, changes to ecosystems, damage to infrastructure and human settlements, illness, and mortality, and even repercussions on human wellbeing and mental health are profound. The most saddening part is that the burden of these changes is unevenly distributed across countries and economies making the most vulnerable persons and communities as the bearer of the brunt. With the adverse risks of climate change and related weather phenomena looming over the global population, many nations and communities have prioritized preparedness and mitigating the effects of climate change, regardless of their economic strata. In climate change, research and regulation, terms like catastrophe risk management, natural resource management, and climate-adaptive methods have become popular since the last few decades.

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xxii

Preface

For instance, showing how collaborative effectiveness is crucial for enhancing India’s climate change adaptation practices. One of the many negative consequences of displacement is social disarticulation, which manifests as dispersed social networks and ruptured patterns of trust and reciprocity. As support networks, which are essential for coping with hazards, are destroyed, resettlement sites become especially vulnerable to risks. People require social capital and financial and tangible assets to adapt to and cope with threats. Community resilience, defined as the process of dealing with disturbances and the ability to adjust to them based on social assets, has been theorized to be a potential catalyzer of climate change adaptation behavior, particularly in poorer communities with few alternative support mechanisms and resources. Thus the goal of this book is to improve the understanding of the effects of community resilience and risk assessment on climate change adaptation behavior. This book provides a critical theoretical framework for understanding the effects of community resilience and risk assessment on climate change adaptation behavior. This framework is based on theoretical and case study empirical analysis of 26 chapters, which comprises of pioneer projects from various regions. The book is organized into three major parts, which reflect the interconnection between theories and practices. Section 1 explains the introduction which reflects the paradigm shift of climate change, community response, and resilience. Section 2 covers the sectoral case study and practice on climate change,

community response, and resilience. This section talks about the roles of community resilience and risk appraisal in climate, local strategies to build climate resilient communities, community-based responses to climate hazards, community-based tourism as a strategy for building climate resilience, climate change on community mental health, vulnerability and resilience to climate change in coastal community, livelihood security and climate change, cultural identity and climate change, stakeholders’ perceptions and strategies to climate change resilience, and climate change and community resilience for sustainable development. Section 3 describes climate change, ecological impacts, and resilience. This section throws the lights on climate change, ecological stress and livelihood choices, climate change and drought: vulnerability and community resilience, climate change and flood: vulnerability and community resilience, community response to flash flooding case study, climate change and cloud burst: vulnerability and community resilience in the hilly region, strengthening community and ecosystem resilience against climate change impacts, the role of socioecological resilience in the coastal zone, socioecological vulnerability to climatic change, water security and climate change, and climate change and agriculture: impacts, adoption, and mitigation. Uday Chatterjee, Rajib Shaw, Gouri Sankar Bhunia, Martiwi Diah Setiawati, Soumita Banerjee

Acknowledgments This book has been made possible with the immense efforts made by the academicians, engineering experts, urban planners, policymakers, and local activists around the world to create a sustainable disaster resilience framework. We would like to sincerely thank the outstanding chapter contributors for their efforts to make this book happen. We also appreciate the relentless endeavor put out by the anonymous reviewers whose constructive comments and suggestions helped to elevate the extent of the research studies and the book.

As life-long learners, we are very much appreciative of the great assistance we have received from our coworkers, students, parents, family members, teachers, and collaborators in thinning the efforts we have made day in and day out while editing this book to add more value and contribute effectively to the knowledge of sustainable disaster management. Last but not least, we would really like to applaud our publisher, the publishing editor, and the production manager, Elsevier, for their never-ending support and encouragement.

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S E C T I O N

1

Introduction

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C H A P T E R

1 Evaluation of community response and resilience on climate change: a bibliometric analysis Suddhasil Bose1, Subhra Halder1 and Snehamanju Basu2 1

School of Water Resources Engineering, Jadavpur University, Kolkata, West Bengal, India 2 Lady Brabourne College, Kolkata, West Bengal, India

1.1 Introduction Climate change is defined as a change in the state of the climate that can be determined (e.g., using statistical tests) by changes in the mean and/or variability of its attributes and that lasts for an extended period, generally decades or longer. Framework convention on climate change (UNFCCC) defined climate change as, a change of climate that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods (IPCC, 2022a). Recently, marine, freshwater, and terrestrial ecosystems have been altered by climate change (Ha¨der & Barnes, 2019). Extreme climate events and constant change, in the long run, have caused the loss of local species, an increase in disease, and mass mortality events all over the world. The effects of climate change have severely impacted climate-sensitive species (Hulme, 2005). The probability of negative climatic effects will show its dominance in the future also. With climate change, it is estimated that for native species evaluated in hotspots, the danger of extinction increases with warming, with endemic species facing a tenfold increase from 1.5 C to 3 C above pre-industrial values (IPCC, 2022b). The concentration and interconnectedness of people, infrastructure, and assets within and between cities, as well as in rural areas, create threats and solutions to climate change on a global scale (Dow, 1992; Wamsler et al., 2013). Exposure to climate-driven impacts such as heatwaves, urban heat islands, excessive precipitation, and storms, combined with growing urbanization and a lack of climate-sensitive planning are impacting marginalized populations as well as critical infrastructure to climate change. Every planning and management framework toward sustainability significantly depends on the community. Response and resilience against climate change from a community have come

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00001-1

3

© 2023 Elsevier Inc. All rights reserved.

4

1. Evaluation of community response and resilience on climate change: a bibliometric analysis

up naturally with time. Relation between man and the environment, its conjunction, and clash developed the civilization. From the local scale to a global frame, the concept has been redefined with different approaches and aspects. Satisfactory instances of communal integrity from the historic cave age to the contemporary postmodern world have been observed pointedly (Betsill, 2001; Gentle et al., 2018; Measham et al., 2011; Saavedra & Budd, 2009; Storbjo¨rk, 2007; Taylor Aiken et al., 2017; Wall & Marzall, 2006; Walther, 2010; Wilson, 2006). Keeping its complexity and wide-ranging impacts, responding to the climate emergency requires ‘transdisciplinary’ knowledge, which blurs the boundaries between core academic disciplines, and recognizes and respects different forms of knowledge and expertize (Apgar et al., 2009; Bernstein, 2015). Already researchers from different dimensions have been unified about the alarming issue of climate change (Castree, 2017). Different approaches and perspectives from climatology, geomorphology, atmospheric science, computer science, economics, sociology, and other subjects have been integrated to discover the impact of climate change and possible measures that lead to sustainability. Researchers published various review articles, journal articles, book chapters, and conference papers in the research field that mainly focused on the community response and resilience to climate change. This paper aims to identify those valued researches to identify and integrate the response and resilience capability of communities all over the world that primarily focused on climate change. From viewpoint of the decision maker, researcher, or enthusiast, a comprehensive study based on research already performed, helps significantly to qualify and quantify the subject matter and inclusive view regarding this. In this frame of reference, the bibliometric analysis offers itself naturally as an instrument (Ellegaard & Wallin, 2015). Traditionally, review articles have been referred to for this process. In contrast, bibliometric analysis is focused on statistics related to bibliographic data. Copious amount of research publication and free remote access to a worldwide publication database has attracted researchers for bibliometric study. The number of publications through bibliometric analysis is increasing in the research community and it has become a professionally accepted tool (Gao et al., 2022). Climate change and its related consequences have been addressed already through bibliometric analysis in recent time (Becerra et al., 2020; Fu & Waltman, 2022; Rana, 2020; Sweileh, 2020; Wang et al., 2014, 2018). The main objective of this chapter is to review the evolution of community response and resilience to climate change from relevant and vast number of researches that have already been published in research communities through journals, books, and other accepted mediums and already produced distinct viewpoints in the research sector. The scientometrics method is a significant approach to accomplish this. Thus, bibliometric analysis has been approached for this chapter. A clear concept will be generated about the pretext of community response and resilience against climate change, a trend will be identified and potential future directions will be discovered through this approach.

1.2 Methodology for bibliometric analysis Bibliometric data (e.g., number of publications and citations) can be analyzed by applying a quantitative approach that can be termed bibliometric analysis. The process of bibliometric analysis started in the middle of the 20th century with the motive of performance evaluation. This study focuses on information-based analysis from bibliographical

1. Introduction

1.2 Methodology for bibliometric analysis

5

databases. Retrieval of the material focuses on the informatic study based on secondary data. The quantitative nature of bibliometric analysis ultimately procures the qualitative spirit of the study (Wallin, 2005). Apart from a generalized knowledge about a specific subject, bibliometric analysis can be divided into the following two categories for the purpose of analytical technique (Donthu et al., 2021): 1. Performance analysis: This process evaluates productivity and impact on the subject area, 2. Science mapping: Relational techniques are involved in it uncovering knowledge clusters. Table 1.1 shows a clear view of the basic purpose of bibliometric analysis. Important other terms related to bibliographic analysis have been discussed below. Network Mapping: Network analysis is needful because documents’ attributes are connected to each other through the document itself (e.g., author(s) to journal, keywords to publication date). These connections of different attributes can be represented through a matrix Document 3 Attribute (Aria & Cuccurullo, 2017). Then it is represented using graphical visualization. It is termed network mapping.

TABLE 1.1 Brief on the purpose of analytical techniques for bibliometric analysis. Performance analysis Analytical technique

Purpose of analytical technique

Analytics of publication metrics

To determine productivity

Analytics of citation metrics

To determine impact

Analytics of hybrid metrics

To determine impact relative to productivity

To identify social dominance or hidden biases

To identify social dominance or hidden biases

To identify social dominance or hidden biases

Science mapping Analytical technique

Purpose of analytical technique

Co-authorship analysis

To uncover social relationships To identify social dominance or hidden biases

Co-citation analysis

To uncover relationships among cited publications, where cited publications converging into a cluster represent a common theme

Bibliographic coupling

To uncover relationships among citing publications, where citing publications converging into a cluster represent a common theme To triangulate with co-word analysis

Co-word

To uncover relationships among author listed or natural language processing extracted keywords, where keywords converging into a cluster represent a common theme To triangulate with bibliographic coupling

1. Introduction

6

1. Evaluation of community response and resilience on climate change: a bibliometric analysis

Factorial Analysis: Sometimes factorial approach makes it easy to understand the conceptual structure of the study. Common concepts related to the study can be understood using numerical analysis of multivariate categorical data (MCA). It is an exploratory multivariate technique for graphical and numerical analysis and it is a low dimensional euclidean representation of the selected data. The result is interpreted based on the relative positions of the points and their distribution along the dimensions and closer words define more closeness with each other. Co-occurrence Network: Co-occurrence network is mainly used to identify such properties from the documents that have reoccurred in different documents and co-existing and corelated with other documents, using such words a network matrix can be created. The aim of the co-word analysis is to map the conceptual structure of a framework using the word co-occurrences in a bibliographic collection. The analysis can be performed through dimensionality reduction techniques such as multidimensional Scaling (MDS), correspondence analysis (CA), or multiple correspondence analysis (MCA). K-means clustering is used to identify clusters of documents that express common concepts. Results are plotted on a two-dimensional map. The conceptual structure includes natural language processing (NLP) routines to extract terms from titles and abstracts. In addition, it implements Porter’s stemming algorithm to reduce inflected (or sometimes derived) words to their word stem, base, or root form (Aria & Cuccurullo, 2017; Cuccurullo et al., 2016). In this chapter, the above-mentioned concepts have been applied to perform bibliometric analysis (Fig. 1.1). This process significantly depends on processing and visualization techniques. Several software already made their dominant impact in the research

FIGURE 1.1 Methodology for bibliometric analysis.

1. Introduction

1.3 Data collection

7

field, between theme some are proprietary and some are open-source software (example: BibExcel, CiteSpace, VosViewer, etc.) (https://liu.cwp.libguides.com/c.php? g 5 225325&p 5 4966525). Bibliomatrix is an alternative approach (https://www.bibliometrix.org/home/#); it is a package under R programming language. Exclusively built to perform bibliometric analysis. An efficient and free-to-use approach has made it most useful. Along with bibliometrix package, biblomterixdata, tidyverse, reshape2, and other software packages (https://cran.r-project.org/) have been utilized for this chapter.

1.3 Data collection In this chapter, one bibliometric database has been selected for the data collection. There are several scientific publishing databases are available that handles published materials from over the world, Scopus, Web of Science, and PubMed are top priorities among researchers. Information from the Scopus database has been used for further analysis. Scopus is a part of Elsevier (an information analytics company) and one of the largest also most used research databases that publish vast number of journals, books, conference preceding, etc. (Schotten et al., 2017; https://www.elsevier.com/en-in/solutions/scopus). Scopus is useful as it covers large number of diversified fields and parallelly it can be accumulated for studies like bibliometrics. Community response and related resilience against climate change is a very intriguing topic in the contemporary research world. Scientists and researchers from different fields relate their research to effect of climate change. Along with climatic and physical approaches, economic, social, geopolitical, and biological approaches are also collineating in the matter of the community-based resilience approach. Therefore, keeping in mind, the process has been limited to focus on the environmental and social approaches for this paper. An outline query has been performed with the query string such as ALL (“climate change” AND “community response” AND “community resilience”) AND (LIMIT-TO (SUBJAREA, “ENVI”) OR LIMIT-TO (SUBJAREA, “SOCI”) OR LIMIT-TO (SUBJAREA, “EART”)). This search query on scopus website database has covered 497 published materials mainly from environmental science, 260 published materials from social science and other related fields. Certain parameters have also been maintained such as all the data that have been used are published data, all the documents are written in English, and all the data have been collected within a contemporary time frame, i.e., 21st century. Scopus database has been accessed in June 2022 and this modification in query has been done for the purpose of identification of significantly qualified data in recent decades (2000
22). A total of 497 documents have been analyzed for this bibliometric analysis from more than 271 variant sources. All the collected data have been serving the community for more than 4 years on average. The integrity of such documents has been referenced by at least 29 more authors on an average. The number of citations are increasing with number of 3.748 per year. All the documents assessed for this study have been used by more than 75,000 references collectively. A total of 366 articles have been used in this study along with 36 whole books and 48 chapters from books. About 37 writings have been published a review on this topic has been incorporated into this study. Keywords in Table 1.2 depicts such information that has been automatically generated by the Scopus database based on their relevance. For this study, more than 2000 words have been culminated by the Scopus

1. Introduction

8

1. Evaluation of community response and resilience on climate change: a bibliometric analysis

database. It also quantifies that 1904 authors contributed their valuable thoughts through their writings. A total of 87 authors singularly published their thoughts through their writings and the collaboration index calculated the value of 4.46, which signifies that, on average, more than four authors collaborated on each paper (Table 1.2). TABLE 1.2 Overall information acquired from Scopus database with the query. Description

Results

Timespan

2000:2022

Sources (Journals, Books, etc)

271

Documents

497

Average years from publication

4.49

Average citations per document

29.31

Average citations per year per doc

3.748

References

75,775

Document types Article

366

Book

36

Book chapter

48

Conference paper

7

Editorial

2

Letter

1

Review

37

Document contents Keywords Plus (ID)

2097

Author’s Keywords (DE)

1495

AUTHORS Authors

1904

Author Appearances

2109

Authors of single-authored documents

75

Authors of multi-authored documents

1829

Authors collaboration Single-authored documents

87

Documents per author

0.261

Authors per document

3.83

Co-authors per documents

4.24

Collaboration index

4.46

1. Introduction

1.4 Result

9

1.4 Result 1.4.1 Most relevant sources After the bibliometric analysis, we shortlisted the top ten relevant journals according to the number of productivities on the allied subject. It has been observed that the International Journal of Disaster Risk Reduction (https://www.sciencedirect.com/journal/international-journal-of-disaster-risk-reduction) has published 36 articles in between the specified time frame under Elsevier. On the second rank, in terms of productivity, e.g., Natural Hazards (https:// www.springer.com/journal/11069) with 15 publications from Springer Nature. Ecology and Society (https://ecologyandsociety.org/) has produced 14 articles related to this topic. Sustainability (https://www.mdpi.com/journal/sustainability) an open access journal ranks as fourth and along with these Global Change Biology (https://onlinelibrary.wiley.com/journal/ 13652486) has produced about 9 articles about climate and related community resilience.

1.4.2 Source dynamics Assessing the total number of documents, it has been observed that, a steady growth in productivity flourished through academic literature from 2004 to 2022 (Fig. 1.2). Studies on climate-related issues and mitigation about it started to take a leap after 2012. All of the top journals have produced a greater number of documents from that time. This signifies increasing interest of researchers in the topic. After 2016, a breakpoint can be observed through the diagram, which depicts more seriousness about the relevant issue among the researchers and publishers also. A steady significant growth can be observed till 2020, and after the worldwide issue of pandemic, the growth of scientific production became steady nowadays. International journal of disaster risk reduction excelled its production in multiple folds after 2016, it was less than 10 articles in 2016, but in 2021, it published more than 35 articles.

FIGURE 1.2 Relevent source and its growth with time.

1. Introduction

10

1. Evaluation of community response and resilience on climate change: a bibliometric analysis

1.4.3 Most relevant authors Diego Thompson from Mississippi State University with expertize in rural and environmental sociology has produced six documents related to this climatic resilience and response (Thompson & Lopez Barrera, 2019) (Fig. 1.3). Kathrine Suding from the University of Colorado has worked on community ecology and global change in climate and published five documents (Suding et al., 2008). Katrina Brown an emeritus professor, made her contribution through emerging topics such as resilience, vulnerability and adaptation, and global environmental change and published four documents (Nelson et al., 2007). Professor Fikret Berkes from the University of Manitoba worked on social-ecological resilience and human ecology and published four documents about these issues (Olsson et al., 2004). Dr. Paton, Dr. Lam, Dr. Rivera, and others also made significant impact on social response to climate change.

1.4.4 Country wise scientific production Community response and resilience to climate change is an emerging issue on a global scale (Fig. 1.4). Developed countries such as the USA and Australia ranking the top of the list for intensive research about the issue. Researchers from the USA have published more than 400 documents about this topic. On the next rank, another developed country, Australia, has produced 145 documents, and researchers from the United Kingdom have also produced more than 100 documents in between 21st century. Other European countries such as Germany, Italy, Spain, and France also made a significant impact. Developing countries such as China, India, and Brazil have also attempted to make a limited contribution to the research field despite their infrastructural and economical constraints in academic sector.

1.4.5 Most globally cited documents Significant research articles that are most relevant for a better understanding of community response and resilience to climate change is mentioned in Table 1.3 according to their

FIGURE 1.3 Eminent authors with the total number of articles.

1. Introduction

11

1.4 Result

FIGURE 1.4 Country wise scientific production.

TABLE 1.3 Most cited documents according to the number of citations. Total Total citations citations per year

Rank Author year

Title

1

Cutter et al. (2008)

A place-based model for understanding community resilience to natural disasters

2

1938

129.2

Tompkins and Adger (2004) Does adaptive management of natural resources enhance resilience to climate change?

655

34.4737

3

Aldrich and Meyer (2015)

Social Capital and Community Resilience

617

77.125

4

Johnstone et al. (2010)

Changes in fire regime break the legacy lock on successional trajectories in the Alaskan boreal forest

383

29.4615

5

Nelson et al. (2007)

Global environmental change

343

38.1111

6

Badjeck et al. (2010)

Impacts of climate variability and change on fishery-based livelihoods

288

22.1538

7

De Lange (2010)

Ecological vulnerability in risk assessment—A review and perspectives

267

20.5385

8

Folke (2016)

Resilience

256

36.5714

9

Murphy (2007)

Locating social capital in resilient communitylevel emergency management

246

15.375

10

Stwart (2013)

Mesocosm Experiments as a Tool for Ecological Climate-Change Research

188

18.8

1. Introduction

12

1. Evaluation of community response and resilience on climate change: a bibliometric analysis

ranking by a higher number of citations. Most relevant articles from Table 1.3 has been reviewed manually to incorporate the author’s perspective in this chapter. Cutter et al. (2008) prepared a model for the understanding of community resilience to natural hazards. The main objectives of the paper are to provide a global perspective on the conceptual framework of disaster resilience and the identification of variables to measure the impact of resilience. Authors identified that increasing suburbanization is a serious reason for the increase in potential loss from natural hazards. Space crunch in a specific area forces people to reside in such places prone to disasters. The significance of this paper lies in the conceptualization of the DROP (i.e., Disaster Resilience of Place) model. The model was designed to represent the relationship between vulnerability and resilience. They also identified indicators that are useful for community resilience (Table 1.4). TABLE 1.4 Different dimensions of community resilience. Dimension

Candidate variables

Ecological

Wetlands acreage and loss Erosion rates % Impervious surface Biodiversity

Social

Demographics Social network and social embeddings Community values-cohesion

Economic

Employment Value of property Wealth generation Municipal finance

Institutional

Participation in hazard reduction Emergency services Zoning and building standards Community of operational plans

Infrastructure

Transpiration network Commercial and manufacturing establishments

Community competence

Local understanding of risk Counseling service Healthy and wellness Quality of life

Adopted from Cutter, S.L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). Global Environmental Change, 18(4), 598
606. https://doi.org/10.1016/J.GLOENVCHA.2008.07.013.

1. Introduction

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Tompkins and Adger (2004) questioned the research community about whether the effectiveness of adaptive management of natural resources truly enhances resilience to climate change or not. This paper discovered the potential benefits of co-management in building resilience to cope with climate change. They specified this exploration from a coastal community on a Caribbean Island. They suggested certain adaptive pathways related to implication through governances (Table 1.5). A parallel investigation has been done by Aldrich and Meyer (2015) on the role of social capital and networks on disaster survival and recovery apart from governmental mega infrastructures and economic implementations. They identified three types of social capital: bonding, bridging, and linking. Bonding social capital describes making emotional relations with near ones that will help in times of disaster. Bridging social capital defines social and communal bonding between different social groups, classes, or races. Linking social capital describes interlinkage between people that will help to make chain to resolve the disastrous situation with minimum time. They suggested several measures to make effective social capital that will fruitfully help in times of disaster (Table 1.6). Brown (2014) focused on socializing resilience in his study. The emergence of literature on social-ecological systems after 1980 was identified in this study. The author divided socializing resilience into three parts: First, the author described community resilience and integrated relevant components that were idealized by eminent researchers. Second, it was identified that global environmental change would certainly make radical, unplanned, and detrimental transformations through climate change and resilience will be an inevitable effect. The author further drew on the needfulness of resilience transitions. The application and core concepts found by the author proved to be valuable guidelines for the research community. Badjeck et al. (2010) described the impact of climate variability and related changes among fishery-based livelihoods. They discussed various types of climate change and its effect on the marine world and the fish industry related to it. They identified pathways of climate change and variability that focused on assessing the capital assets component of TABLE 1.5 Different adaptation strategies in different social dimensions. Adaptation strategy

Social dimensions necessary for implementation

Urban planning and zoning to avoid climate-related hazards

Community governance and participatory structures are effective means of dealing with social exclusion and the urban underclass

Planning for long-term demographic and consumption transition

Social mobility, coherent regional identity, social tolerance, and mixing

Large-scale infrastructure development for adaptation

Social acceptance of developing technological solutions that have had detrimental environmental or social impacts on excluded groups in the past

New technologies in agriculture and natural resource use

Social acceptance of technologies that are potentially risky and socially disempowering recognition of existing lay and indigenous knowledge and technologies

Policies and plans for natural areas and ecosystem conservation

New institutional structures for conservation that overturn part models of executive protected areas and exclusion of people-cantered conservation social acceptance of shared resilience goals

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1. Evaluation of community response and resilience on climate change: a bibliometric analysis

TABLE 1.6 Measures for effective social capital. Field

Applications

Concepts

International relations

Understanding military and terrorist threats

Security; Critical infrastructure

Social-ecological systems

Managing complex systems in times of change informing adaptive management strategies

Adaptive cycle; Adaptive capacity; Transformations; Linking social and ecological dimensions of resilience

Disasters and disaster risk reduction

Minimizing risk and supporting recovery

Vulnerability; Community resilience

Climate change

Adapting to and minimizing the impacts Adaptation; Adaptive capacity; Climate change of climate change

Human development

Coping and thriving in times of adversity Individual response to crisisPoverty traps

Individual resilience; Human well-being; Capacity agency

Organizational science and social innovation

Managing change

Social learning

Planning

Urban regional planning

Urban resilience

the sustainable livelihood framework. Identification of adaptation planning measures for fisherfolks has been discussed in this paper. They advised some measures such as: (1) reducing fisherfolk vulnerabilities; (2) understanding livelihood strategies to inform planned adaptation; (3) harnessing opportunities brought by climate change; (4) addressing conflicts and synergies between adaptation strategies; (5) contributing to mitigation to make a change in the livelihood of fisherfolks. De Lange et al. (2010) reviewed ecological vulnerability in risk assessment. Ecological vulnerability analysis method has been approached through this paper for populations, communities, and ecosystems. They found a close relationship between vulnerability and resilience. According to their perception, the vulnerability had been incepted from social science, whereas resilience originated from ecological research but in the contemporary research field, it is being sued in social science also. They remain focused on ecological vulnerability along with resilience. Folke (2016) dedicated an entire paper to the term resilience. He showed the way to connect resilience with the social-ecological system in the context of sustainability. He focused on the perspective of the Anthropocene about resilience. Defining resilience, he found a deep motive for resilience, as it deals with complex adaptive system dynamics and true uncertainty along with how to learn to live with changes and make use of it. Thought of this paper found that communities can seize on the windows of opportunity created by climate-induced shocks to generate sustained social-ecological improvement. Murphy (2007) described the role of the municipality versus the role of communitylevel initiatives in times of emergency. Assessing several researches she found that risk assessment is distinct between local authorities and local communities. She focused on

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building social capital as it constructs a new lens to assess risk management. She identified the usefulness of resilient communities as members easily create social bonding on regular basis and all members share some degree of preferences or beliefs. It is not necessary that all of the members of a community will be a part of governing body or directly involved in the planning but at the time of emergency, communities will feel an adequate level of internal capacity.

1.4.6 Most frequent words Bibliometric analysis generated the most frequent words used in community response and resilience on climate change-related research. Collecting data from abstracts, keywords, and technically generated keywords, it has been found that climate change has been used as the most used word in documents. It has been used more than 100 times. Next is community response, which is obvious for the study. For management-related issues, the authors relied on the concept of disaster management and adaptive management. Identification of vulnerability and risk assessment has been used to understand the actual situation. Word Flooding has been used mostly among all other climate change-related topics. Researchers have also focused on the effect of climate change in Coastal zones and related resilience. Authors have repeatedly emphasized decision-making, sustainable development, and disaster planning through such words. Some authors relied on governance approach from governmental institutions and other authors found effectiveness through local participation. The authors also highlighted biodiversity, ecosystem and ecosystem resilience, and its interactions with humans.

1.4.7 Word growth Collecting data from the author’s keyword it has been found that the growth of the word resilience has increased cumulatively with a steep slope in the 21st century. The growth of the word climate change has come after it. At a time after 2019, community resilience has overpassed the word climate change in terms of usage. Usage of the words social capital, vulnerability, and adaptive capacity has increased at a generalized rate along with other terms. Observing the data, it can be stated that research on climate change and related resilience was limited till 2010, but from 2011, this has taken a leap. A steep growth can be observed between 2011 and 2014. After 2015, the volume of research increased multiple times as can be seen from the usage of such relevant words (Fig. 1.5).

1.4.8 Co-occurrence network After creating a co-occurrence network using all abstracts it has been found that Community is the most used topic in this type of research, with all nodes of the network connection with the word community (Fig. 1.6). Resilience is also highly related to other topics, all of these nodes somehow joining topic change and relative change is related with impacts. Most collected significant words from abstract can be grossly divided into two clusters, where the first cluster is defining how community is related to natural, environmental ecosystems and response of community are related to climate change and both are

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1. Evaluation of community response and resilience on climate change: a bibliometric analysis

FIGURE 1.5 Growth of words from 2004 to 2022.

FIGURE 1.6 Mapping of co-occurrence network on community response and resilience on climate change.

related to global and local approaches, but local community resilience is more interconnected with other nodes. The second cluster shows mainly social processes that are interrelated with management, development, knowledge about disaster and recovery policies. Both clusters are interrelated with each other simultaneously. It is clearly observable that climate change strategies can be identified through resilience based on social adaptation strategies with a proper response but this is limited to local communities. Along with it, knowledge about development, risk management vulnerability, and policy about this process is also relevant subject matter.

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Analyzing the co-occurrence network using the author’s keyword, it can be found that authors have given priority to certain terms and those are interrelated with others, mostly priority has been given to community response, climate change, adaptive community, and disaster management. This network analysis identified four clusters according to their usage in abstracts. Community response is significantly related to climate change and in this approach disasters like flooding have been given more priority than others; thus, this signifies flood-related issues all over the world. Some authors have given priority to risk assessment processes that are related to disasters and humans are prioritized here. Another significant observation through this study has been found that describes resilience in the ecosystem where biodiversity and environmental monitoring are the main issues due to global change in the ecosystem and climate change.

1.4.9 Thematic evolution With the beginning of the 21st century, climate change-related issues became the most trending topic all over the world, reflected in the research sector as well (Fig. 1.7). It has been analyzed that social-ecological systems are the most trending theme through the decade that started from 2004. Climate change, climate resilience, and disaster-related topics were also under the spotlight. In the next years (2016
17), climatic disturbances, adaptation, and adaptive capacity were the most discussed topics for climate change-related issues. Climate change, community resilience, and disaster resilience were also prioritized. In the next 1 year (2018
19), social networks became relevant and indigenous knowledge about climate resilience was rejuvenated at that time. The revaluation of social capital was taken seriously. Along with these themes, community-based flood risk management attracted researcher planners and policymakers. After 20 years of the millennium, community resilience, discussion on flood risk, perception of disaster-related risks, and building social capital were highlighted repeatedly through scientific literatures and reviews. In recent days (2022), the identification

FIGURE 1.7 Thematic evolution of topics from 2004 to 2022.

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1. Evaluation of community response and resilience on climate change: a bibliometric analysis

of social vulnerability and sustainability are the new addition with prior themes such as climate change, community resilience, and disaster risk reduction. Theme related to community response against climate is interlinked with each other. The disaster-related issue was a primary theme from 2004 to 2015, and repetitive review on this topic opened up a combatively new perspective on disaster resilience; further, this concept was divided into different issues and the concept of social capital got introduced in the research sector. Disaster resilience further merged into climate change adaptation that ultimately idealized the theme of climate change and community resilience. The relevance of topics and their developmental magnitude have been represented in thematic with cartesian coordinates (Fig. 1.8). It can be observed from the diagram that the basic theme of this study is focused on climate change adaptation, indigenous knowledge about locality, and adaptation. These themes are somehow merging with disasters and disaster risk reduction processes. Community resilience and social capital can be treated as basic themes of the study. Resilience, vulnerability, adaptive capacity, climate change, and biodiversity are part of motor themes. Issues such as risk perception and socio-ecological systems can be termed as niche theme, as it has limited usage. Indirect effects of climate change also consist of a niche theme that defines why indirect effects of climate are being neglected. Flood risk is being treated as an emerging concept and community-based flood risk management is also an emerging concept for research. Emergency management and community developments have become basic themes from emerging themes.

FIGURE 1.8 Thematic map generated with author’s keyword.

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1.4.9.1 Factorial analysis After the calculation of MCA, the factorial approach has been adopted to create a conceptual structure from the collected data (Fig. 1.9). It has been identified that the selected topic can be divided into two clusters based on two dimensions. The red cluster has covered most part of climate change and community resilience-related issues. Vulnerability, sustainable development, climate change, and coastal zone management community response are near the central point of the rest cluster that defines relative dependencies among the topics. Flooding, risk assessment, natural hazard, social capital, and local participation are also related to relevant emerging topics. Environmental change ecosystem, environmental change, and ecosystem resilience are relatively far from a central point, which defines declined usage of such topics. On the other hand, another cluster has been formed with a focus on disaster, resilience, and human-related approach.

1.4.10 Country collaboration map It has been observed that research collaboration about the topic is performed all over the world (Fig. 1.10). USA, Canada, Australia, China, and other West European countries have taken part in this research sector. It has been observed that the USA has collaborated most of the time with other European countries such as the UK, Germany, and France.

FIGURE 1.9 Mapping of factorial analysis using multivariate categorical data.

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FIGURE 1.10

1. Evaluation of community response and resilience on climate change: a bibliometric analysis

Country collaboration map generated from bibliometric analysis.

Australia also collaborated with European countries. A comparatively low amount of contribution has been received from the South American and African continents. In the African continent, it is limited to South Africa only. Countries from the Asian continent have produced a limited amount of research materials and nominally participated in collaboration except few studies from China. From the above observation, it can be said that significantly developed countries have understood the effect of climate change on the environment and they have already taken action against it and started to identify resilience measures to make their environment sustainable. It is certainly reflected through their collaboration but developed countries have produced a limited amount of research materials, scientific infrastructure, and economical constraints, which have limited their scope to excel.

1.5 Discussion Community is apparently a sociological term but the main essence of this concept lies in people’s minds. For the ages, people have felt the need for the community to protect, preserve and pursue their lives unitedly. After any natural disaster from economical distress to biological threat human society has gathered collectively and prepared itself to resist the situation and created resilience against it. In this chapter, community response and resilience to climate change have been realized through valuable researches conducted previously. The bibliometric analysis helped to make a clear view of the concept related to the subject matter. It has been observed that several documents are increasing with time and the number of authors also increasing who are interested in this topic. Relevant

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journals have been identified that are giving priority to this topic and producing valuable materials frequently from the International Journal of Disaster Risk Reduction is the most frequent one. Eminent researchers are approaching each and every aspect related to the topic and getting acclaimed with an increasing number of citations for noteworthy contributions to the topic. In country-wise scientific productions, the USA, Australia, and Great Britain really take this contemporary issue seriously through their research. Next, highly cited documents have been curated to gather valuable information about topic. It has been observed that researchers mainly focused on theoretical concepts related to resilience to build a proper framework. They defined variables and terms related to this topic and helped to make difference in each individual concept that is closely contemplating with each other. They identified indicators from different dimensions and variables related to climate resilience, and they have also taken a remedial approach with a significant adaptation strategy that can be followed by social communities with effective implications. The importance of social capital and its relevance in the community have also been appreciated with legitimate applications. Socializing resilience also got much acceptance in the research sector. Such highly accepted research papers in the scientific research field created a definite standpoint that can be helpful for further research. In addition, it can be significantly useful for risk management planners and policymakers. Most relevant words have been identified that replicated importance among researchers. Creating different cooccurrence networks, it has been identified that, climate change, community response, adaptive management, vulnerability, and disaster risk assessments are the most illuminating issues in the contemporary world. Through this bibliometric analysis, it has come forward that climatic events from floods and drafts are the most vulnerable issue in the aspect of climate change and to fight against those climatic changes, communities prefer to rely on local adaptation and these local adaptation methods differ with the varying environment. Developed countries in the world have taken the climate change issue seriously and resilience and response through communities have become intriguing issues for researchers from those countries. However, underdeveloped countries with a lack of infrastructure and a weak economy in the research field are unable to produce significant research that would be beneficial to their countries.

1.6 Conclusion Bibliometric analysis for any given topic mainly focuses on the statistical analysis of the bibliographic data. It is more scientific and informative than the classical review process. Vast number of literature can be covered by this approach but the bibliometric process is not free from minor disadvantages. Collecting data from a single database can reflect the chance of inclinations and valuable research papers can be found missing in data. It mostly focuses on the statistical approach and thus qualitative points of view may be ignored sometimes. As limited studies have been done through bibliometric analysis approach, it can be reflected in conceptualization and writing also. In this chapter, it is observed that bibliometric analysis covered a significant amount of documents within a certain time frame, and delivered significant results that helped to understand a clear scenario about the evolution of community response and resilience

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against climate change. These comprehensive studies cannot be treated as ultimate in this sector. Further detailed interpretation can be executed by accessing each and every piece of literature. This study is limited within the contemporary time frame; however, a bibliometric study on this subject will find more about the topic in the future. The present study has given sufficient observation about the perception of the community from the researchers’ point of view about climate change. It is hoped that after reading this chapter, researchers, policymakers, and other enthusiasts who are comparatively new in this field and finding a path to take for further studies will benefit.

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275. Available from https://doi.org/10.1111/j.1742-7843.2005.pto_139.x; https://onlinelibrary.wiley. com/doi/10.1111/j.1742-7843.2005.pto_139.x. John Wiley & Sons, Ltd. Walther, G.-R. (2010). Community and ecosystem responses to recent climate change. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1549), 2019
2024. Available from https://doi.org/10.1098/ rstb.2010.0021; https://royalsocietypublishing.org/doi/10.1098/rstb.2010.0021. The Royal Society. Wamsler, C., Brink, E., & Rivera, C. (2013). Planning for climate change in urban areas: From theory to practice. Journal of Cleaner Production, 50, 68
81. Available from https://doi.org/10.1016/j.jclepro.2012.12.008; https:// linkinghub.elsevier.com/retrieve/pii/S095965261200652X. Elsevier. Wang, B., Pan, S.-Y., Ke, R.-Y., Wang, K., & Wei, Y.-M. (2014). An overview of climate change vulnerability: A bibliometric analysis based on Web of Science database. Natural Hazards, 74(3), 1649
1666. Available from https://doi.org/10.1007/s11069-014-1260-y; http://link.springer.com/10.1007/s11069-014-1260-y. Springer. Wang, Z., Zhao, Y., & Wang, B. (2018). A bibliometric analysis of climate change adaptation based on massive research literature data. Journal of Cleaner Production, 199, 1072
1082. Available from https://doi.org/10.1016/ j.jclepro.2018.06.183; https://linkinghub.elsevier.com/retrieve/pii/S095965261831833X. Elsevier. Wilson, E. (2006). Adapting to climate change at the local level: The spatial planning response. Local Environment, 11(6), 609
625. Available from https://doi.org/10.1080/13549830600853635; http://www.tandfonline.com/ doi/abs/10.1080/13549830600853635.

1. Introduction

S E C T I O N

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C H A P T E R

2 Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh Mst. Shifat Rumana1, Ummey Kulsum2, Md. Rayhan Ali3, Hasan Mahmud3, Dalce Shete Baroi4, Nafia Muntakim4, Zihad Ahmed4, Md. Mizanoor Rahman4 and Md. Zahidul Hassan4 1

Department of Geography and Environmental Science, Begum Rokeya University, Rangpur, Bangladesh 2Department of Geography, University of Bonn, Germany 3Institute of Environmental Science, University of Rajshahi, Bangladesh 4Department of Geography and Environmental Studies, University of Rajshahi, Bangladesh

2.1 Introduction Bangladesh is one of the most disaster-prone countries in the world. Various types of disasters affect the country every year. The geographical settings and meteorological characteristics have made it vulnerable to different geo-hydro-meteorological hazards/disasters (Rahman et al., 2017). The country is known as a land of rivers; more than 700 rivers and their tributaries and distributaries cross the country, forming a river system network (Islam & Rashid, 2012). Morphologically, the rivers of Bangladesh are at the old stage where the siltation is higher than the transportation. In the meanwhile, all the rivers have lost their carrying capacity. As a result, substantial rainfall along with massive river discharge overflows its surrounding land areas and creates a flood situation (Mohamed & El-Raey, 2020). According to Khalequzzaman (Khalequzzaman, 1994), an unusual or above-normal surfacewater flow that inundates high ground is called a flood. However, a flood turns into a

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00002-3

27

© 2023 Elsevier Inc. All rights reserved.

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2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

disaster when it causes massive damage to life, agricultural land, settlements, and infrastructure (Abebe et al., 2018; Dottori et al., 2016). Almost every year, it comes as a disaster in Bangladesh, typically in the Ganges-Brahmaputra-Meghna (GBM) basin (Bhuiyan & Baky, 2014) in any of the following five forms: (1) riverine, (2) rainfall-induced, (3) flash, (4) tidal, and (5) cyclonic/storm surge (Rahman & Salehin, 2013). Although normal floods are considered as a blessing for Bangladesh providing vital fertility and moisture to the soil through the alluvial silt deposition (Rahman & Salehin, 2013), the destructive effects of floods have long been highlighted as the main obstacle to the economic improvement of the nation. Flooding causes deaths and injuries to people, and every year more than 300
500 people lose their lives, and millions of other people become homeless and suffer from starvation. Flooding brings too much water, which leads to the damage of roads, collapse of bridges, creation of traffic congestion, destruction of farmland, and affects the daily life of all concerned. Because of floods, Bangladesh experiences an economic loss of approximately 30 billion USD every year, and after a big flood government has to inject many resources into aid and reconstruction, which also brings extra economic stress to the public (Rahman & Ali, 2014). Bangladesh consists of 54 transboundary rivers, and Teesta is one of them. It is the fourth largest river in Bangladesh, originates from the TsoLamo Lake fed by the Teesta Kangse Glacier of Sikkim, Himalayas, and enters into Bangladesh at Chatnai, Nilphamari district (Islam et al., 2004). Before pouring into the Brahmaputra River in the Rangpur division of Bangladesh, the river travels through the Sikkim and West Bengal states of India. The length of the river is 414 km, with a total catchment area of 12,159 km2. The Teesta basin is home to around 30 million people, 2% in Sikkim, 27% in West Bengal, and 71% in northwest Bangladesh of which 78% is rural and 22% is urban (Noolkar-Oak, 2017). More than 21 million people in Bangladesh depend directly or indirectly on the Teesta River for their livelihood. However, there are two large barrages on the Teesta that divert water mainly for irrigation purposes: one at Gajoldoba in India and the other at Duani in Bangladesh (Syed et al., 2017). Besides, hydropower plants of India on the Teesta River create anxiety for Bangladesh. There are at least 26 projects on the river, mostly in Sikkim, that reduce the river’s flow during the dry season, greatly hindering the livelihoods of thousands of farmers, fishermen, and boatmen in Bangladesh. Contrariwise, during the monsoon season, Indian dams on the Teesta release excess water, causing heavy floods and again disrupting thousands of livelihoods in Bangladesh. However, the inhabitants of the Teesta River basin area in northwestern Bangladesh contend with floods almost every year. In the monsoon, heavy rainfall and upstream flow multiply the flooding several times in this region, which enhances the sufferings of the inhabitants due to the colossal loss of lives and properties. To deal with the adverse environmental and socio-economic effects of flooding, people have developed local wisdom and indigenous practices that reduce flood vulnerability. They try to adapt to inundation through their previous experience, believing and understanding about floods through their socio-economic, cultural, and environmental conditions (Sultana et al., 2018). Although they are trying to apply their best practices, they could not achieve the expected results. It is because they are unknown of the shortfall and lack their applied strategies as well as proper information on flood recurrence and intensity. Keeping this view in mind, the present chapter makes an endeavor to analyze the flooding intensity, recurring trend, and risk in the Teesta riparian area, and to discuss the indigenous coping strategies to mitigate the problem.

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2.3 Materials and methods

29

2.2 Objectives of the study The specific objectives of this chapter are as follows: • To assess the intensity and recurrence trend of flood in the study area. • To analyze the flood risk in the study area. • To discuss the role of applied indigenous coping strategies to mitigate flood hazards in the study area.

2.3 Materials and methods Study area selection: Teesta basin is one of the most flood-prone areas of Bangladesh, severely affected by floods frequently. Keeping this view in mind two unions, Lakshmitari from Gangachara Upazila and Kaunia Balapara from Kaunia Upazila of Rangpur district were selected purposively as the study area. Both of the unions are situated in the Teesta basin, as shown in Fig. 2.1. Data sources and collection procedure: The study has two parts: first, to analyze the flood intensity and recurrence trend as well as flood risk, the required historical flood data was collected from different Governmental Organizations (GOs) like Bangladesh Flood Forecasting and Warning Center (FFWC), and Bangladesh Water Development Board (BWDB). Second, to gather data on indigenous coping strategies, a well-structured questionnaire survey was conducted in the study area.

FIGURE 2.1 Map of the study area.

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2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

Analysis: To assess the flooding intensity, the danger level of flood (m), the peak of the water discharge (m), and days of inundation above the danger level were considered and calculated through the following procedure (Ali et al., 2013; Tithi et al., 2021). Flood Intensity Index (FII) 5 Maximum inundation depth for a particular year 3 The flooding duration. Maximum inundation depth 5 Height of the water level above the danger level (m) 5 Peak of the flood level (m)
The danger level (m). Here, flooding duration is determined by how many days the flood water depth was above the danger level. First, the FII for different flooding years was determined, and second, the floods were characterized based on the average FII. According to the value of the index and its classification, floods are categorized as: very low flood, low flood, moderate flood, high flood, and very high flood. On the other hand, there are different techniques for computing the flood frequency and magnitude, such as the California method (Barnett, 1975; Chow, 1964; Suhaiza et al., 2007; Weibull, 1939), Log-Pearson type 3 (Bobee & Robitaille, 1977; Griffis & Stedinger, 2007), etc. In this study, flood frequency analysis was conducted using Gumbel’s (Gumbel, 1941) extreme value distribution method based on theoretical probability distributions (Bhagat, 2017; Islam & Sarkar, 2020; Kumar & Bhardwaj, 2015; Naz et al., 2019; Solomon & Prince, 2013), and Gumbel and Weibull distribution functions for estimating flood peak with respect to different return periods and probability of occurrences. The value of the maximum peak flood from a given catchment area for a large number of successive years is arranged in decreasing order of magnitude. The return period or recurrence interval (Tr) is the average interval, in years, between occurrences of two discharges of equal (or greater) magnitude. This relationship, known as the Weibull equation, can be written as: Tr 5

n11 m

(2.1)

where n 5 Total number of observations, and m 5 rank of the value. The Annual Exceedance Probability (P) is the probability (expressed in percentage) that a flood of that magnitude or greater will occur in a given year, and is assumed by: P5

1 3 100 Tr

(2.2)

In the Gumbel method, the expected peak flood (XT) of the different return periods (Tr) and the probability of exceedance (P) are designed using the following algorithm.



XT 5 X 1 KT σ

(2.3)

where XT 5 the maximum value of expected peak flood, X 5 mean peak flood, σ 5 standard deviation of peak flood, and KT 5 frequency factor which is measured using the following formula. KT 5

YT
Yn Sn

2. Climate change, social response and resilience

(2.4)

2.3 Materials and methods

31

where YT 5 reduced variate, Yn 5 mean of reduced variate, and Sn 5 standard deviation of reduced variate which is calculated using the following equation. KT 5 In:In

Tr Tr
1

(2.5)

The reduced variate can be used to establish whether the observed flood data follows the Gumbel distribution or not. If the plot of the reduced variate and flood peak follows a linear pattern, it can be concluded that the Gumbel distribution fits well with the observed data. Flood risk is generally perceived by the two dominant paradigms, for example, subjective (perceived) view and objective (statistical) view (Smith, 2013). Such assessment might be done probabilistically, scenario-based, or by composite indexing. The probabilistic risk assessment could afford good information on future disasters by providing likelihoods and impacts of all possible scenarios (Kheradmand et al., 2018). However, the indicatorbased approach was conceived in this study for assessing flood risk. Indicators used in the study were chosen through an extensive literature review for each dimension of flood risk assessment. The selected indicators were scrutinized in consideration of the local conditions and adjusted accordingly. The primary datasets were standardized using respective weights for the computation of the flood risk index (Rana & Routray, 2016). A subjective weighting technique is also used to allocate values to classes of phenomena for each indicator and formulate indices based on the following equation. WAI 5

n X W1 1 W2 1 W3 1 :::::::::::::Wn Wi 5 5 n n i51

(2.6)

where WAI is the weighted average index, W1 to Wn are respective transformed values assigned to the weight value of ith variable, and n is the number of variables used for computing the individual weighted index. Ensuing the principle, Hazard Index (HI), Exposure Index (EI), Vulnerability Index (VI) and Resilience Index (RI) were calculated. n P i Hazard Index, HI 5 HW n . iP n i Exposure Index, EI 5 EW n . i P n i Vulnerability Index VI 5 VW n . n i P i Resilience Index, RI 5 RW n . i

Finally, the Flood Risk Index (FRI) was calculated using the following equation (Rana & Routray, 2016). FRI 5

HI 3 EI 3 VI RI

(2.7)

For the computation of indices, the existing values of the classified parameters have been transformed from 0 to 1 based on the level of vulnerability, risk factors, and importance of the variables. The values closer to 0 indicate the lowest vulnerability, while the values closer to 1 indicate the highest vulnerability. Each variable was then classified

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2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

based on its inherent characteristics. The values were categorized by 0 and 1 in dual classes. In the case of three classes, the values were assigned as 0.33, 0.67, and 1; for four classes, 0.25, 0.50, 0.75, and 1; for five classes, 0.2, 0.4, 0.6, 0.8, and 1. Thus, the flood risk index for each parameter lies between 0 and 1. However, FRI integrates variables concerning the characterization of river flood hazards and the related exposure, vulnerability, and resilience, and was presented for assessing the flood disaster risk resilience of Teesta riverine households. During the field survey, the key person of the households was requested to rank selected coping strategies against a 0
4 scale, where 0 is never adopted option and 4 is the most adopted option. Based on the weighted average index (WAI), the coping strategies for each period were ranked and calculated by the following equation modified after Devkota et al. (2014) and Jahan et al. (2014). WAI 5

ðF1 3 W1 Þ 1 ðF2 3 W2 Þ 1 ðF3 3 W3 Þ 1 ðF4 3 W4 Þ 1 ðF5 1 W5 Þ::::: 1 Fn 3 Wn F1 1 F2 1 F3 1 F4 1 F5 ::::::::: 1 Fn P WAI 5

F 3 Wi Pi Fi

(2.8)

(2.9)

where, F 5 Frequency of the respondents. W 5 Weight of each scale. i weight (4 5 Mostly adopted, 3 5 Frequently adopted, 2 5 Occasionally adopted, 1 5 Rarely adopted and 0 5 Never adopted). Besides, GIS techniques were used to produce the flood zonation map using ArcGIS 9.3 software. Moreover, various types of statistical tools and techniques, especially tabular and graphical, were applied to analyze and interpret the collected data.

2.4 Results and discussion 2.4.1 Discharge pattern of the river Teesta in the study area Teesta is a perennial, rain- and snow-fed river characterized by extreme variability in its flows throughout the year. The magnitude of the discharge depends on the month and climatic conditions, especially varies significantly between dry and wet spells. Over 90% of the flow occurs in the rainy season, from June to September, while the remaining 10% occurs during the remaining eight months. The yearly discharge pattern shows that a decreasing flow occurs for six months, from November to April, while an increasing flow persists for two months, from May to June, then a highly increasing flow continues for four months, from July to October. Besides, as a lower riparian, Bangladesh is completely dependent on India, the upper riparian, for keeping minimum or maximum flows in the River Teesta. 2.4.1.1 Mean monthly discharge in the river Teesta Historically, the Teesta has experienced an average maximum flow of 280,000 cusecs and a minimum of 10,000 cusecs at Dalia, the upstream of the Teesta Barrage (Khalid, 2013).

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2.4 Results and discussion

33

Evidently, the river flow is highly controlled for hydro and irrigational projects both in India and Bangladesh (Haque et al., 2014). Hence, this flow comes down to about 1000 cusecs and even 500 cusecs during the dry season due to excessive control over the Teesta flow (Khalid, 2013). The study result reveals that the amount of discharge starts to rise after April and following an upward trend, reaches its peak in August (1575.40 m3/s). However, in 3 months, from July to September, discharge remains at the highest level, while the amount of discharge begins to fall after October and touches the bottom in February (24.17 m3/s). But in the 3 months, from January to March, discharge remains at the lowest level Fig. 2.2. To understand the temporal variation in maximum and minimum flow patterns of the Teesta River, discharge data from 1985 to 2020 at Kaunia station was analyzed. The maximum discharge pattern shows a fluctuating tendency as well as discharge irregularities in the Teesta River flow. In the years 1998, 1993, 1997, 1998, 2004, 2007, 2010, and 2017, the discharge exceeded 6000 m3/s. Among them, the maximum discharge peak was about 8577.81 m3/s on 21st April 2004. The average maximum discharge at Kaunia station from 1985 to 2020 was found to be 4274.19 m3/s. During the same period, the minimum discharge pattern was awful, except in 2004 (145.26 m3/s), as the minimal discharge had been decreasing gradually since the construction of the Gajoldoba Barrage in 1985, India in the Teesta upstream (Mullick et al., 2010; Sandep & Futehally, 2013). The recorded lowest minimum discharge was a mere 12.34 m3/s on 7 February 2019. However, the average minimum flow was observed at 78.41 m3/s in the study area, as shown in Fig. 2.3. The availability of water in the Teesta River basin changes each year due to local and global, natural, and anthropogenic phenomena. Although the highest discharge and the highest stage, may not always be corresponding. This is because it depends on a number of factors such as river width, depth, topography, velocity of flow, sedimentation pattern,

FIGURE 2.2 Monthly distribution of Teesta river discharge in the study area.

2. Climate change, social response and resilience

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2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

FIGURE 2.3 Maximum and minimum discharge in the study area (1985
2020).

and so on. The summarized picture of the changing level of water over the years is described herein. The result of historical discharge data analysis (from 1985 to 2020) indicates that September is the highest maximum flow month, reaches to 30.52 m, which is 0.52 m above the danger level. Moreover, the maximum discharge remains at the danger level in May, June, and October, above the danger level from July to October, while the rest of the months remain below the danger level. Contrarily, the lowest maximum discharge occurs in February, 27.9 m. However, the monthly minimum flow of Teesta River follows the pattern of maximum flow (Fig. 2.4). 2.4.1.2 Temporal variation in water levels of the river Teesta The yearly data analysis result exposes that the maximum water level fluctuates over time in the study area. During 1985
2020, the maximum water level crossed the linear danger line (30 m) by seventeen times (Fig. 2.5). In this time, the highest water level was recorded at 3.52 m in 1968, which is 0.04 m higher than the second highest, 3.048 m, recorded in 1998. However, the recent scenario shows an alarming trend as the maximum water level exceeds the danger level by 10 times from 2004 onwards. Excessive sedimentation on the riverbed after the construction of the two barrages, namely the Gajoldoba (India) and the Teesta (Bangladesh), has reduced the carrying capacity of the River Teesta, mainly responsible for such flooding occurrences. On the contrary, a downward trend can be seen in minimum water level discharges, as India withdraws excess water during the dry season through Gajoldoba Barrage. Such activities of India pose the Teesta to be slothful during the dry season, and the minimum flow reaches the bottom line. However, the trend analysis indicates that the minimum water level has decreased over the years, and in 2013, it was recorded as ever at its lowest,

2. Climate change, social response and resilience

2.4 Results and discussion

35

FIGURE 2.4 Monthly variation in maximum and minimum water level of Teesta river in the study area (1985
2020).

FIGURE 2.5

Yearly recorded maximum and minimum water level of Teesta river in the study area

(1985
2020).

25.39 m. After 2013, almost a similar amount of discharge was observed in the case of minimum water flow of the River. Similarly, as shown in Fig. 2.5, a widening trend between maximum and minimum flow can be seen over time, indicating the increasing acuteness of flooding in the wet season or drought in the dry season.

2. Climate change, social response and resilience

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2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

2.4.2 Flood Intensity in the study area The flood occurrences are categorized based on the average FII of the respective flood. According to the FII score and classification, the study area has experienced two very high floods, four high floods, two moderate floods, five low floods, and four very low floods during 1985
2020 (Fig. 2.6). Among them, floods in 1988 and 1998 were severe and much more devastating, as they posed the highest FII scores, 24.64 and 31.68, respectively. Correspondingly, floods in 1993, 2004, 2007, and 2017 were considered high floods with FII scores of 15.58, 15.17, 16.65, and 15.6, respectively. Accordingly, the floods of 2000 (11.31) and 2010 (12.92) were measured as moderate flood events. FII scores for low floods originated in the years 1987, 1999, 2008, 2012, and 2014 were 8.51, 5.94, 5.04, 5.13, and 6.16, respectively. However, very low floods were found in the years 1994, 2009, 2016, and 2020. Upstream monsoon downpours and riverbed sedimentation, as well as the topography of the Teesta basin area, are found to be equally responsible for such flooding events.

2.4.3 Recurrence trend of flood in the study area The return period of the floods was measured based on the time series of peak floods by using Weibull’s method, while the expected flood discharge was estimated at different recurrence intervals according to Gumbel’s extreme value distribution. The obtained analysis results are shown in Table 2.1, while reduced variate and peak flood are plotted in Fig. 2.7. It is revealed that the maximum flow was 8577.81 m3/s in 2004, whereas the lowest was 1684.47 m3/s in 2006. The mean flood flow of 36 years was recorded at about 4274.19 m3/s with a standard deviation (SD) of 1752.6 and a coefficient of variability (CV) is 41% (Table 2.1).

FIGURE 2.6 Yearly flood intensity and classification of floods.

2. Climate change, social response and resilience

37

2.4 Results and discussion

TABLE 2.1 Calculation of return period of peak flood.

Year

Peak flood (m3/s)

Peak flood in descending order (m3/s)

Order (m)

Return period

Reduced variate

Percentage of probability

1985

4106.44

8577.81

1

37.00

3.597

2.70

1986

3440.81

7671.31

2

18.50

2.890

5.41

1987

4720.56

7540.18

3

12.33

2.470

8.11

1988

6810.44

6810.44

4

9.25

2.168

10.81

1989

3512.92

6630.05

5

7.40

1.930

13.51

1990

3606.43

6440.23

6

6.17

1.732

16.22

1991

3422.12

6290.42

7

5.29

1.562

18.92

1992

4120.66

6197.91

8

4.63

1.412

21.62

1993

7540.18

5625.62

9

4.11

1.278

24.32

1994

3770.37

5359.64

10

3.70

1.155

27.03

1995

2250.71

5080.77

11

3.36

1.042

29.73

1996

2421.38

4840.57

12

3.08

0.936

32.43

1997

6630.05

4720.56

13

2.85

0.837

35.14

1998

7671.31

4120.66

14

2.64

0.744

37.84

1999

5359.64

4106.44

15

2.47

0.654

40.54

2000

5625.62

3978.57

16

2.31

0.568

43.24

2001

2355.26

3770.37

17

2.18

0.486

45.95

2002

3215.18

3606.43

18

2.06

0.406

48.65

2003

2987.52

3512.92

19

1.95

0.328

51.35

2004

8577.81

3458.83

20

1.85

0.251

54.05

2005

2889.55

3440.81

21

1.76

0.176

56.76

2006

1684.47

3422.12

22

1.68

0.102

59.46

2007

6197.91

3405.53

23

1.61

0.029

62.16

2008

3978.57

3215.18

24

1.54

2 0.045

64.86

2009

3405.53

3210.36

25

1.48

2 0.119

67.57

2010

6440.23

2987.52

26

1.42

2 0.193

70.27

2011

2790.08

2910.36

27

1.37

2 0.269

72.97

2012

4840.57

2897.28

28

1.32

2 0.346

75.68

2013

3458.83

2889.55

29

1.28

2 0.426

78.38

2014

5080.77

2871.87

30

1.23

2 0.510

81.08 (Continued)

2. Climate change, social response and resilience

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2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

TABLE 2.1 (Continued)

Year

Peak flood (m3/s)

Peak flood in descending order (m3/s)

Order (m)

Return period

Reduced variate

Percentage of probability

2015

3210.36

2790.08

31

1.19

2 0.598

83.78

2016

2897.28

2778.66

32

1.16

2 0.694

86.49

2017

6290.42

2421.38

33

1.12

2 0.800

89.19

2018

2871.87

2355.26

34

1.09

2 0.921

91.89

2019

2778.66

2250.71

35

1.06

2 1.071

94.59

2020

2910.36

1684.47

36

1.03

2 1.284

97.30

Mean

4274.19

SD

1752.60

CV

41.004

FIGURE 2.7 Reduced variate and flood peak (m3/s) for the Teesta river.

Using the peak flood and obtained recurrence interval data set, adjusted R2 gives a value of 0.9716 from the trend line equation. Hence, the value of r 5 0.9857 explains that the pattern of the scatter is narrow, and Gumbel’s method is suitable for predicting the expected flood in the river. From the flood frequency analysis, expected peak floods were also computed for different return periods of 2, 3, 5, 10, 20, 30, 50, 100, 150, and 200 years. However, other values were not considered which can be extrapolated or measured using the same method.

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39

2.4 Results and discussion

TABLE 2.2 Calculation of expected peak flood. Return period

Percentage of probability

Reduced variate

Frequency factor

Expected peak flood (m3/s)

2

50.00

0.37

1.29

6534.46

3

33.33

0.90

2.17

8083.65

5

20.00

1.50

3.94

11,182.02

10

10.00

2.25

8.36

18,927.97

20

5.00

2.97

17.20

34,419.85

30

3.33

3.38

26.04

49,911.74

50

2.00

3.90

43.72

80,895.51

100

1.00

4.60

87.92

158,354.95

150

0.67

5.01

132.11

235,814.38

200

0.50

5.30

176.31

313,273.82

However, the mean peak flow in the river is 4274.19 m3/s having a return period of 2 years, and the percentage of probability is of about 50%, as shown in Table 2.2. On the other hand, the highest peak, more than 8577 m3/s, indicates a 20% of probability with around 5 years return period. Therefore, the prediction of floods in the study area using Gumbel distribution is nearly accurate. Such prediction can be utilized in the river reach for the designing of important hydraulic structures and bridges. Moreover, in cases of extreme flooding, emergency evacuation of people can be carried out well in advance (Table 2.2).

2.4.4 Inundation area in different flooding years Chronological flooding events with inundation area and percentage of the total area were well examined and shown in Figs. 2.8 and 2.9. The results convey that the flood occurrences in 1988 and 1998 were the most devastating, as the study area was completely inundated, and more than one million people were displaced, resulting in massive economic losses. Although, the inundation of 20%
25% area of the country is merely beneficial for the crop production as well as the ecological balance (FFWC, 2016). However, inundation of more than 20% is caused by direct and indirect damages and substantial inconveniences to the people. It is evident that the yearly flooding area in the case of both Upazila was quite uniform, but year-to-year fluctuations in flooding areas are prevalent. In the recent trend, the number of extremely high floods has altered to moderate flooding events, but the frequency of floods has increased. It is clearly stated that the study area of the Teesta basin is highly vulnerable and equally susceptible to flood. In terms of inundation area, analysis of historical flood records picturizes that Kaunia Upazila is a more flood-prone area than Gangachara Upazila (Fig. 2.9).

2. Climate change, social response and resilience

40

2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

FIGURE 2.8 Yearly inundation area of the study area.

FIGURE 2.9 Flood-prone area.

2. Climate change, social response and resilience

41

2.4 Results and discussion

2.4.5 Impacts of flood in the study area The Teesta River has distinctive agricultural and economic impacts on its adjoining areas. Besides, people’s livelihood, cultural, and social status are influenced by this river. Due to the high frequency and intensity of floods, homesteads, crops, agricultural fields, property, livestock, poultry, etc. are being affected repeatedly. Scarcity of pure drinking water and food are common problems during a flood. Besides, people suffer from different types of health problems during the flooding and post-flooding periods. However, a summary of people’s perceptions of flood effects is shown in Table 2.3. TABLE 2.3 Overall impact of the flood and flood-induced riverbank erosion in the study area. Sectors of impact

Status

Level of concern

Damage of house

Increase

High

In both research areas, a number of houses are fully or partially damaged due to flooding and riverbank erosion.

Income

Decrease

High

Income highly decreases due to floods.

Damage of crops

Increase

High

Due to flood and flood-induced riverbank erosion, crop production decreases remarkably.

Loss of livestock

Increase

Low

Due to floods, people sell their livestock at cheaper rates which affects their income.

Loss of poultry

Increase

Moderate

Due to floods, many people sale of their poultry, and some poultry dies due to adverse environment, which also affects their income.

Fish culture

Decrease

Low

Due to floods, many fish ponds/farms go under water and farmers lose their fishes.

Vegetation

Decrease

Moderate

Due to floods and flood-induced riverbank erosion, vegetation (production) decreases.

Health problem

Increase

Moderate

People are being affected by various types of waterborne diseases during and post-flooding time.

Damage of different institutions and infrastructures

Increase

Moderate

Many institutions (houses, schools/collage, mosques/ temples, etc.), and infrastructures (roads, bridges, culverts, embankments, offices, tube wells, toilets, etc.) are being damaged due to flood and flood-induced riverbank erosion.

Pure drinking water facility

Decrease

Moderate

Due to floods, many tube wells were fully or partially damaged.

Sanitation problem

Increase

High

A number of toilets were damaged due to the flood. For this reason, people face sanitation problems.

Food availability

Decrease

High

During floods, people face severe food scarcity.

Loss of work

Increase

High

People become workless during floods.

Poverty level

Increase

High

Poverty level increases due to loss of work and income reduction.

Remarks

(Continued)

2. Climate change, social response and resilience

42

2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

TABLE 2.3 (Continued) Sectors of impact

Status

Level of concern

Educational facility

Decrease

Moderate

During floods, access to education losses due to the damage or destruction of educational institutions or for being used as flood shelters.

Fish availability

Increases

Moderate

As many fish ponds/farms are being submerged due to floods, fish availability increases in natural water bodies.

Soil fertility

Increase

High

Huge particles come with flood water from upstream and deposit in the flood-affected area, which increases soil fertility in the post-flooding period.

Crop production in post-flood

Increase

Moderate

Soil fertility is increased during the post-flood period due to deposition.

Remarks

Source: Data from, Field Survey and FGD by authors (2020).

2.4.6 Flood risk assessment in Teesta flood-prone area To assess the flood risk, the calculated WAI for different indicators is shown in Table 2.4. The flood proneness of the settlements in the study area is very high. Almost 70% of the respondents said that once in every two years their settlements are affected by flood hazards and their localities have flooded more than five times in the last decade. Moreover, they informed us that their houses remain flooded for more than 10 days during all the events. Overall, the intensity of the flood hazard was found too similar in both of the study Upazilas. The exposure index indicates that the livelihood properties of around three-fifth of the respondents were at risk. Besides, they were physically vulnerable as most of their homesteads were located between the levee and the riverbank, and in the Teesta floodplain within 2 km from the riverbank. Here, the level of income of the people is very low. Similarly, their income source is not enough diversified to cope with floods and riverbank erosion. Therefore, the economic vulnerability was found to be very high for all the locations studied. In the case of resilience, the majority of the households do not have access to financial institutions as well as physical, socio-economic, environmental, and monetary assets (e.g. employability, good health, and knowledge), even safe shelter. Besides, only a few people have flood disaster-related training, but not about their actions after receiving a flood warning. In the case of emergency preparedness, only one-third of the households have access to livelihood assets and emergency plans. However, to reduce the flood risk, onethird (32%) of households apply limited structural and non-structural measures as coping strategies. Fig. 2.10 and Table 2.5 reveal insights into each dimension of flood risk, through the average index values of the components. It exhibits slight variations of risk components in the study area. Results show that the households of Gangachara and Kaunia are facing high levels of flood hazard, and have a similar level of flood resilience, although there are significant differences in their levels of flood exposure and vulnerability, as well as

2. Climate change, social response and resilience

TABLE 2.4 Indicators and transformed values for flood risk assessment in Teesta Flood-prone area (1985
2020). Factors Hazard

SL. No. 1

Transform value

Indicators

Class

Magnitude of flood

, 2000 m s 3

1 3

0.75

3

4000
6000 m s

0.50

. 60,000 m s

0.25

,5

0.20

5
10

0.40

10
15

0.60

15
20

0.80

. 20

1

, 30.0 m

0.33

30.0
30.20 m

0.67

. 30.20 m

1

, 10 days

0.33

10
20 days

0.67

. 20 days

1

Katcha

1

Semi Pacca

0.67

Pacca

0.33

Yes

1

No

0

Yes

1

No

0

2000
4000 m s

3

2

3

4

Exposure

1

2

3

Recurrence trend of flood

Height of flood

Duration of flood

Housing type

People at risk

Infrastructure at risk

Explanation

Empirical Studies

Higher incidence of peak floods implies higher risks

Wang et al. (2011), Yankson et al. (2017)

Higher recurrence trend of floods denotes higher risks

Wang et al. (2011), Yankson et al. (2017)

Higher flood levels indicated more severity and resultant damages

Rana and Routray (2016), Yankson et al. (2017)

Longer duration of inundation indicates severity of flood

Rana and Routray (2016)

Type of materials used for construction would affect the structures.

Gain et al. (2015), Thouret et al. (2014)

Affected by previous flood implies that HH is exposed

Hahn, Riederer and Foster (2009), Huong et al. (2019)

Damage to infrastructure from the previous flood indicates the higher exposure

Yankson et al. (2017)

(Continued)

TABLE 2.4 (Continued) Factors

SL. No.

Indicators

Class

Transform value

4

Household properties at risk

Yes

1

No

0

Yes

1

No

0

Between levee and riverbank

1

Floodplain

0.67

Upland

0.33

Yes

1

No

0

Yes

1

No

0

Yes

1

No

0

,5

0.33

5
10

0.67

. 10

1

No schooling

1

Primary

0.8

High School

0.6

College

0.4

University

0.2

5

Vulnerability

Outside properties at risk

Explanation

Empirical Studies

Loss of HH goods from previous flood indicates the exposure of household

Yankson et al. (2017)

Damages of properties from previous floods indicate an exposure of household.

Huong et al. (2019), Rana and Routray (2016), Yankson et al. (2017)

Location of the house in between the levee and riverbank indicates higher exposure

Balica et al. (2009), Thouret et al. (2014)

Physical Vulnerability 1

2

3

4

Flood-prone settlements

Household without tube well Household without sanitary Toilet Household without electricity (Solar energy)

The vulnerability of HH increases Rana and Routray (2016), Yadav and Barve (2017) without a tube well The vulnerability of HH increases Rana and Routray (2016), without access to a sanitary toilet Toufique and Islam (2014) The vulnerability of HH increases Rana and Routray (2016), without access to electricity Yadav and Barve (2017)

Social Vulnerability 1

2

Household size

Literacy rate

The larger household size, the vulnerability might be higher

Birkmann et al. (2013), Cutter et al. (2003)

The low literacy rate reduces the access to information and communication, and increases vulnerability of household’s

Hahn et al. (2009), Pandey and Jha (2012)

3

Medical service

, 1 km

0.25

1
5 km

0.50

5
10 km

0.75

. 10 km

1

, 10,000 TK

1

10,000
20,000

0.8

20,000
30,000

0.6

30,000
40,000

0.4

. 40,000

0.2

Govt. service

0.2

Business

0.4

Agriculture

0.6

Day labor

0.8

Unemployed

1

, 5000 TK

1

5000
10,000 TK

0.67

. 10,000 TK

0.33

Yes

1

No

0

Yes

1

No

0

Yes

1

No

0

The higher distance to medical service, higher the vulnerability

Arma¸s (2012), Rana and Routray (2016)

Lower-income poses to higher vulnerability

Balica et al. (2009), Cutter et al. (2003), Phung et al. (2016)

Unsecured sources of income increase vulnerability

Arma¸s (2012), Phung et al. (2016), Pandey and Jha (2012)

Savings increase the coping capacity with a flood and thus help in recovery

Rana and Routray (2016)

Safe shelters/Shifting HH opportunities increase the coping capacity with a flood

Huong et al. (2019)

Knowledge and attitude of flood risk management enhances by receiving training from experts

Huong et al. (2019), Nhuan et al. (2016)

HH who were able to recover from the last flood using their own resources are considered more capable

Yadav and Barve (2017)

Economic Vulnerability 1

2

3

Resilience

1

2

3

Income

Occupation of household head

Savings

Shelter/Safe location

Awareness and Capacity building Access to Livelihood assets

(Continued)

TABLE 2.4 (Continued) Factors

SL. No.

Indicators

Class

Transform value

4

Access to emergency fund

Yes

1

No

0

Yes

1

No

0

Yes

1

No

0

5

6

Early warning system

Coping strategies

Explanation

Empirical Studies

Emergency fund from neighbors or GO’s & NGO’s helps HH to cope with floods

Nhuan et al. (2016), Rana and Routray (2016)

Access to early warning system indicates HHs are more aware of the floods

Hahn et al. (2009), Huong et al. (2019), Rana and Routray (2016), Yadav and Barve (2017)

HH having indigenous coping are Yadav and Barve (2017) considered better prepared than those without

47

2.4 Results and discussion

FIGURE 2.10 Average Index of the study area.

Flood

Risk

TABLE 2.5 Criteria of assessment. Very low

Low

Moderate

High

Very high

, 0.20

0.21
0.40

0.40
0.60

0.61
0.80

. 0.80

FIGURE 2.11 Index score of the risk components.

aggregated flood risk. As compared to Lakshmitari, households of Kaunia Balapara are at higher flood risk (Fig. 2.11). As per the index score, flood hazard and exposure dimensions were much higher than others, indicating people are lowly resilient which significantly enhances the flood risk and vulnerability. Overall flood risk score is too high (0.642), indicating a strong need to launch awareness-building programs, assistance from emergency

48

2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

groups, and design flood risk mitigation strategies to enhance the flood risk perceptions of the communities and engage the local institutions with the communities to implement flood risk reduction strategies effectively.

2.4.7 Indigenous coping strategies Coping strategies that are perceived by the people are discussed below. 2.4.7.1 Coping strategies during pre-flood period During pre-flood, the Teesta riverine peoples use various precautionary techniques as safeguards to protect against excessive damages from flood (Table 2.6). The study result indicates that among the pre-flood coping strategies, early warning system is ranked at the top (WAI 2.60), which is followed by most people. As elderly people are experienced in hydro-climatic variables, they observe sky and weather motion, animals’ behavior, especially the movement of ants, and river flow, and make people aware by announcing in the mosque, so that people can take the necessary steps to save their life and properties. The structural change of the house is the second most popular strategy (WAI 2.58) that people use. People make houses with movable materials like corrugated iron sheets and thatch so that they can easily shift them to a safer place from being flooded. Moreover, to protect homesteads either they raise plinths of houses or put sandbags or shift the house to a higher place that offers relatively effective protection against flood. Besides, early crop harvesting and selling crops in advance (rank 3rd and WAI 2.53) are adopted by a large number of respondents as a precautionary practice regarding flood coping. Besides, people store crops in mud pots or stack them on mucha (one kind of stage made of bamboo and rope) covered by polythene. Among the other techniques, people preserve dry food items, especially traditional dry foods, e.g., sugar-chapatti, biscuits, bread, puffed rice, flattened rice, pulses, molasses, etc. which play a significant role in coping with floods (WAI 2.24). Accordingly, people stack TABLE 2.6 Pre-flood period indigenous coping strategies. Extent of adaptation Strategies

Mostly Frequently Occasionally Rarely Never WAI Rank

Early warning system

28.81

34.63

16.62

7.76

12.19

2.60

1st

Protecting homestead

21.05

42.94

16.62

12.19

7.20

2.58

2nd

Dry food preservation

13.57

19.11

45.43

21.88

0.00

2.24

4th

Fuel collection and preservation

4.43

21.05

31.30

28.25

14.96

1.72

5th

Collecting fast aid kits and medicine

3.32

6.93

42.94

36.01

10.80

1.56

6th

Early crop cutting and selling crops in advance 19.94

36.01

28.81

7.76

7.48

2.53

3rd

Precautionary money savings

10.25

13.57

49.31

22.99

1.23

7th

3.88

2. Climate change, social response and resilience

49

2.4 Results and discussion

dried cow dung and leaves under a tin shed as fuel (5th ranked, WAI 1.72) which is used for cooking purposes during floods. Similarly, another pre-flood coping strategy is to collect first aid medicine and store it for human beings and livestock. They also reserve personal hygiene and safety kits for emergency situations and make homemade saline before the flooding period. Accordingly, few people save money to meet up the necessary costs during floods, although the amount of savings depends on expected losses and economic conditions. 2.4.7.2 Coping strategies during flood According to the impact and severity of the flood, Teesta riverine community takes different indigenous coping strategies, which are discussed in Table 2.7. Protecting different household products from floods is the prime need for the affected people, and stakeholders follow it (WAI, 2.32). According to the respondent’s opinion, they shifted household properties on the mucha or corrugated iron sheet or to a safer place during floods. But in critical flooding conditions or long inundation periods, people take shelter in neighbors’ or relatives’ houses that are located in a flood-free zone. Besides, a number of people take shelter in the flood shelter. On the other hand, to save livestock and poultry from flood, people take different steps like shifting them to a safer place in the neighbors’ or relatives’ houses or higher ground or roadside. On the other hand, to save livestock and poultry from flood, people take different steps like shifting them to a safer place in the neighbors’ or relatives’ houses or higher ground or roadside. People in the study area also practice various types of fisheries protection strategies, such as raising pond banks, using nets, bamboo nets, and so on for flood protection. During the flood, drinking water crisis is very common. The maximum number of people in the study area have to drink tube well water without using any drinking water purifying system (rank 6th and WAI 1.02). Among the respondents, only one-third had the TABLE 2.7 During flood indigenous coping strategies. Extent of adaptation Strategies

Mostly

Frequently

Occasionally

Rarely

Never

WAI

Rank

Protecting household properties

22.99

31.30

20.50

5.26

19.94

2.32

1st

Drinking water purification

3.32

7.76

21.05

23.82

44.04

1.02

6th

Portable stove for cooking

14.68

21.88

26.32

13.57

23.55

1.91

3rd

Food habit change and reducing the number of meal

7.76

14.68

28.25

22.99

26.32

1.55

5th

Alternative sources of electricity

1.39

5.82

11.63

16.07

65.10

0.62

8th

Indigenous transportation mode

12.19

16.07

26.32

27.70

17.73

1.77

4th

Treatment and medicine facilities

4.43

8.86

14.13

17.45

55.12

0.90

7th

Borrowing money

21.05

26.32

23.82

13.57

15.24

2.24

2nd

2. Climate change, social response and resilience

50

2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

opportunity to use water purifying tablets during flood, which has been provided as relief material. Besides, a few people purify water by using fitkari (potassium aluminum sulfate), and by boiling the water. Moreover, a number of affected people collect safe drinking water from the neighbor’s house, where the tube well is installed in a higher place. The cooking condition poses higher vulnerability during the flooding period. During floods, the traditional portable mud stove (Chula) for cooking is the alternative technique (rank 3rd and WAI 1.91). Most people use this portable stove, which might be arranged on a mucha, road, embankment, etc. Change in food habits and reducing the number of meals (rank 5th and WAI 1.55) are very common techniques to cope with floods. Most people take limited food according to their capacity. Sometimes people take dry and inexpensive food items. They also take rice with onion, salt, chili, etc., and mashed food items (e.g., sutki, sidol, suktani) rather than starvation during floods. Though the households have electricity connection, it often becomes disconnected during floods. People use Prodip (a lamp made of earth and cloth), as they could not afford solar panels as an alternative for the load shading or power outages situation in flooding periods. During floods, the road transport network is often being destroyed, people use rafts (made of bamboo and banana) as a mode of transportation. Very often, waterborne diseases, such as diarrhea, fever, cough, and scabies, break out as an epidemic during the flood. According to the respondents’ opinion, supply of relief medicine is not sufficient enough, and regular doctor visit is also costly. So, they have to depend on herbal treatments, especially tulsi (Ocimum sanctum), basak (Adhatoda vasica), turmeric (Curcuma longa), etc. Moreover, they use Kacha kola (Musa balbisiana) to get rid of diarrhea and apply mustard oil, coconut oil, and kerosene to scabies-affected skin during floods. On the other hand, monetary crisis is very common during the flood. Most people borrow money from informal sources, especially from Mohajon (Landlord) with higher interest due to the unavailability or complexity of getting governmental/institutional loans. Besides, they sell household properties, especially those that have no income from nonfarm sources. However, those involved with farming sell their labor in advance to the Mohajon. 2.4.7.3 Coping strategies in post-flood period After a flood, inundation for a few days has a long-term impact on the livelihood of the community people. To overcome the problems, affected people take some indigenous coping strategies, as shown in Table 2.8. In the post-flood phase, the main issue of the affected people is to repair or reconstruct the damaged house (rank 1st and WAI 2.04). They use mainly iron sheets, bamboo, wood, and thatch for repairing their house. In that case, government and non-government organizations help the affected people by providing house repair/reconstruction materials. The 2nd largest challenge (WAI 1.93) of the affected people is to get any type of work at once. As a result, they diversify their activities rather than fixed ones, as they did before the flood. During this time, domination of informal activities, basically day labor, agricultural worker, van/rickshaw puller, etc., is found. Male people move to a nearby city and very often work on a daily wage.

2. Climate change, social response and resilience

51

2.5 Conclusion

TABLE 2.8 Post-flood indigenous coping strategies. Extent of adaptation Strategies

Mostly

Frequently

Occasionally

Rarely

Never

WAI

Rank

Reconstruction of houses

19.11

24.93

19.11

14.13

22.71

2.04

1st

Community defense

6.37

13.57

30.19

28.81

21.05

1.55

4th

Changing cropping patterns

12.19

18.56

22.99

26.32

19.94

1.77

3rd

Educational coping

4.43

6.93

11.63

21.05

55.96

0.83

5th

Livelihood diversification

13.57

22.99

21.05

27.70

14.68

1.93

2nd

On the contrary, people try to cultivate varieties of crops (rank 3rd and WAI 1.77) rather than previous selected ones, on the basis of the conditions of agricultural fields, availability of inputs such as seeds, fertilizers, pesticides, and market demand. Among the crops, those with short-durations such as nut, pumpkin, potato, maize, mustard, pulses, etc. are preferred in the list. Besides, people cultivate a late variety of rice like Naizershail, Poranga, BR-22/23, etc. However, to recover the damaged common properties and betterment of the area people work together. For example, releasing flood water through road cutting, making earthen barriers and staking sandbag in the passage of flood water can control inundation. Similarly, repairing damaged infrastructures like mosques, madrasas, schools, clubs, etc. can be done on a volunteer basis, a food-for-work basis, or a payment basis. Due to the insufficient carrying capacity of flood shelters, educational institutions use as temporary flood shelters, and educational activities are closed during floods. Along with the situation, the economic crisis poses school dropouts. Child labor and early marriages are other adaptive measures to cope with the situation (5th Rank, WAI 0.83).

2.5 Conclusion Water balance in the Teesta River basin area fluctuates year to year and throughout the year due to local and bilateral, natural, and anthropogenic phenomena. The extremities, both dryness in the dry season and overflow in the rainy season, have increased. Although the contribution of climatic factors behind the situation is important, two manmade constructions like the Gajoldoba and the Teesta Barrage are more significant. Results of the study indicate that during 1985
2020, the minimum water level has been downwarding in the dry season, and up-warding in the rainy season. This is because, through the Gajoldoba Barrage, India controls and withdraws excess water in the dry season, and releases excess water in the rainy season. Generally, the water level increases gradually after March and crosses the danger level between May to September. During 1985
2020, the maximum water flow crosses the danger level by 17 times. However, the mean peak flow in the Teesta River is 4274.19 m3/s having a return period of 2 years (50% of probability), and the highest peak flow is more than 8577 m3/s having 20% of probability with

2. Climate change, social response and resilience

52

2. Assessing flood risk, intensity, recurrence trend, and indigenous coping strategies of the Teesta riverine people of Bangladesh

5 year return period. Lower levels of flood resilience (0.408) offer higher levels of flood risk (0.642) in the Teesta flood-prone area. Nonetheless, flood-affected people adopt different indigenous techniques in different flooding times like pre-flood, during the flood, andpost-flood. Measures of pre-flood times are preventive, during-flood are surviving, and post-flood are recovering. In the pre-flood time, people develop early warning systems such as announcing in the mosque, raising plinths of houses, early harvesting of crops, and preserving dry food, fuel, and necessary medicines. Besides, during the flood, people follow techniques of saving household properties, manage shelters for themselves and pets, manage transport like boat, change food habits, manage fuel and light, etc. On the other hand, after the flood, people give priority to reconstructing the damaged house, recovering the agricultural field and cultivating crops, and managing their occupation. Moreover, people work together to recover the common damage, e.g., roads, embankments, religious and educational institutions, etc. done by a flood. For this, different GOs and NGOs patronize and guide them by providing money and necessary logistics. However, the application of indigenous coping strategies of the affected people has a significant role to minimize damages and manage flood disasters in the study area. But now, it is hard because of changing dimensions and the recurrence of floods. So, collective measures for flood prevention are of utmost need. The government should formulate a comprehensive flood disaster mitigation policy and programs, especially in collaboration with different national and international organizations, that can help the people’s indigenous coping strategies to be more effective against flood hazards and ensure a sound livelihood for the flood-affected people.

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C H A P T E R

3 Socio-economic and livelihood vulnerability in view of climate resilience: A case study of selected blocks of Sundarban, India Semanti Das Department of Geography, Chandrakona Vidyasagar Mahavidyalaya, Paschim Medinipuir, India

3.1 Introduction In view of the United National Development Programme (UNDP), vulnerability can be defined as “a human condition or process resulting from physical, social, economic and environmental factors, which determine the likelihood and scale of damage from the impact of a given hazard” (UNDP, 2004). Social vulnerability is intrinsically related to social inequalities. Social inequalities are analyzed in terms of the susceptibility of social groups to the impacts of hazards, their resilience, or their adaptive capacity (Cutter & Emrich, 2006). Sundarban is the world’s largest mangrove forest ecosystem where frequent environmental vulnerability in terms of regular cyclones, floods, and storm surges culminate in the capacity of being resilient (Das & Das, 2017). Deltas are complex systems that provide the habitat for resource-dependent groups that are susceptible to environmental, economic, political, and social changes. As a result of these factors, a large number of people are migrating in response to the effects of climate change (Seto, 2011). According to surveys in the Sundarban, roughly 7000 people have been forced from their original habitations and have become environmental refugees or migrants as a result of sea-level rise, coastal erosion, cyclones, and embankment breaches over the last 30 years (Hazra et al., 2010). Environmental migrants are "persons or groups of people who, for compelling reasons of sudden or progressive change in the environment that adversely affects their lives or living conditions, are forced to leave their habitual homes, or choose to do so, either temporarily or permanently, and who move either within their country or abroad,"

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3. Socio-economic and livelihood vulnerability in view of climate resilience

according to the International Organization for Migration (IOM) in 2007 (IOM, 2009). “A livelihood is sustainable if it can cope with and recover from stressors and shocks, as well as preserve or improve its capacities and assets in the present and future, without jeopardizing the natural resource base” (Carney, 1998). A thriving and sustainable livelihood system aids in the development of “layers of resilience” in order to survive “waves of adversity,” and allows people to turn many adversities into opportunities (Glavovic et al., 2003). Smit and Pilifosova (Smit & Pilifosova, 2001) proposed that “adaptation” in socioecological systems was a moderate to long-term adjustment. “Coping strategies,” on the other hand, are short-term efforts taken by households to mitigate the negative effects of climatic variability on their livelihoods (Engle, 2011). Vulnerability assessment is one of the most important techniques for determining the extent of climatic hazards’ influence on human and ecological systems, as well as documenting victims’ responses to threats (Adger et al., 2007). Household-level vulnerability is inevitably linked with social vulnerability, which is the exposure of groups or individuals to stress owing to unexpected social and environmental change (Neil Adger, 1999). According to Chambers and Conway (1992), the sustainable livelihoods approach emphasizes five types of household assets: natural, social, financial, physical, and human capital. This approach helps to design development programming at the community level (81. United Nations General Assembly, 1997). The Department for International Development (DFID) assessed the Sustainable Livelihoods (SL) framework for asset endowment of the rural poor with the help of the above-mentioned five capitals (DFID, 2000). Adaptive capacity can be defined as “the ability of systems, institutions, humans, and other organisms to adjust to potential damage, to take advantage of opportunities, or to respond to consequences” (IPCC, 2014). Based on the sustainable lifestyles analytical framework, Boateng (2013) developed the Livelihood Asset Status Tracking (LAST) technique for evaluating the effects of a development program on agricultural productivity and poverty reduction in Ghana (Boateng, 2013). Bond and Mukherjee (2002) documented the LAST method as a useful tool for rapidly assessing impacts in livelihood improvement projects through a formative process evaluation system (Bond and Mukherjee, 2002). Household-level vulnerability is inevitability linked with social vulnerability, which is the exposure of groups or individuals to stress owing to unexpected social and environmental change (Neil Adger, 1999). The MS Swaminathan Research Foundation (MSSRF) created the Sustainable Livelihood Security Index (SLSI) techniques in 1993. The Household Livelihood Security (HHLS) model has become the foundation for CARE’s (one of the world’s largest international relief, and development not-for-profit organizations) program analysis in Kenya, India, and Sri Lanka (Lindenberg, 2002). It is an established fact that a vibrant relationship exists between vulnerability and resilience through the works of different scholars such as Cutter et al. (2008), Joakim et al. (2015), and Usamah et al. (2014). Many studies on vulnerability and adaptability focus on adaptive capability while ignoring actual adaptation behaviors. Little research has been done on the delta’s recovered populations (Samling et al., 2015). This research is inspired by studies of tussles over vulnerability, resilience, and access to natural resources in order to maintain sustainability in coastal zones, especially in response to climate-induced shocks. The objective of this chapter was to look at the coastal community of the islands’ socio-economic vulnerability as a result of climate change using the LAST matrix,

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Economic and Social Vulnerability Index (ECVI), and SLSI tool, as well as the relationship between adaptable capacity and adaptability. This research is timely and applicable as it focuses on the above-mentioned terminologies which are burning issues in the selected coastal blocks of Sundarban, especially the Sagar and Gosaba.

3.2 Materials and methods 3.2.1 Study area On Sagar island, the study area includes three villages: Bankimnagar (Dhaspara Sumatinagar II GP), Kamalpur (Rudranagar GP), and Gangasagar (Gangasagar GP) (Fig. 3.1).

FIGURE 3.1 Study area showing surveyed villages of Sagar Block of Sundarban, India.

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3. Socio-economic and livelihood vulnerability in view of climate resilience

Sagar island is located at the mouth of the Hugli estuary in the Sundarban Delta, and is bordered on the north and west by the Hooghly River, the east by the Muriganga River, and the south by the Bay of Bengal. Bankimnagar, Kamalpur, and Gangasagar are three villages that cover 3.08, 5.72, and 12.26 km2 and serve 3885, 6602, and 10,340 people, respectively (Census, 2011). Villages of Gosaba Block were selected as study areas based on their geographic location, adjacent to the Sundarban Reserve Forest (SRF) in the Indian part (Fig. 3.2).

FIGURE 3.2 Surveyed villages of Gosaba Block of Sundarban, India.

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River Bidya bounds the region in the west and rivers Gomar and Raimangal in the east (Ghosh & Mistri, 2020). The surveyed villages of Bijoynagar, Birajnagar, Pakhiralay, and Pathankhali belong to Bali II, Rangabelia and Pathankhali GP, respectively, cover 6.53, 6.15, 4.79, 3.45 km2, and serve 6507, 5328, 3946 and 1414 people, respectively (Census, 2011). Climate change, sea-level rise, and recurrent cyclones have all put the coastal villages and the villages adjacent to the forest fringe in the Sundarban at risk of coastal hazards. Sundarban is the most vulnerable to natural (floods, cyclones, riverbank erosion), quasi-natural (saline water intrusion, embankment breaching), and man-made hazards (coastal hazards). Therefore, Sagar and Gosaba blocks have been selected as the areas that are crucial to a case study.

3.2.2 Methodology Primary data were collected through semi-structured interviews with randomly selected households by lottery method (n5352) as part of a door-to-door household survey. This sample size was deemed sufficient, as there was a negligible variation in the quality of life. The sample size for the household survey was calculated based on a formula with a 5% margin of error (95% of confidence level), and a 50% response distribution. The eldest respondent was 85 years of age, and the mean age of respondents was 52 years. Female respondents accounted for 50% of the total population at Jibantala-Kamalpur, 10% of Gangasagar Colony, and 0% of Bankimnagar Colony. Sample households (n 5 160) from Gosaba block were drawn from the villages of Bijoynagar (n 5 50), Birajnagar (n 5 50), Pakhiralay (n 5 30), and Pathankhali (n 5 30). Secondary data were collected from the Village Directory of India at the District and Sub District level, and the Census of India 2011. Socio-economic challenges of economically marginalized islanders of deltas can be analyzed by documenting the vulnerability, and sustainability of livelihoods. Vulnerability assessment is one of the most important techniques for determining the extent of climatic hazards’ influence on human and ecological systems, as well as documenting victims’ responses to threats (Adger et al., 2007). The LAST matrix and the SLSI are useful tools for assessing vulnerability. ECVI helps to analyze the risk exposure of the community with their adaptive capacity. 3.2.2.1 Livelihood asset status tracking (LAST) matrix: an analytical framework Using the LAST matrix, qualitative assessments of capital assets can be transformed into quantitative scores and aggregated for different spatial and temporal analyses, where word pictures (quality of life indices) are the main tool for gathering and reorganizing data. Respondents help to depict their household circumstances as a “best case” (100% response score) and “worse case” (0% response score) snapshot, or somewhere in between. The score is assigned against positive and negative responses. The score interval was chosen as 0
20, 20
40, 40
60, 60
80, and 80
100 following the method adopted by Boateng (Boateng, 2013). A precise absolute score was assigned to each response, along with their specific score interval, based on the middle value of the interval score corresponding to that response (Boateng, 2013). Based on the financial capital of the randomly selected surveyed families of Sagar block, an assessment sheet was created. The sum of total percentile

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scores from household responses in the LAST sheet for each family was divided by 1.00 to document the LAST Matrix (Table 3.1) for each household. X  ðY1 1 Y2 1 ? . . . YnÞ =M (3.1) Here, Y stands for the percentile score of responses for each of the indicators comprising the capital asset of households up to the nth response score for a capital asset. Based on the response score from 0 to 100, Boateng categorized respondent households into the following four groups: Category 1: Extreme poor where the LAST index score ranges between 0.01 and 0.30. Category 2: Vulnerable where the LAST index score ranges between 0.31 and 0.59. Category 3: Viable where the LAST index score ranges between 0.60 and 0.79. Category 4: Sustainable where the LAST index score ranges between 0.80 and 1.00. 3.2.2.2 Sustainable livelihood security index (SLSI) SLSI of the rural poor households was developed to identify potential households that can cope with natural hazards. Aggregate indices based on normalized and summed indicators of socio-ecological vulnerability are useful tools for identifying hotspots where multiple vulnerabilities occur (Abson et al., 2012). For calculating SLSI, we followed the formula (Singh & Hiremath, 2009) as shown below: SLSIij 5

Xij-minj Xij i 5 1; 2 . . . ; I; j 5 1; 2 . . . ; J: maxj Xij- minj Xij

Where Xij is the value of the SLSI related to the jth entity of the sampled villages. Xij is the value of the variable representing the ith component of SLSI related to the jth entity. Each indicator has different units so they should be standardized using the method adopted by United Nations Development Programme (UNDP) while calculating the Human Development Index (HDI). This index was constructed following Hahn et al.’s study (Hahn et al., 2009) where indicators were identified and given equal weight. SLSI included eight components or dependent variables of livelihood security; income and assets, food and nutrition, education, community participation, drinking water, sanitation or hygiene, and primary and reproductive health. These elements were grouped into five sub-components as livelihood security areas such as economic security, nutritional status assessment (based on nutritional centers’ availability), educational security assessment, community participation, and health security. CARE implemented an ordinal scale of one to five for quantifying village status in terms of the household livelihood security index (Lindenberg, 2002). Based on availability, a score was assigned for each element. Thus, the Village Index was prepared on a scale of one to five, as shown in Tables 3.2 and 3.3. 3.2.2.3 Economic and social vulnerability index (ECVI) ECVI has been constructed for the Gosaba block to analyze the risk exposure to the community with their adaptive capacity. For calculating ECVI, indicators are standardized using the method adopted by United Nations Development Programme (UNDP). Then the standardized indicators are combined by computing a simple average of each capital with

2. Climate change, social response and resilience

TABLE 3.1 Computation of livelihood asset status tracking (LAST) index. Village

Indicator

Percentile score based on response score (village sequence-wise)

Bankimnagar, Kamalpur Gangasagar

Govt. grants for migrants

No Relief (Y1) 0, .00090, 0

BPL*(Y2) .00229, .00270, .00265

Household Income in Rs./ Year

5000
10,000 (Y6).00076, .00090, .00088

10,000
50,000 50000
100000(0Y8) 0, .00450, .00442 (Y7) .00229, .00270, .00265

Properties possessed by each household prior to migration

, 20 Bigha*** 20
40 Bigha (Y11) .00229, (Y10) .00076, .00090, .00088 0, .00265

40
60 Bigha(Y12) .00381, 0, .00442

80-150 Bigha (Y13) 0, 0, .00619

Number of poor migrated people including women enjoy credits facilities

From Self-Help Group (Y14) .00229, .00270, .00265

From Cooperative Banks, Agricultural credit society (Y15) .00381, 0, 0

From Commercial Bank(Y16) 0, 0, .00796

Availability of information on weather forecast

Local Administration always gives early warning (Y17) .00687, .00810, .00796

Support from local administration to support local income-generating activities.

No support from local administration (Y18) .00076, .00090, .00088

Government Legislation

Unstable Local income generation activities due to strict Government legislation (Y20).0. 00076, .00090, .00088

*BPL, Below poverty Level. **PMAY, Pradhan Mantri Abas Yojana, 1 Bigha. ***5 0.13378 hectare, Y1 to Y20. . .Indicators. Source: Primary Survey, Local Panchayat Office, 2018

PMAY**(Y3) .00381, .00450, .00442

BPL 1 PMAY (Y4) .00534, .00630, .00619

BPL 1 PMAY 1 old
age allowances (Y5).00687, 0, 0 100000 to 150000 (Y9) 0, .00630, 0

Job Card holders get support (Y19) .00229, .00270, .00265

Perfect Summation(M) 1.00

LAST Index 0.045 (Bankimnagar) 0.045 (kamalpur) 0.058 (Gangasagar)

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3. Socio-economic and livelihood vulnerability in view of climate resilience

TABLE 3.2 Indices values of the sustainability indicators for the block Gangasagar.

Assessment indicator Economic Security Index

Village

Total score obtained based on amenities Standardized availability value

Bankimnagar 1

0.125

Kamalpur

1

0.125

Gangasagar

1

0.125

Nutritional Bankimnagar 3 Potential Index Kamalpur 3

0.375

Gangasagar

0.375

Bankimnagar 8

1.000

Kamalpur

4

0.500

Gangasagar

6

0.750

Participation and empowerment Index

Bankimnagar 2

0.250

Kamalpur

2

0.250

Gangasagar

2

0.250

Health Security Index

Bankimnagar

0.125

Kamalpur

0.156

(1) Water Availability Index (2) Sanitation Index

(3) Primary Health Care Index (4) Reproductive Health Care Index

Rank

Bankimnagar (0.375)

1

2.25

2

Kamalpur (0.281)

3

1.875

3

Gangasagar (0.369)

2

2.875

1

Name of the village with SLSI

0.375

3

Educational Security index

Village index (total score obtained /total number of Rank indicators)

Gangasagar

0.344

Bankimnagar 1

0.125

Kamalpur

1

0.125

Gangasagar

2

0.250

Bankimnagar 1

0.125

Kamalpur

3

0.375

Gangasagar

1

0.125

Bankimnagar 1

0.125

Kamalpur

0

0

Gangasagar

6

0.750

Bankimnagar 1

0.125

Kamalpur

1

0.125

Gangasagar

2

0.250

2. Climate change, social response and resilience

TABLE 3.3 Indices values of the sustainability indicators for the block Gosaba. Assessment indicator Economic Security Index

Nutritional Potential Index

Educational Security index

Participation and empowerment Index

Number of subcomponents Village

Total Score obtained based on amenities availability

Standardized Value

6

1

0.143

Pathankhali 1

0.143

Bijoynagar Birajnagar

10

0.1430

Pakhiralay

2

0.286

Pathankhali 3

0.429

Bijoynagar Birajnagar

21.5

0.286 0.214

Pakhiralay

5

0.714

Pathankhali 7

1.000

Bijoynagar Birajnagar

62

0.857 0.286

Pakhiralay

2

0.286

Pathankhali 2

0.286

Bijoynagar Birajnagar

0.286 0.143

3

12

3

Health Security Index

Pakhiralay

Pakhiralay Pathankhali Bijoynagar Birajnagar

(1) Water Availability Index

21

6

0.286 0.250

Name of the Village with SLSI

Village index (total score obtained /total number of Rank indicators)

Rank

Pakhiralay (0.343) Pathankhali (0.422)

31

2.25

2

2.50

1

2.00

3

Bijoynagar (0.350)

2

0.179
0.179

Pakhiralay 13 Pathankhali

0.143 0.429

Bijoynagar Birajnagar

0.143 0.286

12

(Continued)

TABLE 3.3 (Continued) Assessment indicator (2) Sanitation Index

(3) Primary Health Care Index

Number of subcomponents Village 12

8

(4) Reproductive 9 Health Care Index

Total Score obtained based on amenities availability

Standardized Value

Pakhiralay 33 Pathankhali

0.429 0.429

Bijoynagar Birajnagar

0.286 0.286

22

Pakhiralay 30 Pathankhali

0.4290

Bijoynagar Birajnagar

11

0.143 0.143

Pakhiralay 11 Pathankhali

0.143 0.143

Bijoynagar Birajnagar

0.143 0

10

SLSI, Sustainable livelihood security index. Village Directory of India at District and Sub District level, Census of India 2011.

Name of the Village with SLSI

Village index (total score obtained /total number of Rank indicators)

Rank

Birajnagar (0.164)

4

4

1.188

3.3 Results

67

equal weight to each factor. By way of construction higher values of ECVI would denote higher vulnerability and thus are assigned a higher rank. The Borda Rule as a method of rank-order scoring has been applied to reach an ordinal aggregator toward this end.

3.3 Results 3.3.1 Implementation of LAST tool, SLSI, ECVI Natural, financial, physical, human, and social capitals are the five capital models used by researchers to execute the LAST tool for sustainable livelihoods frameworks. However, only financial capital was used in the current study to implement LAST (Table 3.1). In the villages of Bankimnagar, Kamalpur, and Gangasagar of Sagar block, the LAST indices have values of 0.045, 0.045, and 0.058, respectively. According to Boateng, the respondent families fall into category 1, which denotes severely bad living conditions (Boateng, 2013). The educational security index received the highest score (1.000) in the case of village Pathankhali of Gosaba block and village Bankimnagar of Sagar block, after standardizing sustainability indicators followed by the nutritional potential index (0.429) at Pathankhali of Gosaba block, health security index (0.344) at Gangasagar of Sagar block, participation and empowerment index (0.286), and economic security index (0.143) at Pakhiralay, PathankhaIi and Bijoynagar villages of Gosaba block, respectively. The village of Bankimnagar had a standardized SLSI value of 0.375 and was ranked first, followed by Gangasagar (Rank 2, SLSI value 0.369), and Kamalpur (Rank 3, SLSI value 0.281) (Table 3.2). The village of Pathankhali of Gosaba block had a standardized SLSI value of 0.422 and was ranked first, followed by Bijoynagar (Rank 2, SLSI value 0.350), Pakhiralay (Rank 3, SLSI value 0.343), and Birajnagar (Rank 4, SLSI value 0.164) (Table 3.3). CARE (one of the world’s largest international relief, and development not-for-profit organizations) adopted the concept of relief to rehabilitation to development continuum. The village indexes of Bankimnagar, Kamalpur, and Gangasagar were calculated based on the total score of the amenities available, which were 2.25, 1.875, and 2.875, respectively. Similarly, the village indexes of Pakhankhali, Pakhiralay, Bijoynagar, and Birajnagar portray scores of 2.50, 2.25, 2.00, and 1.188, respectively. Therefore, the village scores lie in between serious threats to livelihood security (village index 5 1), and well-protected livelihood security (village index 5 5). Thus, villages are in the rehabilitation stage (Tables 3.2 and 3.3). ECVI depicts the village-wise capital-based scenario as quite better at the village Pakhiralay, followed by the villages Pathankhali, Birajnagar, and Bijoynagar (Table 3.4).

3.3.2 Relationship between adaptive capacity and adaptation in the light of SLSI and LAST matrix and SLSI and ECVI Adaptation sometimes suffers from social and political intervention, affecting human experience, attitude, and behaviors. Therefore, adaptation can become complex and unpredictable (Adger et al., 2013; Dilling et al., 2019). A positive relationship should be established between adaptive capacity and adaptation if adaptive capacity has explanatory power for adaptive behaviors (Mortreux et al., 2020). A weak positive relationship

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3. Socio-economic and livelihood vulnerability in view of climate resilience

TABLE 3.4 Calculation of the economic and social vulnerability index (ECVI). Village-wise capital-based ECVI rank P

Village

Human

Physical

Natural

Financial

BSESI (

score of capitals)

Borda rank

Bijoynagar

18

21.5

18

18.5

76

4

Birajnagar

15.5

18

16.5

19

69

3

Pakhiralay

14

10.5

11.5

11

47

1

Pathankhali

13.5

10

14

10.5

48

2

BSESI, Borda Score based on the ranks of economic and social indicators.

TABLE 3.5 Relationship between adaptive capacity and adaptation at the village level through SLSI and LAST index. Village

R2

SLSI LAST R

Bankimnagar 0.375 0.045 Kamalpur

0.281 0.045

Gangasagar

0.369 0.058

Sig

Unstandardized coefficient Eigen Value Durbin
Watson

0.450 0.202 0.703 0.272

1.992

1.006

TABLE 3.6 Relationship between adaptive capacity and adaptation at village level through SLSI and ECVI index. Village

SLSI standardized

ECVI standardized

R

Bijoynagar

0.027

1.086

0.659 0.434 0.341 2 0.659

Birajnagar

2 1.400

0.611

Pakhiralay

0.466

2 0.883

Pathankhali 0.906

2 0.815

R2

Sig

Unstandardized coefficient

Durbin
Watson 2.383

(R 5 0.450) was observed between SLSI and LAST Index at the village level in the case of Sagar block (Table 3.5). Only 20% of the variation of actual adaptation depends on adaptive capacity. If adaptive capacity remains unchanged, almost 27% of adaptation is assured. The Durbin
Watson value (1.006) indicates that positive autocorrelation is detected in the sample. As a result, the adaptive capability suggested that the villages’ livelihood security was at risk. Because of their genuine adaptive behavior, households also lived in deplorable conditions. In the case of Gosaba block, a quite strong positive relationship (R 5 0.659) was observed between SLSI and ECVI Index at the village level. Only 43% of the variation of actual adaptation depends on adaptive capacity. The Durbin
Watson value (2.383) indicates that no autocorrelation is detected in the sample. The adaptive capability suggested that the villages’ livelihood security was at moderately vulnerable condition (Table 3.6).

2. Climate change, social response and resilience

3.4 Discussion

69

3.4 Discussion 3.4.1 Livelihood status of economically marginalized people In terms of qualitative assessments of capital assets which can be transformed into quantitative scores through the LAST matrix, villages Bankimnagar and Kamalpur are the most vulnerable followed by village Gangasagar of Sagar block. While identifying the potential households that can cope with natural hazards through the measurement of SLSI, village Bankimnagar stood in a better position followed by villages Gangasagar and Kamalpur. Therefore, based on the overall performance, village Kamalpur will be treated as the most vulnerable village, followed by villages Bankimnagar and Gangasagar among the studied villages of Sagar block. Actually, Deltas are complex systems that provide a habitat for resource-dependent groups that are susceptible to environmental, economic, political, and social changes. As a result of these factors, a large number of people are migrating in reaction to the effects of climate change (Seto, 2011). According to surveys in the Sundarban, roughly 7000 people have been forced from their original habitations and have become environmental refugees or migrants as a result of sea-level rise, coastal erosion, cyclones, and embankment breaches over the last 30 years (Hazra et al., 2010). Climate change, sea-level rise, and recurrent cyclones have all put the islands of Ghoramara, Lohachara, and Khasimara in the Sundarban at risk of coastal hazards. Ghosh et al. (2014) documented how the submergence of the villages of Khasimara, Khasimara Char, Lakshmi Narayanpur, Bagpara, and Baishnabpara of Ghoramara Island forced people to migrate to the neighboring Sagar Island. Because of their proximity to Lohachara, low-lying tidal areas of Sagar island began to be reclaimed by local government officials in the 1970s in order to resettle islanders. The Bankimnagar, Gangasagar, and JibantalaKamalpur colony areas have the highest human concentrations (Harms, 2015; Chakma, 2014). The Ghoramara, Khasimara, and Lohachara islands resettlement project began in 1972 in Bankimnagar, then in 1981 at the Gangasagar colony, and in 1983 at the JibantalaKamalpur colony. The project was started by the West Bengal State Government through the local administrative authority (Harms, 2013, 2015; Mukherjee, 2014). The total number of displaced persons fluctuates between 4000 and 7000 (CSE, 2007; Mukherjee, 2014; Ghosh et al., 2014). According to the Sagar Panchayat report, the government provided 0.20067 hectares as patta or grants to individual environmental refugees. After rehabilitation, however, many residents of the Jibantala-Kamalpur colony did not receive titles or patta. Initially, sufficient quantities of rehabilitation packages were supplied, but as the number of resettlers grew, the packages began to dwindle (Samling et al., 2015). The current government began distributing patta as part of a special package known as ’Nijo Bhumi Nijo Griha’ (own land, own house). Harms conducted a survey of displaced persons with legal titles (patta) and discovered that they are still concerned about the possibility of losing their land. Respondents from the Khasimara and Lohachara islands migrated to the Bankimnagar colony and continued to catch fish in the Battala River (a distributary of the river Chemaguri that falls into the river Muriganga). Daily wages ranged from Rs. 100
150 (US $1.32
1.98). Below Poverty Line (BPL) card holders accounted for 45.45% of the respondents. PMAY didn’t provide homes to all of them (Pradhan Mantri Abas Yojana). However, this colony’s economic situation is far superior to the other two.

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3. Socio-economic and livelihood vulnerability in view of climate resilience

The Jibantala colony (1 and 2) has very poor infrastructure. They don’t have enough safe drinking water wells. Tube wells were about 1.5 km from their homes. The majority of people do not have access to basic housing. Poor roads (kachcha or mud roads) and sanitation conditions made it nearly impossible for them to maintain their standard of living. Most of the respondents of Adibashi Mahalla catch prawn spawn from the river Chemaguri and fetch Rs. 50
100 (US $0.66
US $1.32) per day. Climate change impacts disproportionately affect the poor, young, elderly, sick, and otherwise marginalized people (Kasperson & Kasperson, 2001). Hence, for immediate relief, they often need the services of philanthropists. Only non-government organizations like Tagore Society for Rural Development and Sagar Mangal, at Jibantala-Kamalpur, were performing benevolent activities such as mother and child development programs, sanitation programs, drinking water testing centers, vocational training programs, and self-help group formations. However, in the Gangasagar colony, only 16.13% of households were under the BPL category which reflects quite a satisfactory performance in SLSI (0.369). In terms of ECVI, village Bijoynagar of Gosaba block is the most vulnerable, followed by Birajnagar, Pathankhali, and Pakhiralay among the study villages as higher values of ECVI would denote higher vulnerability and thus is assigned a higher rank. While identifying the potential households that can cope with natural hazards through the measurement of SLSI, village Pathankhali stood in a better position followed by villages Bijoynagar, Pakhiralay, and Birajnagar. Therefore, based on the overall performance, village Birajnagar will be treated as the most vulnerable village, followed by villages Bijoynagar, Pakhiralay, and Pathankhali among the studied villages of Sagar block. Most of the houses of Birajnagar, Bijoynagar G.P. are kutcha, and materials used for dwelling units are mud, straw, and tin. Some houses of Pathankhali G.P are semi-pucca and the building materials used are brick and tin. Most of the kutcha houses were severely damaged by natural catastrophes. Actually, a large number of people live on Khas (government-owned land) known as Chargheri. With the shifting of rivers, they also move on from one place to another. The picture is different in the case of Pathankhali G.P., where some villagers got sanctioned amounts under the Indira Abas Yojana. For catching fish, using of Boat License Certificate (BLC) from Forest Department (FD) is a must. Villagers had to buy a permit from the FD for entering the forest, which costs US $2.70 for 7 to 10 days per person. In reality, however, marginal people often by compulsion enter the forest without permits to avoid paying the fees. If they were caught by FD officials while entering the core areas they have to pay US $67.42 as a penalty. Due to the dearth of land holdings and capital assets, banks often refuse to give them loans; therefore, the villagers should depend on local merchants for money and physical capital including boats and nets. Making of boat costs Rs. 40,000 and the cost of making a net for catching fish is Rs. 1 to 1.5 Lakhs (according to Primary Survey, 2019). Sometimes fishers depend on Bandhan Bank for loans. However, they give loans below 60 years of age, and repayment of loans is done on weekly basis, which is not possible for poor fishers. Hence, informal sources of credit play vital roles in rural economies.

3.4.2 Management strategy Some management techniques have already been implemented by local governments to ensure the long-term viability of the Sagar block. Economically marginalized villagers did

2. Climate change, social response and resilience

3.5 Conclusion

71

not have access to physical capital. As a result, resilient agriculture techniques should constantly be advocated. In this regard, mechanized agricultural practices such as the development of chili, dubbed "Sagar-beauty," and watermelon, dubbed "Sagar-sweet," should be promoted. Fishing, pond and canal excavations for irrigation, poultry farming, animal husbandry, small-scale cottage businesses, and people in non-formal business sectors should all have access to micro-financing. According to Wild Life Protection Act, there should be provisions for alternative packages for the livelihoods of marginalized people of Sundarban. People living in the selected villages of the Gosaba block remain economically marginalized due to the low priority assigned to their problems. It is important to improve access to basic services, such as health, education, training and capacity building, and basic infrastructure. This would help in developing other skills and in reducing pressure on natural resources in the long term (Patel & Rajagopalan, 2009). The pressure on resource utilization can be eased to the extent where alternative livelihoods are available. This is a challenge for both governmental bodies and NGOs providing education, microcredit, and access to markets (Chowdhury, 2010). Last but not least, the livelihoods of forest-dependent people can improve only if policymakers focus on practical livelihood problems such as lack of alternative income-generating activities during bans on fishing, a money lending system, and a serious lack of infrastructure for health and sanitation (Sarker, n.d.).

3.5 Conclusion The analysis reveals that livelihoods in the villages of Kamalpur, Gangasagar, and Bankimnagar of Sagar block, where environmental migrants are placed by the local authority and Bijoynagar, Birajnagar, Pakhiralay and Pathankhali of Gosaba block are in poor shape. Villagers are impeded from getting proper housing conditions, sanitation, safe drinking water, education, and livelihood-generating opportunities. One of the main issues highlighted by fishing communities is that the process for the distribution of BLC was problematic, as the fishers were required to register within a month of notification. Many fishers, particularly those in remote villages, were unable to do so and were thus not issued BLCs. Informal arrangements exist within villages for active fishers who wish to fish, to lease BLCs from the owners, thus making the BLCs a “leasable property.” Decentralized planning and community-based adaptation measures are urgently needed to help economically marginalized islanders to become economically selfsufficient and overcome climate change-related livelihood crises. To move ahead, further study should be required to identify the latent factors behind the adaptation, especially considering the socio-psychological aspects of economically marginalized villagers.

Acknowledgments The authors would like to thank all the villagers for offering their best support for data collection and assistance during the field survey. We express gratitude to the authors of various Government reports, which have been generously used in the preparation of this paper.

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C H A P T E R

4 Building resilient city in coastal urban areas: case study of community adaptation and response toward climate change and tidal floods in Semarang, Indonesia Henny Warsilah Rural and Urban Studies, Center for Community and Cultural Research, National Research and Innovation Agency (PMB BRIN), Jakarta, Indonesia

4.1 Introduction: problems of community adaptation and response toward climate change Most urban and strategic areas in Indonesia are located on the coast of Java, such as Jakarta, Semarang, and Surabaya. The characteristics of coastal urban areas are mainly viewed from their various diversity of ecosystems, their richness in natural resources, and natural beauty, which generates economic added values for the surrounding communities. These areas are vulnerable to natural hazards, notably climate change and tidal floods. It is projected that tidal floods will dramatically increase the risk of coastal urban environments in the future due to sea-level rises along the coast, climate change, and global warming. Intergovernmental Panel on Climate Change (IPCC, 2007) predicted that the sea level will globally rise by 18 to 59 cm by 2100. The rise has increased annually by 0.21 to 0.68 cm/year. Walker et al. (2004) state that: “A Resilient City is one that has developed capacities to help absorb future shocks and stresses to its social, economic, and technical systems and infrastructures so as to still be able to maintain essentially the same functions, structures, systems, and identity.” Wildavsky said that a resilient city is a concept where a system is more resilient to disaster, not just immune to the changes, but also how the system can bounce back,

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00004-7

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mitigate, and recover from disasters. The general characteristics of a resilient system are as follows: redundancy, diversity, efficiency, autonomy, powers, interdependence, adaptation, and collaboration (Djalante & Thomalla, 2010). The problem is to what extent major cities in Indonesia have stepped forward to replicate sustainable development and resilient city. This question is certainly worth studying, as stated in the book report of Semarang and 100 Resilient cities (Government, 2016). One of the impacts of climate change is causing tidal flooding that often occurs in coastal cities in Indonesia, one of which is the city of Semarang in Central Java. According to Marfai, flood phenomena in coastal areas, many referred to as tidal floods, have threatened the northern coast of Java for many years. It is worsened by the dramatic increase in sea-level rise as the impact of climate change and global warming (Suryanti & Marfai, 2008). Mitigation and adaptation as responsive actions have been carried out by the Municipality of Semarang to anticipate tidal floods. This program is a set of policies to put into action urban governance and build resilient cities for a better future. Having collaborated with other 100 cities across the world, Semarang is the only city in Indonesia that has been represented in the Resilient Cities network (100 RC) initiated by the Rockefeller Foundation. This network focuses on empowering cities worldwide to develop resilience in finding better solutions to city problems, either physical aspects or socio-economic problems toward natural disasters. This chapter aims to investigate the process of adaptation and response of the community toward climate change and tidal floods conducted by the residents who dominantly work as fishermen in kampong Tambak Lorok, in northern Semarang. This chapter underlines social and cultural approaches as a part of the adaptation and response process and in contrast how poverty is likely to have resulted in people’s less awareness of the environment and led to environmental degradation. Urban development tends to prioritize physical aspects, rather than socio-economic aspects which will make the city less resilient and the community vulnerable. Moreover, this monolithic view can reduce the capacity of the community to adapt to disasters and climate change.

4.2 Materials and methods This research is qualitative with the data collection method is qualitative by conducting recording interviews, video production, observation, and in-depth interviews to explore a respondent’s deep point of view, a multi-level Focus Group Discussion (from district to subdistrict and with communities related to environmental matters, including private sectors and environmental NGOs). This research uses descriptive analysis to elaborate Focus Group Discussion data collection, which is important and relevant to the topic. The research location is a fishing kampong in Tambak Lorok, North Semarang, which is a poor fishing kampong and is vulnerable to tidal flooding due to climate change and subsidence. The objective of this research is: (1) This study is not only about understanding the implementation of smart resilience in Semarang but also finding out the process of building a resilient community related to climate change disasters and the consequences of the socialecological crisis. (2) This case can be a lesson for other coastal cities in Indonesia. Tidal floods occur twice a day, in the morning and evening. This research is focused on urban coastal planning on the coast of Java. That would elaborate on the term resilience in

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coastal cities. The key factor of resilient city implementation is public participation that would engage the decision-making process on urban planning and urban development. Two paths of cultural approaches underline the role of local government’s role in adopting the cultural values of coastal communities and integrating those values into a single policy.

4.3 Disaster risk reduction (DRR) in Indonesia 4.3.1 Impacts of climate change on coastal urban areas in Indonesia The coast is a transition zone between land and sea and a mixture of freshwater and saltwater, with a distinct peculiarity and characterized by tidal force or tidal rising, winds, and the salinity of the water. The coastal zone is where the interaction of the sea and land processes meet. It also refers to sediment deposited by rivers due to human-induced environmental impacts and devastates habitat and environment (Dahuri, 2001). The transformation of the coastal zone into settlement areas has created complex problems regarding intertwined aspects related to socio-economic aspects as well as generating cultural and political aspects of the community involved (Brahtz, 1972). Now, 65% population of Java Island lives in coastal areas. They rely on the quality and the number of coastal resources. In addition, the growing population in coastal areas has increased significantly which amounts to 2.2% per year (with population densities above the national average). As a result of this high population growth and when disasters hit the coastal areas, preparedness is needed to reduce the risk. It is evident that almost 3000 districts in coastal areas of Java are vulnerable to tidal floods every year. There are 90 locations in coastal areas of Java which exposed to coastal erosion to the nearest dozen kilometers in the last 10 years (Walhi, 2007). One of the issues being discussed is that global climate change affects the balance of Earth’s energy, and it leads to the dramatic transformation of urban ecosystems in coastal areas in the form of tidal floods, sea-level rises, land subsidence, global temperature changes, particularly in the coastal area of Semarang. It is estimated that within 20 years, the coastal areas will be flooded by sea-level rises reached by 16 cm with an affected area of 2672.2 hectares. Meanwhile, it is projected that the land subsidence on account of sea-level rise will worsen the impact of tidal floods by 10 cm in many coastal areas of Semarang (Elevation Zone Map of Semarang City in (Murdohardono, 2006) and Kodoatie in BBC Indonesia). It extends the area and fills it with seawater intrusion due to decreases in groundwater levels and rises in seawater levels. In February 2009, for example, the severity of tidal flooding posed a risk to many coastal areas in Semarang and halted people’s activities because of the high level of seawater rise, reaching about 10 cm to 2 m (Republika, 2009). The problem of tidal flooding is related to global climate change which affects the earth’s energy balance and causes dramatic changes in urban ecosystems in coastal areas. Disaster risk reduction (DRR) is a systematic practical approach to identify or recognize, assess and reduce risks arising from disaster events. The definition of disaster risk reduction is also described by Twigg (2009), as a systematic approach to identifying, assessing, and reducing disaster risk. DRR aims to reduce socio-economic vulnerability to disasters as well as address environmental hazards and other hazards that

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trigger them. According to Lassa (2009), the notion of community-based disaster risk reduction is an approach that encourages communities to manage disasters at the local level. These efforts require public interpretation to evaluate all disaster risks, indeterminate priority management, plan their area, determine priority management, plan disaster risk reduction activities, and evaluate their performance in disaster risk reduction efforts.

4.3.2 Indonesia’s climate change mitigation and adaptation strategies Indonesia is anticipating the global impacts of climate change, such as extreme conditions, urban heat, coral bleaching, high tides, tidal floods, sea-level rises (on average of 0.57 cm per year), land subsidence in coastal areas (on average 0.8 cm/year), etc. (Hayu, 2012). A large share of global greenhouse gas emissions is ultimately attributable to cities, including misuse of electricity, widespread use of refrigerant gases in freezers or air conditioners, and carbon emissions from vehicles, industries, and other household products. Similarly, this condition has been estimated (Kirschbaum, 1999) that the global temperature will increase from 1.3 C to 4.6 C and global warming will raise the sea level by 100 cm by 2100. Being the third-largest greenhouse gas emissions producer, pollution levels in Indonesia are major concerns, with 85% of the largest single source of air pollution from road transport. It is unavoidable that Jakarta is the third-highest polluted city after Beijing and Mexico City (Sukamto, 2013). Urban areas, particularly those located on the coast, are vulnerable to the impacts of climate change due to the high level of pollution. Many large cities with large populations in Indonesia such as Jakarta and Semarang are anticipating the city’s urban sprawl along with its complicated problems, the massive use of public infrastructures, high-cost economic activities, and a large share of low-cost income communities residing in the coastal area. This community is the most vulnerable to this condition, due to limited access to resources and they have less preparedness to anticipate climate change’s impacts. Mitigation, moreover, in regard to climate change is a collective approach to reduce the risk of environmental problems, particularly greenhouse gas emissions. On this basis, it is necessary to develop urban strategic planning embedded in urban governance principles and integrated into a sustainable development framework by making cities located in coastal areas resilient to climate change impacts. First, in order to control urban sprawl, local governments, for example, in Semarang start to control land-use planning by building mass settlements appropriately for the public and ensuring mobility of the population by building road infrastructures, providing many public models of transport, bridges, providing other low-cost, comfortable, and secure public transports. Second, the rapid growth of the population is fueled by the migration of poor communities to urban slum settlements, particularly in coastal areas. It is required for local governments to relocate and provide them with appropriate settlements supported financially by Local Government Working Units. Third, empowering poor urban slum communities through job skills training and providing them with economic opportunities by supporting them to start small-medium businesses in many sectors. Fourth, community development of poor populations can be treated by delivering them cultural values and character building, transforming them from being indifferent to

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caring for the environment, and early childhood environmental education by reducing the use of non-recyclable plastic stuff. In this chapter, climate change adaptation refers to strategies and actions exercised by governments, local communities, and related stakeholders in dealing with climate change impacts and their risks to the city and its inhabitants from devastating natural disasters in coastal areas. According to Hayu (2012), mitigation and adaptation should not be applied separately, but these efforts should be addressed together at the same time to achieve their common objectives. Whereas failed mitigation will affect the process of adaptation in dealing with climate change and disaster mitigation. As a consequence, there should be synergy between the coastal development driven by local governments-stakeholders and the community itself by enhancing public participation or community-based participation in the running process (as shown by Fig. 4.1). The first policy is in the form of a commitment between the local government and stakeholders to manage coastal areas; second, the participation of the private sector in managing and maintaining the coastal environment; and third, community participation and community empowerment for coastal area conservation.

4.3.3 Disaster risk reduction (DRR) and resilient city campaign in Indonesia The word resilient refers to capacities to revive and adapt to hardship and difficult situations. Walker et al. (2004) stressed that: a “Resilient City is one that has developed capacities to help absorb future shocks and stresses to its social, economic, and technical systems and infrastructures so as to still be able to maintain essentially the same functions, structures, systems, and identity”. According to Wildavsky, resilience means a concept or ability to endure disasters and not only be invulnerable to changes but also how it can revive, mitigate and recover from disasters. Wildavsky also stated that the general characteristics of resilience are redundancy, resourcefulness, efficiency, autonomy, strength, interdependency, adaptation, and collaboration (Djalante & Thomalla, 2010). FIGURE. 4.1 Government, stakeholder, and community policies in coastal city areas. Source: Data from Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

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The problem is to what extent major cities in Indonesia have stepped forward to replicate sustainable development and resilient city. The resilient city concept generally derives from sustainable development and the green city concept where the notion of development refers to organizing resources, the continuity to provide for the needs of future generations, and increasing the human quality of life and well-being without degrading or endangering the natural environment and the availability of natural resources. Sustainable development is also recognized as principles and practices in relation to responsibility and regeneration of resources without sacrificing future life (Brundtland, 1987): “Sustainable development is the development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. The green city concept moreover relates to urban planning with ecological concerns and city development. It is obviously important that the new paradigm of city development would gradually boost the market-driven economy, social dimension, environment, and culture, and later on, bring social justice to the community. With a large share of the population and 75% of people living inside the city area, the city is more vulnerable to global climate change and natural disasters. The losses from natural disasters are also part of the government’s responsibility to take further action on relief. The major droughts in Semarang, for instance, resulted in financial losses, and local governments also have had to reallocate resources for relief and reconstruction efforts. Physical, social, and financial losses had been burdened by the government. Roads and bridge reconstruction is the first priority in the post-disaster recovery in order to reconnect mobility goods and people. Another immediate action is a reconstruction of health services and public schools in order to help the community to return to normal life. In its aim, on the whole, the concept of a resilient city strives to be interpreted into an applicable program in order to bring its context to build the future of urban development that is based on resilience against futuristic hazards. Public participation has a valuable role in coastal development to enhance the democratic nature of the process and build cooperative collaboration with other stakeholders. This task moreover needs to encourage information or knowledge exchange to bring the gaps among stakeholders involved and to acknowledge patterns of coastal planning. It will result in better coordination among stakeholders to improve the efficacy of development and conduct preventive action against coastal hazards in the coast. This holistic approach is needed to protect and anticipate the hazards because of the valuable resources of coastal cities contributing to economic growth, of which almost 70% of the Gross Domestic Product has been circulated in coastal urban zones. Sustainable urban development refers to three aspects, notably environmental, social, and economic aspects, are connected to each other. In other words, for instance, economic development is supposed to enhance social development in the ongoing process of development. The main trajectory of economic development not only results in economic growth but is supposed to deliver economic prosperity to all people. The concept of a resilient city is supposed to maintain social as well as economic conditions in the postdisasters by enhancing social functions, social structures, and systems and maintaining their local identities. The community involvements notably among urban citizens who are prepared with social modalities are the ultimate factor to achieve applicable programs and actions in the resilient city concept. The local governments have significant roles and responsibilities to take immediate response. Participation from local communities is urgently encouraged to equip their social modality with awareness and preparedness for natural disasters.

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In the midst of efforts to build a disaster-resilient city, in addition to infrastructure development, social development through the involvement of the community is needed to make the city a resilient city. 1. Assessments: It is to define vulnerability in estimated areas and to conduct a costbenefit analysis in order to deliver appropriate mitigation. 2. Planning: It is to conduct a set of planning as primary sources for policymaking in order to define mitigation efforts and certain vulnerabilities and how a resilient city develops infrastructures in a land-use planning context to achieve urban resilience. 3. Implementation: It is a set of actions conducted in the post-projection and planning to develop community development, and it is the key factor to building a resilient city. 4. Finance: It is to provide financial sources and assets to support resilient city projects. 5. Government: Local governments have valuable roles and responsibilities to give a shape resilient city in the local context.

4.4 Case study: social cultural adaptation process of urban coastal community in Tambak Lorok toward rob flood 4.4.1 Rob flood and land subsidence in Tambak Lorok Kampong Flood phenomena in coastal areas or many refer to tidal floods have been threatening for many years on the northern coast of Java. It is worsened by the dramatic increase in sea-level rise as an impact of climate change and global warming (Suryanti & Marfai, 2008), a case in part of Sukoharjo Regency, Indonesia. Mitigation and adaptation as responsive actions have been carried out by the city Municipality of Semarang to anticipate tidal floods. This program is a set of policies to put into action urban governance and build resilient cities for a better future. Semarang is the only city in Indonesia that has been represented in the Resilient Cities network (100 RC) initiated by the Rockefeller Foundation. This network focuses on empowering cities across the world to develop resilience in finding better solutions to city problems, either physical aspects or social and economic problems toward natural disasters.

4.4.2 Overview of Semarang city: urbanization and development of slum areas in coastal cities of Semarang Semarang city is one of the industrial cities located strategically on the northern coast of Java and one of the most populous metropolitan areas in Indonesia (BPS, 2015). With a population of 1.5 million inhabitants, 76.06% of the population work in the service sector. In contrast, 114,939 families, or 367,848 people live in poverty. Semarang is gradually expanding through the massive development of settlements, trade expansions, and industrial zones. Economic growth will certainly generate higher incomes for communities. The intensive use of space in regard to massive land use for settlements and land-consuming economic activities will result in another problem of land use in urban planning. The

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recent data shows that land-use expansion and conservation have increased significantly from 2005 to 2016 by 4580.85 hectares (BPS, 2015). The fishermen’s kampong in Tambak Lorok has been settled permanently on the coast of the Java sea. These kampongs will soon create coastal slum areas as they suffer from unstable economic conditions in communities and are marginalized in society. Rapid urbanization and slum upgrading soon lead to the increase of uncontrollable population in coastal urban areas. It is projected that by 2050, the average urban population will likely increase by 64% in Asia and the same situation had been predicted that the urban population in Indonesia will as well reach 67.5% in 2025. Urban agglomeration, however, contributes to the national economy (20.37% from metropolitan cities and 15.34% from large cities) and particularly to Regional Gross National Product (BPS, 2015). In contrast, urban population growth is obviously correlated to environmental degradation and the increase in greenhouse emissions. Based on data released by Sasongko Purnomo (Coastal Rehabilitation for Developing Economic Growth In Semarang Coastal Area, Poverty-Environment Partnership 19th meeting 21
23 May 2014, EThekwini Municipality, Durban, South Africa), it is shown that substantial increase in settlement areas is in a linear relationship with the mobility of the population to the city, by 33.06%. On the contrary, the forest area is less than 4% and the public space area is 1.1% of the total area. The population density of the city (as shown in Fig. 4.2) also generates typical urban problems such as air pollution, traffic jams, improvements to slum areas, floods, water pollution, and environmental degradation. Fig. 4.2 shows the increasing distance between coastal areas and land by 2.5 km due to climate change impacts. The effects of soil erosion which reached 46.8%, go beyond the loss of productive fishponds, industries, and fishermen’s settlements. The tidal flood poses the risk of disrupting daily life activities socially and economically and it also causes paralyzing public infrastructures such as airports and railway stations. Based on the Focus Group Discussion conducted by the Indonesian Institute of Sciences/ LIPI (2019
2020), many settlers in Tambak Lorok had complained about the impacts of the tidal floods that had drowned their houses in the last 10
15 years. They had to raise their land yards of houses for each of the last 10 years by 2.5 m. In the next 20 years, they should be at least 5 m of rise. For that, they have to rent truckloads to dump soil combined with solid wastes onto the land in layers. It costs obviously a lot of money. Waste is an alternate filament combined with soil until it reaches a certain elevation. They have to rent in total at least six dump trucks and the rental cost is about 600,000 rupiah per truck, excluding building costs for their homes. Those who cannot afford to rent truckloads, dump their homes with waste until they have sufficient funds to elevate the land with soil. It is undeniably a part of their adaptation to anticipate tidal floods, but it poses another vulnerable condition when the water stream is blocked by solid waste. The sewer blockage could stagnate the area with water and it will result in flooding because the stream cannot go straight into the rivers. The pile of waste aggravates this condition during the flood which generates another problem for Tambak Lorok due to the unavailability of waste dumping sites. This tendency of ecological deterioration in the coastal area should be included in Disaster Risk Management to reduce disaster risks in the future (Fig. 4.3).

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FIGURE 4.2 Population and land use and the classical problems in urban area. Source: Data from Sasongko Purnomo (Coastal Rehabilitation for Developing Economic Growth In Semarang Coastal Area, Poverty-Environment Partnership 19th meeting 21
23 May 2014, EThekwini Municipality, Durban, South Africa).

Fig. 4.3 shows a resident’s house that was affected by land subsidence and tidal flooding that occurred twice in one day. This condition made the house damaged and become unfit for habitation. Fig. 4.4 shows a residential village that is submerged in water due to high tides and high waves, and rising sea levels. Their settlements are submerged by seawater, making it sad and difficult to carry out daily activities.

4.4.3 Current mitigation and adaptation to climate change strategies in the coastal city of Tambak Lorok Kampong Two paths to confront climate change impacts are mitigation and adaptation, which permanently reduce community risks and hazards. Conducted by the Semarang municipality, these efforts have targeted local communities to bring social justice and economic prosperity due to major concerns on hydrological deterioration, land subsidence,

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FIGURE 4.3 Land subsidence. Source: Data from Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

FIGURE 4.4 The residential village that is submerged in water. Source: Data from Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

environmental degradation in the sea, and extreme poverty within the community, where slum urban coastal settlements in high population density. Fig. 4.5 Demonstrating coastal community participation models and increasing social resilience. Increasing community resilience in coastal areas can be done by improving ecosystems. Efforts to improve the ecosystem can be done by restoring the ecosystem by planting mangrove trees and building a wave retaining wall; then by gathering information to increase community capacity against climate change through the establishment of climate field schools, and developing alternative livelihoods, as well as cultivating climate

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FIGURE

4.5 Coastal community participation model and improvements to social resilience. Source: From Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

change-resistant fish. Also, it can be done by involving stakeholders through advocacy and documentation, to replicate and expand the rehabilitation of mangrove ecosystems. The Semarang municipality has conducted various programs to anticipate tidal floods by developing physical infrastructures, stormwater pump stations, embankments, flood boxes, and improvements to the drainage systems. Another future project is the coastal Jatibarang embankment project, which has been started in 2009, and river normalization projects on Kali Garang, Kali Asin, and Kali Baru are estimated to spend a total of 1.7 trillion rupiahs (Merdeka, 2009). Other special programs in regard to Climate Change and Disaster Risk Reduction have been launched by the Semarang Municipality, as follows • Improvements to the drainage systems to manage overflow debit of rainwater and tidal floods • Improvements to flood management by building flood water retention (retention ponds), stormwater pump stations to pump away large volumes of water • Building urban flood canal systems, for instance, the West flood canal project financed by the JICA project from Japan and the East flood canal project which will be funded by the Netherlands government. There are more than 50 storm water pump stations operated in Semarang to support the canal flood functions in all zones impacted, widening as well as deepening the river, performing routine transports of river wastes, improvements to drainage systems, and raising the houses impacted from tidal floods. Urban planning and infrastructure development in Semarang Municipality are keys to mitigating tidal floods in order to respond to the diverse need of people. Derived from 100 Resilient Cities programs, a comprehensive approach of “City Resilience Frameworks” means to articulate city resilience and strategies for climate change impacts. The framework is based on four important dimensions in city development: (1) health and prosperity, (2) social and economic, (3) environment and infrastructure, and

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the last (4) leadership and strategy. The City Resilience framework has developed tools to measure the capacities of cities collectively to endure, adapt, and transform from climate change impacts.

4.4.4 Identified impacts of mitigation and adaptation The long-term support to the program on mitigation and adaptation has been conducted by the Municipality of Semarang to accelerate community development in the northern Semarang district. Located on the coast of the Java Sea, Tambak Lorok is well recognized as a fisherman’s kampong. Poverty and the lack of public infrastructure remain chronic conditions for this community, the general condition of almost every community in many coastal areas of Indonesia. The kampong was actually built on mangrove swamps and reclamation land where they gradually convert the land into settlements. Land reclamation activities are operated by the local government for a bulk of container and terminal complexes, but it results in soil degradation and risks the soil’s capacity to absorb rainwater and tidal floods. Milkfish cultivation flourished many years ago and it was once growing as a future fish industry in the 1990s when many fishermen become suddenly rich and spent their money performing Hajj to Mecca. In the previous golden age of milkfish cultivation, the fish auctions sold nearly 74,037 kg of fish products with an estimated value of 198,183,700 rupiahs in 2009. In the next year, the total production of fish products decreased significantly to 50,052 kg, but it increased its total value to 271,668,500 Rupiah (Maritime and Fisheries Board of Semarang, 2011).

4.5 Community perception toward tidal floods The multi-faceted perception of this community toward tidal floods is generally related to the condition that the causes of tidal floods refer to the rise and fall of the tides all year, the rise of sea level, and climate change impacts. Built on previously mangrove swamps, the kampongs are vulnerable to rainwater as well as seawater inundation and land subsidence. According to local people, the cause of tidal flooding is more due to sea level rise, climate change, and land subsidence (Fig. 4.6). Even though the fishing communities are very poor, they have knowledge about tidal flooding and land subsidence in coastal areas. Their parents are fishermen who have lived in coastal areas for a long time, and they are very familiar with coastal and marine ecology, so they have knowledge about disasters that are passed down from the next generation.

4.5.1 Adaptation and strategies adopted by the community toward tidal floods The community’s adaptations toward tidal floods are mainly driven individually as well as collectively to implement strategies, adapt, and anticipate environmental changes and social implications. Communities in Tambak Lorok have used to adapting to frequent tidal floods as soon as when tidal floods occur. Tidal floods usually start at 4 pm in the afternoon when the tides are high. The tides then subside on the following day by

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87 FIGURE 4.6 Causes of tidal floods according to local communities. Source: Data from Warsilah, H. (2019). Indepth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

FIGURE 4.7 Forms of community’s adaptation toward tidal floods. Source: From Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

gravitation forming low water on the sea coast. Community adaptation of Tanjung mas district can take forms, as shown in Fig. 4.7. Fig. 4.7 shows the adaptation process for the Tambak Lorok community to the flood disaster in the form of raising houses, raising roads, and embankments. People’s knowledge of disaster adaptation is not focused on physical adaptation by building houses, roads, and other public facilities. They also have socio-cultural adaptation, by planting mangroves, performing village clean-up rituals, performing “Wayang” performances with the theme of Disaster and Adaptation to disasters, and strengthening social capital by strengthening social networks and social cohesion. The Tambak Lorok population is dominated by local fishermen. This community has a shared bond of cultural and environmental values where the sense of belonging does embed within the community in forms of collectivistic values such as togetherness, helping each other, and high environmental concern. Many fishermen learn how to recycle leftover food from hotels and restaurants as animal feed. Shrimp and crab shells are used for animal feed as additional feed for chickens or ducks. It is the best feed for producing

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high-quality salted eggs. This local knowledge has been practiced as part of improving the quality of salted eggs. As fishermen, this community has something in common and is found by cultural values as a social modality to live in harmony with the environment. The Tambak Lorok community is more resilient and adaptive in realizing its cultural values, especially in nature conservation. A local NGO called “Cemara” which was founded in 2011 and funded by Pertamina’s CSR helps the community. The aim is to provide ecotourism and economic added value for fishermen. In the future, Tambak Lorok village is projected to become a marine tourism destination. The aim of the project is to develop the tourism industry and create economic opportunities for the local community. Fig. 4.8 described about the Attitudes of the Tambak Lorok Community Toward Tidal Floods. Showing their attitude in dealing with tidal floods, because of poverty they chose to stay in the flood area, some tried to rebuild their houses, and most of them filled their houses with plastic waste. Lack of knowledge among the population about the risks of disasters and disasters from plastic waste forces them to use plastic waste to stockpile their land even though plastic cannot be decomposed. Due to the poor economic condition of the family, they filled up the destroyed soil with plastic material which was done by choosing sand, even though this will also cause the housing to collapse. As shown in Fig. 4.9, a resilient city can be translated as a city that is able to withstand the various types of growing threats, both coming from nature such as natural disasters that developed as a result of human actions. A resilient city can be transformed into a resilient society throughout its community and is able to maintain stable social, economic, and infrastructure after certain changes or disasters while maintaining its function, structure, systems, and previous identity. Community involvement, in this case, of the citizens can be done through the strengthening of social capital in the community. Furthermore, after a disaster, it is crucial to improve community preparedness in urban areas. As conceptualized above, establishing a disaster-resilient city should be based on community participation. Cities from the perspective of environment and climate change have a central role as both “cause” and “affected” of climate change and threat of disaster. Based on Fig. 4.10, to develop a resilient city, the local government must develop a strategy from a resilient city to a resilient community, namely by integrating physical aspects with sociocultural aspects. For example, by completing policy-making related to flood prevention strategies, tidal disaster management, and land subsidence, as well as strengthening the role of leadership in the regions. FIGURE 4.8 Attitudes of Tambak Lorok and Kamijen community toward tidal floods. Source: From Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

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Be Resilient

Preparedness in the face of disasters

Economic Stability

Social Stability

strengthening Social Capital

Resilient Society Insfras tructure Stability

Environment Conservation

Public Participation

Function & Structure Systems & Identity

FIGURE 4.9 Resilient society strategic. Source: From Warsilah, H. (2019). In-depth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

FIGURE 4.10 The strategy of resilient city to the resilient society. Source: Data from Warsilah, H. (2019). Indepth interview and Focus Group Discussion with local district and subdistrict staff and fisherman community.

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To make the community resilient, it is strengthened by community participation, adaptation, and strengthening of social and cultural capital. illustrates a model of the community’s resilience to the tidal flood. This figure is the result of an analysis that describes the condition of the community that does not have resilience, and vice versa, the community is resilient. A less resilient society generally lacks complexity and is socially unacceptable. On the other hand, resilient communities are much more complex and socially acceptable. The Tambak Lorok community tends to be more resilient than other village communities in the context of tidal flood adaptation. They have social preparedness and social anticipation in dealing with the threat of tidal flooding. This community is more socially able to reduce disaster risk, and they are less complex because all fishermen have more social resilience in dealing with climate-change disasters. Fig. 4.10 describes a model of community resilience to tidal floods. This figure is the result of an analysis that describes the condition of a less resilient community and a more resilient community. Communities that are less resilient are generally unresponsive to the complexity of problems and lack social cohesion. However, resilient societies are much more complex and social cohesion is high. The Tambak Lorok community tends to be more resilient than other village communities in the context of tidal flood adaptation. They have social preparedness and social anticipation in facing the threat of tidal floods. These communities are socially better able to reduce disaster risks, and because of them fishermen have higher social resilience in facing climate change disasters. Fig. 4.11 explains the balance of the attitude of community resilience to tidal floods. Compared with other villages, the people of Kampung Tambak Lorok tend to be more resilient in facing tidal floods. The Tambak Lorok community has more factors of social preparedness and social anticipation of the threat of tidal floods. The local government also has the resources to help socialize about tides in Tambak Lorok Village, and other villages affected

FIGURE 4.11

Analysis of resilience of resilient communities in the Tambak Lorok coastal village, Semarang.

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by the tidal floods. The government built a climate information system for fishermen from 2013 to 2016. Through this climate information, the people of Tambak Lorok are socially better able to reduce disaster risk and have higher social resilience in facing climate change disasters. Referring to the research findings, the very complex condition of the Tambak Lorok area shows that the condition of the area is becoming more resilient and the community also has resilience.

4.6 Recommendation Coastal cities on Java Island, especially the north coast of Java such as Semarang City, will face climate change which will result in severe tidal flooding and social vulnerability because the coastal areas of the two villages studied are not resistant to tides. flooding and land subsidence. Even in the Demak area, as we can see in the BBC video, there are already submerged villages. It is projected that if the land subsides every 10 years by 2.5 m, in the next 15 years, all the houses in the area will sink eventually. The only unpopular decision was that the local government should relocate residents for a shorter or longer time to dry land. However, as explained above, the remaining area is only in a protected forest area which aims to preserve the diversity of natural habitats and the environmental balance in the area. Other options include building a retention pond, a rainwater pumping station to pump large amounts of water, and the East Canal project and building a sea wall. The final option is to build resilient cities and resilient communities. Based on the ACCCRN City Resilience Strategies Model, from the experience of the Asian Cities Climate Change Resilience Network program, there are four basic components in preparing a CRS: (1) Climate risk context and stakeholder mapping; (2) Resilience action plan and strategy; (3) Consultation and prioritization; (4) Finalization and implementation.

4.7 Conclusion This socio-cultural capital is an important component to develop disaster-resilient cities and communities that have high social resilience, or resilient communities. The various impacts of climate change in the two villages studied have caused many socio-economic activities of the two communities, namely: (1) disruption of the functions of coastal areas and cities located on the coast, (2) disruption of public infrastructures such as roads, ports and airports, (3) disruption of community settlements and economic activities, (4) decreased agricultural productivity, (5) increased risk of disease which will affect community resilience. They still have strong social capital, by separating social ties, social networks, and social cohesion. The social cohesion that unites communities is the main capital to help them anticipate tidal floods and land subsidence disasters. Communities have a high participation in flood disaster mitigation and adaptation, thus tending to make them more resilient than other rural communities. Building a resilient city does not only focus on infrastructure development but must refer to community development that is integrated with social aspects.

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References BPS. (2015). Semarang city in figures. Brahtz, J. F. P. (1972). Coastal zone management: Multiple use with conservation. John Wiley and Sons, Inc. Brundtland Report. (1987). Sustainable development—Our common future. Dahuri, R (2001). Management of coastal and ocean resources on a regular basis. Jakarta: PT. Pradnya Paramitra. Djalante, R., & Thomalla, F. (2010). Community resistant to natural hazards and climate change impacts: A review of definitions and operational frameworks. Hayu, P. (2012). Urban policy regarding climate change. Ministry of National Development Planning. IPCC, J. J. (2007). Climate change 2001: Impacts, adaptation, and vulnerability, contribution of working group II to the third assessment report of the intergovernmental panel on climate change. Kirschbaum, M. (1999). The temperature dependence of organic-matter decomposition-still a topic of debate. Lassa, J. (2009). Right tips to reduce community based disaster risk. PT. Grasindo Jakarta. Maritime and fisheries board of Semarang. (2011). Merdeka, S. (2009). Available at: www.Suara Merdeka.co.id. Murdohardono, D. (2006). Semarang land subsidence. Republika. (2009). Semarang is surrounded by floods. Available at: www.republika.co.id. Resilience: 40 years of resilience research and thinking. (2011). Semarang City Government. (2016). Resilient Semarang: Moving together towards a resilient Semarang. Sukamto. (2013). Air pollution kills 2 million people per year. T. Tempo.co.id/News Magazine. Suryanti, E. D., & Marfai, M. A. (2008). Adaptation of coastal area communities Semarang against the danger of tidal flood (Rob). Journal Indonesian Disaster, 1(5), 335
346. Twigg, J. (2009). Characteristics of a disaster-resilient society: An introductory note, OXFAM. Walhi. (2007). Ecological disaster and sustainability of Indonesia. Walker, B., Holling, C. S., Carpenter, S. R., & Kinzig, A. (2004). Resilience, adaptability, and transformability in social-ecological systems. Ecology and Society, 9(2).

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5 Climate change indicator, impact, adaptation, and innovation at the local level: learn from the peoples’ experience of the coastal plain of Probolinggo, East Java, Indonesia Suyarso Suyarso, Martiwi Diah Setiawati, Indarto Happy Supriyadi and Bayu Prayudha Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia

5.1 Introduction The world’s attention currently focuses on climate change and its physical and socioeconomic impacts. It includes increases in air and sea temperatures, changes in rainfall, groundwater availability, rising sea levels, coastal erosion, and their effects on biodiversity and ecosystem services. Sea-level rise (SLR) and extreme precipitation are some of the island countries’ significant socio-economic challenges. According to recent literature, the global mean sea level (GSML) continued to increase by 3.2 mm/year over the period 1993
2015 and 3.6 mm/year over the period 2006
15 (Oppenheimer & Glavovic, 2019; Nerem et al., 2010; Leuliette, 2015; Watson et al., 2015) and this phenomenon is kept accelerating (Oppenheimer & Glavovic, 2019). Moreover, anthropogenic impacts have led to the widespread escalation of extreme precipitation with moderate confidence on a global scale (Seneviratne et al., 2012). This condition severely threatens coastal cities, small islands, and tropical regions worldwide. Therefore, considering population expansion and urbanization, increased GSML by 2060 would expose more than 117% of the global population (200 million people) to coastal hazards (Neumann et al., 2015).

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The potential threats include disturbance of coastal ecosystem processes, changes in the natural drainage system, continuous or short submersion, and soil salinization. Moreover, regardless of the scenario, the SLR will become more frequent and intense (Oppenheimer & Glavovic, 2019), in which more areas are more exposed to coastal flooding.

5.1.1 Climate change and tidal flood in Indonesia As an archipelagic country, Indonesia has a significant potential for exposure to climate change and its proxies (i.e., sea-level rise, extreme rainfall or drought, extreme tidal waves, etc.). It also has been identified as one of the most vulnerable countries in the Asian continent in the face of climate change. The country consists of 17,508 islands (Andre´foue¨t et al., 2022) and is home to varied geography, topography, and climate, ranging from sea and coastal systems to peat swamps and mountain forests (Leitmann et al., 2009). Of the territory, two-thirds is the ocean, with a significant potential for exposure to climate change. Indonesia’s annual average air temperature rose roughly 0.3 C every decade, while daily precipitation increased by 0.21 mm/day per decade (Boer & Faqih, 2004; Supari et al., 2017). In addition, the rainfall pattern has changed/shifted, with yearly rainfall decreasing in the southern part of the country and increasing in the northern region (Supari et al., 2017). Yet, temperatures in the country are expected to surge by about 1 C by 2030 and by 1.6 C
2 C by 2060 (McGregor et al., 2016). Meanwhile, the precipitation trends in 2060 vary from 0% to 5%, with some increases in the rainy season and fall in the dry season (McGregor et al., 2016). Furthermore, the increasing temperature in the region by 1.2 C in 2100 will increase the frequency and intensity of extremes, and the sea level could rise by 1 m (Butler et al., 2014; Jackson & Jevrejeva, 2016). A previous study has also shown that Indonesia will experience an SLR increase for a 90% probability range of 1.6 m between the period of 2080
2100 under the high-end scenario (Jackson & Jevrejeva, 2016). These change in evidence and future projection dramatically affects many aspects of the country, including Indonesia’s economy, vulnerable communities, human health, and the environment. For instance, increasing SLR and extreme precipitation create coastal cities in the country (i.e., Semarang, Jakarta, Pekalongan, etc.) vulnerable to frequent flooding. A previous study reported that the estimated economic loss due to extreme coastal flooding in Jakarta was about 4.27 billion USD (i.e., the business area is the most affected) during the baseline period, and by 2100, the damage exposure will increase by four to five times (Ward et al., 2011). Another report also revealed that under the RCP 4.5 scenario, coastal flooding in Indonesian cities will boost the number of infectious gastroenteritis cases to 35% in Medan and 101% in Jakarta by 2030 (Masago et al., 2018).

5.1.2 Tidal flood on the Northern Coast of Java The urban population in many South-East Asian countries, including Indonesia, is expanding because people tend to move to megacities where the typical topography is

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low-lying coastal zones. Therefore, natural hazards in low-lying coastal areas will increase the exposure of people and assets. A study Andreas et al. (2017) reported that the tidal inundation on the Northern Coast of Java Island has been going further inland in recent years. Many urban and other areas, like farming areas, fishponds, etc., have suffered tidal inundation and become worse in times. First, it was only a few centimeters of inundation and came only at a high tide, but now it can be more than half of a meter and coming at the regular tide, and even has come permanently in certain places. Handayani et al. (2020) reported that more than one thousand flood events were recorded on the north coast of Central Java province from 2009 to 2018. Another paper also revealed that flooding is more commonly occurring in urban or potentially urban areas, especially within the area of 10 km from the shoreline area (Rudiarto & Pamungkas, 2020; Handayani et al., 2020). For instance, Semarang experienced a very dynamic shoreline in the past 10 years due to land subsidence and climate change, where villages, business cores, fishponds, and mangroves were eroded (Wetlands INTERNATIONAL, 2019). Another district in North Java, called Demak, also faces serious issues such as massive coastal erosion and declining mangroves and aquaculture (Handayani et al., 2020). Thus, tackling flooding in this coastal area is very crucial to conduct. In general, there were four actions to reduce the impacts of coastal flooding: climate change mitigation, avoiding human build-up in areas prone to subsidence, improved flood protection, and limiting the rate of exposure growth (Nicholls, 2004). Local authorities usually utilized the options mentioned above, numbers two and three, called adaptation measures (Harwitasari & van Ast, 2011). However, flood adaptation solutions that enable municipalities to be much more dynamic and adaptive to catastrophic weather events are either underdeveloped or face significant challenges in transitioning from design to implementation. In many nations, including Indonesia, the responsibility for developing and implementing flood mitigation policies is spread among several government entities, which frequently remain tightly in their respective mandate (Saito, 2013). Furthermore, stakeholder inclusion and open communication are critical for developing trust and a mutual understanding of the issue, as well as ensuring the participation of the interests of the most vulnerable groups; however, flood risk management practices are frequently insufficient for such participatory public engagement (Laeni et al., 2020). Probolinggo is a city at the eastern end of the north coast of Java Island (Fig. 5.1) which has a similar threat such the cities on the northern coast of Java Island. In 2016, this city faced tidal flooding, which caused 56.8 ha of fish and salt ponds to be damaged and three villages submerged [Kalibuntu Village (Pajarakan sub-district), Randutatah Village (Kraksaan sub-district), and Pajurangan Village (Gending sub-district) (Budiman & Supriadi, 2019)]. In 2022, the same villages in Probolinggo were submerged with an inundation depth between 80 cm and 1 m (detikJatim, 2022). Therefore, it is crucial to conduct a climate change impact assessment for the repetitive tidal flooding for physical aspects (coastal morphology, coastal dynamics, topography, land cover), and social elements (communities adaptation and policy intervention). This chapter also showed the climate change evidence in the local context, how the Probolinggo community in dealing with coastal inundation, and what kind of local context recommendation can be applied to the regional level for dealing with the same issues.

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FIGURE 5.1 Map location of the study area.

5.2 Materials and methods 5.2.1 Materials We used multi-temporal Landsat imageries from 1973, 1989, and 2013 to monitor the coastline change at the pilot sites during the study period. Moreover, a high spatial resolution Digital Globe of the Google satellite in 2021 was utilized to map the detailed land cover information using on-screen manual digitizing. We also used Shuttle Radar Topography Mission (SRTM) to generate Digital Elevation Modeling (DEM) data for food extension estimation. In addition, Sea-Bird Electronics (SBE) was applied to record tidal change based on water pressure in the sensor deployed at Probolinggo harbor. Furthermore, the daily rainfall dataset from 1980 to 2018 was utilized to assess the extreme events, the dataset was obtained from the Meteorology and Geophysics Agency at the Juanda Airport rain gauge station, Surabaya. Then, we utilized the geodetic instruments Sokhiza B2C and prism pole (measuring stick) to measure the height difference among targeted land cover (ponds, settlements, roads) relative to the sea waters at the time of measurement. At last, we used Garmin 76 XL Global Positioning System (GPS) with an accuracy of 10 m to plot the interest targets such as eroded ponds, abandoned ponds, and collapsing pond dykes. Table 5.1 presents detailed research materials used for this study.

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TABLE 5.1 Detailed dataset and instruments used in the study. Material and instruments

Target/output

Landsat 4 MSS 29 July 1973

Shoreline 1973

Landsat 4 MSS 1 June 1979

Shoreline 1979

Landsat TM 5 28 March 1989

Shoreline 1989

Landsat 5 TM 25 June 1995

Shoreline 1995

Landsat 7 ETM 22 May 2003

Shoreline 2003

Landsat 7 ETM 28 May 2011

Fluvial deposits on the rice fields

Landsat ETM7 2013

Shoreline 2013

Google satellite high resolution 2021

Shoreline 2021 and land cover land use detail.

Shuttle Radar Topography Mission (SRTM) data

Digital Elevation Modeling data of the research area.

Tidal data 5 May 2012
11 May 2012

Land position relative to sea level

Tidal data 5 May 2012
4 June 2012 Rainfall data 2002
2018 from Juanda Airport Surabaya (East Java)

Rainfall anomalies related to climate change.

Sea-Bird Electronics (SBE)-26 tidal recorder

Tidal data of the research area.

Leveling Sokhiza B2C and prism pole (measuring stick)

Detailed Coastal profile relative to sea level.

Garmin 76 XL Global Positioning System (GPS)

Geographic Positioning plot for interest target

Forum Discussion Group (FGD) with local government, residents, and Non-government Organizations (NGOs)

Addressing the climate change impact, possible/ existing solutions, and how the local government and communities minimize the impacts.

5.2.2 Methods Fig. 5.2 shows the overall research framework used for this chapter. We utilized satellite data, in situ data, literature review, and focus group discussion (Table 5.1) on showing the local evidence of climate change indicators and their impact along with their adaptation action in the study area. Climate change indicator was described by the frequency of extreme precipitation conditions, the regional trend of sea level rise, and the frequency of El Nino/La Nina years. The extreme condition was calculated based on the 99 percentile of daily precipitation data over 37 years from the local rain gauge dataset (Table 5.1). Meanwhile, regional trends in sea level rise and the frequency of El Nino/La Nina years were obtained from literature reviews [i.e., (Bott et al., 2021) and in modification of (Smith & Sardeshmukh, 2000)]. We also analyzed climate change impact assessment using satellite data, in-situ data, and a literature review. Since the local disturbance was not only influenced by climate factors, we added another anthropogenic pressure in this chapter. Therefore, calculating coastal area profiles such as geological information, land use land cover, and shoreline dynamics is crucial. With that information, we can understand the current/baseline

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FIGURE 5.2 Research framework to assess climate change indicator, impact, and adaptation scenario at local level.

condition in the study area. For the climate change impact assessment, we only focused on tidal flooding. In this case, we did a field survey and flood model with various wave height scenarios and assessed the affected area in the region. Based on this assessment and FGD, held in March 2016, we built an adaptation scenario with high feasibility for local communities. The detailed methodology was explained in the following section. 5.2.2.1 Shoreline changes In the first step, we conducted some corrections for Landsat images, such as radiometric and atmospheric corrections. The radiometric correction was utilized to convert the satellite image’s digital number (DN) to spectral radiance (at the sensor) and reflectance. After radiometric correction was conducted, we removed some atmospheric effects due to absorption and scattering called an atmospheric correction. Those two steps of correction were applied to improve the satellite image of Landsat data. Furthermore, the following Landsat images such as Landsat TM 5 1973, ETM7 1989, and 2013 radiometrically were corrected using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm, whereas the further correction technique was explained in the Landsat 4-7 Collection 1 (C1) Surface Reflectance Product Guideline. Each Landsat image dataset was digitized manually on-screen to detect the shoreline change using QGIS Madeira vers 3.4 software. In addition, geometric correction for Landsat imageries is based on the geometrics of the Google satellite image platform. 5.2.2.2 Coastal profile Sokhiza B2C leveling is used to determine the height of target points (pond dykes, pond depths, settlements) to sea level at the measurement time. The measurement data is the distance between targets and the height difference to sea level at the time of

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measurement. After correcting the tidal information, the measurement results are a detailed coastal profile relative to the mean sea level obtained from tidal data analysis using the least square method. 5.2.2.3 Flood modeling The DEM data used in this chapter is the shuttle radar topography mission (SRTM). The data was then corrected and adjusted to the coastal profile through linear equations to produce a derivative of contour maps of the research area. Furthermore, the contour map added with the wave height will generate an inundation map at the time of the highest tide accompanied by waves. Based on the extent, the inundation map is divided into three zones. Zone 1 occurs during the highest tide accompanied by a wave height of 0.3
0.7 m, Zone 2 occurs during the highest tide accompanied by a wave height of 0.7
1.5 m, and Zone 3 occurs at the highest tide accompanied by wave heights .1.5 m. Overlaying between the land cover land use map and inundation map will inform the economic loss of the research area. 5.2.2.4 Focus Group Discussion Forum Group Discussion (FGD) is a media facilitated by the research team from Research Center for Oceanography—National Research and Innovation Agency (BRIN) with local community representatives, Non-government Organizations (NGOs), local community leaders, and local authorities represented by the Probolinggo Regency Regional Planning Board looking for alternative solutions in reducing tidal inundation disasters. Probolinggo Regency Regional Planning Board will provide technical experts and funds. At the same time, the affected community and NGOs, due to their experience, will understand more about the appropriate coastal protector models suitable applied in Probolinggo.

5.3 Result and discussion 5.3.1 Geological formation of the Probolinggo coastal plain The topography of the coast of Probolinggo is an open coastal plain 82.8 km long and 5 km wide, bordering the Madura Strait in the north (Fig. 5.1). Moreover, in the south direction, the slope increases up to 2%, with sandy and muddy material coming from young volcanic alluvium deposits of Mount Bromo. Moreover, Camus et al. (2000) stated that volcanic eruptions in Indonesia frequently released intermediate materials with a pH higher than seven, containing minerals extremely rich in nutrients for plant fertility. Therefore, it is challenging to relocate populations when a catastrophic eruption occurs due to the soil richness around the volcano. Stratigraphically, according to (Bogie & Mackenzie, 1998), the Probolinggo Regency is a part of the volcanic system’s distal facies, or the plains surrounding the volcanic cone, where it typically consists of sand material. Numerous river mouths, from west to east including the Bayeman River, Sepaser, Kedungbajul, Banyubiru, Pekalen, Rondoningo, and Pancargelagas, can be found along Probolinggo’s coastline. The Rondoningo River, one of the rivers transporting debris from

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Mount Bromo’s eruption, is one of Probolinggo’s longest rivers, measuring 95.2 km. The formation of the Probolinggo plain involves both fluvial and marine processes, as well as many river flows that originate from Mount Bromo and create a delta. The community develops the fluvial-processed plains as agricultural regions, while the marine-processed plains are cultivated as aquaculture areas.

5.3.2 Land cover and land use of the Probolinggo coastal area The current land cover detail of the Probolinggo Regency is described in Fig. 5.3, where the blue, yellow, light green, and pink color indicates the ponds, settlements, ricefield, and harbor coverage area. The 83 km coast of Probolinggo is mainly covered by ponds covering 300 m wide, and some even reach more than 1 km inland. These ponds are divided into intensive, traditional, and salt ponds. Intensive ponds are less than 10% compared to traditional ponds. Due to the lack of flowing rivers, and river water is only enough to irrigate rice fields, salt ponds dominate the eastern part. Even many traditional ponds have been abandoned, and mangroves are allowed to grow. Unlike the west coast, the rivers flow directly from Mount Bromo in the more significant discharge and carry a lot of material from the eruption. Fig. 5.3 and Table 5.2 show that rice fields (53.1%) are the most dominant land cover in the study area, followed by ponds (25.2%), settlements (12.8%), and mangroves (8.8%). However, along the coastline, ponds and mangrove covers almost all of the area. Ponds in the west (Tongas—Dringu) are relatively thinner, only 300
500 m wide, but in the east (Gending-Paiton), the area is more expansive, reaching 900 m inland. Regarding natural capital, only mangroves exist in the study area, whereas it dominates the western coast of Probolinggo (Tongas to Gending), reaching a width of up to 450 m. Meanwhile, in the east part (Pajarakan to Paiton), the area is relatively thinner, only 200 m wide. In addition, mangroves are planted in an accreted coastal environment, making them easy to grow. Settlements are generally far from the coastline, except Kalibuntu Village in Kraksaan

FIGURE 5.3 The current status of land cover in Probolinggo Regency.

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TABLE 5.2 The land cover type and its area in each sub-district in Probolinggo Regency. Districts (in hectares) West

East

Coverages

Tongas

Sumberasih

Dringu

Gending

Pajarakan

Kraksaan

Paiton

Mangroves

92.42

159.90

83.15

174.94

76.93

122.46

39.39

Ponds

141.23

146.49

71.32

486.55

395.08

510.38

387.98

Settlements

128.97

39.49

193.57

99.76

73.78

306.92

247.00

Rice fields

466.19

398.46

478.94

645.59

649.30

865.98

999.14

sub-district and Randutatah in Paiton sub-district, where residential areas are located on the coast with quite a large area and are densely populated. In this study, we only estimated the affected land cover by inundation from the coastline to the Surabaya—Bali State road (Table 5.2) (i.e., we did not evaluate the road since it has a high elevation and will never be inundated by tidal waves). Fig. 5.4 shows the coastline profile on the east and west sides of Probolinggo. The profile shows different primary conditions among those locations. The west coast is generally vegetated with mangroves, whereas in some places, mangroves reach up to 500 m wide from the shoreline. This condition is different from the east coast, where generally, the dykes of the outer ponds are the border and the barrier between land and sea. Moreover, the west or east, behind the pond, towards the ground, are rice fields and residents’ villages. Despite the different conditions, the dyke height among those locations is similar (i.e., 1.5 m). Therefore, during the highest tide (i.e., 3.8 m in May), the East side of Probolinggo faces a severe risk of tidal flood.

5.3.3 Shoreline dynamic of the coast of Probolinggo Fig. 5.5 shows an analysis of shoreline dynamics by using multi-temporal Landsat image data from 1973 to 2013. The result indicates that Sumberasih and Gending subdistrict beaches experienced rapid accretion between 1973 and 1979. Moreover, between 1979 and 1989, coastal accretion persisted in all parts of Probolinggo Regency, where Sumberasih has the highest accretion, followed by Gending, Kraksaan, Tongas, Dringu, and Pajarakan sub-districts. Meanwhile, from 1989 to 1995, there was a decline in the coastal accretion process in all sub-districts in Probolinggo Regency. It is interesting to note that during this period, the coastal erosion process was more prominent than the accretion process, especially in the eastern part of the Probolinggo Regency. In the western region, coastal accretion continues, although the intensity has decreased. In the period 1995
2003 in the eastern part of Probolinggo Regency, the speed of coastal accretion for 8 years ranged from 5 to 10 ha, while coastal erosion was under 5 ha, so it can be said that although erosion occurred in some places, in other parts of the coast accretion process is still going on. Moreover, period from 2003 to 2013 showed almost the same conditions as the previous period. The accretion process was still ongoing in the western region, 10
40 ha,

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FIGURE 5.4 Profile of Probolinggo’s coastline with mangrove-West (A) and without mangrove-East (B).

while there was no coastal erosion process. The accretion process is still ongoing in Probolinggo Regency’s western part; however, in its eastern region, which includes Kalibuntu Village in the Kraksaan sub-district, the erosion rate is almost comparable to that of accretion. Fig. 5.5 depicts the Probolinggo Regency’s beaches in 1973, 1979, 1989, 1995, 2003, and 2013. As shown in Fig. 5.5 (lower left), Banjarsari Village and Pilang Village in Sumberasih sub-district experienced a land expansion of up to 450 m between 1973 and 1979. At the same time, the beach in Pajurangan Village, Gending sub-district (lower right), had an increase in land area of up to 250 m to the sea. depicts erosion and accretion intensity of each sub-district based on the period, where from 1973 to 2013 accretion was dominant, but from 2013 forward erosion was a major phenomenon. Moreover, the local community then utilized this land accretion by the fluvial process as agricultural land (i.e., rice fields). At the same time, those formed through the marine process were used as aquaculture areas- fish ponds (Fig. 5.7). Also, according to Landsat image coverage from 2011, a 46.5

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FIGURE 5.5 Shoreline dynamics by using multi-temporal Landsat image data from 1973 to 2013 (upper), where Banjarsari and Pilang villages in Sumberasih sub-district (lower left), and Pajurangan village in Gending sub-district (lower right) had an increase in the land area.

ha ricefield area in the Sumberasih sub-district was submerged by cold lava sand deposits brought about by fluvial processes from Mount Bromo. Furthermore, the rapid development of infrastructure over the past two decades, both at the district and city levels and the provincial level, has sucked up sand as a building

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FIGURE 5.6 Shoreline variation in each period for each sub-district in Probolinggo Regency.

FIGURE 5.7 The Sumberasih sub-district shoreline and rice fields in 1973 (left), the development of rice fields and fishponds in 1989 (middle), and sand materials from fluvial deposits which inundated rice fields covering an area of 46.5 ha in 2011 (right).

material. Along with the need for sand materials, several locations upstream of the river in the study area have turned into legal and illegal sand mining areas (Fig. 5.8). Moreover, until October 2015, in Probolinggo, there were 39 illicit mining sand locations (KOMPAS daily news 2015; Radar, 2019). Therefore, a significant shoreline change trend was observed from 2013 until 2021 in the region, whereas coastal erosion was dominant (Fig. 5.6). Also, many studies have examined the impact of upstream sand mining and dam construction on coastline development. McCusker and Daniels (2008) revealed coastal erosion in Connecticut, US, due to the construction of dams in the river’s upper reaches. Similarly, Kumar and Jayappa (2009) stated the impact of shoreline changes due to reduced material supply from upstream rivers by anthropogenic factors, especially illegal sand mining on Someshwar beach, India. (Gupta et al., 2012) also revealed that illegal sand and gravel mining, a common phenomenon in rivers in Asia, significantly affect shoreline changes.

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5.3 Result and discussion

FIGURE 5.8 Sand mining activities in Probolinggo. Source: From Radar Bromo. (2019). Tak Berizin, Tambang Pasir di Tegalsiwalan Masih Beroperasi (Unregistered Sand Mine in Tegalsiwalan Probolinggo Continues to Operate). https://radarbromo.jawapos.com/daerah/23/10/2019/tak-berizin-tambangpasir-di-tegalsiwalan-masih-beroperasi/.

5.3.4 Climate change indicator in Probolinggo Fig. 5.9 shows the frequency of extreme daily precipitation in the pilot study from 1981 to 2018. In this study, extreme precipitation was defined as the magnitude of daily precipitation higher than the 99 percentile from 1981 to 2018 (Setiawati & Miura, 2016). The extreme value was equal to or higher than 75.8 mm/day. The result stated that the highest frequency of extreme events occurred seven times a year with an annual average of four times a year. The most increased extreme precipitation occurred in 1991, 1998, 2003, and 2010 with the positive anomaly of extreme events (i.e., the annual frequency is higher than four times in a year) frequently occurring after 1990. The occurrences of extreme precipitation are somewhat related to the ENSO phenomenon, as shown in Fig. 5.10. For instance, the highest events of extreme precipitation occurred during 1991
93, 1997
98, 2002
03, 2008
10, and 2015
16. Furthermore, an indication of the frequent La Nina (blue color) phenomenon in recent years is also shown in Fig. 5.10. Fig. 5.11 presents the regional and local sea-level change derived from radar altimetry (Bott et al., 2021). Based on Fig. 5.10, the sea level offshore Probolinggo shows a positive 3.6 mm/year (Bott et al., 2021), which is higher than the global SLR average (i.e., 3.2 mm/year) (IPCC, 2014). In addition, previous research also found that the maximum wave height in the study area during the west monsoon is 0.71 m, with the average wave height being 0.13 m (Budiman & Supriadi, 2019). Meanwhile, the average wave height during the East Monsoon is 0.2 m, with a maximum wave height of 0.57 m (Budiman & Supriadi, 2019). However, the frequency of occurrence is relatively high, where 2654 days over 7 years was categorized as high wave ( . average), resulting in tidal floods (Budiman & Supriadi, 2019). Our research also found that Probolinggo Regency, especially Kalibuntu Village, often experiences tidal disasters, namely floods caused by tidal motions during spring tide. This phenomenon occurs when the Earth, sun, and moon are nearly in alignment, and average tidal ranges are slightly larger and occur twice each month. The tidal disaster mainly occurred in the Sumberasih sub-district (western part), Dringu, Gending, Pajarakan, and Kraksaan sub-districts (eastern part). The tidal wave phenomena in Kalibuntu Village

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FIGURE 5.9 Frequency of extreme precipitation (i.e., Percentile of 99 of daily precipitation from 1981 to 2018) at Juanda Airport Station.

(Kraksaan sub-district) was more substantial compared with other areas due to the densely populated area (it reached 1800 people/km2) (BPS Probolinggo Regency, 2016). Moreover, according to the BNPB dataset from 2006 to 2018, the Probolinggo regency almost every year was hit by flooding, with the highest frequency occurring in 2010, 2011, 2013, and 2017 with the worst destruction occurring in 2017
18 (BNPB National Disaster Management Authority, 2022).

5.3.5 The impacts of climate change in Probolinggo Extreme events such as storm surge significantly impact inundation, particularly its compounding effects with SLR (Little et al., 2015). According to a study on Florida’s Big Bend Region (Hagen & Bacopoulos, 2012), storm surge is enhanced by the shore elevation and other physical characteristics such as coastline angle, wide continental shelf, and basin geology. Storm surges are mainly induced by tropical cyclones (TCs) in the Pacific, North Atlantic, and the Indian Ocean and cause catastrophic hazards in the coastal region (Mori et al., 2014). Storm surge occurs when the wind pushes water onshore and can also be caused by the atmospheric pressure gradient (Kim, 2019). The storm surge amplitude

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FIGURE 5.10 El Nino/La Nina years and the BEST index (the combination of SST and SOI indices). Source: The data was obtained from Smith, C.A., & Sardeshmukh, P.D. (2000). The effect of ENSO on the intraseasonal variance of surface temperatures in winterInternational. Journal of Climatology, 20, 1543
1557.

FIGURE 5.11 Regional sea level trend from 1993 to 2020. Source: From Bott, L.-M., Scho¨ne, T., Illigner, J., Haghighi, M. H., Gisevius, K., & Braun, B. (2021). Land subsidence in Jakarta and Semarang Bay
The relationship between physical processes, risk perception, and household adaptation. Ocean & Coastal Management, 211, 105775. https://doi.org/10.1016/j. ocecoaman.2021.105775.

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depends on the track’s orientation to the coastline, intensity, size, wind speed, and bathymetric features (National Oceanic Atmospheric Administration (NOAA), 2020). In the west monsoon of Probolinggo, particularly in November
December, and the East monsoon from June to July, the highest tides in coastal and inundation areas tend to be more widespread. At the same time, wave height and wind speed change so that inundation impacts coastal lands such as milkfish ponds, salt ponds, crab cultivation, rice fields, settlements, and infrastructural in coastal environments. Direct measurements at some point location during the inundation show that the village is affected region wider than other villages (Table 5.3). As an indication of the increase in seawater level, some people have made efforts to raise the house’s ground floor. Moreover, along the coast of Probolinggo, the land facing the sea is generally an aquaculture area. Companies generally manage intensive shrimp ponds with capital by building supporting infrastructure and equipment, while local communities traditionally manage fishponds and salt ponds. However, many ponds have been abandoned in the last 10 years. According to the interview with the related stakeholders, various factors caused the cessation of intensive pond business activities. It includes diseased shrimp, shrimp mortality caused by poor pond water quality due to factory waste, and increased tidal flood frequency, which frequently causes damage to pond infrastructure. Salt farmers face the same problem due to the uncertain changes between the rainy and dry seasons in recent years. When the rainy and dry seasons can still be determined, pond farmers can choose and determine alternative sources of livelihood. The salt pond business is a livelihood activity during the dry season. In the rainy season, salt farmers make a living from other sources of income, such as farming or fishing. Since the rainy season is erratic, salt farmers find it difficult to determine when the dry season begins to be able to decide on the time of pond business activities. The coastal environment is constantly changing, both natural and anthropogenic. Natural factors include wave activity, wind, tides, currents, and sedimentation in deltas and river mouths. In contrast, anthropogenic factors are carried out by residents around the coast; among them is the conversion of mangroves into aquaculture and land reclamation areas (Sukardjo, 2010). Threat anthropogenic impacts can be in the form of loss of various kinds of marine life, increasing pollution, and loss of livelihood in local communities (Harley et al., 2001; Gilman et al., 2008). Fig. 5.12 shows the coastal flood scenarios based on topographical measurements in Probolinggo Regency. This area is divided into three categories: those that occur at high tide with wave heights of 0
0.7 m, 0.7
1.2 m, and 1.2
1.7 m. The land boundary used in the model is the Surabaya
Bali main road with a height of more than 2 m. Thus, the road can act as a natural barrier if there is TABLE 5.3 The inundation depth at Kalibuntu Village. No.

Locations

Longitude

Latitude

Depth (m)

1.

Kalibuntu village

113.417307

2 7.737197

1.10

2.

Gending-1 village

113.312262

2 7.769192

0.86

3.

Gending-2 village

113.311814

2 7.769567

0.98

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FIGURE 5.12 Flood model based on topographic measurement with various wave heights scenarios in Probolinggo Regency.

a maximum rob. The relationship between the flood extent driven by the wave height category and its land cover is presented in Table 5.4. Moreover, tides in Probolinggo waters are semi-diurnal whereas, they have little impact on agricultural land, which is only inundated for a few hours. However, this coastal flooding significantly impacts fish/salt ponds, salt production, and settlements. Also, the figure shows that the inundation width is relatively narrow in the western part of the Probolinggo Regency. At the same time, the eastern area is much broader and more inundated in settlements and pond areas.

5.3.6 Adaptation and resilient of climate change on the local peoples in Probolinggo Despite the high confidence that SLR will persist into 2100 (IPCC, 2014), many researchers contend that coastal regions will inevitably retreat due to coastal flooding (Cao, Esteban, Onuki, et al., 2021; Cao, Esteban, Valenzuela, et al., 2021). Moreover, this flooding has disrupted daily human activity, affected domestic economic growth, and created problems with public health. Before the authorities launch official adaptation efforts, locals typically adjust using their own resources. This kind of effort is called reactive adaptation, where the adaptation was conducted after the impact was observed. This type of reactive adaptation has also been seen in the coastal cities of the South-East Asia region (i.e., Manila, Jakarta, Ho Chi Min Cities), including Probolinggo.

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TABLE 5.4 The extent of affected land cover by flood inundation under three different wave height scenarios in Probolinggo Regency. Sub-districts West

East

Wave height

Coverages

Tongas

Sumberasih

Dringu

Gending

Pajarakan

Kraksaan

Paiton

0
0.7 m

Mangroves

79.85

146.44

66.49

56.90

70.56

97.74

31.56

Ponds

48.50

88.79

23.99

42.71

151.16

139.80

129.07

Settlement

0.00

2.17

2.29

1.43

1.66

5.92

17.18

Rice fields

3.30

44.78

11.01

0.01

0.01

0.15

0.51

Mangroves

88.27

155.80

72.43

74.37

75.92

107.41

37.12

Ponds

86.55

111.16

47.88

127.19

340.85

335.10

279.45

Settlements

1.49

4.72

8.66

2.71

6.99

39.15

32.28

Rice fields

36.72

96.49

39.83

7.99

23.18

24.82

14.73

Mangroves

92.34

156.80

78.64

91.13

76.93

119.76

39.45

Ponds

105.56

126.55

55.50

202.68

394.48

479.01

364.87

Settlements

7.44

8.75

18.04

3.92

13.90

68.24

51.81

Rice fields

66.50

177.22

94.62

28.28

150.13

147.15

95.06

0.7
1.2 m

1.2
1.7 m

According to FGD and interviews with the related local stakeholder, some local adaptation by local authorities and communities was found in the study area as follows: • Mangrove reforestation along the coastline Fig. 5.13 shows the mangrove reforestation effort in the study area as part of the reactive adaptation to climate change. To protect the coast, which is often exposed to abrasion, mangroves were planted along the study area’s coastline by the local communities and NGOs independently. The effort shows that mangrove planting in the western part looks more successful. It may be due to most of the coast accreting, so mangroves can grow and develop rapidly. In contrast, in the eastern part, there are many places where coastal erosion occurs; thus, mangrove planting is less successful. Also, in the east part of the study area, some regions show the dykes of ponds as a barrier directly facing the open sea, which creates a less favorable habitat for the growth of mangroves. Mangroves are believed to be able to dampen sea waves before hitting the coast. Also, wind and swell waves are rapidly reduced as they pass through mangroves, lessening wave damage during storms (Spalding et al., 2014; Sun & Carson, 2020). With rising sea levels and increasingly intense storms associated with climate change, there is substantial interest in ecosystem-based adaptation as a defensive measure for protecting low-lying coastal communities against coastal flooding. • Seawall Construction by Local Communities and Government

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FIGURE 5.13 Mangrove reforestation on the west coast of the study area. Planting mangroves near the beach (A) and in ponds (B).

Fig. 5.14 shows the seawall construction by local communities (A and B) and local authorities (C, D, and E). Since the east coast had less success growing natural capital such as mangroves, the seawall construction was mainly conducted in the East part of the region. Fig. 5.13 shows the seawall built by the local communities, in this type of seawall is made using bamboo along the coastline. Meanwhile, local authorities built three kinds of construction models with varying durability; type c river stone construction arranged in a trapezoidal shape (not cemented) parallel to the shoreline (seawall) and perpendicular to the shoreline (groins), type d with the same type (seawall and groins) cemented and type e seawall cemented. Based on the experience of the local community, type c is more resistant and has more extended durability due to the wave-absorbing properties in the pores of the stones. • Elevating the foundation of the house To avoid the threat of tidal inundation, the local people raise the foundation of their homes (0.5
1 m). The foundation height is adjusted to the rob conditions that occur yearly with an increasing trend (Fig. 5.15). This kind of adaptation is also conducted in other mega cities like Jakarta, Manila, and Ho Chi Min City (Cao, Esteban, Onuki, et al., 2021; Cao, Esteban, Valenzuela, et al., 2021). • Innovating in salt production Climate change causes seasonal changes that are difficult to predict. Thus, it is becoming more challenging to carry out their activities. It can be seen that there is a tendency to shift people’s activities from high-risk to low-risk businesses such as salt pond farmers. Most salt producers in East Java are traditional salt farmers who follow a seasonal pattern, underutilize expertize and marine resources, and utilize the old method to process salt products (Yuliati et al., 2019). However, in Probolinggo, salt production innovations have been recorded; they use plastic to cover rain protection on the high salt content of pond plots. Apart from being a salt producer, the plastic-roofed room is also used as an alternative medicine to improve public health (Fig. 5.16).

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FIGURE 5.14

Seawall by local communities (A and B) and local authorities (C, D, and E).

FIGURE 5.15

Elevating the foundation in the new house construction.

5.3.7 Discussion The primary purpose of this study is to find evidence of climate change indicators, impact, and adaptation at the local level of the Probolinggo regency. This chapter reveals that extreme annual precipitation occurs (Fig. 5.9) in the study area with the highest

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FIGURE 5.16 Innovative salt production using plastic roofs in the salt pond plots.

frequency during the La Nina events (Fig. 5.10). This condition makes the site location vulnerable to annual flooding due to extreme precipitation. Moreover, Fig. 5.11 also revealed that the SLR in the region tends to increase by 3.6 mm/year, which means the Probolinggo regency will also face worse tidal flooding. The fact also supports this condition that out of 105 natural disasters in the region, 70% are climate-related disasters (BNPB, 2022). The current condition of climate factor creates Probololinggo susceptible to climate change. This condition is getting worse by additional anthropogenic pressure from the surrounding. As shown in Figs. 5.5 and 5.6, shoreline changes exist in the study area. From 1973 to 2013, coastal accretion was dominant in all areas (six villages), but from 2013 to 2021, five out of six villages experienced coastal erosion. These shoreline dynamics create a land use change driven by local communities. For example, the accretion of the fluvial process which occurred from 1973 to 2013, made additional land in the area (Fig. 5.7), which was then used as rice farms, and those formed by the marine process used as fishponds by the communities. This phenomenon gave an excellent effect on additional income for coastal communities. Still, people who move to those areas for a long time will face a routine climate-related disaster, such as coastal flooding, which negatively impacts their economy. This condition worsened when coastal erosion occurred in recent years (Fig. 5.6). People who live near that area face economic damage to their businesses, and the probability of flooding reaching their homes is higher. This current trend of coastal erosion was may due to the massive sand mining in the highland area (Bromo Mountain), which creates small materials from the upper region to flow to the coastal areas. As shown in Fig. 5.12 with various wave height scenarios, the study area along the coastline will get a negative impact of coastal flooding. According to the field survey data (Table 5.3), the range of inundation depth was between 0.98 and 1.1 m, which means all agricultural and aquaculture businesses will be damaged. By utilizing three different scenarios, Table 5.4 shows that more than half of the affected area was ponds. Moreover, using the worst-case scenario, ponds and rice fields are the most affected area (75%), followed by mangrove and residential areas (Table 5.4). This situation creates a significant loss for both local communities and local authorities.

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Using this fact and previous FGD in 2016, we built a possible adaptation scenario for the local level, as shown in Figs. 5.13
5.16. The adaptation option shown in the result section is the action initiated by the local communities and authorities in the region, where most of them were reactive adaptation plans. For example, during the reforestation program, all mangrove was planted using the same technique along the coastline without considering the coastal profile. As a result, only the west side is successful, but on the east side, mangroves can’t grow. Therefore, proper planning and combining science and policy is crucial for regional development program implementation. The FGD also shows high awareness of local climate change impacts by the local communities and authorities. However, proper adaptation planning in the long term has various challenges. For example, climate change adaptation was still a voluntary activity by the local government, which means they have difficulty allocating the budget for adaptation-related programs. Moreover, the high resolution of the climate dataset is not accessible for the lines ministry, which creates a financial burden for modeling the future climate change impact assessment for a particular sector. Referring to the local development plan (RPJMD) in Probolinngo Regency, the coverage of green open space and mangroves in 2016 was just 1.2% of the entire region (2019), which means groundwater infiltration and coastal proception from storm surge is deficient in the study area. However, in the current condition, the open green space is about 7%, and they plan to reach 20% in 2024 (2019). Adaptation program and how to tackle climaterelated disaster needs to be clearly explained in the RPJMD. However, these local authorities applied a qualitative index to measure disaster risk index within the region. This science approach helps local authorities to measure their effort in disaster prevention. Moreover, the local authorities also set early warning systems for disaster prevention as part of their target. A soft approach also was implemented in the region, such as creating a Disaster Resilient Village as part of their annual target.

5.4 Conclusion This chapter revealed that evidence of climate change in the study area exists. It includes increasing the trend of extreme precipitation, increasing the rise of annual SLR whereas higher than the global average, and often experiencing tidal disasters. Since the Probolinggo coastline was dominated by ponds (i.e., the width of the pond in the west is only 300
500 m wide inland, but in the east, it reaches 900 m from the shoreline), this area received a significant impact of climate change, especially from tidal floods and storm surges. Especially in the eastern region, tidal inundation is extensive in the aquaculture and residential areas. Moreover, the sand mining activities in the upstream river reduced the supply of sedimentary material through rivers, and the cessation of the accumulation process led to increased coastal erosion. In dealing with the tidal flood, the local community and authorities are trying to make various efforts, including planting mangroves to inhibit the rate of coastal erosion, elevating house foundations, and seawall wave breaker construction of both permanent and semi-permanent, and innovating in accelerating salt production.

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Author contributions All authors did conceptualization, data collection, and data processing. Suyarso wrote the initial manuscript and processed the shoreline change. Martiwi Diah Setiawati conducted the review, revised the initial manuscript, and processed the meteorological dataset for climate indicators. Bayu Prayudha did flood modeling. Indarto Happy Supriyadi did socioeconomic data collection. Suyarso and Martiwi Diah Setiawati have equal contributions.

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12034.

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C H A P T E R

6 Climate change and geopolitical risks: cases of riverine communities of Teesta and Brahmaputra rivers of India Parama Bannerji1 and Radhika Bhanja2 1

Department of Geography, Nababarrackpore Prafulla Chandra Mahavidyalaya, Kolkata, West Bengal, India 2Department of Geography, Presidency University, Kolkata, West Bengal, India

6.1 Introduction Water as a commodity to be used in a region is governed by the communities’ sociospatial position, environmental exposure as well as economic positions. It is also pointed out by scholars that there are few regions in the world that has so much water insecurity like Himalayan Asia (Engelke & Michel, 2022) with billions of people living in China, Pakistan, India, and Bangladesh relying on them. South Asia here faces severe waterrelated challenges including declining per capita water resources for the ever-increasing populations, This region also has a dependence on irrigated agriculture coupled with poor water management. South Asia’s transboundary water resources consist of two major river systems: the Ganges
Brahmaputra
Meghna and the Indus river systems (Bandyopadhyay et al., 2020). Climate change has further aggravated the problem with retreat of glaciers or the building of dams to harness renewable resources. This study focuses on the relatively understudied Brahmaputra Basin and its tributaries. The two selected cases are the tributaries of the Brahmaputra river basin—the Teesta Basin of West Bengal and the Ranganadi Basin affecting North East India. While the changing water level distress in the Teesta River valley during the monsoon and non-monsoon period has invoked distress among the residents of the IndoBangladesh border, a multitude of new dams in the Brahmaputra River basin, to meet India’s

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00006-0

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growing energy demands, has led dam-builders to prioritize hydroelectricity generation over flood control, as has been observed in the Ranganadi Hydroelectric project. The paper follows an integrative review approach to draw new conclusions from the existing literature. It insists on providing attention to the development of adaptive mechanisms by the riparian communities that are already vulnerable to climate change hazards due to their low economic status. Further, it also concludes that the South Asian Region will face increased demand for water and it is much needed that the riparian rights are marked by agreement with the agroecological communities to popularize less water-intensive agriculture. Sharing of data between governments and between regional governments, and civil society may also promote sustainable water use.

6.2 Literature review Geopolitics, as studied from a geographical-political perspective, can be explained as a discourse of social practices that illustrates the perception and functioning of highly politically relevant entities with a larger regional framework to global worldviews. The pluralistic nature of the domain encapsulates discourses as representations of space and power, decision-making in different geopolitical scenarios, and political positioning from scientific analysis. As an evolving discipline, the heterogeneous nature of discussion attracts views from geodeterminist stance, positivist and empirical findings, merged with structuralist to post-structuralist approaches (Babalova, 2017). The nation-state is a representation of a territorial container defined by regional authorities, where the foreign and internal administrative policies are manifestations of the governing body, serving as a spatial container of its occupants. The power of nation and space infiltrated the mind of every man by the end of the 19th century, such as the conflicts between humanity in terms of birth, occupation, religion, race, and culture. The geopolitical representations of man-nature relationships are more than the binary imaginations of the hostilities between the East and the West, counties dominating land and sea, or having a foothold of the heartland and rimland. The environmental consciousness and its consequent governance involving global politics surfaced at the end of the 20th century. With a wide range of collaborative networks and transnational medium of environmental governance, decision-making and geopolitics are no longer restrained by the hierarchical authority of the state. International consensus and multilateral regulations currently dominate traditional state-based jurisdictions over a wide array of complex regional and global affairs (Bulkeley et al., 2014). The era of the Anthropocene has revived the geopolitical discussions on rising sea levels, melting glaciers, and climate-induced calamities into the re-territorialization of global affairs. Loss of land and nationalities from new environmental threats is leading to social and economic disturbances, while gradually reshaping the geopolitical map. The increasing diaspora and identity crisis has forced counties to open borders for environmental refugees, leading to unaccounted demographic changes in the borderland and strict climate refugee policies. Turbulent and meandering rivers, especially during floods, are responsible for causing border disputes and displacements of people. On the other hand, dry regions with limited water supply have a profound influence on water accessibility and security issues, thereby the administration is always in negotiation with a

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relatively water-secured nation to alleviate its chronic water problem. Water can be a matter of life or death; therefore, it is of great economic and political importance. Thus, water resource management through regulatory and tariff structures helps in systematic water resource monitoring. State’s inability to manage water resources, to utilize modern technologies for water conservation and reuse, to exploit water as an economic commodity, water taxation, and allocation of water appropriately between different users results in politicized decision-making (Selby, 2005). The diplomatic relations between riverine communities sharing international or regional borders are always under stress. The geopolitical implications of water cannot be limited to water accessibility, security, and resource management. The policy regulations governing riverine communities involve water distribution protocols for domestic, industrial, and irrigation purposes, flood hazards control and mitigation, dams and reservoir construction, hydro-power generation, and pollution control. In such circumstances, it has been observed that the regions situated at the source of the river always have an upper hand over the states in the lower reaches of the river. Riverine communities of meandering streams suffer from land inundation and many families living near the banks of the river get displaced frequently. Further, the people living downstream suffer from a lack of irrigation water during dry seasons despite the construction of irrigation canals close to their settlements. They suffer not only in terms of environmental injustice, but also are a victim of distributive injustice and procedural injustice. Power relationships among the stakeholders determine the efficacy of the judgment of their grievances. Further, the scale at which the problems are addressed is of notable concern. The monetary compensation and security of such people are determined by the regulations inked by the constitution of the governing body. Lack of awareness and recognition of their problem also influence governance process and produce injustices (Bandyopadhyay et al., 2022). In an interesting article by Franklin Templeton Investment Institute (2021), it has been discussed how climate change has acted as a powerful force to multiply of geopolitical risk with accelerated population growth and declined water resources. The article also mentions specifically the water rights and climate change effects as drivers of geopolitical conflicts in various areas. The article’s authors indicated that two of the three most exposed river basins in the world are in India namely the Indus River and the Ganges
Brahmaputra River basin. However, this whole interlinkage needs to be understood on a case-to-case basis. Unless contextualized, it would be difficult to understand the situation. For a developing country like India, this challenge is expected to be unsurmountable considering the fact that the riverine communities are not always economically well-off. In India, the average temperatures across the Himalayas and the Tibetan plateau have risen at an alarming rate and are higher than the global average. According to an article in Observer Research Foundation (2016), as a result of this warming of the Himalayas, snow-covers and glaciers are melting at an alarming rate, swelling Himalayan rivers and causing huge floods. Transboundary rivers, such as the Ganges, Indus, and Brahmaputra, have defined the culture of South Asia for centuries. They are also critical to economic growth as well as food and energy security region (Bandyopadhyay et al., 2022). However, over the last few decades, these rivers have come under considerable pressure due to urbanization and population explosion. This situation has been further aggravated by improper domestic

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governance of water resources and climate-change-induced variability in rainfall and climate patterns making South Asia highly susceptible to floods, droughts, and natural disasters. Bandyopadhyay et al. (2022) pointed out that geopolitics and the history of cross-border disputes have threatened national security. Riparian households in this region are vulnerable to regular flooding and waterlogging because of their proximity to the river, and climate change has only multiplied the frequency of flooding as pointed out by Alam (2018). Alam (2018) also pointed out that this has led to huge economic loss for the riparian community of Bangladesh and it has led to a massive exodus of the community in search of livelihood as they lacked the adaption techniques. According to UNCT, Bangladesh (2021) Brahmaputra river system, as an impact of climate change, has experienced increased water flow in the upstream area and it has led to frequent river flooding events along the Brahmaputra River and low-lying areas of Bangladesh, downstream. Brahmaputra river system is also facing one of the greatest damming activities upstream. Thus, this river system calls for immediate attention for not only being relatively understudied compared to Ganga or Indus but also for its rapid expected impact due to the upstream damming activities to combat the energy crisis due to climate change. It also has a transboundary impact on India and Bangladesh. In general, its behavioral change also calls attention while being fed by Himalayan glaciers, which are retreating due to global warming.

6.2.1 Research gap A gap in the literature was observed as to how specifically climate changes are affecting the riverine communities. Although a number of studies, in general, have pointed to changes in water surface temperature or flash flooding due to damming rivers to tackle the energy crisis, most of them have not pointed out if, within a single river system, there may be variations of impact in different regions. A gap in literature was further observed on the above-stated tensions for two of the three most exposed river basins in the world which are in India namely the Indus River and the Ganges
Brahmaputra River basin. It was also observed that the Brahmaputra river basin is relatively less understudied. Hence, this study focuses to understand the power asymmetry of this region, preferably on a case-by-case basis. The study attempts to explore these issues and understand the interlinkage of geopolitics and water as a commodity within the context of climate change.

6.3 Rationale of the study Although there had been a multitude of studies on the opportunities as well as challenges for water management in South Asia, a need was perceived to include specific cases so that it can be managed to keep the socioeconomic, infrastructural, environmental, and institutional aspects in mind. Climate change has brought a number of reactions to water management. This is particularly true in the case of rivers which are either snow-fed or rainfed, with precipitation being impacted directly by climate change. It has been pointed out that climate change has brought about changes in the flow and pattern of snow-fed

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Himalayan rivers which is again bringing changes in the water dynamics of the communities. The reaction has taken many forms in terms of reduced and variable water flow because of changes in precipitation, changes in river water temperature, etc. All these have impacted the riverine communities economically and socially. However, a direct impact of climate change on the riverine community was observed by the row of large dam construction to tackle the energy crisis. Among the Indian case studies on major river basins in the region, such as Indus or Ganges, Brahmaputra is relatively understudied one. Hence, for this chapter, Brahmaputra has received attention to understanding climate changes affecting its riverine communities, specifically pointing to the tributaries of Brahmaputras where such dams have been constructed.

6.3.1 Objectives The following are the main objectives of the study: • To understand how climate changes are affecting river and the riverine communities, in general, and enumerate its impact on the specific communities under study. • To identify the nature of stress faced by the riverine communities of the selected study areas. • To understand if the damming of rivers is an immediate reaction to tackle energy crisis affecting the communities. • To understand if, within a single river system, there may be a regional variation of impact. • To explore the coping techniques by the community of the case sites, generally in specific and South Asian regions.

6.4 Material and methodology The study adopts two approaches-integrative research review as well as a case studybased approach. The study uses secondary data for it. This study, in the first place, reviews the relevant literature and then analyses, synthesizes, and evaluates information in the content area. Once done, it includes the authors’ observation which is based on the available evidence. It follows a five-stage process of formulating the problem, collecting data or literature search, evaluating the data, data analysis, and interpretation of results. Thus, this study focuses to synthesize the relevant literature sources on the research inquiry. This approach is used when the objective of the review is not to cover all articles ever published on the topic. It rather combines the perspectives to create new theoretical models and draw new conclusions from the existing data after identifying the Knowledge gap (Snyder, 2019). The study reviews the existing and available published literature on various climate-induced changes in rivers and the coping techniques of the riverine community. To make the study evidence-based, the study narrows to two tributaries of the Himalayan river, Brahmaputra, as case studies. It was observed that the Brahmaputra river basin was relatively understudied. Both the Teesta basin of West Bengal and

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Ranganadi, another tributary of the Brahmaputra River basin is chosen for the study. The riverine communities of Teesta and Brahmaputra, like any other community of the global south, are dependent on the multidimensional role that the river plays in their life namely agriculture and industry, drinking and sanitation, hydroelectricity, transportation, etc. The study attempts to understand the changes which have been brought on to such communities. The workflow is indicated in Fig. 6.1.

6.4.1 Profile of the case sites The two selected case studies are the climate-change-induced water management tensions of the tributaries of the Brahmaputra River basin affecting North East India, namely the Teesta river basin and the hydel power project on Ranganadi, another tributary of Brahmaputra. The Tibetan Plateau is the Asian is source of all major rivers and South Asia rivers are usually shared by two or more countries. Originating in the Himalayas, the Brahmaputra River passes through Southwest China (Tibet), two Indian states (Arunachal Pradesh and Assam), and Bangladesh. One of the main right-bank tributaries of the Brahmaputra River is the Teesta River which originates from North Sikkim Himalaya, India, and joins the Brahmaputra River in Bangladesh. It is a perennial, rain-and-snow-fed river characterized by extreme variability in her flows. Thus, it flows through three states/divisions of two countries. In India, the river flows through the Sikkim
West Bengal border and then through West Bengal, primarily through the northern districts of Darjeeling, Kalimpong, Jalpaiguri, and Cooch Behar (Fig. 6.2). Riverine communities refer to communities that live along a river basin and where the majority of the people practice and sustain their livelihoods with water-related activities (Oriola, 2017). FIGURE 6.1 framework.

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FIGURE 6.2 Teesta river basin. Source: Using Google Earth Pro imagery Landsat 8 (captured 26-5-2022).

The riverine community of Teesta includes both the communities in Sikkim and West Bengal. In North Bengal, it is a mixture of ethnic Bengalis with tribal communities such as Mech, Rabha, Rajbanshis, etc. as well as Gorkhas and other Nepali communities in the Darjeeling Hills. In Sikkim, the three major communities of Sikkim are the Lepchas, Bhutias, and Nepalis. Agriculture is the major economic sector in the basin which employs more than 90% of its rural population. In rural Sikkim, fisheries are an important source of sustenance. However, there is very little commercial value attached to the primary activity and no significant industry is there in the Teesta river basin. Coming to the next case, with regard to the Brahmaputra River and its tributaries, Arunachal Pradesh is considered a new hotspot for India’s dam-building effort. The state houses numerous indigenous tribal groups and is also one of the most geographically isolated states in India. Naturally, it has attracted the attention of dam builders (Babalova, 2017). Thus, 168 massive dams were proposed in Northeast India, the majority of which were planned to be located in Arunachal Pradesh, including the largest of them all, the Lower Subansiri Hydroelectric project. Ranganadi Hydroelectric Project is located in the

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Lower Subansiri District of Arunachal Pradesh with a capacity of 405 MW. It started operation in 2001
02. The beneficiary states include Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, and Tripura. The River enters Assam near Johing (27 20’38.96” N, 094 01’56.23” E), flows for another 60 km, and joins the Subansiri river, in the Lakhimpur district of Assam.

6.5 Results 6.5.1 Climate-induced changes of Teesta basin and its Impact on geopolitics 6.5.1.1 The river basin and its community The Teesta river originates in the Teesta Khangste glacier in the high-altitude area of Sikkim in India. It then flows through the northern parts of West Bengal and enters Bangladesh. The situational analysis identified climate-related hazards such as floods, droughts, extreme precipitation events, and landslides adding to the vulnerability of the people in the region. According to a study by Himalayan Adaptation, Water, and Resilience Consortium, the Teesta river is 414 km long and has a total catchment area of 12,159 km2. Around 30 million people (2% in Sikkim, 27% in West Bengal, and the remaining from Bangladesh) are dependent on the Teesta basin. Teesta river basin extends from Sikkim in India in the eastern Himalayas, through West Bengal (Darjeeling, Jalpaiguri, Cooch Behar, Uttar Dinajpur, Dakshin Dinajpur, and Malda districts) to Bangladesh. There the river joins the Brahmaputra before finally flowing into the Bay of Bengal. 6.5.1.2 Situational analysis of the river basin due to climate change A number of dams have been constructed along the Teesta river, the largest being the Teesta barrage project at Gajoldobha in Jalpaiguri district of West Bengal in India. India and Bangladesh also constructed major irrigation projects on the Teesta River to reduce poverty and bring food security, triggering demand for water in that region. In the study by the Himalayan Adaptation, Water and Resilience Consortium (2017), there had been a trend toward rising temperatures and increased variability in precipitation. The upper part of the basin has sub-zero temperatures in winter rising to a maximum of 20 C during summer season. The study also conducted field survey where the local people of the mid-hills (Pendam and Melli-Dara in Sikkim, and Possyor and Teesta Valley in Darjeeling), reported that warm days are increasing. In the downstream area as the river and its tributaries are mostly rainfed, rainfall is crucial, and this changing climate has been affecting the water availability in the river. The study also reported that it has affected the availability of water for agriculture, fisheries, household use, and drinking. The upstream areas are also facing problems of melting of glaciers, damming of rivers, and drying up of springs. Around 80% of the rural population in Sikkim depend on natural springs while the springs and streams are dependent on rainfall hence as a cascading effect the drying of string has affected the water availability. Sikkim has experienced shifting and disappearance of springs, partly as a result of the Government of India program, that is, Pradhan Mantri Gram Sadak Yojana (PMGSY).

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In the lower part of the Teesta basin in Jalpaiguri, West Bengal, due to the construction of dams upstream, the river has dried up extensively. The river is characterized by chars, the local name for the islands or sandbars. These chars are prone to flooding during the monsoon, which is affecting settlements and agriculture. Fishing, a livelihood for many, has also been affected by the low volume of water. 6.5.1.3 Conflicts due to climate-induced changes on Teesta river River water sharing can be a source of both cooperation and conflict. According to ORF (Observer Research Foundation, 2016), 83% of Teesta’s catchment area lies in India while the remaining 17% is in Bangladesh. However, there had been no mutually acceptable framework for a water-sharing arrangement for over 30 years. While for India, Teesta is the major river draining two of its geopolitically important state—Sikkim and West Bengal. In Bangladesh, Teesta is the primary river for its dry, drought-prone north and northwest region, an economically weak region in the country. According to an article in the Telegraph newspaper (dated 8th March, 2021), West Bengal had been resisting the ongoing Teesta agreement on account of “global warming”. The stand of the West Bengal government was as many of the Teesta basin’s glaciers have receded, any agreement may dehydrate northern West Bengal and harm farmers. Over the years, river water sharing has become both a political weapon, as well as a battleground challenging a critical relationship in the neighborhood. According to Observer Research Foundation (2016), the ratio of Bangladesh’s external dependency on river water is over 90% and a substantial amount of that water comes from India. International transboundary water disputes are common and another case may be the Indus system with Pakistan and the Brahmaputra with China. 6.5.1.4 Impact of renewable (hydroelectricity) energy projects on Brahmaputra basin and its impact on geopolitics The generation of hydropower is a strategy to tackle climate change. According to IHA (International Hydropower Association, 2018), the use of hydropower helps in reducing fossil fuel reliance and an additional 4 billion tonnes of Greenhouse gases is being emitted in the atmosphere. Hydropower generation for dam construction continues to advance at a rapid pace. This trend is mainly observed in the “developing countries” of Southeast Asia, South America, and Africa. It has been pointed out (Borgohain, 2019) that the Ganges
Brahmaputra basin (in India and Nepal) and the Yangtze basin in China will have the “highest dam construction activity in Asia”. 6.5.1.5 The study area The rich water resources of the Himalayan region, in South Asia, are shared and contested between multiple countries. They hold economic and political significance than the mere production of renewable energy. India’s Hydro Power Policy (2008) identified 148,701 Megawatts of Potential hydropower Capacity. Of this, 34% (50,328 MW) was in Arunachal Pradesh. However, some of the hydropower projects are under criticism due to environmental factors.

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6.5.1.6 Situational analysis Because of climate change, melting Himalayan glaciers and parallel changes in the South Asian monsoon pattern have led to an increase in the frequency of severe floods. Even if the situation is grave, a multitude of new dams is under construction in the Brahmaputra river basin, to meet India’s growing energy demands. According to Sen (2008), these projects have come under a lot of criticism because although these dams could provide flood protection for downstream communities, it has not been doing so. Political and economic factors have led dam-builders to prioritize hydroelectricity generation over flood control. It is pertinent to mention a study by Rampini (2022), where the author uses a case study of the Ranganadi Hydroelectric Project to assess the vulnerability of riverine communities in Northeast India, in the context of dam-building efforts, due to the effect of climate change on the Brahmaputra River. The author used data from key informants as well as semi-structured interviews with households downstream of the Hydroelectric Project. The study revealed that, by focusing on hydroelectricity generation over flood control and environmental flows, dams along the Brahmaputra have worsened floods and increased overall flow variability as an impact of climate change on river flows. Another study by Mali and Chuti (2017) revealed that there had been changes in water temperature subsequent to changes in water levels to meet the demands of power generation. This has affected the fish spawning habitats, creating unfavorable conditions for the fish and hence their decline. Also, the reduction in flow and water level in the dry months has impacted causing forest and wetland ecosystems. Table 6.1 and quarrying provides income to local TABLE 6.1 Summary of findings. River basin

Upstream

Midstream

Downstream

Teesta

Increasing warm days; It has affected the availability of Reduced flow river water; water for agriculture, fisheries, Decreasing river water levels household use, and drinking

Extensive drying of riverine plains; Flooding of chars or sandbars

Reason

Climate change impact on Himalayan glaciers; Changing impact of Indian summer monsoon

Construction of dam to tackle energy crisis

Construction of dam to tackle energy crisis

Ranganadi Changes in water temperature; Decreased river water flow

Variability in water temperature and diurnal water flow, siltation, flooding, flash floods

Changes in water temperature have affected fishing; Damming has increased flooding, mudflows, and siltation; Sand quarrying activities have been affected by decreased flow

Reason

Construction of dam to tackle energy crisis

Construction of dam to tackle energy crisis

Climate change impact on Himalayan glaciers Indian summer monsoon has affected the flow of the Brahmaputra river

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people who are also affected due to decrease in the flow of the river. The resultant consequence is the loss or depletion of livelihood options for the community. It has forced the people of the affected area to become daily wage earners.

6.6 Discussion As per the findings, the socioeconomic or environmental impact of climate change both directly and indirectly may vary both in nature and scope, but the implications are deep. It acts as a cascading effect and has deepened inequalities among the poor riparian communities, at the microlevel while at the macrolevel, it has induced new stressors affecting geopolitical risks.

6.6.1 Macrolevel impact At the macrolevel, as discussed climate change and its impact on Himalayan rivers in one way or the other has induced tension both between countries or at a smaller scale, between regions. As discussed in the above section, Teesta’s retreating and melting glaciers due to climate change have led to shortage of water in the dry season and has widened the gap in the Indo-Bangladesh relationship. Another case that the study focuses is on how the development of hydroelectric dams projects to combat climate-change-induced energy crisis has led to several problems in the region. Competing for water, abrupt changes in livelihood options have brought unprecedented tensions as demonstrated in the second case. In general, South Asia’s declining per capita water resources and its huge population with its dependent agricultural population coupled with low water use efficiencies have combined to form this region as one of the most vulnerable regions to intense water-related disputes. One also cannot ignore that, South Asia contains two nuclear-armed rivals (India and Pakistan), bordered by a third nuclear power, China, that is upstream of both countries. Interestingly, the much-feared “water wars” have not occurred. Researchers (Babalova, 2017) believed that these conflicts never actually lead to war because there exists asymmetrical power between the riparian states. China or India is on the upstream while downstream is the weakest political actor, namely Bangladesh. River water sharing can be a source of both cooperation and conflict. South Asian Region will see, in the near future, increased demand for water. It is much needed that the riparian rights are marked by the agreement while the agroecological communities also need to promote less water-intensive agriculture and sharing of data between governments and civil society, which may in turn promote sustainable water use.

6.6.2 Microlevel impact Local communities draw advantage of living close to the rivers in terms of food, both subsistence and commercial, fishery, leisure, affordable housing, etc. However, they are more vulnerable to the impact of climate-induced changes. It has been illustrated in Table 6.2.

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TABLE 6.2 Key impact of climate change on riparian communities. Socioeconomic impact

Environmental impact

Institutional impact

Agriculture, fishing, sand quarrying and other economic activities were affected. Recurrent flood damages to riparian households. Crop failure due to flooding Riparian households causes a lack of income-generating activities. Migration of the riparian community

Floods, Storm Surges, Saltwater intrusions in agricultural fields, Shifting of natural streams, Mudflows due to siltation and damming of water

Government support and services are often interrupted in connection to extreme events. Transboundary tensions over sharing of river water in case of damming

There had been a number of factors contributing to increasing in vulnerability. The livelihood strategies which are mostly primary activities like farming or fishing were affected due to reduced and variable river levels, flooding, and siltation due to damming of water. Flooding also has affected access to food, water, and health facilities. Vulnerability also increased due to their low livelihood status coupled with the climate-change impact on river morphology. As reported in the literature, this has driven river-bank erosion and sometimes loss of land. This has been reported to decrease the potential of economic activities in the riparian area. In the case of Teesta, the char land has been most affected by occasional flooding, and downstream has been extensively drying. In the case of Ranganadi, out-migration, both upstream and downstream, has been reported due to frequent flooding, mudflows, and siltation as an impact of damming. Targeted policies and developmental approaches are needed to enhance the adaptive capacity of char land and river-bank households across Bangladesh.

6.7 Limitation of the study The study is based on the available peer-reviewed literature in English but could not access or refer to literature in the local language. This by far is the main limitation of the study. Further, lack of adequate previous studies on the research theme had been one of the main limitations of the study. Limited access to data and time constraints were other limitations of this study.

6.8 Recommendation It is evident from the study that climate change has significantly threatened the ecosystem and livelihood. Mitigating these detrimental impacts of climate change requires policy measures so that the resilience of riparian communication, increases. Training may be conducted for farmers to design new production techniques to combat saltwater intrusions after the flooding. In the agricultural sector, crop diversification and crop rotation need a greater boost. Diversification of economic activities like tourism, and developing local

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handicrafts may assist the local community. There should be improved access to health care for immunization or treatment of vector-borne diseases—in hazard-prone areas and is of immediate need. An early warning system regarding the dam’s flood gates opening is also required. At a macrolevel, the assumption of riparian rights to water should be governed by a detailed agreement regarding quantity, quality, or seasonality for the river basins. Secondly, agroecological researchers have to popularize less water-intensive crop production technologies. There is a need for the transparent sharing of information between governments, civil society, and researchers for implementing sustainable use of water for the people and places of South Asia. Framing of developmental approaches is needed to enhance the adaptive capacity of char land and river-bank households.

6.9 Conclusion This chapter has provided an overview of the various climate-changing risks affecting riparian communities in the Indian part of the Brahmaputra river system and their socioeconomic impacts. The results show that riverine communities are vulnerable to this threat and also have a rather low level of resilience. A significant contribution to local climate resilience includes the urgency to develop long-term planning initiatives in the governance processes. It is extremely crucial to develop an efficient system to survive during the hazards and also recover afterward. The study, at a higher level, also concludes that the question of control over water resources is at the heart of the conflict in many territories. Climate change has only aggravated it and this type of conflict will multiply in the future. For the case sites under consideration, climate change is expected to have a significant effect on the hydrology and water resources of the river basins. While riverine communities in the region rely upon a variety of coping techniques to live with these destructive floods, melting Himalayan glaciers and changes in the pattern South Asian monsoon has aggravated the problem and increased tension among communities. Even the dams which could have provided flood protection for downstream communities, prioritized hydroelectricity generation over flood control. Thus, it is needed that some kind of “water diplomacy” or agreement marking riparian rights should be introduced. At the same time, the agroecological communities also need to popularize less water-intensive agriculture, and governments and civil society enter into partnerships to promote sustainable water use.

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C H A P T E R

7 Vulnerable countries, resilient communities: climate change governance in the coastal communities in Indonesia Andi Luhur Prianto and Abdillah Abdillah Department of Government Sciences, Universitas Muhammadiyah Makassar, Indonesia

7.1 Introduction Climate change is a very serious global threat to the coastal and marine environment, the impacts of which include increasing sea level and surface temperature, as well as increasing the intensity and frequency of tidal waves/tsunamis (Burrel et al., 2007; Paul et al., 2018). Derived impacts cause damage to coral reefs (coral bleaching and weakening of coral aragonite structure), submergence or shifting of mangrove formations toward the mainland, warming of algae, decreased reproductive ability of fish, changes in sex-ratio in turtles, and changes in the composition of species assemblies (Linneweber, 2013; Stys et al., 2017). Anticipating locally to reduce climate change is almost useless, so coastal and marine area managers must immediately adapt to global climate change (Klein et al., 2001; Spalding et al., 2014; Sa´nchez-Arcilla et al., 2016; Tobey et al., 2010). In 2010, in Indonesia, the impact of extreme climate with high rainfall almost all year round reduce food crop production by between 60% and 70% (Pardosi et al., 2020; Novianti et al., 2016). In addition to shrinking the area of agricultural land due to being submerged in seawater, an increase in sea level will also increase the salinity (salty) of the soil around the coast (Fagherazzi et al., 2019; Zhou et al., 2017). Salinity in the soil is toxic to plants so it interferes physiologically and physically in plants, except for marine and coastal plants or adaptive varieties (Orr & Ostrowska, 2006; Monaco & Prouzet, 2016). Indonesia as an archipelagic country has very long coastlines and stretches of coast so the

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reduction of agricultural land for coastal communities due to rising sea levels is widening and expanding (Surya, 2021; Ditjen & Kemen, 2017; Pratama, 2020). One of the impacts of climate change is the shift at the beginning of the rainy season which further affects fishing patterns (Stys et al., 2017; Linneweber, 2013). Fishermen also need weather information (e.g., fish processing seafood and salt industry craftsmen) to determine when they have to carry out postharvest management, start salt-making operations, and the month when they can no longer go to sea (Orr & Ostrowska, 2006; Linneweber, 2013; Monaco & Prouzet, 2016; Stys et al., 2017). Efforts to see the social vulnerability of the community, of course, do not only look at static social indicators but also dynamic indicators of social vulnerability (Klein et al., 2001; Spalding et al., 2014; Tobey et al., 2010). Therefore, social vulnerability indicators do not only describe individual characteristics, such as age, income, livelihood, and ethnicity but also describe conditions of inequality or social disparity as the main factors influencing response actions to disasters due to the impact of climate change (Cutter et al., 2009; Spalding et al., 2014; Sa´nchezArcilla et al., 2016). Fig. 7.1 (Overlay visualization) describes the identification and analysis of research problems through Vosviewer with the keyword "vulnerability of coastal communities" from 700 previous research articles indexed on Google Scholar in 2010
22, found the novelty of research from the keyword "community vulnerability coastal areas” identified and analyzed in 2014
18 namely; (1) Decrease in the number of fish in coastal areas; (2) vulnerability of coastal communities; (3) clean water problems in coastal areas; (4) species vulnerability in coastal areas; (5) vulnerability of coastal zones due to Covid-19; and (6) storms and sea level

FIGURE 7.1 Identification and analysis of research problems. Source: Processed from Vosviewer (2022).

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rise due to climate change. The findings from the identification and analysis of the research problem form the basis of this research focusing on the vulnerability of coastal communities as community resilience to the country’s vulnerability through climate change governance in Indonesia. Inequality of development between regions and the center and between regions with each other is a natural thing because of differences in resources and the beginning of the implementation of development between regions. Inequality between regions, in reality, cannot be eliminated in the development process of a region (Todaro & Smith, 2009). With inequality, it will encourage underdeveloped regions to be able to try to improve the quality of their development so that they are not left behind by developed regions (Kim et al., 2003; Krugman, 1999; Mok, 2007; Tosun, 2001). In addition to the positive impact of inequality in development, the negative impact is that the higher the inequality between regions, the more economic inefficiency will occur, thereby weakening social stability and solidarity, and high inequality is generally considered unfair (Burgoon, 2006; Rodrı´guez-Pose & Storper, 2006; Wahyuningsih et al., 2020). Some of the main factors that cause or trigger inequality in regional development (Taringan, 2007; Todaro & Smith, 2009) are geographical, historical, political, policy, administrative, social, and economic factors. Normatively, coastal communities should be prosperous communities considering the huge potential of coastal and marine natural resources. However, the reality shows that most coastal communities, especially fishermen, are still part of the disadvantaged community (Popova et al., 2019; Prianto, 2012, 2014; Voyer et al., 2017). Socio-economic vulnerabilities, such as poverty, social inequality, limited access to education and health, weak social institutions, and difficulties in accessing business capital, technology, and markets, are multidimensional (complex) and interrelated problems (Cinner et al., 2010; Popova et al., 2019; Roslinawati, 2013; Sutton-Grier et al., 2015). The Pangkep Regency Coastal Area has the potential for marine and fishery resources. Based on BPS (Central Bureau of Statistics) Pangkep Regency (BPS Kabupaten Pangkep, 2018) coastal areas have transportation and port functions, industrial areas, agribusiness and agro-industry, recreation and tourism, as well as residential areas and waste disposal sites. The condition of coastal area of Pangkep Regency has the potential for port transportation that is included in the industrial area. This area is located in Ring One of Semen Tonasa with agribusiness potential in the form of fish ponds. The government’s cooperation through village funds and Semen Tonasa’s CSR (Corporate Social Responsibility) funds has made a major contribution to making Bulu Cindea Village a tourist village. Nevertheless, the poverty rate is still relatively high even though government programs in the field of handling the poor already exist such as the Family Hope Program (PKH), Non-cash Food Assistance, and the Joint Business Group Program (KUBE) (BPS Kabupaten Pangkep, 2018, 2022; Parawangsa & Lestari, 2020). Coastal water ecosystems in East Lombok Regency are mangrove ecosystems and coral reefs, but both ecosystems have been damaged due to human activities such as bombing, in addition to natural factors. The opening of salt ponds and milkfish ponds is a factor that causes damage to coral reefs and mangroves in coastal areas. Efforts to overcome ecosystem damage have been carried out by the government, especially the mangrove

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ecosystem by replanting mangrove plants since 2003 (BPS Kabupaten Lombok Timur, 2022; Novianti et al., 2016). Not much different from the problems that occur in the coastal area of Rembang Regency due to climate change causing drought, flooding, forest fires, sea level rise, and abrasion. The disaster poses a threat to the social, economic, and environmental conditions of coastal communities in Rembang Regency. Changes in the characteristics of rainfall resulted in the emergence of a flooding phenomenon with a height of 30 cm within a period of 3
5 days. Drought also occurs almost throughout the year in the coastal area of Lasem District causing a crisis of clean water for the needs of life and the economy of the community. Drought also caused forest fires in Binangun Village, Rembang Regency. Therefore, coastal communities in Rembang Regency need to have the ability to deal with and reduce disaster disturbances due to the impact of climate change which is referred to as coastal community resilience (Maulana et al., 2016; BPS Kabupaten Rembang, 2018, 2022; Roziqin & Kismartini, 2016). Based on these problems, this study aims to describe the coastal natural resource management system in three areas of Pangkep Regency, East Lombok Regency, and Rembang Regency which are prone to natural disasters currently in Indonesia. At the same time, as an effort to develop appropriate strategies to improve the welfare of coastal communities, and reduce the risk of vulnerability due to climate change, analyze the relationship between marine ecotourism management and the economic resilience of coastal communities. It also analyzes the economic development and prosperity of coastal communities in terms of four components, namely health, education, per capita expenditure, and settlements in the three areas of Pangkep Regency, East Lombok Regency, and Rembang Regency, Indonesia.

7.2 Methods This study uses an in-depth qualitative-exploratory method with a case study approach (Creswell & Poth, 2016) in three areas (Pangkep Regency, East Lombok Regency, and Rembang Regency) in Indonesia to explore problems until getting the latest findings, as well as developing a scheme as a solution to the problem of the resilience of coastal communities due to climate change in disaster-prone areas in Indonesia, using interactive data analysis techniques then interpreted in depth through the Nvivo 12 tool to get the best conclusions. Data and facts are used through library research by studying, reading, and studying books, journals, official documents, and other relevant data sources so as to produce qualified research. Once obtained, the data are then analyzed and interpreted as developed with the stages of data collection, data reduction, data presentation, and data verification, to produce conclusions in the form of new findings that will be useful for readers (Miles et al., 2018). Then assisted with qualitative research tools Nvivo 12 Pro (Woolf & Silver, 2017) and Vosviewer to profoundly explore the problems that occurred and get the best conclusions. Qualitative data analysis was carried out with the Nvivo 12 Plus application, through the stages as shown in Fig. 7.2.

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FIGURE 7.2 Data collection and analysis techniques with NVivo 12 Plus. Source: Processed from Woolf, N. H., & Silver, C. (2017). Qualitative analysis using MAXQDA: The five-level QDAs method. In Qualitative Analysis Using MAXQDA: The Five-Level QDA Method (pp. 1
208). United States: Taylor and Francis. https://doi.org/10.4324/978131526856.

7.3 Results and discussions 7.3.1 Evidence of coastal communities vulnerability in Pangkep Regency, East Lombok Regency, & Rembang Regency, Indonesia Coastal areas are the areas most vulnerable to the adverse impacts of climate change as the accumulation of land and ocean influences (Rusnaedy et al., 2021; Surakusumah, 2011; Wacano et al., 2013). Losses suffered by traditional local fishermen in Indonesia such as Pangkep Regency, East Lombok Regency, and Rembang Regency due to the failure of the adaptation and mitigation agenda for change are more than IDR 73 trillion per year (Hastuti, 2021; Novianti et al., 2016). This fact shows that the productivity of fishermen’s catches is decreasing and making fishermen more distant to catch fish (Mulyasari et al., 2020; Susilo et al., 2021). Fishermen have knowledge about sea currents which according to some informants usually occur between the 17th and the 21st of each month. Outside of these dates, the ocean currents are usually calm. Should a sudden change occur, they can feel it through the rising waves. They can also tell that the wind that resembles a cloud that is seen hanging over the surface of the sea is a sign of strong winds. In such conditions, they do not dare to go down to the sea or “ngadon” (which means go to sea in Indonesian) (Hastuti, 2021; Novianti et al., 2016). Based on the explanation in Table 7.1, it is identified that people living in coastal areas are most at risk of having impacted by increasingly extreme climate change (Afjal Hossain et al., 2012; Sinay & Carter, 2020). It is important to increase the capacity and mitigation of this group through the formulation of an inclusive policy design for climate change in Indonesia’s coastal areas (Kesuma, 2021; Tamitiadini et al., 2019) To that end, the Indonesian Institute of Sciences (LIPI) through the Deputy for Social Sciences and Humanities in collaboration with the UNESCO Office Jakarta, the University of Indonesia (UI), and the University of Gadjah Mada (UGM) held a “Launching of the Study Results of the National

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TABLE 7.1 Conditions of coastal areas in Pangkep Regency, East Lombok Regency, and Rembang Regency and climate change. No. Regencies name

Conditions of community in coastal regencies in Indonesia

1

Pangkep Regency

The largest percentage of poor households is 13.84% compared to Boriappaka Village of 8.84% which is a village in the ring II area of PT Semen Tonasa. The total population of Bulu Cindea Village is 4601 people consisting of 2318 men and 2283 women (BPS Kabupaten Pangkep, 2018, 2022). The majority of the people work as additional farmers (425 people), fishermen (343 people), laborers (315 people), and factory workers (574 people). The poor households in Bulu Cindea Village are 115 households out of 607 households or about 18.95% of all existing households (BPS Kabupaten Pangkep, 2018, 2022).

2

East Lombok Regency

The location of East Lombok Regency is included in a landslide-prone area. One of the disaster-prone areas is in Sambelia District, East Lombok Regency, consisting of a suitable area of 10,565.82 Ha, while an unsuitable land area is 21,182.09 Ha, and residential areas located in an unsuitable area are 472,365 Ha, while the residential area in the appropriate area is 75,285 ha (BPS Kabupaten Lombok Timur, 2018, 2022).

3

Rembang Regency

The Rembang Regency has a tropical climate with a maximum annual temperature of 33 C and an average temperature of 23 C. Wet months in Rembang Regency occur for 4
5 months each year, while the rest are categorized as moderate to dry months. Meanwhile, the highest rainfall in 2021 will occur in January 2021 with an average of 298 mm. Meanwhile, the area with the highest rainfall during 2021 is Sale District, reaching 1689 mm. The high rainfall that occurred in Sale District was due to the fact that during 2021, Sale District experienced a fairly high number of rainy days, as many as 112 days (Arini et al., 2014; BPS Kabupaten Rembang, 2022).

Action Plan for the Design of Inclusive Climate Change Policies in the Indonesian Coastal Zone” (LIPI, 2019; Hidayati et al., 2017). The study on the National Action Plan for Inclusive Climate Change Policy Design in Coastal Areas of Indonesia aims to provide input on the National Action Plan for Climate Change Adaptation (RAN API) on the resilience of special areas, namely coastal areas and small islands by using the UNESCO Analysis Framework for policy formulation Inclusive (Aziz, 2019; LIPI, 2019; Hidayati et al., 2017). In general, in Pangkep Regency there are approximately 160 Heads of Families (KK), especially in coastal areas, who are very dependent on marine products so the community is more dominant by profession as fishermen whose income is uncertain to support their economic needs. Most of the people mainly in coastal areas whose livelihoods as fishermen are below the average income of Rp. 1,000,000/month; these conditions will affect the needs of their socioeconomic conditions (Rahman et al., 2020). The geographical condition of Pangkep Regency is an area with three types of regional characteristics with different natural conditions (known as a three-dimensional area), while areas located in the archipelago are very rich in potential for marine products as well as areas located in mountainous areas having potential for food crops, vegetables, forestry, mining, and quarrying, and being a center for industrial activities, especially the cement industry and marble stone industry in the area eastern Indonesia. Natural conditions and existing resources have made Pangkep Regency one of the pillars of the South Sulawesi economy (Rahman et al., 2020). Vulnerability of coastal communities in East Lombok Regency vulnerability sensitivity that villages in East Lombok Regency have a high level of sensitivity, although there are

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villages that have a moderate level of sensitivity. This shows that most of the coastal areas of East Lombok Regency are very influential on changes in seasonal conditions that occur, so that it has an impact on the productivity of the livelihoods of the majority of coastal communities in rural East Lombok Regency which is highly dependent on seasonal conditions such as the agricultural, plantation and fishery sectors (Pertanian, 2011). The condition of the coastal area of Rembang Regency is a bay area located in the northern coastal area of Java Island and is included in the category of open water so that the wave energy heading toward the coast affects the dynamics of the coastal process, the coastal area in Rembang Regency has experienced the impact of critical land changes as a result of beach erosion or erosion. Abrasion in the northern part of Central Java Province causes damage to the ecosystem of mangroves, seaweed, coral reefs, and ponds which also occurs in Rembang Regency and has an impact on the level of vulnerability of coastal communities living there (Arini et al., 2014; BPS Kabupaten Rembang, 2022; Maulana et al., 2016). The level of vulnerability of coastal communities in Indonesia is shown in Fig. 7.3. Several research results emphasize the importance of increasing the adaptation and mitigation capacity of coastal communities through increasing public awareness with specific approaches according to the needs of each community group in Pangkep Regency, East Lombok Regency, and Rembang Regency (Maulana et al., 2016; LIPI, 2019; Kafle, 2012; Rahman et al., 2020), provision of basic services for groups with special needs (persons with disabilities, women, children, the elderly), as well as access to information, appropriate technology, and capital for economically vulnerable groups in accordance with the type of work and utilization of coastal area resources (LIPI, 2019). In addition, it is also necessary to revitalize the knowledge and local wisdom of coastal communities in an effort to adapt to climate change (Ross et al., 2019).

FIGURE 7.3 Percentage of vulnerability in three Regency of Indonesia. Source: Processed from various sources, 2022.

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The results of other studies recommend several important inputs for the design of inclusive climate change policies in Indonesia’s coastal areas. “The first recommendation is to involve all coastal community groups, including vulnerable groups” (Maulana et al., 2016; Hidayati et al., 2017; Kafle, 2012; Malik, Abdillah, et al., 2021; Malik, Prianto, et al., 2021; Rahman et al., 2020; Rusnaedy et al., 2021). Center for Population Research LIPI as the coordinator of the research. According to Badan Perencanaan Pembangunan Nasional BAPPENAS (2014), this group consists of vulnerable groups due to physical and health conditions such as persons with disabilities, pregnant women, the elderly, and economically vulnerable groups such as the poor. In addition, the results of the study see the importance of coastal resilience through the provision of facilities and infrastructure that are friendly to vulnerable groups and equipped with easy-to-use applications, development infrastructure, early warning systems to reduce disaster risk, and the provision of adaptation action plans for coastal community activities (Malik et al., 2021b; Badan Perencanaan Pembangunan Nasional, 2014). Equally important is the increased participation of all coastal communities in the construction, maintenance, and supervision of coastal protection and early warning facilities and the preservation of coastal resources (Bennett et al., 2014; Kafle, 2012; Rahman et al., 2020; LIPI, 2019; Maulana et al., 2016; Badan Perencanaan Pembangunan Nasional, 2014).

7.3.2 Level of welfare of coastal communities and challenges of vulnerability due to climate change in Indonesia In order to improve the welfare of coastal fishermen throughout Indonesia, the Ministry of Maritime Affairs and Fisheries (KKP) through the Directorate General of Marine Spatial Planning (Ditjen PRL) and PT Pertamina (Persero) signed a cooperation agreement. The collaboration is in the form of a synergy between the Directorate General of the PRL program and the utilization of Pertamina products in the Marketing Operation Region (MOR) VIII working area which includes Maluku, North Maluku, Papua, and West Papua (Ian Montratama et al., 2021). The signing of this cooperation is a follow-up to the Memorandum of Understanding (MoU/Memorandum of Understanding) between KKP and PT Pertamina Number: 08/MEN-KP/KB/VII/2017 and Number: 10/C00000/2017-SO concerning Synergy in Management and Development of Resources Marine and Fisheries Power (Ian Montratama et al., 2021; Badan Perencanaan Pembangunan Nasional, 2014). The level of welfare of coastal communities in Pangkep Regency where the income of fishermen in Pangkep Regency is generally determined by profit sharing, so there is rarely a fixed salary/wage system received by fishermen. Then the system used will be reduced operational costs incurred during operation, plus the cost of selling the results. This profitsharing activity is sometimes less profitable for labor fishermen in Pangkep Regency. Seeing the conditions in the HR improvement program in Pangkep Regency, it has a fairly good achievement, marked by the distribution of the most SPP Education assistance in South Sulawesi Province so that people are strongly encouraged to continue education in order to improve the family economy. The challenge of vulnerability due to climate change that occurs in Pangkep Regency is seen from vulnerability to emergency conditions such as when there is a very extreme seasonal change, fishermen with limited facilities

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and infrastructure are unable to work, and that means they do not earn income. This helplessness is the limited skills and capital they possess, leaving them unable to break out of poverty. Business capital for fishermen includes boats, fishing gear, fuel, preservatives, and processing equipment, which are not small in nominal terms (Jermsittiparsert et al., 2021; Rahman et al., 2020). Potential marine and fishery resources in East Lombok Regency are capture fisheries, aquaculture, salt, fishery product processing, and eco-tourism. White sand beaches and underwater scenery with coral reef ecosystems and ideal waves are the potential for the development of marine tourism. Marine capture fisheries have the potential for sustainable fish resources (MSY) in 2008 reaching 18,242.0 tons/year, including pelagic fish at 7752.8 tons/year and demersal fish at 10,489.2 tons/year. East Lombok Regency also has potential for aquaculture fisheries, including pearl production of 0.22 tons; Grouper of 5.40 tons; Lobster Shrimp of 82.90 tons; and Seaweed of 60,471.00 tons. Freshwater aquaculture is also a large pond to be developed with a production capacity of 851.00 tons; Mina Padi is 3.60 tons and cages are 1.40 tons. Types of fish that are cultivated are carp, tilapia, carp, catfish, pomfret, catfish, and Tawes fish. In addition to freshwater aquaculture, there is still potential for brackish water aquaculture, namely 1074.50 tons of vanamme shrimp; 352.70 tons of tiger prawns and 7.9 tons of milkfish. East Lombok Regency is not only abundant in the capture fisheries sector but the aquaculture sector is also a potential and has a great opportunity to be developed in the context of industrialization. However, in the midst of the natural resources potential of East Lombok Regency, the condition of poverty is increasingly visible in the fishing communities on the coast which is a challenge in itself, generally, coastal communities still live in conditions of deprivation. Factors causing poverty include low technology in business, debt bondage, and the tendency of consumerism in society. When the catch is abundant, they tend to spend their income on nonproductive household needs such as electronic goods. This condition causes them to have no savings to face famine conditions. Lack of savings causes them to take out loans from their bosses or leaders during famine, such as moneylenders or middlemen (Nurlaili et al., 2016; Kab and Timur, 2022; Badan Perencanaan Pembangunan Nasional, 2014). The potential that exists in Rembang Regency is on the north coast. The marine fishery potential of Rembang Regency is quite proud with the amount of production being ranked first out of 16 regencies/cities of Center Java, Indonesia whose territory is a coastal area. Based on data on marine fishery production in Central Java, Indonesia, in 2018, it can be seen that marine fishery products in Rembang Regency are still the largest with a production percentage reaching 28.5%, far exceeding other surrounding regencies/cities that have coastal areas. In terms of the number of Fish Auction Places (TPI), Rembang Regency has more than 10 Fish Auction Places (TPI), while Pati Regency has fewer with 8 Fish Auction Places (TPI). With this condition, basically, Rembang Regency has great potential in the fisheries sub-sector, especially marine fisheries. However, in general, the welfare of the people in the Rembang Regency is still lacking. Rembang Regency is still ranked in the bottom five in Central Java, Indonesia based on the 2018 poverty rate, which is around 15% of the total population in Rembang Regency. This is a challenge for the government and business actors how to manage the potential of Rembang Regency to further advance the region and also prosper its people (BPS Kabupaten Rembang, 2018, 2022).

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Brahmantya (in the Deputy for Cabinet Work Support, S. K. R., 2022) said that this collaboration was carried out for the synergy of the Ministry of Maritime Affairs and Fisheries (KKP) program with PT Pertamina to support the activities of coastal communities which include fishermen, fish cultivators, marketing processing groups (Poklahsar), community monitoring groups (Pokmaswas), and community mobilizing groups. According to Brahmantya, after the sovereignty over the Indonesian seas has been achieved, the government wants the Indonesian people living on the coast, on 17,504 islands to be sovereign and make a living in the marine and fisheries sector. To achieve this, sovereign energy support is needed, one of which is the construction of Fisherman’s Fuel Filling Stations (SPBN) in various Integrated Marine and Fisheries Centers (SKPT) (Pamungkas et al., 2022; Putra, 2022). The purpose of the Integrated Marine and Fisheries Center (SKPT) is to accelerate the economic growth that can be obtained from fisheries and marine affairs at the forefront points of Indonesia’s borders with other countries. The sea in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency, Indonesia has the potential for a high-selling value so sufficient support for fuel oil (BBM) is needed for fishermen there (Pamungkas et al., 2022; Putra, 2022). However, the addition of quotas for fuel oil (BBM) in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency must also be accompanied by an assessment with the principle of prudence so that it is balanced with supervision. The Indonesian government at the Ministry of Maritime Affairs and Fisheries (KKP) manages data on fishermen of 30 Gross Tonnes and above, of which 10
30 Gross Tons are recorded in the province. Pertamina can use this data to carry out calculations to ascertain whether there is really a need for additional quotas so that they are truly on target (Pamungkas et al., 2022; Putra, 2022). If the needs are confirmed, where they are located, there is an affirmative from the local government, affirmative policy from the central government through the Ministry of Maritime Affairs and Fisheries (KKP), Indonesia. Availability of Fuel Oil (BBM), coastal communities also need some other support from Pertamina such as musicool environmentally friendly coolers (Putra, 2022). The need for coolers for fish storage in several fishing ports so far still uses freon materials which may not be environmentally friendly. Pertamina has a musicool that can be used and socialized together as a domestic product in Indonesia. However, if the community, cold storage entrepreneurs, and fisheries entrepreneurs are suitable, the goods must continue to exist because the sustainability of supply is the key along with the availability of agents who sell these goods (Putra, 2022). Looking at the natural wealth in Indonesia (Pangkep Regency, East Lombok Regency, and Rembang Regency) which are abundantly formed by several factors: From an astronomical perspective, Indonesia is located in a tropical area that has high rainfall so that many types of plants can live and grow fast. Indonesia is the largest maritime country in the world. Most of Indonesia’s territory consists of oceans. The oceans in Indonesia are rich in various types of fish. Marine natural resources in the form of marine life, offshore oil mines, and iron sand are present. Indonesia’s marine fish potential reaches 6 million tons per year and Indonesia’s marine potential ranked fourth in 2009, Indonesian capture fisheries production is ranked second in the world (Henriksson et al., 2017; Prianto et al., 2022). However, from the potential of Indonesia’s amazing natural resource wealth, it will be in vain if the management of the threat of climate change is not carried out properly.

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The threat of vulnerability due to climate change disasters on the Indonesian coast will have an impact on the country’s vulnerability. Therefore, it is necessary to strengthen the resilience of coastal communities in areas in Indonesia from the threat of climate change by utilizing the potential of marine resources (such as beaches, mangroves, and aquaculture) to improve the economy of coastal communities.

7.3.3 A model of resilience in facing the vulnerability of coastal communities due to climate change vulnerable to disasters in Indonesia The potential of natural resources in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency is an opportunity to increase the resilience of coastal communities due to climate change in Indonesia. The fishery product processing sector in Pangkep Regency, East Lombok Regency, and Rembang Regency also have opportunities to be developed. The availability of raw material sources is a driving factor for the development of fishery product processing. The potential of marine and fishery resources is quite large but in reality, they still provide a low contribution to the regional income of East Lombok Regency, proper natural resource management to improve for coastal communities who are vulnerable due to the threat of climate change. Considering that the contribution of the marine and fisheries sector as a natural resource for coastal areas in Pangkep Regency, East Lombok Regency, and Rembang Regency which is incorporated in the primary sector is still far from the tertiary sector such as trade and hotels which are the contributors to regional income. Potential marine and fishery resources in the community are still not able to provide optimal welfare. Various factors cause, among others, factors of technology, human resources, and culture owned by the community. From the technological aspect, the low level of technology used by the community causes the community to not be able to optimize the use of available resources. The scale of business in the community is still small and generally has not led to large business productivity. Likewise, human resources are still low with subsystem, consumptive, and individualist cultural tendencies. From the cases described above, here the support system is also an important factor to increase the resilience of coastal communities. The support system includes the implementation of periodic disaster early warning simulations, the implementation of research and periodic studies in the medium and long term on the potential and risk of disasters, as well as the allocation of funds for capacity building and the provision of basic services to coastal communities for sustainability and preservation of coastal resources (Bencana, 2020; Danar, 2020). This study is very important as input for the preparation of a national action plan for Indonesia to improve the resilience of coastal communities in Indonesia, by involving related institutions such as the Ministry of National Development Planning/ Bappenas, the Ministry of Social Affairs, the Meteorology, Climatology and Geophysics Agency, and the National Disaster Management Agency. It is hoped that this study can strengthen the strategy of the National Climate Change Action Plan (Dianty, 2007). The management of coastal areas in Indonesia is seen from the case of the management of coastal areas of three regencies (Pangkep Regency, East Lombok Regency, and Rembang Regency) in Indonesia which shows an increase in the economy of coastal communities.

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It can be seen in two ways: the contribution of increasing sustainable fisheries and the development of marine tourism (Mangrove Forest Tourism and Coral Reef Tourism). Previous research studies (Chen et al., 2013; Cutter et al., 2009) stated that social vulnerability related to environmental disasters can be divided into three models: (1) social vulnerability studies that focus on identifying individual and place vulnerability conditions due to extreme natural events; (2) a vulnerability study that starts from the assumption that vulnerability is a social condition which is a measure of social resistance and resilience to a disaster; and (3) a study that explains the interaction between potential exposure and social resilience in a particular place or area. The first model emphasizes more on climate change exposure and socioeconomic change; risks involved, or the possibility of a disaster occurring that could lead to undesirable outcomes. Risk is the loss that occurs in the livelihood system due to certain natural disasters. In the world of fisheries, for example, the loss of fish resources and the cost of fishing is increasing. Climate change exposure refers to the presence of disasters in individuals, households, or social groups. Therefore, vulnerability is often defined as "a function of exposure to risk or as a measure of coping capabilities" (Tuler et al., 2008). One effort to measure indicators of social vulnerability departs from the understanding that social vulnerability refers to exposure, namely acceptance of exposure to a hazard or the presence of stress conditions at the group or individual level due to exposure to a hazard. The level of community vulnerability is strongly influenced by factors of access to natural resources and the diversity of income sources. Vulnerabilities can change at any time in the short or long term depending on how much adaptation changes: the character of the threat, exposure to the threat, sensitivity, and recovery efforts that get quick results. The second model is related to the resilience of coastal communities, also often referred to as resilience to hazards triggered by climate change. Community resilience also means the adaptive capacity of the community to maintain its condition from the dangers of climate change. Therefore, the development of community adaptation is a way that must be done in restoring community resilience due to climate change. Strategies to increase community resilience to hazards triggered by climate change with the aim of restoring the original state can be carried out in various ways of adaptation, including communitybased adaptation approaches through networks or networking, or cultural and local wisdom approaches. The development of community adaptation can also be driven by government policies. In addition, the third model that explains the interaction between potential exposure and social resilience in a particular place or region is more focused on sensitivity. Sensitivity refers to the degree to which an individual or group suffers a loss when a disaster strikes. Sensitivity relates to the frequency with which people or groups face a disaster. Therefore, how much an individual or social group has certain characteristics. This understanding of sensitivity is often confronted with resilience. In the world of fisheries, for example, efforts to increase vulnerability often have an impact on fishermen not having sensitivity to disasters that may cause the risk of loss (Tuler et al., 2008). Fig. 7.4 showed the strategic model for fisheries management in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency of Indonesia. Based on Fig. 7.4, it can be concluded that the findings of this study, the strategic model for fisheries management in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency, namely optimal use of marine resources, avoiding

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FIGURE 7.4 The strategic model for fisheries management in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency of Indonesia. Source: Processed with Nvivo 12 Pro, 2022.

excessive marine exploitation, catching fish in potential areas with environmentally friendly fishing gear, business diversification, increasing knowledge coastal communities, improving the skills and capacity of fishermen, regulating marine governance, optimal use of facilities and infrastructure and appropriate technology, utilization of fishing zones, and expanding access to capital and markets. At the same time, the use of the Recirculating Aquaculture System (RAS) is able to increase production, obtain quality seeds, reduce human dependence on nature, and be more adaptive to climate change. Efforts to develop marine tourism are carried out in Pangkep Regency, East Lombok

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Regency, and Rembang Regency, as potential areas for coastal areas in Indonesia, in order to achieve an increase in the economy of coastal communities with optimal allocation of space utilization such as beaches, coral reefs, and mangrove forests. The economic value of this tourism is very promising in improving the welfare of coastal communities in Indonesia, especially in the three regions.

7.4 Conclusions The strategic model for fisheries management in the coastal areas of Pangkep Regency, East Lombok Regency, and Rembang Regency, includes optimal utilization of marine resources, avoiding excessive marine exploitation, catching fish in potential areas with environmentally-friendly fishing gear, diversifying business, increasing knowledge of coastal communities, increasing skills and capacities of fishermen, arranging marine governance, using facilities and infrastructure optimally and utilizing appropriate technology, utilizing fishing zones, and expanding access to capital and markets. At the same time, the use of the Recirculating Aquaculture System (RAS) is able to increase production, obtain quality seeds, reduce human dependence on nature, and be more adaptive to climate change. Tourism development efforts are carried out in Pangkep Regency, East Lombok Regency, and Rembang Regency, as potential areas for coastal areas in Indonesia, in order to increase the economic improvement of coastal communities by optimizing the use of space such as beaches, coral reefs, and mangrove forests. The economic value of this tourism is very promising in improving the welfare of coastal communities in Indonesia, especially in the three regions. Limitations in this study are in the institutional and legal scope, potential conflicts of authority often arise as a consequence of the uneven distribution of authority which is divided according to the administration of the Provincial and Regency/City Governments. Therefore, future research should focus on studies on the division of tasks that are expected to increase the utilization of coastal and marine resources, along with accountability in their management. In order to strengthen and confirm the findings of this study, it addresses the weakness of legal instruments in the use of coastal and marine resources as well as law enforcement which causes much uncontrolled use of resources. Therefore, the integration of legislation regarding coastal and marine spatial planning will determine the success of implementing the management of coastal and marine areas.

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C H A P T E R

8 Solar-powered drip irrigation managed by women farmer groups as climate change adaptation for gender equality and social inclusion in East Lombok, Indonesia Ayu Siantoro, Endang C. Purba, Anak Agung Ngurah Agung, Bayu Tumewu, Elvi Tambunan, Krishna Silalahi and Fransisca Novita Wahana Visi Indonesia, Jakarta, Indonesia

8.1 Introduction Climate change is an environmental phenomenon that necessitates research and innovation. It is also a security, economic development, and child rights issue. Girls and women are prone to experience the gendered impacts of climate change without equal representation in decision-making or policy. The differential impacts of climate change on men and women are pronounced in settings that are also affected by violent conflict, political instability, and economic conflict. Women and men are shaped by the societies in which they live, and societal expectations affect the roles both women and men play. This means that women and men often do different work, have differentiated access to resources and information, and experience natural disasters differently. Girls and women tend to be marginalized from political and economic power and have limited access to financial and material resources, particularly in disaster and less

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economically developed settings, which can increase their vulnerability to the impacts of climate change. Lombok, an island in West Nusa Tenggara, Indonesia, that was recently affected by earthquakes in 2018, is also susceptible to drought, floods, and water issues. The situation was exacerbated when the COVID-19 pandemic happened. Lombok, as a touristic area, was significantly affected. Responding to those circumstances, WVI through LENTING (Leverage Girls and Women Climate Resilience Through Improved Renewable Energy) Project developed solar-powered drip irrigation so that the people in Sembalun, Sajang, and Bilok Petung villages are more resilient to climate change. Strengthening agriculture sector capacities can improve household food availability and dietary diversity which has an impact on reducing poverty, improving food insecurity, and increasing resilience to climate change (Choudhury & Abbas, 2017; Premanandh, 2011). Resource-poor smallholder farmers in the arid East Lombok were facing water scarcity, especially during the dry season. The situation affected farmers’ activity and during most of the dry season, they were not able to grow food plants. Hence, drip irrigation was an essential part of agriculture, especially for farmers who faced the problem of being denied access to water for their drip kit gardens during the dry season (Moyo et al., 2006). Design automatic drip irrigation integrated with solar energy is optimizing the use of water and energy. Renewable energy could be a better alternative to reducing dependency on fossil fuels. LENTING Project is installing solar-powered drip irrigation as a renewable energy source for farming to be managed by six women farmers groups in three villages (Sembalun, Sajang, and Bilok Petung) in East Lombok, West Nusa Tenggara, Indonesia. In previous research, solar-powered drip irrigation has proven to be beneficial for gender equality, community economic resilience, household food security, and diet diversity (Alaofe` et al., 2016; Burney et al., 2010). Women and children, as the most severely impacted community members, have to be involved and take a decision-making role in renewable energy management, such as solar-power technology, so they can gain the most benefit. By taking a central role in managing renewable energy, its usage can be focused to ensure women’s and children’s well-being, rights, and protection. The drip irrigation system itself has been proven to contribute to gender equality (Dawit et al., 2020). Dawit and colleagues compared 10 smallholder farmer groups with intervention and pilot schemes in three kebeles (the smallest administrative region in Ethiopia) to gain perception and acceptance of the drip irrigation system and independent water supply. The study found drip irrigation system combined with hand-dug wells reduces over-exploitation of water and labor-intensive manual irrigation. The system has the effect of reducing women’s working time to irrigate agricultural land and giving them the flexibility to grow their land or also grow crops, which is also expected to help them mitigate the negative impacts of climate change on water resources and crop productivity. Further contributions of drip irrigation to gender equality were also found in Nepal (Upadhyay et al., 2005). In the project location, women contribute more extensively to vegetable farming than men in almost all aspects of production and marketing. Before

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drip irrigation, they used 1
2 hours to irrigate the land per 0.0127 ha. Women’s time allocation for work was reduced by 50% after the use of drip irrigation systems. The spare time was then allocated for childcare, socializing, resting, and taking care of livestock. Saving time also gives women the opportunity to engage in other innovative activities. For example, forming a self-help group and operating a savings and loan group which will be useful in times of crisis. These groups also provide a platform for women to share experiences as well as increase networking, self-esteem, and self-confidence. Drip irrigation also contributes to changes in income access and power for women. In most cases, men tend to be favored over women in terms of food allocation, so sometimes women have to eat leftovers. However, the post-intervention assessment of the LENTING Project showed that the food and cash produced by women will tend to be controlled by themselves. The increase in total family income and income control of women has a positive impact on household livestock production and consumption. Furthermore, women were becoming more often consulted by men before making household decisions. Men tend to be more willing to share domestic work since women contribute to earning income. This shows that economic independence not only changes the gender division of labor but also encourages changes in power relations in households. The LENTING Project includes action research that aims to study the effectiveness of climate change adaptation to reduce the risk of vulnerability, hence reducing the tendency of Gender Equality and Social Inclusion problems for women and children (e.g., domestic violence) in the community. The chapter will investigate how far the LENTING project can support vulnerable communities to adapt to negative climate change impacts, while also supporting children and women to increase their resilience by increasing protection and livelihood through renewable energy. The result of this action research can be used to conduct advocacy for climate change and child protection policy, develop a more strategic approach for ending violence against children by inserting climate change adaptation programs in communities that are prone to climate crises, and provide more evidence on program effectiveness for future reference.

8.2 Limitations of the study This action research has not optimally monitored the results caused by solar-powered drip irrigation as a renewable energy-type intervention for climate change adaptation due to the short project duration (9 months). Baseline assessment was conducted after the wet season while the endline data was measured after the dry season, causing the crops’ yields before and after the intervention to be of different seasonal planting periods. Moreover, LENTING Project was closed in November 2021 when the harvesting season is yet to be finished, resulting in only the crop yield from the first harvest recorded. This short project duration also made measuring significant changes at the village community level impossible. Hence, while the baseline could be conducted via survey with randomly sampled community members, endline assessment could only be conducted via group discussions with the direct solar-powered drip irrigation participants.

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8.3 Materials and methods 8.3.1 Research questions 1. How does solar-powered drip irrigation managed by women farmers contribute to community resilience related to livelihood and food security (which are often found to be the root causes of violence against women and children) through: • Increased agricultural productivity? • Adoption of sustainable management of natural resources? 2. How does solar-powered drip irrigation managed by women farmers contribute to GESI in terms of improved access, participation, decision-making, system, and wellbeing for women?

8.3.2 Sample The LENTING community development project was implemented in Sembalun, Sajang, and Bilok Petung villages in Sembalun sub-district, East Lombok District, West Nusa Tenggara Province, Indonesia. Detailed populations in those three sub-districts are shown in Table 8.1. Solar-powered drip irrigation was managed by six Women Farmers Groups (Kelompok Wanita Tani or KWT) as representatives of participating communities from the three villages. The women farmers’ groups are: 1. 2. 3. 4. 5. 6.

KWT KWT KWT KWT KWT KWT

Mele Maju, Sembalun Village (19 members) Bunga Berseri, Sembalun Village (19 members) Pade Girang, Sajang Village (22 members) Barokah Ikhlas, Sajang Village (21 members) Berjuang, Bilok Petung Village (18 members) Maju Bersama, Bilok Petung Village (22 members)

Each member of Women Farmers Groups represented her whole household. Hence, the total direct beneficiaries of solar-powered drip irrigation are 121 households. Their routine activities in the field are illustrated in Fig. 8.1.

TABLE 8.1 Population in three target villages (Sembalun, Sajang, and Bilok Betung). Village

Households

Adults

Children

Bilok Petung

853

2092

896

Sajang

1103

2618

1122

Sembalun

476

1799

771

Data from LENTING Project Concept Note, Wahana Visi Indonesia.

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FIGURE 8.1 Women farmers group at Sajang village. (A) Wahana Visi Indonesia. (B) Wahana Visi Indonesia.

8.3.3 Research procedure Solar-powered drip irrigation as a renewable energy intervention for climate change adaptation was implemented with the following steps: 1. Developing plans for renewable energy installation and solar-powered drip irrigation design, implementation, and maintenance plan. 2. Increasing community capacity to manage the renewable energy system is developed with training on drip irrigation operation, as well as solar renewable energy operation and maintenance.

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3. Supporting government and community care systems to protect children, families, and vulnerable groups with training on climate change adaptation for adults and child groups. 4. Construction and operation of the renewable energy system, followed by planting, maintenance, and harvesting of crops using solar-powered drip irrigation. Prior to the solar-powered drip irrigation intervention, we conducted a baseline assessment and endline or impact assessment after the intervention. Those assessments were conducted in the catchment areas of the three villages (Sembalun, Sajang, and Bilok Petung) in Sembalun sub-district, East Lombok District, West Nusa Tenggara Province, Indonesia. The baseline or pre-intervention survey was conducted to investigate community resilience and GESI. The survey involved 94 randomly sampled household respondents (F 5 41) and 32 purposively selected child respondents (F 5 22). Endline or post-intervention data collection was conducted via focus group discussions (FGD) involving the six Women Farmers’ Groups in three villages as direct beneficiaries of the project (total participants 5 121). Quantitative data were analyzed using descriptive statistics, while thematic analysis was employed for qualitative data.

8.4 Results and discussion 8.4.1 Community resilience: increased agricultural productivity Farmers in Sembalun Sub-district (location shown in Fig. 8.2) are smallholders with land sizes ranging from 0.2 to 2 ha. The effect of climate change is becoming more real as farming is more challenging due to inadequate water and long dry months (5 months/year). In this sense, the planting season only takes place once a year compared to 3
4 times/year in cases where irrigation or water is available. This study found that only 20% of the participants plant wetland paddy. Rice productivity is considerably good in Sembalun at 5.3 tons/ha in 2019, a stable rate compared to the previous year (Central Statistics Agency Indonesia, 2019, 2020b). The figure is also better compared to both national and province averages at 5.1 and 4.8 tons/ha respectively (Central Statistics Agency Indonesia, 2020a, 2020c). This study further found that around 43% of the respondents have used hybrid seed implying maize yield in Sembalun could be higher than the current figure. Legumes are the most common crops planted in all villages in which around 70% of the respondent stated to have grown different types of legumes. They can be planted during the rainy season or before entering the dry season. Legumes contribute to organic fertilizer for soil by N reduction, reduce greenhouse gases emission, as they release 5
7 times less GHG per unit area compared with other crops, allow the sequestration of carbon in soils with values estimated from 7.21 g/kg DM, 23.6 versus 21.8 g C/kg year and induce a saving of fossil energy inputs in the systems due to N fertilizer reduction (Stagnari et al., 2017). It was found during baseline that farmers only farm during the rainy season due to no water during the dry season. Considering the higher risks of climate change, in the long run, farmers need to adapt their agricultural practices including a strategic selection of their crops. The technical efficiency of farming has an important role in enhancing agricultural development in Indonesia (Saeri et al., 2019). Farmers are called technically efficient if they

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FIGURE 8.2 Map of Sembalun sub-district. Source: Data from LENTING Project Baseline Assessment, Wahana Visi Indonesia.

have produced at the level of production limits where this cannot always be achieved due to various factors such as bad weather including water scarcity (Battese & Coelli, 1995). Farmers in Sembalun, to some extent, have practiced good agricultural practices including water conservation contributing to lowering the risks of climate change. This study found that 56% of the respondents do not have access to irrigation and therefore rely on rainfed or water from the river. Of those who have access to irrigation (43%), around 7.5% of them have utilized drip irrigation. In addition, around 6% of the respondent have applied drip irrigation from water sources other than irrigation which then make up 13.8% in total. In terms of distribution, more than half of the farmers (56%) in Sajang have access to irrigation while Bilok Petung is the lowest (Fig. 8.3). Although Sajang and Bilok Petung have access now, the water sources come from other villages. For instance, Bilok Petung draws the water from another district (North Lombok). As mentioned in FGD, the volume of irrigation water is decreasing particularly in the dry season; it is very likely Sajang and Bilok Petung may lose access in the future if the water source at the other village is no longer adequate. At this point, the villagers have arranged verbal agreement on the distribution of irrigation water by scheduling ‘open and closed’ pipes in dry season.

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FIGURE 8.3 Distribution of irrigation by target village. Source: Data from LENTING Project Baseline Assessment, Wahana Visi Indonesia.

TABLE 8.2 Harvest of commodities in three villages (Sembalun, Sajang, and Bilok Petung). Village

Hamlet

Women farmers group

Commodity

Harvesting (kg)

Sembalun

Mentagi

Mele Maju

Potato

150

Dasan Tengak Baret

Bunga Berseri

Potato

200

Bawak Nao Daya

Pade Girang

Tomato

1500

Bawak Nao Lauk

Barokah Ikhlas

Onion

400

Bilok 1

Berjuang

Tomato

1700

Bilok 2

Maju Bersama

Tomato

1700

Sajang

Bilok Petung

Data from LENTING Project Endline Assessment, Wahana Visi Indonesia.

It is interesting to find out that 13.8% of the respondents have applied drip irrigation though there has not been strong evidence of its effectiveness and efficiency. The existing literature mentions that drip irrigation is suitable for individual irrigating crops such as horticulture or fruit trees but less effective for close-growing crops, e.g., rice paddy (FAO, 2017). Solar drip irrigation has the same advantages as other methods of drip irrigation (Burney et al., 2010). The latitude of Sembalun is suitable for sunlight availability which in turn enhances solar power efficacy. Solar drip irrigation is undeniably contributing to climate change mitigation by enabling the development of low-carbon irrigation agriculture. The project results that the farmer was able to grow horticulture plants during the dry season. Table 8.2 shows the harvesting of each commodity per 10 acres. The farming happened in the dry season from July to September except for the potato in August to November. The farmers also implemented organic farming to improve the soil nutrients. Before the intervention, the group only grew onions and potatoes in Bawak Nao Lauk,

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FIGURE 8.4 Women farmer groups’ harvest. (A) Wahana Visi Indonesia, (B) Wahana Visi Indonesia, (C) Wahana Visi Indonesia.

Mentagi, and Dasan Tengak Baret during the rainy season. Its average harvest was 1600, 400, and 500 kg, respectively, for farm women’s groups from October 2019 to September 2020 (examples of their commodities are shown in Fig. 8.4).

8.4.2 Community resilience: sustainable management of natural resources Sustainable agriculture is the effective management of resources to fulfill human needs while maintaining the quality of the environment and conserving the natural resources, involving conservation of crop diversity, conservational tillage, efficient water management, integrated management of nutrients, weeds, and pests with crop diversification (Kumar et al., 2020). Based on the observation in the research area, sustainable natural resources management was limited to the application of good agriculture practices (GAP) and water conservation. The GAP followed in sustainable agricultural development such as intercropping, crop rotation, terracing, proper tillage, organic farming, droughtresistant seeds, tree planting in the agricultural plot, and solar-powered drip irrigation (see Fig. 8.5) which contribute to climate change adaptation. The baseline study found that 26.6% of households practiced sustainable natural resources management with regard to good agriculture practices and water conservation. The figure is relatively low presumably due to poor awareness of the effect of climate change and a lack of information on agricultural practices. It is worth noting the majority of the households (76%) have already practiced intercropping mainly due to its economic benefits. In general, farmers cultivate two crops together, i.e., maize and legumes, which are good genotype combinations. Literature highlights two important potentials of intercropping, which are maintaining and improving soil quality and fertility (Gardarin et al., 2022; Maitra et al., 2019).

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FIGURE 8.5 Demonstration plot at Bilok Petung village. (A) Wahana Visi Indonesia, (B) Wahana Visi Indonesia.

Soil is a central resource of farms, and it relates to both climate change mitigation and adaptation actions. Evaporation can be lower and water use efficiency can be higher with intercropping. Plants with differing root structures can take up water from varying depths, and adding deep-rooted or drought-resistant crop genotypes can reduce the between-crop competition for scarce water. In addition, the increased productivity per acreage in intercropping can also contribute to higher soil organic matter accumulation and carbon sequestration, which is important for greenhouse gas mitigation in low-carbon soils. Crop rotation has similar benefits to intercropping but fewer farmers (12%) opt to apply this practice. It is inopportune since crop rotation is an important tool for improving the climate resilience of the agricultural production system (Yu et al., 2022) and the rise in required water for crop irrigation (Ouda & Zohry, 2018). Other good agricultural practices applied are terracing, organic farming, and planting trees in the field or on the perimeter. Terracing was only practiced by 5% of the respondents which is inopportune considering the spatial analysis highlights the majority of the land (74%) is classified as moderately steep.

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In terms of water conservation, two major practices applied by the farmers are rainfed (63%) and water trap (61%). Those are good practices considering Sembalun is classified as “moderately dry” according to Schmidt’s classification with 5 dry months within a year. There should be efforts to manage available water sources, i.e., rivers and watersheds. As mentioned earlier, there are eight watersheds that have been found in the Sembalun sub-district, which Beburung watershed is the biggest with 8.419,9 Ha or accounting for 45% of the total area of the Sembalun sub-district that can potentially be used for irrigation and other purposes. In Bilok Petung and Sajang villages, the change in behavior relatively supports the improvement of land productivity by using organic materials (47.8%), while in Sembalun village there were still many people not familiar with the land regeneration method. At the same time, the communities use more water sources from drilled wells (see Fig. 8.6); therefore, they protect the surrounding well by conducting horticulture practices. Surprisingly, 37.5% of Women Farmers Group in Bilok Petung experienced a water FIGURE 8.6 Solar-powered drilled well at Sembalun sub-district. (A) Wahana Visi Indonesia, (B) Wahana Visi Indonesia.

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TABLE 8.3 Post-intervention data on water availability and natural resource management by Women Farmers Group. Question

Bilok Petung

Water availability increased from last year

Sajang

Sembalun

1 (6%)

Water availability same as last year

10 (62,5%)

15 (88%)

11 (85%)

Water availability decreased from last year

6 (37,5%)

1 (6%)

2 (15%)

Covering land with organic materials

4 (25%)

17 (100%)

1 (8%)

Planting Fruit and Wood Trees

3 (18,75%)

2 (15%)

Planting Plants Around Water Sources Letting plants around the land grow

9 (56,25%)

Doing nothing

(77%)

Data from LENTING Project Endline Assessment, Wahana Visi Indonesia.

availability reduction compared to the last year. At least two factors are in place to justify this finding. First, the farmers’ understanding of defining water availability is only limited to surface water, excluding the drilled well. Second, monitoring periods are not comparable between baseline (wet season) and endline (dry season) conditions. Table 8.3 shows the perspective and activity of women farmer groups in water availability and natural resource management.

8.4.3 Gender equality and social inclusion (GESI): improved access, participation, decision-making, system and well-being for women The Household Survey for LENTING Project baseline involved 41 women and 53 men to represent their households. Most heads of the household surveyed (95%) were men. There were five households headed by widowed or divorced women. Aside from the survey for adult respondents, there were 22 girls and 10 boys who participated in the child/ adolescent survey. The baseline determines that women lack access to agricultural assets and income, training/capacity building or skill/knowledge, assistance, services, and infrastructures, as well as participation in farming activities, compared to men. Women also had yet to have equal and inclusive decision-making power in family agricultural methods, farm management, i.e., choices of crops to grow, and household income utilization; they were dominated by men/husbands. At the village community levels, women and children also lack participation and decision-making ability. It was mostly men who are involved in the public discussion forums, organizations, and other civic activities in the village community, as well as influencing, controlling, or making decisions on village matters. In the LENTING Project baseline, we assessed the first three layers of GESI components, namely, (1) access, (2) decision-making, and (3) participation at two levels, which are (1) household or family and (2) village community according to World Vision GESI Approach and Theory of Change (World Vision, 2020). In terms of access to asset ownership, 62%

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of household respondents reported that most lands were registered under the husbands’ name and 25% said that men own all family assets. Almost all (94%) respondents said that husbands were always or mostly become the only member of households who access agricultural training (see Fig. 8.7). This finding is not surprising as men were revealed to have overwhelmingly greater access to village activities (82%) and village meetings (86%). Fig. 8.8 tells us that husbands (30%) still received social assistance more often than wives (20%). Furthermore, it was still husbands (49%) who decided on the utilization of government aid. No wonder, 62% of respondents agreed that husbands decided on how to use household income, and only 25% said that both husband and wife could make FIGURE 8.7 Household members’ access to agricultural capacity building. Source: Data from LENTING Project Baseline Assessment, Wahana Visi Indonesia.

FIGURE 8.8 Household members’ involvement in social assistance management. Source: Data from LENTING Project Baseline Assessment, Wahana Visi Indonesia.

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FIGURE 8.9 Decision maker on crops management. Source: Data from LENTING Project Baseline Assessment, Wahana Visi Indonesia.

such decisions. Despite the majority (72%) of husbands and wives surveyed jointly deciding what crops to grow (for food) as well as when and where to grow the crops, husbands (27%) still have more decision-making power than wives (1%) (see Fig. 8.9). Outside of household matters, 76% of respondents said men also have more control over village finances, as 94% of respondents agreed that men have full or most decision-making power on village matters. The unequal decision-making power that disserves women is related to the lack of opportunity for women to participate in the public domain. Men were found to immensely dominate participation in the village community. As much as 85% of respondents reported that it was men who are able to express opinions in village meetings. Hence, only men could also participate in influencing village decision-making, as stated by 81% of respondents. Nonetheless, 17% of women but only 2% of men said that both women and men could influence village decision-making. Furthermore, the majority of respondents revealed that it was men who participated in village organizations (84%) or took part in volunteer work in the community (82%). However, 15% of women stated that both sexes participated in volunteer work, compared to 2% of men who said so. In terms of participation in the domestic domain as shown in Fig. 8.10, 68% of men reported that childcare is a joint responsibility of husband and wife. However, 29% of women disagreed; it was wives who take care of children. Because women participate in more childcare work compared to men, those mothers could gain decision-making roles in their children’s lives and some control over household management. This conjecture was supported by the child/adolescent survey, in which 66% of girls and boys revealed that mothers have the most role in deciding household resources and energy usage (see Fig. 8.11). It is suggested that women have an important role in determining the wellbeing of children at home, but the gender relation system in the community hampered their abilities to do so. Despite their essential role, women still lack equal access, decision-making power, and participation. The LENTING project aimed to ensure that women groups in the

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FIGURE 8.10

Child-care responsibility. Source: Data from LENTING Project Baseline Assessment, Wahana Visi

Indonesia.

FIGURE 8.11 Decision maker on energy source and usage in households. Source: Data from LENTING Project Baseline Assessment, Wahana Visi Indonesia.

community have equal access to learn and use sustainable management of natural resources using solar-powered drip irrigation, as well as decision-making power and full participation on how the solar-powered drip irrigation should be managed through public consultation (see Fig. 8.12). By doing so, women could claim more contributions and benefit from the increased agricultural productivity resulting from climate change adaptation via solar energy. It would then become the basis for women and girls to gain equal and inclusive access, participation, and decision-making to livelihood resources, which in turn strengthen the community’s resilience.

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FIGURE 8.12

Public consultation with women farmers to discuss renewable energy design. (A) Wahana Visi Indonesia, (B) Wahana Visi Indonesia.

After women farmers managed solar-powered drip irrigation as a renewable energy intervention, positive changes can be seen in more equal and inclusive access, participation, decision-making, system, and well-being for women. The GESI improvements are as follows: 1. Access It was found that after the intervention in which women farmers groups were facilitated to manage demo plots with renewable energy, i.e., solar-powered drip irrigation, they had access to productive activities outside the home without relying entirely on their husbands. Access to agricultural training and assistance that was previously dominated by husbands, after the LENTING project can be available for wives who can receive agricultural training with renewable energy. In addition, the women farmers groups also have access to income from agricultural yield demo plots, However, this income was not much because the market prices were low. The women farmers group stated that, usually, the income is used for the benefit of the family, especially for children’s needs, or for further farming capital, e.g., buying seeds.

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2. Decision-Making Women started to gain the ability to influence their social relations (husband, neighbors, family) related to agriculture methods because they master renewable energy technology with experience using solar drip irrigation. But they were not yet able to influence decisions formally in the village. The women can make decisions related to the management of the demo plot and the use of its income; husbands mostly do not interfere with women farmers’ group matters. Most members of the women farmers group started to have the confidence to discuss and contribute views regarding the family farm decision to the husband. Some women were able to discuss and then take a family agricultural decision together with their husbands, but they were not many. Not much changed related to decision-making about agricultural affairs at the village level. Such decisions were still mostly controlled by men; only a little improvement from before with some women having the confidence to speak with the village’s farmer champions. 3. Participation In this LENTING project, women farmers group members can participate fully in farming using solar drip irrigation. The administrators and members of the women farmers group are women only. It was all women who lead and manage the women farmer groups’ activities in the demo plot. Women learned to do all the farming activities that are typical of men. Although contrary to traditional gender roles, participation in the demo plot may be considered as part of the role of the wife to help the husband and the household economy; newly wedded wives were tolerated when they could not participate fully. As women’s participation in the demo plot was for the benefit of the whole family, their husbands helped with heavy work (e.g., hoeing the land) or when the wife is pregnant or in a sick state, her role is temporarily replaced by her husband. 4. System In relation to encouraging a system that accommodates women’s conditions, women farmers groups in three villages agreed that solar-powered drip irrigation makes it easier for women physically because it is not too heavy. Solar-powered drip irrigation has also made farming can be done by women according to the traditional gender roles in their cultural context, i.e., irrigation in the villages is usually done at night when women cannot get out of the house, drip irrigation drops automatically so women farmers did not need to visit the farm at night to irrigate. To stimulate an equal system related to agriculture, the women farmers’ group felt they have proven they can do farming activities usually done by men. However, there isn’t yet a formal mechanism in the village to regulate and support this. 5. Well-Being The well-being of women is still related to the role of the wife: they are more confident and prouder because they can help the husband and the family economy, not yet for themselves. Women farmers groups tend to construe their farming activities using solar drip irrigation as a part of their duty as a wife to their families. Hence, the output/outcomes from women farmers’ group activities were mostly utilized for the well-being of the women’s whole family, less for the women’s own well-being. Hence, there were no reported backlash from the male population when the access and participation to the new renewable energy, i.e., solar drip irrigation knowledge and technology mastery were dominated by women.

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This study finding is in line with a previous study in Northern Benin, West Africa (Sehgal, 2011). Solar-powered drip irrigation has enabled women to gain agency, such as successfully learning to choose the right plants to plant, and securing market access for them. The women also learned to transition from subsistence farming to marketing farming which will contribute even more greatly to their economic independence and well-being. However, both studies also identified difficulties to ensure that women farmers actually accept and adopt solar-powered drip irrigation after the project ends. It is because, during the project duration, the construction and management of solar-powered drip irrigation were free.

8.5 Recommendations This chapter has identified several strengths, weaknesses, opportunities, and threats during the intervention as follows: Strengths: solar-powered drip irrigation is a new technology approach that is very appropriate to be used according to natural conditions in dry areas. In addition, this technology approach might become an alternative solution to agricultural practice in adapting to climate change impacts; Weaknesses: the impact of the intervention cannot be monitored due to short-period of activity implementation. During the implementation process, the farmers have difficulties in accessing groundwater as the water-source, since there are only few vendors providing well-drilling service. Additionally, some farmers or land-owners are reluctant to give small portion of their land to solar-power infrastructures. During the initial phase of this intervention, some farmers are not interested in organic farming. This practice cannot involve children due to their responsibility to their own formal education; Opportunities: through this technology approach, women farmers can contribute to the effort of their collective welfare and improve their position in the society. Even though empowering women is not common practice, further study might help to provide a better understanding of this gender or social roles in the future or different areas. Post-intervention analysis should be conducted in an ideal intervention duration to have clearer perspectives on the impacts; Threats: empowering women in the masculine area are not common in most communities, and this might create wrong or different perceptions in some areas. In practice, farmer groups cannot buy the vegetable seeds at local markets because it has very limited stocks.

8.6 Conclusion 8.6.1 Related to the technical approach in agriculture and natural resource management (community resilience) 1. The period of the dry season in research areas is worsened by the impact of long-term climate change. Currently, the farmers have experienced 5 months of the dry season and possibly longer periods in the future. Therefore, to adapt to that particular circumstance, solar-powered dripping irrigation was developed based on the need for water from farmers during the relatively long dry season.

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2. Solar-powered dripping irrigation is not yet a proven effective and efficient intervention to increase the harvest period during one year of the planting calendar. According to the geographical area of the research areas, sunlight is suitable for powering the dripping irrigation. It provides more possibility to grow crops during the dry season and potentially generates additional income for farmer groups. 3. Solar-powered dripping irrigation provides sustainability for at least 26.6% of households in regard to agricultural practices, water management, crop harvesting, and farmers’ income throughout the year. 4. By using this technology, the communities contribute to improving their land productivity (with additional organic materials/fertilizer) by approximately 47.8%. On the other hand, the water source from drilled well made them more protective of their surrounding environment.

8.6.2 Related to gender equality and social inclusion (GESI) 1. Access. Women farmers can access a complete novel farming capacity building, not merely partial like they usually did. Moreover, the women’s mastery of solar-powered drip irrigation farming techniques was also appreciated by their husbands. 2. Participation. Women farmers claimed that they can learn how to meaningfully participate in productive farming. Some Women Farmers Group members said that they became more confident to participate in discussions with farmer champions in the village. 3. Decision-making. At first, 62% of respondents agreed that husbands decide on almost everything in their household. After the intervention, women farmers said that they can make agricultural decisions fully, even though it is limited in the Women Farmers Group regarding the demo plot matters. However, decision-making at the community level is still dominated by men. 4. System. The installation of solar-powered drip irrigation introduced a new agricultural system using renewable energy that enabled people to farm during the dry season, not only during the wet season anymore. This new system is more accommodating for women than the usual farming system in which irrigation was done at night when women cannot go out of the house. Nevertheless, as the project duration is short, it is yet to be known whether this new farming system introduction will bring about a systemic change. 5. Well-being. Women were enabled to acquire additional income or food for their children and family as they can produce harvest by farming the unused land during the dry season. They can also do farming activities with more comfort and less time as drip irrigation is automatic. Nonetheless, psychological well-being improvement, i.e., increased confidence and self-worth, that women felt from mastering a new technology was still limited to their role as a wife in helping their husbands to farm, not yet to developing themselves as an individual.

Acknowledgments LENTING Project was implemented by Wahana Visi Indonesia with generous support from World Vision Korea. The authors thank the Lombok Earthquake Emergency Response (LEER) team who implemented LENTING Project in the field. Utmost gratitude is given to the government and community of Sembalun, Sajang, and Bilok Petung villages, Sembalun Sub-District, East Lombok District, East Nusa Tenggara Province for the participation and support for the LENTING project and action research.

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C H A P T E R

9 Climate change, local vulnerabilities, and involuntary migration in drought-prone Bundelkhand region of central India Debarghya Chakraborty1, N. Savitha1 and Kunaljeet Roy2 1

Department of Social Sciences, School of Social Science and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu, India 2School of Social Sciences and Languages, Vellore Institute of Technology, Chennai, Tamil Nadu, India

9.1 Introduction As per Human Development Report (2007
08), UNDP by 2009, the world is expected to be on average between 1.8 C and 4 C hotter than it is now. Large areas are expected to become drier—the share of land in constant drought is expected to increase from 2% to 10% by 2050. Meanwhile, the proportion of land suffering extreme drought is predicted to increase from 1% at present to 30% by the end of the 21st century. Rainfall patterns will change as the hydrological cycle becomes more intense. Such changes in climate regimes will challenge the adaptive capacities of many different communities, and engulf some, by interacting with and exacerbating existing problems of food security, water scarcity, and the scant protection afforded by marginal lands. Robert McLeman unpacks the drivers of forced migration into two discrete groups. First, there are the climate drivers. These themselves are of two types: climate processes and climate events (McLeman & Hunter, 2010). Climate processes are slow-onset changes such as sea-level rise, salinization of agricultural land, desertification, growing water deficiency, and food insecurity. Equally significant though are the non-climate drivers. It is clear that many natural hazards are, at least ‘manmade’. A natural hazard only becomes a ‘natural disaster’ if a group of people is particularly vulnerable to its impacts. A community’s vulnerability, then, is an outcome of its exposure to climatic conditions (such as a coastal location) and the community’s adaptive

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capability. At some point, that land becomes no longer capable of sustaining livelihoods and people will be forced to migrate to areas that present better opportunities. Temporary migration as an adaptive response to climate strain is already evident in many areas. But the picture is nuanced; the capacity to migrate is a function of mobility and resources (both financial and social). In other words, the people most susceptible to climate change are not necessarily the ones most likely to migrate. Forced climate migrants fall through the loopholes of international refugee and immigration policy—and there is a considerable confrontation to the idea of expanding the definition of political refugees to incorporate climate ‘refugees’. Meanwhile, large-scale migration is not taken into account in national adaptation strategies which tend to see migration as a ‘failure of adaptation’. So far there is no ‘home’ for forced climate migrants in the international fraternity, both literally and figuratively (Bhagat, 2017). The association is so unpredictable that the science of climate change is complex enough—let alone its blow on societies of different resources and varied capacities to adapt to external fright. Partly, it is because individual migrants’ decisions to depart their homes vary so extensively: deciding causality between economic ‘pull’ and environmental ‘push’ is often highly subjective. Finally, disaggregating the role of climate change from other environmental, economic, and social aspects requires an ambitious analytical step into the dark. In short, drawing a causative, linear line between climate change and forced migration is very difficult. One immediate controversial issue is whether people displaced by climate change should be defined as ‘climate refugees’ or as ‘climate migrants’. This is not just semantics; its meaning becomes generally accepted and will have very genuine implications for the obligations of the international community under international legislation (Bhagat, 2017). The word ‘refugee’ reverberates with the general public who can sympathize with the implied sense of duress. It also carries fewer negative connotations than ‘migrant’ which tends to entail a voluntary move towards a more attractive lifestyle. However, the use of the word ‘refugee’ to portray those fleeing from environmental pressures is not strictly accurate under existing international conventions. The United Nations’ 1951 Convention on Refugees and protocol of 1967 relating to the position of refuges are clear that the term should be restricted to those fleeing persecution: “a refugee is a person who owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group, or political opinion, is outside the country of his nationality, and is unable to or, owing to such fear, is unwilling to avail himself of the protection of that country”. There are other problems with using the term ‘refugee’. Sternly speaking, categorization as a refugee is dependent on crossing an international border; sometimes displaced within their own country is an ‘internally displaced person’ (IDP). Given that on the current predictions, the majority of people displaced by climate change will settle within their own borders, restricting the definition of those who cross international borders may seriously devalue the extent of the problem. Second, the notion of a ‘refugee’ tends to imply a right of return once the persecution that triggered the original flight has ceased. This is, of course, impossible in the case of sea level rise and so again the term distorts the nature of the problem. Third, and perhaps most significantly, there is concern that expanding the definition of a refugee from political persecution to encompass environmental stressors would reduce the available international mechanisms and goodwill to cater for existing refugees. If the term ‘climate refugees’ is problematic, it is widely used,

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in part, for the absence of a proper substitute. The term ‘climate evacuee’ implies momentary movement within national borders. ‘Climate migrant’ implies the ‘pull’ factors of the destination area more than the ‘push’ factors of the source nation and carries negative connotations which lessen the implied responsibility of the international community for their welfare. However, due to lack of an adequate definition under international law, environmental migrants are almost invisible in the international system: no institution is accountable for collecting data on their numeric, let alone providing them with basic amenities. Unable to establish political persecution in their country of origin, they fall through the cracks in asylum law. One definition given by Jeff Crisp in the UNHCR is that “People who are displaced from or who feel obliged to leave their usual place of residence because their live, livelihoods and welfare have been placed at serious risk as a result of adverse environmental, ecological or climatic processes and events”. Migration is (and always has been) a central mechanism to deal with climate stress. Pastoralist societies have of course habitually migrated, with their typical mode of life. When climate stress overlaps with economic and social stresses, the potential for forced migration from rural areas increases significantly. In West Africa, the distance that people migrate is a function of their family’s resources; in extreme drought years, they cannot afford to travel far and instead seek to find paid work in adjacent urban areas which is locally marked as ‘eating the dry season.’ Even in the most severe, unexpected natural disasters—migrants, if they have any choice, tend to travel along pre-existing paths—to places where they have family, support networks, historical ties, and so on. Most people displaced by environmental causes will discover new homes within the boundaries of their own countries. Previous literatures have regarded seasonal migration as an essential coping mechanism, especially in relation to shock. In a resource-poor economy, the existing economic ladder collapses during a shock like a crop failure, droughts, morbidity, marriage-related issues, dispute settlement, and population pressure (Choksi, 2021). The disparity in the intensity of migration across households is explained by variation in the following explanatory variable: family size, dependency ratio of the family, diminution of own agricultural land due to drought, bullock loss, and cereal consumption during the rainy season as a percentage to normal cereal consumption. These circumstances are also highlighted by the authors by connecting the case of drought-induced migration of the Bundelkhand region of Madhya Pradesh, India (Thomas et al., 2015) which is also the subject of the present research.

9.2 Rationale and significance of the present study The idea of vulnerability as cited in McLeman and Hunter (2010) provides a base for understanding the spatial and temporal patterns of climate-related migration, as well as its outcomes for societal well-being. In the climate change research fraternity, vulnerability has been denoted as being the degree to which a system is susceptible to and unable to cope with adverse effects of climate change, including climate variability and extremes. The nature and features of vulnerability vary significantly across geographic and ecological regions. Vulnerability also differentially characterizes social systems and indeed communities and households within particular systems. These variations are shaped by a variety of factors including the particular nature of climate impacts; the degree of exposure

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to such impacts; the sensitivity of human systems to such changes; and the capacity of the exposed population and its socioeconomic systems to adapt. Certain types of socioeconomic systems are inherently more responsive to climate-related environmental changes and are therefore more likely to engender adaptive migration. Climate-related exposures most commonly associated with migration fall into two broad categories: sudden-onset events and slowonset events. The latter, such as droughts, land degradation, or oscillations in precipitation patterns, typically do not stimulate permanent relocation as a first-order household adaptation. They may, though, stimulate changes in temporary migration as a short-term adaptation. As the impacts of anthropogenic climate change become increasingly experienced across a range of human populations and environments, greater attention will be remunerated to climate-related population redistribution. Existing research on historical and contemporary environment-migration connections provides important insights into the causal, temporal, and spatial dimensions of this association. On causal connections, analogs suggest climatic conditions and changes represent but one set of ‘push’ and ‘pull’ factors acting upon migration. Ultimately, environmental factors interrelate with socioeconomic, cultural, and political processes to shape migration decision-making. In addition, existing research strongly suggests that environmentally influenced migration is closely connected with adaptive capacity. As such, the nature and scale of future climate migration will depend considerably on the extent to which the global community engages in proactive capacity-building in vulnerable populations and regions (McLeman & Hunter, 2010). Swetelina and Thomas (2016) marked drought as one of the most severe water-related natural hazards and along with desertification is expected to affect as many as one-third of the world’s population. Drought is considered one of the most damaging physical disasters in terms of financial costs (e.g., navigation, electricity generation, societal problems, increased mortality, etc.) and resulting ecological impacts. They further applied the universal criteria of classifying drought events on the basis of the nature of the water deficit, as there are four separate categories of drought events, i.e., meteorological, hydrological, agricultural, and socioeconomic drought (Swetalina & Thomas, 2016). The meteorological drought is related to rainfall deficits which cause decreases in water supplies for domestic and other purposes affecting the flora and fauna of a region. The hydrological drought is caused by the low stream flows that directly affect established water uses under a given water resources management and monitoring mechanism (Anuja et al., 2020). The agricultural drought is linked to crop failure as an outcome of decreasing soil moisture and has no connection to stream flow. Hydrological drought is computed by the propagation of meteorological drought through the terrestrial hydrological cycle and is therefore influenced by the properties of the hydrological cycle. In this research, the authors have taken into account the catastrophic event of meteorological drought and migration in context to the same has been considered as an adaptive coping mechanism of the affected marginal population of parts of Bundelkhand in central India (Sah & Shah, 2005). The NIDM report (2014) documented the case of drought and its adversities affecting the population of the adjacent geographical regions (Gupta et al., 2014). It marked Bundelkhand as a new ‘hotspot’ of prolonged drought, ahead of its predecessor western Rajasthan region (Gupta et al., 2014). This is due to consecutive droughts amidst susceptibility, poverty, and lack of effective mitigation strategies. Although several efforts and schemes are in records, reports, and many of those on the ground as well, launched by Central and State

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governments, the risk has been growing with more and more complexities. Bundelkhand is geographically divided into the jurisdiction of two Indian states, viz. Uttar Pradesh (the northern section) and Madhya Pradesh (the southern section). Here, six consecutive districts namely Panna, Tikamgarh, Damoh, Datia, Sagar, and Chhatarpur have been considered study areas (administrative jurisdiction as per Census 2011) as they constitute the central Indian drought-prone tracts of Bundelkhand, under the state of Madhya Pradesh. Here the parameters taken for studying the impact of drought on temporary seasonal migration are standardized and as per the global scholarly attempts (Devanand et al., 2019). These parameters are precipitation, vulnerabilities to local migrant population, economic vulnerabilities such as reduction of cultivation area, left out women folk due to absence of male counterparts, lack of basic jobs (through MNREGA), irrigational difficulties, etc. Recent reports indicated that around 20 lakhs or more farmers have migrated from this region either temporarily or permanently since last year, as per Bundelkhand Jal Manch (NGO). They conducted the study in about 200 villages of Bundelkhand and found that the average age of drought-induced migrants ranged between 15 and 45 years. Most of the farmers of the drought-stricken district of Panna are seen migrating to Delhi and adjacent NCR regions for jobs in secondary and tertiary sectors as per local media reports indicating a permanent change in the workforce pattern of entire central India. Drought in Bundelkhand of Madhya Pradesh has directly enforced large-scale migration as been reflected in ‘Rabi’ crop sowing which has drastically come down by about 50%
60% in average in districts of Chhatarpur and Tikamgarh. Hence a direct correlation is been observed by the authors by comparing the level of precipitation of this region for the last four decades with the long-term impact of socioeconomic and climatic vulnerabilities resulting drought-induced seasonal migration (sometimes permanent) for the locals. The Standardized Precipitation Index (SPI) has been computed here (McKee, 1993) to find out the probability of precipitation output for the coming times, indicating the proportion of severe drought-related catastrophes which may trigger labor migration streams.

9.3 Research objectives The research problem is a question/query or set of questions worth inquiring about, an issue that evokes scholarly attention or is worth solving; perhaps an issue of contemporary significance, either to academics or the community, or together. This research focuses on the following issues framed as Objectives derived from the literatures and background researches stated above: 1 To analyze the frequency of drought occurrence in the Bundelkhand region of Madhya Pradesh; 2 To assess the effect of drought on migration in this region and related vulnerabilities.

9.4 Methods and materials This section describes the methodology and data sources used in this study. Our research has used various secondary data sources.

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9.4.1 Data sources 9.4.1.1 A POWER release 8 and 901 POWER stands for Prediction Of Worldwide Energy Resources (POWER). This initiative was undertaken by NASA (National Aeronautics and Space Administration), USA to create new data sources for renewable energy sources and satellite-based new climate data sets. The data published under this project is being targeted for basically three user sectors namely Sustainable Architecture, Renewable energy, Agriculture, and climatology. The POWER release 8 was modeled upon GMAO (Goddard’s Global Modeling and Assimilation Office) model, MEERA-2 (Modern Era Retrospective-Analysis for Research and Applications). The solar-based information/parameters in POWER Release 8 were derived from satellite observations, and NASA’s Global Energy, Water Exchange Project (GEWEX)/Surface Radiation Budget (SRB) Release 3 and NASA’s CERES Fast Longwave and Shortwave Radiative project then inverted them to surface solar insolation (FLASHFlux). The surface insolation measurements used in the POWER solar data are derived from satellite observations. The MERRA-2 assimilation model serves as the foundation for the meteorological parameters in this data set. Based on the comparisons with surface measurements, uncertainty estimates were created. The MEERA-2 data set covered the period from 1981 to the present times. POWER release 901 added more data from the recent releases of NASA CERES SYN 1-deg, GEWEX SRB Release 4, and FLASHFlux Version 4A. A global grid with spatial resolutions equivalent to the input data is used to present the data/parameters in POWER Release 901. For the radiation data sets, this resolution is 1.0 degrees latitude by 1.0 degrees longitude, and for the meteorological data sets, it is 12 degrees latitude by 58 degrees longitude. WGS84 is the grid reference system. The addition of hourly data for several metrics over the previous edition is another enhancement of the POWER data set. Each of the parameters in this data set has either been obtained from MEERA-2 or directly calculated from it. The model optimizes and assimilates data from the observational findings and estimates of different climatic variables. The various observational parameters used in this data set are surface pressure, sea level wind and pressure from sea level surface observation data, height wind, moisture data from the upper air radiosondes, remote sensing data for cloud motion, wind activity, and different other parameters using various geostationary satellites. The data archived are then transformed into UTC coordinated system to solar time system. All of the parameters in the MEERA-2 have provided with various time scale values from hourly to monthly data. All of these parameters and their values are representatives of averaged data of a specific grid. For this study, the precipitation data has been collected from the POWER data set for the calculation of average annual rainfall and Standard precipitation Index (SPI) (Fig. 9.1). The precipitation data has also been used to analyze the trend of the yearly average rainfall for the past four decades from 1981 to 2021. The precipitation parameter taken into account here is the bias-corrected average of the total amount of precipitation on earth, measured in water mass that also includes water content in snow.

9.4.2 Standard precipitation index Over the years, there have been many indexes used for the measurement of different types of drought used by researchers all over the world. Those indexes are ranged from

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FIGURE 9.1 This flow-diagram is denoting the layout of data analysis and resulting methods adopted for this study.

very simple indexes such as percent normal precipitation to very complex indexes such as Drought Severity Index by Palmer. The SPI index was formulated by McKee et al. (1993). It is an easy yet effective index to measure the severity of drought in a region. It was designed to quantify precipitation deficit over a period of time in a given space. The overthe-period time scales of precipitation deficit depict the effect of drought on various climatic factors. SPI calculation is derived from a long time period of precipitation data over a geographic location. The collected long-term data values are fitted to a probability distribution. Then to make sure that the mean SPI for a specific geographical location is zero this time series data is turned into a normal distribution. The negative values of the SPI index depict that the precipitation is less than the median precipitation whereas the positive SPI values depict that the precipitation is higher than the median value (Svoboda et al., 2012). In the formulation of SPI index, a probability of gamma density function has been fitted in a provided frequently distributed rainfall data. gð xÞ 5

χ 1 α21 2β e ; x . 0. βα γ x

where β is a parameter of scale, α is the parameter of shape and γ is the function which can be presented as: γ ðαÞ 5

ÐN 0

xα21 e2x dx.

The values of α and β are estimated by the maximum likelihood method. A drought event, as defined by the SPI, takes place when the index consistently achieves an intensity of 21.0 or below. When the SPI turns positive, the event is over. As a result, each drought event has a length that is determined by its start and finish as well as the severity for each month that it lasts. The SPI for each month of the drought episode

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is added together to determine the drought’s size (Hayes et al., 1999). The SPI is the number of standard deviations, for a normally distributed random variable, that the observed value would depart from the long-term mean. presents one interpretation of the outcome values (Tsakiris & Vangelis, 2004). The SPI index can be calculated for various time scales. The Index is generally calculated for five-time scale intervals which are 1, 3, 6, and 12 months. But this index has its own flexibility. The period can be chosen by the researcher as the convenience of the time scale. SPI is a standardized index that is very suitable for comparing drought conditions over various periods of time in different parts of the world with varying climatic conditions. The standardization of the index helps to predict the rarity of the drought event and its future probability. Table 9.1 represents the severity probability of different types of drought (Liu et al., 2021). For the convenience of our study 1- and 3-month SPI has been calculated for the time period of 1981
2021 for the previously mentioned six districts of the Bundelkhand region in Madhya Pradesh.

9.4.3 Temporary migration and local vulnerabilities In India, there is no database that provides monthly data for temporal migration. To assess the impact of drought on migration in the Bundelkhand region of central India the authors has relied on an assessment of existing literature. The review of literature has been extensively done using various sources, e.g., previous research publications, newspaper reports, government press releases, and various reports published by NGOs and other organizations. We have used keywords such as Bundelkhand, drought, migration, male migration, rainfall deficiency, scare irrigation, MGREAGA, etc. has been used find the literatures. Initially, we found 50 journals based on the keywords. Later, we finalized 20 various sources of literature for our final assessment.

9.4.4 Study area This study only focuses on the drought-induced migration in central India’s Bundelkhand region. The study has been concentrated in the six districts Bundelkhand region of Madhya TABLE 9.1

Climatic moisture category/drought categories for SPI.

Climatic moisture category

SPI values

Extremely wet

$ 2.0

Severely wet

1.5 to 1.99

Moderately wet

1.0 to 1.49

Normal

0.99 to 2 0.99

Moderate drought

2 1.0 to 2 1.49

Severe drought

2 1.5 to 2 1.99

Extreme drought

# 2 2.0

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Pradesh which are Tikamgarh, Sagar, Panna, Datia, Damoh, and Chhatarpur as depicted in Fig. 9.2. This region is situated in the central part of India and North Eastern part of Madhya Pradesh State (Fig. 9.2).

9.4.5 Software For the calculation of SPI and other trends in the annual precipitation of the region, the researchers have used MS-Excel and DrinC (version 1.7) software. DrinC is a graphical user interface (GUI) based software that runs on Windows. This software is used for the calculation of various types of Drought Index. This software uses an excel file as the input for the calculation of various indexes. Excel has been used to compute the average annual rainfall data set, input files of DrinC software, and draw the trends of SPI and average annual rainfall over the last 40 years. DrinC software is being used to calculate SPI for the specific time series of the mentioned six districts. ArcGIS (version 10.8.2) has been used for making maps.

FIGURE 9.2 Bundelkhand region of Madhya Pradesh.

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9.5 Result and discussion 9.5.1 Multiscale pattern of rainfall The average annual rainfall of the six study districts from 1981 to 2021 has been shown in Table 9.2. Table 9.3 shows all of the six districts that have less annual average rainfall than the country average for the last four decades (i.e., India’s annual average rainfall is 1200 nm). Datia has observed the least average annual rainfall for the last four decades. Datia’s annual average rainfall is only 736 mm for the time period which is 40% less than the country’s average. The highest average annual rainfall can be observed in the districts of Damoh and Sagar with an average annual rainfall of 1130 mm. Fig. 9.3 shows a clear pattern of the rainfall distribution in the Bundelkhand region of Madhya Pradesh. The southern districts of the region (Damoh, Sagar, and Panna) have had an average rainfall of over 1000 mm in the last four decades whereas the northern districts of Chhatarpur, Datia, and Tikamgarh have lower rainfall as shown in Table 9.2.

9.5.2 SPI evaluation and characteristics of drought Annual average rainfall does not always give a clear picture of the actual drought scenario. There can be a few months in a year that may have received excessive rainfall where as most of the months can be affected by drought. To clear this uncertainty in this study and to show the actual drought scenario, the authors have used a 3-month SPI as a TABLE 9.2 Average annual rainfall of Bundelkhand region in Madhya Pradesh (1981
2021). District

Average annual rainfall (in mm)

Chhatarpur

866

Damoh

1130

Datia

736

Panna

1018

Sagar

1130

Tikamgarh

831

TABLE 9.3 Probability of recurrence of drought. SPI

Category

Number of times in 100 years

Severity of event

0 to 20.99

Mild dryness

33

1 in 3 years

2 1.00 to 21.49

Moderate dryness

10

1 in 10 years

2 1.5 to 21.99

Severe dryness

5

1 in 20 years

, 22.0

Extreme dryness

2.5

1 in 50 years

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FIGURE 9.3 Average annual rainfall of Bundelkhand region in Madhya Pradesh (1981
2021).

seasonal drought index to represent short-time drought. SPI index has been calculated for all six districts of the Bundelkhand region in Madhya Pradesh. The variation of 3-month SPI in different time scales has been shown in Table 9.4. The six districts have total observed 312 drought occurrences in the last 40 years. District of Chhatarpur and Damoh has observed the second highest number of droughts each with 60 occurrences of drought in the last four decades. Panna has observed the highest number of droughts in the last 40 years with 64 total drought occurrences. Tikamgarh has observed the lowest number of drought occurrences in the last 40 years with 41 droughts. According to the SPI values, droughts have been further classified into three classes: moderate, severe, and extreme droughts. The region of Bundelkhand in Madhya Pradesh has observed a total number of 212 moderate drought occurrences, 65 severe drought occurrences, and 35 numbers of extreme occurrences of drought in the time span of the last 40 years. The highest number of moderate droughts can be observed in the district of Panna with the frequency of moderate drought occurrence being 45. The district of Panna has also observed the highest occurrence (17 times) of severe drought. The district of Sagar has not observed any severe drought in the last 40 years but this district has been affected by the highest number of extreme drought occurrences in this region.

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TABLE 9.4 Different types of droughts classified according to the intensity. Drought occurrence (3 months SPI) Districts

Moderate

Severe

Extreme

Tikamgarh

20

15

6

Sagar

34

0

10

Panna

45

17

2

Datia

26

12

5

Damoh

44

10

6

Chhatarpur

43

11

6

Total

212

65

35

A contrast of drought and rainfall scenarios can be observed in this region which is clearly visible in Fig. 9.4. The southern districts especially Damoh and Panna of Bundelkhand region receive more average annual rainfall but these southern districts are more affected by the occurrence of various types of droughts. This can be viewed as the effect of skewed monthly precipitation in those three southern districts. In these districts, most of the rainfall happens only in the months of July and August whereas other months do not receive ample rainfall most of the time the amount of precipitation remains zero. Therefore, other than the monsoon region, most of the time in the years, the districts remain affected by drought. Most of the drought in the district of Damoh has been observed in the months of January to April, whereas only 10 times among the total 60 occurrences of the drought was in the monsoon months of June to August. The Panna District also shows the same scenario. Among 64 total drought occurrences in the last 40 years only 12 times, droughts happened in the month of June and July. Only 2 times severe drought was observed in the month of August in this district. In Panna, drought months span from September to April. This temporal variation of drought indicates that the rainfall distribution in the region of Bundelkhand is very skewed. Apart from the months of the monsoon, most of the months in this part of Madhya Pradesh remain dry. The most recent prolonged drought-affected years can be observed in the years 2013 and 2016
18.

9.5.3 Drought induced temporal migration and other vulnerabilities Bundelkhand is one of the most backward regions in the country in terms of development. There is a lack of resource distribution in this area. Most of the districts are being affected by extreme poverty in this region of India. Nearly 60% of the working population in the Bundelkhand is engaged in the agricultural sector. This region of central India has a very high dependency on agriculture than other parts of the country. The low level of industrialization in this region has resulted in a very low rate of urbanization. Most of the people in this region still remain in the villages.

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FIGURE 9.4 Three month SPI in the districts of Bundelkhand of Madhya Pradesh.

The SPI calculation results have shown that the area is very drought-prone. In the last 40 years, there has been a total of 312 drought occurrences in this region which at least lasted over three months. In the last four decades, Bundelkhand Region of Madhya Pradesh has observed 212 numbers of moderate droughts, 65 numbers of severe drought occurrences, and 35 extreme drought occurrences. This repeated occurrence of drought and inadequate monsoon rainfall results in crop failure. Severe climatic conditions in this region make people, especially the poor marginalized farmer community very much vulnerable to drought

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and crop failure The findings of the Tendulkar Committee Report 2009 suggested that farmers in this region mostly rely on rain-fed agriculture, the amount of poverty in rural regions has grown with periodic drought and agricultural failure. Even though nearly about 45% of this region’s net planted land is irrigated, the inter-ministerial team sent by the central government claims that the water supply is inadequate (Samra, 2008). Chronic poverty is mostly brought on by a lack of access to resources for production, population pressure, declining landholdings, recurrent droughts, and lack of off-farm work opportunities, as well as consumer loans from moneylenders that lock borrowers in a cycle of debt. In this belt, seasonal migration is seen as a crucial coping strategy, especially in reaction to regional vulnerabilities like severe crop failure. For a bare minimum income, people mainly males are forced to migrate from their native villages to the bigger cities of India, mainly Delhi and Mumbai. Recent studies have found that the migrant family from the Bundelkhand region earns merely Rs 12,000 per annum from their migration period. Most of the migrants from this region are temporary and seasonal migrants. Recent studies have found that the repetitive occurrence of drought in the Bundelkhand region has resulted in a very large-scale temporal and seasonal labor migration which is dominated by the male population. Most of this migration has been observed in the last decade in time of Rabi season. Rabi sown starts in the mid of November. From 2016, the Rabi sowing has been down by 40%
60% in most of the districts as reported by district officials. This has led to loss of jobs in the agricultural sector triggering large-scale migration from this region. The analysis done by the authors as explained previously has shown a skewed distribution of rainfall in this area. Our analysis found that only the monsoon period (June to August) receives some amount of rainfall. As per our findings droughts in the region of Bundelkhand start in October and continue till the months of April and May. This prolonged drought period lowers the agricultural employment opportunity in this area. As there are very few alternatives available other than agriculture the male population migrants from this region from the time of Rabi sowing. These findings indicate that there exists a direct relationship between the occurrence of drought and seasonal migration in this area. People are also migrating because the large dams and reservoirs tend to dry up by the end of December after the monsoon ends (Elledge & McClatchey, 2013). According to Bundelkhand Jal Manch, a local NGO, nearly 20 lakh farmers from this area have relocated either temporarily or permanently since the year of 2018. Another research conducted by the Bundelkhand Jal Manch in May, 2018 found that since last November, up to 55% of the Bundelkhand’s farmers had moved, at least temporarily. The majority of the migrants were between the ages of 15 and 45, according to the survey, which was carried out in around 200 localities in the six districts of the Bundelkhand region of Madhya Pradesh. There have been other vulnerabilities in this region also. Most of the population in this region is the tribal population who has been historically dependent upon the nearby forests for their resources. In recent times increase in drought occurrence and a decrease in agricultural output can again be made the tribal population rely on the forests for their daily resources. Especially the large-scale male migration will lead to a large population of left-out unskilled women in the villages of Bundelkhand. The state and union government has failed to provide enough jobs using the Mahatma Gandhi National Rural Employment Guarantee Act. This can lead to over-exploitation of nearby forests which are already shrinking.

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9.6 Conclusion The region of Bundelkhand was a resource-rich region in the past. But now it has become a drought-prone area with increasingly barren land. Scare and unplanned irrigation have made the scenario worst. Bundelkhand, an arid area shared between Madhya Pradesh and Uttar Pradesh, is cleaning out as drought becomes the new norm. According to the India Meteorological Department, the area, which is just under 70,000 km2, used to receive 800
900 mm of rain annually. According to DK Dubey, a scientist with the weather bureau, the number of rainy days in the June to October monsoon period decreased from 52 to 24 during the past 6
7 years, roughly halving the quantity of rainfall. The region is one of the most underdeveloped regions in India. The lack of economic development is pretty obvious here. Most of the population is engaged in the primary sector mostly in agricultural works. There are very few job alternatives for the people living in Bundelkhand except for agriculture. The findings of this study show that in the last four decades, the Bundelkhand region in Madhya Pradesh has received very less annual monsoon rainfall which is below the national average. The study also observed that droughts have been repetitive and prolonged. In the last 40 years, there have been over 300 occurrences of drought which spanned over 3 months. Some of them spanned over 6 months. This repetitive drought has led to crop failure in this region. Repetitive drought, lack of Monsoon rainfall, and crop failure have hampered agricultural work in this region. So a large number of the young male population from these districts of the Bundelkhand region every year migrates to other parts of the country, especially in the Region of Delhi NCR and Mumbai as temporary daily-wage migrant workers. It is obvious that prolonged and repetitive droughts have greatly impacted the lives of the people in this region starting a large stream of temporal outmigration from this area. India’s economic growth has been characterized by growth pole development. There is a very uneven distribution of resources in the country. Most of the economic opportunities in the country are concentrated in a few “tier one and tier two” cities. These cities attract crores of migrants every year. But the cities have very limited resources and infrastructure. In India migration to the cities has become one of the biggest issues. Most of the temporal and seasonal migrants in the cities stay in the slums with the bare minimum available resources which can be turned into a social and health disaster. The Covid pandemic has shown the vulnerabilities of these migrant people. India needs inclusive economic development to achieve its global economic ambitions. Government should focus more on the backward regions such as Bundelkhand. Our study recommends that governments should focus on health care and education in this region to make a more skilled population. Both the state and central government should work together in infrastructural development such as irrigation, dams, etc. Governments should also encourage medium and small-scale industries at the grassroots level which can create an alternative source of employment in the region with easily sanctioned low-interest loans and other needs. This will reduce outmigration and can be a driver for local inclusive economic development. Tribals have always been excluded from mainstream development in this country. The authors also suggest that the tribal population in the region should be included in the developmental policies and its implication. An inclusive sustainable long-term development plan can be very useful to minimize the effect of recurring droughts and related vulnerabilities in this region.

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9.7 Limitations of the study In India, migration as a field of study has always been neglected. The last census data on migration was collected in 2011. There is no secondary database regarding temporal migration in India which can provide data on temporal and seasonal migration on the monthly basis over a large time scale. The constraint of secondary data sets has limited the analysis of temporal and seasonal migration and heavily relied upon the review of the literature. Lack of resources and non-availability of funds has restricted the researchers from conducting field visits and primary surveys in the region, which could have given more in-depth understanding of the drought-induced vulnerabilities in this region.

Acknowledgment The authors would like to express their sincere gratitude to Ms. Labani Sarkar and Ms. Angana Pal for extending their generous cooperation towards this research work.

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C H A P T E R

10 Climate change resilience by community involvement: a case study in Indian base stations for the wellknown Himalayan trek routes of Darjeeling and West Sikkim Sanjoy Kumar Sadhukhan and Premangshu Chakrabarty Department of Geography, Visva-Bharati, Bolpur Santiniketan, West Bengal, India

10.1 Introduction The capacity of communities to cope with the impact of climate change in mountain resorts is one of the sustainability indicators of tourism, which itself is a climatedependent industry. In limited literature available in this context, such adaptive capacity is referred to as climate change resilience which is nevertheless destination-specific (Teye et al., 2002). This not only depends on exposure to climate factors but also on the built capacity of the socio-ecological system based on how communities may accept the change with regard to several resilience-enhancing factors including emotional, physical, and spiritual well-being as well as cognitive and behavioral competencies (Sheppard & Williams, 2016). Identification of ‘climate vulnerability hotspots’ (Simpson et al., 2008) in the tourism sector has emerged as a task with a focus on four broad categories of climate change impacts that are affecting tourism destinations: 1 Direct effect: A serious decline in tourist demand is the result and the resilience of the community is reflected in efforts to adjust the economic activity in order to address the vulnerabilities. In the case of higher latitudes, however, benefit from the consequences of climate change (Jopp et al., 2010) is felt and also conceived as a direct climatic impact determining the suitability of a location for a wide range of tourist activities.

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2 Indirect environmental impacts: Landslides on the way to mountain destinations are one of such impacts triggered by increasing rainfall as the consequence of climate change. In Goeche La trek route in the study area, for example, six numbers such as landslides have been conspicuous (Chakrabarty & Sadhukhan, 2018) making the route seasonally risk-prone. It might be responsible for the reduction of tourism revenue if migration efforts are not appropriate and successful in the long run. 3 Impacts of mitigation policies on tourist mobility: Policies to reduce access to vulnerable areas to combat the effects of climate change might be executed by convincing both hosts and guests. The sensitization of the community on climate change resilience is vital since the impact of such policies on tourist flow adversely affects both the local economy and the environment. 4 Indirect societal change impacts: Climate change-associated security risks bring adverse impacts on societies of ‘climatic change vulnerability hotspots’ (Simpson et al., 2008) studies on which are, however, still at the juvenile stage. The aim of this chapter is to address the research gap in this context with case studies from two such hotspots: Manebhanjyang (latitude 26 59v160 N and longitude 88 07v150 E, the gateway of the famous Sandakaphu-Phalut Trekking Circuit) and Yaksum (latitude 27 22v220 N and longitude 88 13v220 E, the gateway of Goeche La trek route) in the eastern Himalaya. It is noteworthy to mention that an active community organization named Khangchendzonga Conservation Committee (KCC) which was formally registered and recognized by the Government of Sikkim in the year 1997 is operating from Yaksum, while the community involvement is not noticed in a similar scale in Manebhanjyang. Yaksum is the base station of the Yaksum-Dzonri-Goeche La trek that leads one to traverse through Khangchendzonga National Park following pastoral trails through lush green forest and alpine pastures ultimately arriving at snow-peaked mountain passes (Simlai & Bose, 2014). This book chapter attempts to test the hypothesis that the presence of such community organizations makes a significant difference in climate change resilience.

10.2 Study area Darjeeling district of West Bengal and West Sikkim district of Sikkim state which are the two adjoining districts of two neighboring states in the eastern Himalayan regions are selected as the study area with a focus on Manebhanjyang and Yaksum respectively (Fig. 10.1). There are many trekking routes are situated in West Sikkim and Darjeeling districts which are very popular for adventure tourists. The two most popular trek routes which are situated in the Singalila range of the Darjeeling
Sikkim Himalayan region have been taken into consideration for the study of climate resilience in their base stations. One of them named the Southern Singalila trekking corridor has been initiated from Manebhanjyang in the Darjeeling district of West Bengal. Manebhanjyang is a small market town from where trekkers gather their ration guides and porters (Sadhukhan & Chakrabarty, 2018). Further, it is famous for offering the opportunity of Land Rover ride for adventure tourism, which is unavailable elsewhere in India (Chakrabarty & Sadhukhan, 2019). A six-seater heritage vehicle brings tourists to Sandakaphu through a historical road

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10.2 Study area

195

FIGURE 10.1 Himalayan base stations of popular trek routes. Source: Prepared by the Authors, 2022.

recently reconstructed by the Indian armed force on the Indo-Nepal border. The trek zone namely Yaksum
Goeche La trekking corridor is situated in the West Sikkim district of Sikkim state. The landscape around Yaksum is conceived as sacred for Tibetan Buddhism, thereby taken care of (Chakrabarty & Sadhukhan, 2020) as evident from the scrapping of the Rathong Chu hydroelectric project by the government (Gurung, 2012).

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10.3 Materials and methods Methodologically two distinct approaches prevail in tourism studies relating to the study on the impacts of climate change- the direct impact analysis and the integrated analysis acknowledging the existence of non-climatic factors and focusing on the climatesensitive factors of the system within a social framework (Tervo, 2008). The data have been derived from ethnographic surveys involving both the hosts and the guests. The vulnerability of diverse tourism-related land uses has been examined with the application of image processing techniques including NDVI (Normalized Difference Vegetation Index) and NDBI (Normalized Difference Built-Up Index). The variability of environmental conditions and the response of the stakeholders have been analyzed separately for the two hotspots: Manebhanjyang and Yaksum considering the consequences on their credibilities as centers for marketing high-altitude trekking and adventure tourism. The vulnerability comparisons have been undertaken considering both similarities and differences between the two hotspots taken for assessing sensitivity to changing climate and its impact on the tourism business. Questionnaire surveys have been conducted to derive the stakeholders’ perceptions of the direct and indirect effects of climate change. A Varimax-rotated Principal Component Analysis has been attempted using the IBM SPSS software (Version 22.0) based on 11 variables (Table 10.1) derived from a survey involving 104 and 92 stakeholders, respectively, from Manebhanjyang and Yaksum selected by stratified random sampling considering age group and income level. The total population (2021) of Manebhanjyang (data provided by Gorkha Territorial Administration, 2022) and Yaksum (data provided by Yaksum Police station during field visit, 2022) are 2530 and 1740, respectively. Focus TABLE 10.1 Selected variables for climate change sensitization level study. Sl. No.

Description of attributes

1

Global warming consciousness from secondary sources. (impact of social media on the host in this context).

2

Perception of decadal temperature change

3

Experience in changing nature and quantity of precipitation

4

Perennial water sources.

5

Awareness of deforestation.

6

Perception on soil loss due to climate change.

7

Climate change effects on blooming of Rhododendron.

8

Perception on climate change and landslide frequency.

9

Climate change adaption of wildlife.

10

Agricultural productivity.

11

Climate change and seasonality of tourism business.

Prepared by the authors during field survey, 2021.

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10.4 Results

group discussion reveals that the age group 25
50 is dominant in decision making for which more than 50% of samples have been drawn from this age group. As male dominance was found to prevail in decision-making, more than 60% of samples are directly taken from the male population for this study.

10.4 Results Features of tourist supply, as well as demand from which the resilience of destination emerges (Hall et al., 2017), have been taken into consideration during ethnographic surveys. Face to Face interviews and participatory observation methodologies have been adopted to analyze the contribution of human agency and community empowerment in the context of climate change resilience development. In terms of five distinct characteristics, namely inaccessibility of the terrain, fragility, marginality, diversity, and niches (Nyaupane & Chhetri, 2009), regions’ vulnerability to climate change has been assessed by generating data from focus group discussions. With the perception derived from the community specifically on the direct and indirect impact of climate change, KMO and Bartlett’s test (Table 10.2) have been applied to testing the reliability of data for pursuing the factor analysis. The computed KMO score is 0.618 for Manebhanjyang and 0.714 for Yaksum depicting that the sample size is adequate for factor analysis. Bartlett’s test of sphericity w.r.t Chi-Square df value, as shown in Table 10.2, indicates that the significance level of both places is 0.000 which is less than 0.05 and is validated for the factor analysis. A commonality greater than 0.700 has been considered a very significant variable for both base stations. Being satisfied with computed commonalities (Table 10.3) rotated component matrices are derived for comparison between both places Tables 10.4 and 10.5. Four principal factors have been extracted in the case of Manebhanjyang and three factors have been extracted for Yaksum from PCA (Principal Component Analysis). Eigenvalue derived for Manebhanjyang and Yaksum organizational clearly depicts that stakeholders of both places are concerned about global warming, temperature, and precipitation change issues while agricultural productivity and wildlife draw the attention of stakeholders in Manebhanjyang, more attributes relating to tourism business, i.e., seasonality and landslide disaster perceptions have been qualified in case of Yaksum depicting better TABLE 10.2

KMO and Bartlett’s test. Base station

KMO and Bartlett’s Test

Manebhanjyang

Yaksum

Kaiser-Meyer-Olkin Measure of Sampling Adequacy

.618

.714

Bartlett’s Test of Sphericity Approx Chi-Square

711.435

322.688

df

101

91

Sig.

.000

.000

Prepared by authors using IBM SPSS 22 .0 software.

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TABLE 10.3 Communalities values of Manebhanjyang and Yaksum. Extraction Sl. No.

Description of attributes

Initial

Manebhanjyang

Yaksum

1

Global warming consciousness from secondary sources. (impact of social media on host in this context).

1.000

.899

.770

2

Perception of decadal temperature change

1.000

.876

.733

3

Experience in changing nature and quantity of precipitation

1.000

.887

.738

4

Perennial water sources.

1.000

.768

.469

5

Awareness of deforestation.

1.000

.780

.436

6

Perception on soil loss due to climate change.

1.000

.740

.772

7

Climate change effects on blooming of Rhododendron.

1.000

.778

.531

8

Perception on climate change and landslide frequency.

1.000

.581

.790

9

Climate change adaption of wildlife.

1.000

.789

.651

10

Agricultural productivity.

1.000

.794

.605

11

Climate change and seasonality of tourism business.

1.000

.775

.540

Prepared by authors using IBM SPSS 22 .0 software.

TABLE 10.4 Rotated component matrix of Manebhanjyang. Component Attributes

1

Global warming consciousness from secondary sources.

.921

Perception of decadal temperature change

.883

Experience in changing nature and quantity of precipitation

.820

Perennial water sources.

2

3

4

.730

Awareness of deforestation.

.868

Perception on soil loss due to climate change.

.548

2 .626

Climate change effects on blooming of Rhododendron.

.616

Perception on climate change and landslide frequency.

.719

Climate change adaption of wildlife.

.852

Agricultural productivity.

.851

Climate change and seasonality of tourism business. Prepared by authors using IBM SPSS 22 .0 software.

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.771

199

10.4 Results

TABLE 10.5

Rotated component matrix of Yaksum. Component

Attributes

1

Global warming consciousness from secondary sources.

.876

Perception of decadal temperature change

.831

Experience in changing nature and quantity of precipitation

852

Perennial water sources.

.590

2

3

Awareness of deforestation. Perception on soil loss due to climate change.

.849

Climate change effects on blooming of Rhododendron.

.552

Perception on climate change and landslide frequency.

.738

Climate change adaption of wildlife.

.780

Agricultural productivity.

.776

Climate change and seasonality of tourism business.

.726

Prepared by authors using IBM SPSS 22 .0 software.

resilience development. After studying people’s perceptions of climate change impact an attempt is made to analyze the relation between increasing tourism activities and climate change by applying image processing techniques following the changes in vegetation cover for both the places taken for comparative analysis on climate change resilience by the community. The raster calculation method using QGIS software has been used to prepare the maps of The Normalized Difference Vegetation Index (NDVI) for the last two decades for Singalila National Park and Khangchendzonga National park. The satellite images have been procured from US Geological Survey (USGS) Earth Explorer site for the years 2000 and 2020 for analyzing the changes in forest cover. Bands 3, 4, and 5 of LANDSAT-5 and LANDSAT-7 and bands 4, 5, and 6 of LANDSAT-8 have been taken into consideration for image processing. Images of LANDSAT Thematic Mapper (TM) and Enhances Thematic Mapper (ETM1) with 85% cloud-free data have been downloaded with 30-m spatial resolution. The spectral resolution of the Red band is 0.63
0.69 μm, and near Infrared is 0.77
0.90 μm. High reflectance in the near-infrared (NIR) band and high absorption of the Red band is used to calculate NDVI in the remote sensing process. The equation for band calculation for generating the NDVI images is (NIR band
Red band)/ (NIR Band 1 Red band). The result of the NDVI calculation has been categorized on the basis of the NDVI value. Generally, 20.1 to 11.1 represents the barren land, sand or snow cover area, 10.1 to 10.2 represents shrubs and grassland, 10.2 to 10.4 represents sparse vegetation and 10.4 to 10.6 represents more intense vegetation (Fig. 10.2). From NDVI raster analysis for the same area (radius 2 km) of the two popular base stations of Manebhanjyang and Yaksum has been computed with the help of the raster calculator of QGIS software (3.16 versions) (McFeeters, 1996). It is revealed that barren land and

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FIGURE 10.2 Changing NDVI status for Manebhanjyang and Yaksum. Source: Prepared by the authors using LANDSAT image and QGIS 3.16 software.

built-up area have increased more in Manebhanjyang between 2000 and 2020. About 136% of the area has been increased in the last two decades and around Manebhanjyang, whereas only 85% of barren land and the built-up area has been increased in and around Yaksum during the same time period. The NDVI analysis also reveals that the dense and more intense vegetation area is also decreasing for Manebhanjyang and may be interpreted as an indirect impact triggering climate change in the long run. Further, the intense vegetation cover also decreased more for Manebhanjyang than for Yaksum between 2000 and 2020. Due to complications of the spectral response of NDVI to separate barren land from dense vegetation cover, the Normalized Difference Built-Up Index (NDBI) raster analyzing method has been taken into consideration since the built-up area is more prominent in comparison to NDVI (He et al., 2010). The NDBI image analysis is ideal to evaluate the

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10.4 Results

201

FIGURE 10.3 Changing NDBI status for Manebhanjyang and Yaksum. Source: Prepared by the authors using LANDSAT images and Qgis 3.16 software.

built-up area of any settlement (Zha et al., 2003) area, the raster equation (Piao et al., 2021) of NDBI is (SWIR band
NIR band)/(SWIR band 1 NIR band). The built-up area and bare soil reflect more in the SWIR band. The higher positive value of NDBI represents a built-up area and the lower positive value which is close to ‘0’ indicates farmland and vegetation cover (Fig. 10.3). NDBI analysis of two popular base stations indicates that farmland and built-up area are increasing more rapidly in and around Manebhanjyang than in Yaksum. From the comparative analysis of NDVI (Table 10.6) and NDBI (Table 10.7), it is evident that Manebhanjyang is more vulnerable than Yaksum. The better climate change resilience developed by the community is playing a pivotal role in the case of Yaksum. particularly under the influence of community organizations like KCC. The land use and land cover status of Manebhanjyang and Yaksum (focusing on Barren land and built-up area, shrubs, and sparse vegetation including farmland, dense and more intense vegetation) are significantly different. It is further revealed from demographic data derived from the previous census (2001 and 2011) and field sources (2021) that the population pressure of

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TABLE 10.6 NDVI results for comparison of Landuse and Landcover between Manebhanjyang and Yaksum. Base station

Manebhanjyang NDVI analysis

Yaksum NDVI analysis

2000 Area in sq. Km.

2020 Area in sq. Km.

% of Landuse Change

2000 Area in sq. Km.

2020 Area in sq. Km.

% of Landuse Change

Barren Land and built-up area

1.47

3.48

1 136.43

1.26

2.34

1 85.71

Shrubs and Sparse (including farmland)

2.02

3.31

1 63.86

1.97

2.78

1 41.11

Dense Vegetation

4.08

3.57

2 12.5

4.23

3.23

2 23.64

More Intense vegetation

5

2.16

2 56.8

4.58

3.69

2 19.43

Landuse

Prepared by the authors using LANDSAT image and QGIS 3.16.

TABLE 10.7 NDBI results for comparison of Landuse and Landcover between Manebhanjyang and Yaksum. Base Stations

Manebhanjyang NDBI analysis

Yaksum NDBI analysis

Landuse

2000 Area in sq. Km.

2020 Area in sq. Km.

% of change

2000 Area in sq. Km.

2020 Area in sq. Km.

% of change

Built-up area

0.82

1.98

1 141.46

0.67

1.25

1 86.56

Shrubs and Sparse (including farmland)

1.66

3.44

1 107.22

1.23

2.02

1 64.22

Dense Vegetation

5.02

3.36

2 33.06

4.98

3.84

2 22.89

More Intense vegetation

5

3.72

2 25.6

5.21

4.98

2 4.41

Prepared by the authors using LANDSAT image and QGIS 3.16.

Manebhanjyang is found to be more (Fig. 10.4) responsible for less climate change resilience development as revealed from focus group discussions in order to feed its resident population depending on the tourism business, particularly the Land Rover tourism involving fossil fuel consumption. A field survey reveals that 49 vehicles are registered in the office of GTA for much adventure tourism affects the environment (Chakrabarty & Sadhukhan, 2019). By triangulation of NDVI, NDBI, and demographic data for Manebhanjyang and Yaksum, it is evident that more population pressure particularly due to diversified tourism activities is responsible for more changes both in NDVI and NDBI of Manebhanjyang in comparison with Yaksum. The better measures taken by the community organization of Yaksum are also beneficial for Yaksum resulting in lesser changes in NDVI and NDBI in the last two decades which maintained a better environment in Yaksum for both hosts and guests, in comparison with Manebhanjyang.

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10.5 Discussion

FIGURE 10.4 Changing population pressure: Manebhanjyang and Yaksum Source: Census of India 2001, 2011 and field survey 2022.

10.5 Discussion Although the residents of mountain terrain acknowledge the economic benefits of tourism, they are also concerned about environmental degradation from tourism growth (Ali, 2020) for which the socio-ecological system of distinction environment has been influenced. The function of the socio-ecological system (SES) of a destination is determined by community resilience (Chen et al., 2020). In order to maintain sustainable livelihood, people’s access to livelihood capital, i.e. the resources essential to make a living is very much essential, which is determined by political ecology. The Sustainable Livelihood Framework (SLF) of a destination environment is vital and taken into consideration by Manebhanjyang and Yaksum for comparative analysis. Manebhanjyang to Sandakaphu-Phalut known as the Southern Singalila trekking corridor is the most popular among the trek routes which is situated in the JorebanglowSukiapokhri C. D. block of Darjeeling district. The Manebhanjyang to Sandakaphu-Phalut trek route passes through the Singalila National Park in the Southern Singalila Range of the Darjeeling district. Manebhanjyang, being the initiation point of the conventional trek route, is well connected with New Jalpaiguri (NJP) railway junction, the gateway for visitors traveling Darjeeling and West Sikkim. Trekkers assemble their rations, guides, and porters in the small market village of Manebhanjyang, which is located along the Nepal
India border. Travelers also can hire Land-Rover to travel by road up to Sandakaphu from Manebhanjyang. Sandakaphu trekking corridor is prescribed for beginners who wish to undertake the first adventurous trek of their life. The great Himalayan mountain system extends more than 300 km and includes four of the world’s highest peaks, including Mount Everest (8848 m), Khangchendzonga (8586 m), Lhotse (8516 m), and Makalu (8463 m), as well as other 7000-m peaks like Chamlong, North Kabaru, South Kabaru, Chamolari, etc., can be seen magnificently from Sandakaphu. The stony trekking

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track from Sandakaphu to Phalut offers the natural beauties of the snow-clad mountain view of the Greater Himalayan ranges. Table 10.8 is showing the segment-wise attraction of the trekking corridor. The Southern Singalila range of the Lesser Himalaya is characterized by a rough and dissected topography with a steeper slope in comparison with any other part of the Darjeeling Himalaya (Mallet, 1875). The Main Central Thrust (MCT) divides the Singalila range into the Northern portion which is situated in the West Sikkim district and the Southern Singalila range which is situated in the western portion of the Darjeeling district (Mukul, 2010). The southern Singalila range is one of the important watersheds in the Eastern Himalayas. The streams flowing in the western flank of the ridge join into the Koshi river which is a tributary of the Ganges and the streams flowing in the eastern flank flow into the Teesta river is one of the important tributaries of Brahmaputra (Samanta, 2018). The Singalila National Park has a total area of 78 km2 and is renowned for its great biodiversity, which includes endangered species like the Red Panda and the Asiatic Black Bear. The total length of the trekking route started from Manebhanjyang to Srikhola via Sandakaphu and further extended to Phalut is near about 80 km. On the other hand, Yaksum
Goeche La trekking corridor is high altitude moderately difficult trek route that passes into Khangchendzonga National park which is famous for its rich biodiversity and designated as Khangchendzonga Biosphere Reserve (KBR). The Khangchendzonga National Park occupies 25.14% of the total geographical area of the state of Sikkim. The total area of the park is 1784 km2. The Khangchendzonga National Park area of Sikkim is virtually a paradise for which it has been recognized as India’s first Mixed Site on the World Heritage list by UNESCO, 2016 (Kumar & Singh, 2017). The major portion of the park lies in the North district and only 1/3 area of the park lies in the West district (Sarkar et al., 2012). The major portion of the National Park comprises glaciers, mountains, and lakes. There are 18 glaciers among which Tongshiong glacier, Kang TABLE 10.8 The epitome of the Manebhanjyang
Sandakaphu
Phalut trekking corridor. Day-wise trekking segment

Distance in kilometers (km) Major attraction on trekking route

Intermediate halt stations

Day 1: Manebhanjyang (1980 m) 12 to Tonglu (3060 m)

Chitre monastery, Meghama Chitrey, Lameydura, monastery, snow-clad mountain views. Meghama

Day 2: Tonglu (3060 m) to Kalipokhri (3000 m)

14

Rhododendron, lake, cave

Tumling, Gairibus, Kaiyakatta, Batasi

Day 3: Kalipokhri (3000 m) to Sandakaphu (3636 m)

6

Snow-clad mountain view, spring

Bikeybhanjyang

Day 4: Sandakaphu (3636 m) to Phalut (3596 m)

21

Mountain view, varieties of Junipers, and Lake

Molley, Sabarkum.

Day 5: Phalut (3596 m) to Samanden (2350 m)

12

Floral diversity, Wildlife, Cascade

Gorkhey

Day 6: Samanden (2350 m) to Siri- Khola (1900 m)

15

Rapid river, Floral diversity, and Rammam hydroelectric project.

Rammam and Upper SiriKhola

Field survey, 2021.

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10.5 Discussion

Kiong glacier, Jonsang glacier, Onglakthang glacier, and East Rathong glacier are situated in the West Sikkim district. Mt. Khangchendzonga (8585 m) is the world third highest peak which is revered as the guardian deity of Sikkim, along with the other peaks namely Mt Narsing (5825 m), Jupano Peak (5963 m), Mt. Pandim (6691 m), Kabaru North (7338 m), Kabaru South (7317 m), and Goeche La (6115 m) are among the most picturesque mountain peaks are attracting visitors who arrive Yaksum, the initiation point of this 6 days trek route which is 44 km long (Table 10.9). Yaksum is a sacred landscape and respects the religious sentiments of the Buddhists which boosts international tourist arrivals in the region. The National Park draws ecotourists in increasing numbers since it was designated as a biodiversity hotspot by World Wildlife Fund (WWF) in 1992
93. From the aforementioned characteristics of both the trekking routes initiated from Manebhanjyang and Yaksum respectively, it is evident that both the base stations are more or less affected by tourism growth influencing local-level climate change. from the perception survey, it is revealed that the community is already aware of this context and a number of recommendations have been derived. Owing to the dependency of the community on trekking and tourism, the implementation of ecotourism policies is the only feasible option. Diverse practices of KCC while organizations to cope with/adopt climate change in this context involving community resources may include: 1 2 3 4 5 6

The planting/greening initiatives. Green fuel/energy use. Control grazing that affects vegetation cover. Host-Guest interaction followed the awareness program. Diversification of trekking activities/tourism. Involving resident population while formulating tourism-related climate resilience policies at the local level.

Further, the destination management initiatives may include sustainable waste management separating biodegradable from non-biodegradable and provision of training to guides, porters, and other stakeholders of the tourism business to involve them as volunteers resisting environmental degradation and climate change. TABLE 10.9

The epitome of Yaksum
Goeche La trekking corridor.

Day-wise trekking segment (altitude in meters)

Distance in kilometers

Major attraction on trekking route

Intermediate halt stations

Day 1: Yuksam to Sachen

10

Low-height waterfalls, Dense forest

—

Day 2: Sachen to Tshoka

9

Waterfall, landslides

Bakim

Day 3: Tshoka to Dzonri

6

Glacial lakes, conspicuous landslides

Phedung

Day 4: Dzonri to Thansing

8

Mountain peaks, Exposed rocks.

Kockchurang

Day 5: Thansing to Lamuney

4

Sculptures made byPrek Chu river

—

Day 6: Lamuney to Goeche La

7

Glacial lake, moraine mountain peaks, Samiti Lake and cold desert

Field survey, 2021.

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10.6 Conclusion The aim of the chapter is to address the dearth of research beyond the model-based quantitative approach confined among a small number of scholars that seriously undermines the evolution of tourism and climate change as a research domain (Becken, 2013). It accepts the contemporary challenge to participate in the climate change-related discussion from the standpoint of spatial analysis in the Himalayan context estimating the indirect ecological and societal impacts by comparing the two climate change vulnerability hotspots of Darjeeling and East Sikkim in the Eastern Himalayas. The resilience research has already developed itself as a comprehensive theory-driven approach to address climate change stresses with a focus on the innovative and adaptive role of the Destination Management Organization (DMO) on a regional scale. To enhance overall resilience, the function of such organizations from a network governance point of view is found useful worldwide (Luthe & Wyss, 2016). It counters the key challenge in climate change resilience management since the worldwide problem in this context arises from a top-down approach (Muganda et al., 2013). This is why Yaksum is far ahead with respect to the performance scale responses in comparison with Manebhanjyang. The role of the Khangchendzonga Conservation Committee (KCC) which made aware the community of environmental issues including climate change apart from providing training to local youth to act as guides and porters actually makes a difference. The code of conduct imposed by KCC on homestay and other tourism development-related activities (Rubita, 2012) which was noticed even about 10 years back surely had positive impacts on Yaksum that is found absent for Manebhanjyang due to less community involvement to address the environmental issues. The contribution of stakeholders is therefore most vital in climate change resilience building to address the sustainable development goal.

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C H A P T E R

11 Indonesia’s engagement in the climate change negotiations: building national resilience R.R. Emilia Yustiningrum, Athiqah Nur Alami, Ganewati Wuryandari and Nanto Sriyanto Research Centre for Politics, National Research and Innovation Agency (BRIN), Jakarta, Indonesia

11.1 Introduction Indonesia’s engagement in the climate change negotiations, specifically related to various COP meetings and the implementation of the Kyoto Protocol, comes from two Janus faces of international commitment and domestic interest. Murdiyarso (2003b) explains the tenth anniversary of climate change negotiations from the first meeting in Germany in 1995 that came up with the Berlin Mandate up to the meeting in Morocco to create the Marrakesh Accord in 2001 with little engagement of Indonesia on that issue. Murdiyarso (2003a) in his study on the impact of the Kyoto Protocol on developed and developing countries clearly suggests Indonesia’s active role is crucial in order to mitigate the impact of climate change. Pramudianto (2008) highlights two levels of involvement in Indonesia’s environmental diplomacy as total diplomacy coined by Hassan Wirajuda during his tenure as Indonesian foreign minister. Total diplomacy which Pramudianto mentioned refers to utilizing not only first-track diplomacy COP 1 to COP 8 but the NGOs and scholars as part of second-track diplomacy at the bilateral and regional levels. In addition to those studies, other scholars have studied the transformation of domestic institutional agencies and specifically point out that the transformation is an Indonesian way of dealing with climate change issues as a two-level game. Soemarwoto (2005) argues that incorporation of environmental issues into Indonesia’s national development planning and the establishment of the ministry of environment for the first time in 1978 after the Stockholm Conference 1972 was an exit strategy to respond to global issues. Witoelar (2009)

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00011-4

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highlights that the environment is a complex issue in a global setting because it is not only involving the political commitments of various governments but also engages in their implications on diverted domestic settings. In the realm of domestic politics, climate change has never been a single element but it has been entangled with various issues such as regional autonomy, environment, forestry, and energy throughout different governments in Indonesia (Suwarno, 2017). The fact of managing the balance between domestic interest and international commitment exemplifies by certain regulations on Indonesia channeling its domestic interest in international fora. For example, the Indonesian government recognizes the significance of climate change on the national policies and integrates it as one of the prioritized programs on the medium-term development planning regulated in Presidential Regulation 7/2005, particularly in Chapter 8 on Foreign Policy and International Cooperation. This regulation is a complement of the initial regulation titled the Presidential Decree 64/1999 on the membership of the international organizations that are based on national interests. The regulations are an obvious indicator that climate change is one of the strategic issues for Indonesia in maintaining its position in global affairs. Based on previous studies and preliminary empirical facts, this article proposes to use a two-level game approach in order to understand how Indonesia manages the balance between international commitment and domestic interests in climate change negotiations. While most of the previous studies categorize climate change as part of the so-called non-traditional security (NTS) (Buzan et al., 1998), by framing this study with two-level game (Putnam, 1988), this study can dig deeper into the leitmotif of Indonesia as important participants in negotiation on climate change mitigation. This approach basically underlines the importance of the domestic sphere as a venue for molding national interests. Instead of framing foreign policy as mere continuation of domestic interest at the international level, the two-level game treats foreign policy as double-edged diplomacy which means it serves domestic interests while also managing to fulfill international expectations through commitments in certain international fora (Evans et al., 1993). Thus, the two-level game approach can bring to the fore the Janus face and shifting responses of Indonesia toward climate change’s negotiation that serves its domestic interest and international expectation. In the following sections, this article starts with a brief description of the method and study limitations. The main part elaborates on the empirical findings and discusses them based on the two-level game approach. The final part of this article is closed with recommendations based on Indonesian experience in managing climate change mitigation as a double-edge diplomacy.

11.2 Limitations of the study Given the inadequacy of comprehensive studies exclusively on Indonesia’s engagement in climate change negotiations, there is a crucial need for an analytical study on this issue. The study focuses on the period from 1972 to 2021 which incorporates the administration of President Suharto up to Joko Widodo, their dynamics in domestic politics, and foreign policies related to the climate change negotiations most importantly on building national resilience. The period is significant because it shows the dynamic of Indonesia’s engagement in

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the climate change negotiations from fulfilling the need of external pressures to managing the entanglement with domestic needs. The paper answers the following questions: Why did Indonesia engage in climate change negotiations? How did the governmental agencies perceive them as a means to build national resilience? In order to answer these questions, the paper utilizes a two-level game of foreign policy making as an analytical framework. The study builds on the argument that Indonesia’s engagement in the climate change negotiations was based on the commitment to address non-traditional security issues in international affairs. In realizing the commitment, governmental agencies prepared strategies in domestic politics by emphasizing mitigation to support national resilience.

11.3 Materials and methods Two-level game of foreign policy-making recognizes that at least there are three approaches that can be employed in which foreign policy is formulated and the context of both domestic and international politics in the process. The first approach assumes that in foreign policy making, players interpret their interests, objectives, and responsibilities within the context of organizational structure and rules. The second approach is premised on the notion that foreign policymaking is a process or bargaining activity that relies on a degree of political persuasion amongst the players. The third approach assumes that the international environment influences the decision-makers’ view of outside powers.

11.3.1 Two-level game of foreign policy making Foreign policy is an extension of domestic politics in the sense that it is not made in a vacuum of power but also requires the involvement and engagement of various state agencies. Therefore, it is necessary to investigate the perceptions, motivations, positions, and power of the national agencies that influence a state’s foreign policy (Allison & Zelikow, 1999). Foreign policy also depends on how state agencies stand on an issue and how the issue is framed in the domestic politics realm (Allison & Zelikow, 1999). Foreign policy is double-edged diplomacy. It means that foreign policymaking is not only fulfilling the promises of the government in domestic politics but also fulfilling the expectations of the states’ counterparts both at the regional and global levels (Evans et al., 1993). In Indonesian foreign policy making, the more open political system provides a longer process and an expanding number of players. The arrival of democracy turned foreign policy-making into a consultation process between state agencies and some other players thereby creating diverse and multiple interpretations that reflect multiple aspirations and pressures in domestic politics. Furthermore, foreign policy initiatives are not only designed to follow the international rule-based order (RBO) but also to foster domestic political rivalry based on the party system and party identity that sometimes has no relevance to the national interests (Wuryandari et al., 2007). Foreign policymakers tend to strive to reconcile domestic and international imperatives in order to adhere to national interests (Putnam, 1988). This tendency in Indonesia’s case was supported by the fact that the head of government was an internationalist who looked

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for support from the international environment (Putnam, 1988) in order to satisfy domestic pressure (Putnam, 1988). Foreign policymaking can be more effective when it is internationally coordinated (Putnam, 1988) within the appropriate time frame and procedures. In this regard, synergizing between international factors and the domestic goals they reach out to is necessary, by setting the international agenda and linking a specific issue to international negotiations thereby providing policymakers the opportunity to frame the issue within domestic politics (Evans et al., 1993).

11.3.2 Methodology The paper is qualitative in nature. The proposed methodology includes an analysis of secondary sources as well as semi-structured interviews with foreign policymakers from various government agencies (foreign ministry, environment ministry, and agriculture ministry) as well as non-government agencies (journalists, academics) in order to understand the dynamics of the domestic politics and the national interests that led to the engagement of climate change negotiations. Content analysis has been undertaken on written documents such as books, journals, speeches of the President and foreign minister, government official statements, national regulations, regional and international policies, national and local newspapers, magazines, treaties, and transcripts of interviews. The paper asks questions about the unit of analysis and any sub-units (Rowley, 2002). Therefore, the limitations that define the unit of analysis and sub-unit (Gerring, 2004) are significant in terms of searching for evidence (Rowley, 2002). In this paper, the unit analysis is the Indonesian government, and the subunits are the state agencies. The purpose of the paper is to understand why the Indonesian government engaged in climate change negotiations and how the state agencies perceived them as a means to build national resilience.

11.4 Results and discussion 11.4.1 Environment as international concern The environment has been considered as a significant issue in international affairs in two ways. First, the environment is a dynamic issue throughout various global meetings. Environmental issues in the global setting have transformed initially from the domain of environmental degradation, sustainable development, and global warming into the area of climate change. The transformation has happened throughout various global negotiations on the environment starting from the Stockholm Conference in 1972 up to the 26th meeting of COP in Glasgow, Scotland in 2021. Second, the diversified issues within the domain of the environment have caused the change and continuity of the players involved in the various meetings of either state or non-state actors. The complexity of various issues related to the environment has created a small group of states and non-states to respond in diverted ways either to support national interests or partially agreed on specific matters. As a consequence, each global-level negotiation related to global warming and climate change has not only reached a consensus

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among the parties but also has created separated groups of states with specific preferences such as small island countries, forestry countries, and clean technology countries. For example, Annex I was a group of developed countries who have various interests in the Kyoto Protocol—the ratified and the non-ratified parties—including the US. There was another group of developed countries such as Australia, Canada, Japan, and New Zealand, who create the Umbrella Group that have diverted their national interests outside Annex I. Furthermore, Switzerland and South Korea have created Environmental Integrity Group as the developed countries that owned large areas of forests; however, they have had separate interests from the rest members of Annex I. The developing countries have created separate groups related to their national interests. Initially, there were eight states who owned the biggest tropical forests in the world and formed Forestry Eight (F-8) and continuously negotiated to gain compensation funds from the developed countries that successfully applied deforestation. The membership has grown into 11 countries including Peru, Papua New Guinea, Malaysia, Indonesia, Gabon, Democratic Republic of Congo, Costa Rica, Congo, Colombia, Cameroon, Brazil, and has formed Forestry Eleven (F-11) with the same mission. The small island countries both in the Pacific and Indian Ocean such as Nauru, Tuvalu, Vanuatu, Fiji, and Maldives have formed the Alliance of Small Island States (AOSIS), and have claimed that they were the most vulnerable countries to climate change because the raising of sea level would have drawn their territories. AOSIS comprises 44 of the world’s small island states (Barnett, 2007) and has been influential in the climate change negotiations partly because it approximately commanded 20% votes in the United Nations system (Barnett, 2007). During the period of 1972
86, the states were concerned with the environmental degradation that happened worldwide. It was a call for organizing the United Nations Conference on Human Environment (UNCHE) in Stockholm, Sweden, in 1972. Furthermore, in the period of 1987
2002, environmental degradation led to the need to create sustainable development worldwide as it was notified through the global meeting of the World Commission on Environment and Development (WCED) up to the World Summit on Sustainable Development (WSSD). The matters on global warming and climate change have become the spotlight of the international negotiations on the environment since the creation of the Intergovernmental Panel on Climate Change (IPCC) in 1988 up to the last COP meeting in Marrakesh in 2021. The development of environmental issues in international fora has demonstrated the influence of external factors in foreign policy of many countries, including Indonesia, on climate change. Indonesia has shown active engagement in the various international negotiations on climate change although it was burdened by multiple problems in domestic politics. Undoubtedly, climate change has secured its place on the Indonesian foreign policy agenda which was followed by a political commitment to reduce emissions that reached a peak during the Yudhoyono administration. Moreover, the existence of the climate change regime that was followed by the creation of climate change institutions in the global setting has forced Indonesia’s foreign policy to comply with them. The most visible climate change regimes are Bali Action Plan and Kyoto Protocol and with other institutions at the international level, have been ratified and politically accepted by the Indonesian government and have been translated in domestic politics (Suwarno, 2017). In a similar vein, environmental issues have been considered in Indonesia’s foreign policy.

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As a consequence, many national regulations made by the Indonesian government were an extension of international policies such as the ratification of the UNFCCC and Kyoto Protocol (Suwarno, 2017). Indonesia’s foreign policy particularly on the climate change sector and other related sectors has taken shape after the ratifying UNFCCC and Kyoto Protocol. Institutional arrangements and national regulations in domestic politics were also altered to adjust to such compliance. Since environmental issues including climate change are multidimensional, they are related to regional autonomy, development, forestry, and energy, then more institutions have become an integrated part of the whole governance of climate change.

11.4.2 Institutional arrangements and diplomacy toward climate change The dynamics in the domestic politics have influenced Indonesia’s engagement in climate change negotiations. It has happened because the position of Indonesia on the climate change regime is inevitably the product of a two-level game approach. Under this approach, each president tries to maximize the national interests in the forms of actions and inactions based on their reading of the domestic interest (Barnett, 2007; Paterson, 1996; Putnam, 1988). The arrival of Suharto as Indonesia’s second president in 1966 inherited an acute economic crisis from the previous government. During that time, Indonesia suffered annual 650% inflation and 2.5 billion USD of foreign debt (MTI, 2005). As a consequence, Suharto focused on domestic politics to serve the economic development. Suharto applied MPRS Decree XXIII/MPRS/1966 on the Renewal of Economic, Finance, and Developments Policies by improving the tax, bank, and various development stages known as Five Year of Development (Pembangunan Lima Tahun: Pelita). He applied Pelita I as the foundation of New Order economic development that focused mainly on food, settlement, workforce, and mental welfare from 1 April 1969 to 31 March 1974. Under its focus on economic development, Suharto’s administration marked the objectives to gain external support from international affairs to rebuild Indonesia’s economic and political postures. At the same time, the Cold War colored global affairs with the US and Soviet Union as the main players to develop capitalist and communist blocs. However, there was the emergence of various global initiatives to support sustainable development worldwide, particularly related to environmental issues. The first initiative, the Stockholm Conference 1972 was the trigger factor to raise awareness of environmental issues on a global level. In response to the conference, developing countries such as Indonesia and Brazil engaged in environmental issues as a pathway to gain financial aid to support their national economic development (Salim, 2009). During the Stockholm Conference, Indonesia sent its representatives at the ministerial level. The head of the delegation
Emil Salim
has presented Indonesia’s position on the environment issue by stating that “why should developing countries carry the burden of pollution made by the developed countries and to use its scarce resources for global environmental destruction not of the developing countries making? And what, where, and how is the responsibility of the developed country?” (Editor, 2009). Despite confidently stating its position toward the environment, the government has actually limited preparation for the conference (Soemarwoto, 2005). Prior to attending the conference, the

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government organized a seminar on the management of the environment and national development at the Padjadjaran University, Bandung, on 15
18 May 1972 to discuss environmental issues. To follow up on this seminar, the government sent the participants of the seminar as the delegation (Husein, 1993a). The Indonesian government responded to the Stockholm Conference by harmonizing it into the national regulations by issuing Presidential Decree 16/1972 on the Creation of the Task Force on the Development Planning on the Management of Environment which was followed by Presidential Decree 72/1975 on the Creation of Governmental Task Force to the Appraisal of the Natural Resources (Husein, 1993a). Furthermore, Indonesia incorporated the management of the environment and natural resources to support the future generation into the National Guidelines of the Direction of the Country (Garis Besar Haluan Negara: GBHN) 1973 (Husein, 1993a). For the first time, Indonesia established the Ministry of State for Population and Environment Affairs with Prof. Dr. Emil Salim as the first minister in 1978. The governmental commitment to implement the task force on the development planning and the management of environment has come into reality by incorporating the Indonesian Institute of Sciences (Lembaga Ilmu Pengetahuan Indonesia
LIPI) as the national research foundation and the National Development and Planning Agency (Badan Perencanaan dan Pembangunan Nasional: BAPPENAS) as the state agencies responsible for economic and development affairs to conduct research and policies on environment during the period of 1972
77 (Salim, 2009b). President Suharto then applied the Environment Mandate on 5 June 1982 as a pathway to incorporate environmental issues within the national development policies (Husein, 1993b). Indonesia’s engagement in the global meeting on the environment has continued by sending representatives to the Earth Conference in Brazil in 1992. The limited preparation, the lack of understanding of the cross-cutting relations between the environment in the global and domestic settings, and the lack of database and comprehensive information on the environmental issues in domestic politics have made the Indonesian delegations unable to serve the Earth Conference as the avenue to defend the national interests (Soemarwoto, 2005). Nevertheless, in domestic politics, the Indonesian government has ratified the United Framework Convention on Climate Change (UNFCCC) by issuing Law 6/1994 on the same topic. The ratification was based on the awareness and understanding that the increase of greenhouse emissions in the atmosphere has degraded the environment and human conditions (Pramudianto, 2008). Moreover, the Indonesian government passed Presidential Decree 82/1995 on a million peatland programs in Central Kalimantan which was followed by Presidential Decree 74/ 1998 on the same issue. It made the government clear the peatland into rice farming in order to fulfill the need to be self-sufficient in staple food. However, the peatland program caused controversies in domestic politics particularly on the utilization of an initial national budget of 500 billion Rupiah (Tempo, 1997), the socioeconomic problems of transmigration in the same area (Kompas, 2004), the appropriateness of the peatland, the land clearing, and the sustainability of peatland ecosystem. In order to reduce the environmental degradation from forest clearing and peatland, the Suharto administration passed Law 23/1997 on the Management of Environment. However, the new regulation was too late to stop the growing forest clearing, forest fires, and environmental problems in various areas in Indonesia.

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During the New Order era, the central role of the President and executive in policymaking has also been reflected in foreign policies. Indonesia’s government responses toward environmental issues on the international level and the engagement in the various negotiations on the same matter in the global setting were still dominated by the role of the foreign ministry. Other powerful state agencies, such as the military known as Indonesian National Armed Forces (Tentara Nasional Indonesia: TNI) and National Development Planning Agency (BAPPENAS) have remained irrelevant to the Indonesian foreign policy toward the environment. In the late 1990s, along with the beginning of Indonesia’s reform era, Indonesia’s foreign policy prioritized the recovery from the acute global financial crisis, the improvement of Indonesia’s tarnished image in international affairs, and the support to manage the disintegrity and social unrest nationwide (Wuryandari et al., 2008). As a consequence, Habibie’s government paid little attention to Indonesia’s engagement on environmental issues in the global setting. Indonesia did not send a delegation to COP 4 in Argentina in 1998 and COP 5 in Germany in 1999, which clearly implied this lack of interest (Pramudianto, 2008). Moreover, when Abdurrahman Wahid replaced Habibie in 1998, he continued the priorities of Indonesia’s foreign policy on the revitalization of national sovereignty and the search for external funding to support the development of domestic politics (Jemadu, 2008; Smith, 2000; Sukma, 2003). Wahid’s 20 months administration did not consider the environment as one of the strategic issues in Indonesia’s foreign policies. The less commitment to the environment had evidence in the fewer official statements related to the environment, recent global negotiations, and no national delegation on COP 6 in Germany in 2001 and COP 7 in Morocco in 2001 (Pramudianto, 2008). Indonesia only engaged in the Millennium Development Goals (MDGs) Summit in the USA in 2000. Similar to the previous President, Megawati Sukarnoputri, who took the presidential office in 2001 focused her foreign policy on territorial integrity, the recovery of the global economic crisis, the improvement of Indonesia’s tarnished image, and the protection of citizens abroad (DLNRI, 2004). During Megawati’s administration, the foreign ministry has transformed into a well-oiled engine of diplomacy since the minister—Hassan Wirajuda—has initiated internal reform with three main objectives including restructuring of the ministry, the representative offices abroad, and the recruitment of the diplomat by utilizing Presidential Decision 108/2001 on the New Structure of the Department of Foreign Affairs (Wuryandari et al., 2009). Interestingly, as a result of the internal reform within the foreign ministry, the government has attempted to integrate the global issues, including the environment, into one of the strategic issues in Indonesian foreign policy. Environmental issues were handled previously by the Head of the sub-directorate. Under the internal reform agenda, the environmental issue has been managed by the Directorate General of Multilateral, Economy, Finance, and Development and thus went down into the Directorate of Economy, Development, and Environment since 2005 (Tharyat, 2010). Within the foreign ministry, the environment was not a single element, but it has had cross-cutting references to various directorates. The inter-relations between environment and international agreement were the responsibility of the Directorate General of Law and International Agreement as well as the connection between environment and public policy was the authority of the Directorate of Public Diplomacy. During Megawati’s

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administration, the foreign ministry no longer saw the environment as a marginal issue, but it had integrated into a significant and strategic institutional structure in Indonesia’s foreign policy. Besides the establishment of a specific division in the Ministry of Foreign Affairs related to environmental issues, President Megawati showed their commitment through engagement in international negotiations on the environment. During the preparation committee for the World Summit on Sustainable Development (WSSD) in Bali from 27 May to 7 June 2002, Emil Salim was the chairman. Furthermore, Megawati attended the WSSD known as Rio 1 10 Conference in South Africa in 2002 as vice chairman and delivered a speech that stated, “In our effort to overcome those challenges, we need to take into consideration of the different level of capacity between the developed and developing countries” (Sukarnoputri, 2002). It highlighted Indonesia’s commitment to supporting the principles of sustainable development as it was notified at the Common but Differentiated Responsibilities Principle and Precautionary Principle that was codified during the Earth Conference in Brazil in 1992 (UN, 2002). Indonesia’s engagement in the global negotiation on the environment continued when the government ratified the Protocol to the United Nations Framework Convention on Climate Change (UNFCCC) worldwide known as Kyoto Protocol by issuing Law 17/2004 on the same theme. The Ministry of Environment has mandated to implement of the Kyoto Protocol by focusing on some issues including sustainable development, the Clean Development Mechanism (CDM) between the developed and developing countries, the development of low-emission and clean technology, and the reduction of greenhouse emission through replanting the forests (KLHRI, 2004). The arrival of Susilo Bambang Yudhoyono through the direct presidential election in 2004 has given a stronger foundation to Indonesia’s greater engagement in international negotiations on the environment. Yudhoyono’s foreign policy has focused on the improvement of Indonesia’s engagement in global affairs, the creation of world peace, the restoration of Indonesia’s tarnished image abroad, the cultivation of trust from the international community, and the creation of regional and international governance to support the national growth (BAPPENAS, 2009). In the first tenure of the presidency, President Yudhoyono issued Law 32/2009 on the Protection and Management of the Environment. The new law was a replacement for the previous national regulations on the environment such as Law 17/2004 on the Ratification of Kyoto Protocol, Minister of Forestry Decree 14/2004 on the Reforestation Program, Law 23/1997 on the Management of Environment, and Law 6/1994 on the ratification of UNFCCC (KLNRI, 2007). The Yudhoyono administration has greatly highlighted the significance of the climate change issue in the national setting. He established a focal point institution on climate change known as National Council on Climate Change (Dewan Nasional Perubahan Iklim: DNPI) by utilizing Governmental Regulation 46/2008 under the same name. DNPI was a separate institution from the Ministry of Environment that was led directly by the President, the deputies were the Coordinating Minister of Economics and Coordinating Minister of Social Welfare, and the members were 17 ministers related to climate change. In the domain of international affairs, SBY’s commitment to the environment has been delivered through the speech at the 62nd session of the United Nations General Assembly (UNGA) in 2007. SBY expounded that “Indonesia is a country that endured the most on the impacts of climate change. In recent years, we have been hit by a series of natural disasters in

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the form of floods, droughts, forest fires, El Nino, tsunami, and earthquake” (UNGA, 2007). It was followed by the opportunity for Indonesia to be the host of the COP 13 meeting in Bali on 3
15 December 2007 which was attended by 12,000 delegations from 18 countries ( Jenie, 2009). The delegations came from various agencies such as governments, International Governmental Organizations (IGOs), International Non-Governmental Organizations (INGOs), media as well as the head of government of Singapore, Papua New Guinea, Palau, Norway, Maldives, Grande, Australia, and the UN Secretary-General. For Indonesia, the COP 13 had three significant decisions. First, Bali Road Map served as a negotiation platform to respond to climate change in the global setting that was codified in the Bali Action Plan. Bali Road Map was a platform for the parties to apply convention from 2007 to 2012 by utilizing building block mechanisms to include adaptation, mitigation, capacity building, technology transfer, finance, and investment. The section on the way forward has agreed on the creation of an Ad Hoc Working Group on the longterm Cooperation Agreement under the Convention (AWG-LCA) that has to be completed by 2009. Second, COP 13 has inserted the term Degradation (Werdaningtyas, 2009) into the Reducing Emission from Deforestation and Degradation (REDD) that was codified during the COP 11 meeting in Canada in 2005 and was being applied in the developing countries. REDD has obliged developed countries to support capacity building, technical assistance, and transfer of technology to developing countries. Furthermore, REDD served as an avenue for developing countries to reduce deforestation and the application of sustainable forest management by utilizing national and sub-national approaches. Third, COP 13 created an Expert Group on Technology Transfer (EGTT) as a commitment of the developed countries toward the developing states as well as the Adaptation Fund on the convention of climate change related to the issue of adaptation. In the second period of presidency, SBY integrated not only environmental issues in general but also global warming and climate change into one of the priorities on the Medium-term National Development Planning (Rencana Pembangunan Jangka Menengah Nasional: RPJMN) 2010
14 by utilizing Presidential Regulation 5/2010 on the same name and he inserted them into 100 Program of the second United Cabinet (Rangkuti, 2009). SBY’s administration was not only engaged in the negotiation on climate change in the multilateral setting but also on the bilateral setting with Australia, China, Finland, Japan, Norway, the UK, the US, and the European Union. Indonesia’s Minister of Foreign Affairs—Marty Natalegawa—met his counterpart at the Norwegian Minister of Environment and Development—Erik Solheim—and both signed a Letter of Intent (LoI) on the bilateral agreement on forest conservation to reduce carbon emission at cost of 1 million USD as a commitment toward climate change on 26 May 2010. SBY’s greater engagement on climate change issues achieved some international awards such as Global Home Tree in 2010, Champion of the Earth 2014 for Policy Leadership from the United Nations Environment Program (UNEP) in November 2014, and was elected as Council Growth Institute (Sinaga, 2020). The arrival of SBY into power in 2004 gradually transformed democratic values into foreign policy practices. It meant that the institutional arrangement of foreign policy was no longer the major domain of the executive (Wirajuda, 2007). The incorporation of democratic values in foreign policy-making was achieved by adjusting the norms and procedures to the players’ activities within the organizational structure, political system, and

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society, as well as synchronizing more stakeholders in the process and implementation (Wuryandari et al., 2009). Foreign policymaking shifted from state-centric to pluralistic and participatory so the executive had to consider the interests, objectives, and responsibilities of some other actors within the context of organizational structure and rules (Wuryandari et al., 2009). During SBY’s administration, the Indonesian government responded that the environmental issue was not only related to global warming and climate change but also it was entangled with other significant issues such as forestry and energy. The entanglement of these issues led the Indonesian government to formulate a foreign policy that engaged both international negotiations, and national regulations. In the realm of foreign policy making, the engagement of the climate change negotiations was not only the domination of the foreign ministry, but it has involved specialized ministries such as the Ministry of Environment, Ministry of Forestry, Ministry of Agriculture, Ministry of Finance, and BAPPENAS. SBY’s government actively engaged in the negotiations with the UNFCCC by sending the proposal to avoid deforestation (REDD) within which the developing countries would have received the compensation fund for preventing deforestation as part of the international agreement on climate change (Wingqvist & Dahlberg, 2008). Throughout SBY’s both tenures of the presidency, Indonesia successfully gained multiple international fundings for preventing deforestation. However, there were dramatic changes in both domestic politics and institutional arrangements on climate change since President Joko Widodo, known as Jokowi, has administered the Indonesian government since October 2014. Jokowi’s government immediately delivered direct impacts on the ongoing climate governance (Suwarno, 2017). Jokowi merged the Ministry of Environment and Ministry of Forestry into one single ministry known as the Ministry of Environment and Forestry. It was the most dramatic move in the realm of domestic politics concerning climate change since the unification created not only confusion amongst domestic and foreign stakeholders but also multiple problems to manage the tasks between the two ministries (Suwarno, 2017). Furthermore, Jokowi abolished the two competing non-structural agencies related to climate change that was created by the previous government—the National Council on Climate Change (DNPI) and the REDD 1 Managing Agency—and integrated the remaining functions of the two agencies into the Ministry of Environment and Forestry (Suwarno, 2017). Despite not being as internationalist as SBY (Santikajaya, 2014), these dramatic changes in policies were seemingly driven by his administration mostly focusing on domestic issues (Jong, 2015).

11.4.3 State agencies and engagement in the climate change Since the initial engagement in the climate change negotiations, the Ministry of Environment has been the focal point for the Indonesian government. It means that this ministry throughout different administrations has integrated into various sectors and actors related to climate change. The climate change sector is multidimensional. It entails environment and regional autonomy, development, forestry, and energy. Consequently, more institutions have also become an integrated part of the whole governance of climate change (WB, DFID and PEACE, 2007). Based on the distribution of green gas emission reduction responsibility, for example, there are seven ministries involved including the

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Ministry of Environment, Ministry of Agriculture, Ministry of Forestry, Ministry of Public Work, Ministry of Industry, Ministry of Transportation, and Ministry of Energy and Mineral Resources (Suwarno, 2017). The complexity arises most importantly because climate change is largely a land-based sector within which those ministries are in charge (Suwarno, 2017). Climate change issues have evolved alongside the dynamics of international negotiations as well as the adjustment of domestic politics through issuing national regulations and institutional arrangements. These regulatory and practical policies are necessary efforts of adaptation and mitigation to overcome the dire impact of climate change including the extreme to sea-level rise, the disruption of agricultural production, the increase of flood and extreme rain, and the negative effects to achieve SDGs (Wingqvist & Dahlberg, 2008). Some of the concrete adaptation and mitigation steps to climate change are taken by the government, such as promoting the planting of mangroves, restoring watersheds (DAS) to protect water resources, building earthquake-resistant buildings and early warning systems, acknowledging transboundary impacts of climate change, the Indonesian government has also developed cooperation with other countries both developed and developing countries. It should be noted that Indonesia’s involvement in environmental issues and climate change since its inception in 1972 has frequently been brought not only for the sake of its national interests but also the interests of developing countries. Developing countries are part of the strategic solution for mitigating climate change.

11.5 Recommendations Climate change engagement is an unavoidable global interest and a transnational issue that goes beyond national boundaries. Therefore, every country should balance their international and domestic commitment to climate change without omitting the fact of different levels of development. The fact that different levels of capacity in climate change mitigation between developed and developing countries, developing countries should be empowered to become critical players. They are not only the most affected parties but also part of the solution. Despite domestic constraints and limited capacities, developing countries should be able to actively assert their interests in international fora. Balancing domestic interests and international commitment in climate change negotiation is a key to sustainable resilience.

11.6 Conclusions As a two-level game, Indonesian foreign policy on environmental issues (1972
2021) has clearly indicated two-current flows between international and domestic stages. In its infancy, the Indonesian foreign policy on the environment during the Soeharto era was triggered by the growing global concern for the environment as shown by the Stockholm Conference (1972). Indonesia assertively stated that there was a divisional perspective on environmental issues between developing and developed countries. In line with Indonesian foreign policy, Indonesia considered environmental issues to be closely linked

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to the rights to develop for developing countries. While Indonesia kept its international participation, its national interest was actually driven by domestic needs to gain financial aid to support its economic development. Soeharto’s administration created a portfolio ministry of environment to accommodate such interest. In the post-Asian Financial Crisis that led to the democratic transition period, Indonesia maintained limited international involvement and engagement on global environmental forums, especially during Habibie and Wahid. Not only was absent from participating in the international forum, but a huge domestic crisis had also made Indonesia unable to send official statements regarding global environmental issues. The turning moment arrived after Megawati took presidential office. Her administration began with an institutional restructuring policy in the Foreign Ministry which led to elevating the environmental issues from the sub-directorate to a directorate level. The process called Benah Diri (self-improvement) enabled Indonesia to routinely send a proper delegation to international climate change forums and ratified the Kyoto Protocol in 2004. Similar to the previous Indonesian’s standing position, Megawati also highlighted the difference in responsibilities. Indonesia plays a more active role under the leadership of Yudhoyono’s presidency by keeping a balanced performance between the international and domestic stages. Not only active in international fora, but Indonesia also strengthened its institutional capacity and regulation, as well as expanding its international cooperation by organizing multilateral events and building up bilateral cooperation with various countries. In terms of institutional legacy, Yudhoyono established a special agency for managing climate change called DNPI and also amended the previous law on the environment issued by Megawati. These significant policies and engagements are closely linked to the improvement of Indonesia’s political and economic stability. Indonesia’s commitment to climate change issues reached its peak during the administration of Yudhoyono came with an anti-climax when Joko Widodo took the presidency. The new administration put more attention on domestic needs rather than on international engagement. He radically dissolved DNPI and merged the Ministry of Environment and the Ministry of Forestry for the sake of efficiency. These policies have created confusion among stakeholders, particularly regarding the conflicting interests of two different ministries. In the end, the case of Indonesian foreign engagement in climate change evidently shows a two-level game. However, domestic factors play more decisively compared to international factors. Domestic concerns gain more attention because of the increasing uncertainties in the international political economy, especially on the economic-development nexus of environmental issues.

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12 The green economy to support women’s empowerment: social work approach for climate change adaptation toward sustainability development Hari Harjanto Setiawan and Yanuar Farida Wismayanti National Research and Innovation Agency, Jakarta, Indonesia

12.1 Introduction Many recent studies have investigated the impact of climate change on the future of humanity. Even the effects of climate change caused the mass extinction of the earth (Bellard et al., 2012). The Indonesian government has established a green economy as an economic transformation strategy. This system seeks to create a sustainable economy by maintaining the balance of nature. The green economy is also seen as a new value (Knuth, 2017) as a solution to building an economic system that focuses on the environment. The green economy is guided by sustainable growth by maintaining balance in all sectors. Green growth efficiency is the key to sustainable development. Green Economy is an economic concept that seeks to improve people’s welfare and social equality to reduce the risk of environmental damage. A Green Economy can also be defined as an economy that emits little or no carbon dioxide into the atmosphere, conserves natural resources, and is social. The difference between a green economy and other economic ideas is the direct valuation of natural capital and services as economic value (Nadiroh & Emilkamayana, 2021). The impact of climate change is causing increased natural disasters and raising the issue of women in poverty. Women are the group most vulnerable to the effects of climate change. Gender inequality makes it more difficult for them to adapt quickly. Real action is

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needed to change the position of women in climate change situations. Empowering women through a green economy is mitigating the impacts of climate change to improve livelihoods. Adaptation and mitigation must be considered in sustainable economic development (O’Neill et al., 2014). Climate change impacts increasing poverty, a significant concern for the Indonesian government and the international community. Climate change causes the community’s economy to decline, so women are the most vulnerable. Social protection programs for women in Indonesia improve the welfare of the green economy through the Social Entrepreneurship Program. Practicing social entrepreneurship will contribute to social transformation (Cavalcanti, 2021). This social transformation will change women considered weak and vulnerable to climate change to become strong women in adapting to climate change (Cavalcanti, 2021). Zero poverty is the primary goal of the Sustainable Development Goals (SDGs). Indonesia’s efforts to alleviate poverty are through social entrepreneurship programs. This program began to be implemented in 2020. The participation of women in this program is very high compared to men. As many as 96% of the thousand beneficiaries are women. This program is implemented in five areas: Majalengka Regency, West Bandung Regency, DKI Jakarta, Semarang Regency, and Bantul Regency. Beneficiaries are provided with a one-time venture capital of IDR 3,500,000/$237.03 USD per family. This program aims to increase the economic independence of low-income families to reduce their dependence on government assistance. Entrepreneurship is one of the essential variables chosen to encourage economic growth. Improving the welfare of the green economy through social entrepreneurship is an instrument of sustainable development. Social entrepreneurship needs to be considered in adapting to climate change (Me´ndez-Picazo et al., 2021). We are learning from Kenya that entrepreneurship aims to increase the contribution of gross domestic product (GDP) by at least 10% per year. Increased innovation, employment, and income access will ultimately lead to prosperity and livelihoods (Ngare et al., 2021). Social entrepreneurship should be supported to encourage increasing the role of women in adapting to climate change. The social entrepreneurship model is one alternative to improve the community’s economic welfare (Setiawan et al., 2021). The Indonesian government seeks to empower women by strengthening the green economy through social entrepreneurship programs. This program considers the balance of nature, social value creation, and business innovation as the core of social entrepreneurship. The social business target will impact empowering women in efforts to adapt to climate change. Social entrepreneurship encourages green economic development to realize the adaptation agenda to climate change (Iyer, 2016). The relationship between sustainable development and climate vulnerability risks is significant for countries to formulate evidence-based policies (Prabhakar, 2017). Future social research must balance business and social goals as a sustainable business model (Garcı´aJurado et al., 2021). This chapter focuses on the following: (1) Human activities cause global warming, which has an impact on climate change; (2) The impact of climate change on women as a vulnerable group; (3) Efforts to adapt women’s groups to climate change through social entrepreneurship to create a green economy. This chapter contributes to academics and practitioners studying climate change issues. The tangible form of this contribution is a model of a social entrepreneurship program to support a green economy for poor women in adapting to climate change through a social work approach.

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FIGURE 12.1 Framework to adaptation to climate change.

Environmental damage encourages humans to build resilience through sustainable development (Galindo-Martı´n et al., 2020). The Paris Agreement is an international agreement on climate change mitigation, adaptation, and financing, signed by 196 countries in 2015. Climate mitigation and adaptation policies must be designed to respond to climate change risks in a way that supports accelerating a green economy (Hasselmann, 2013). Efforts to involve stakeholders are significant in climate change adaptation (Olabisi et al., 2021). Indonesia is committed to climate change adaptation, involving stakeholders from various sectors (Berrang-Ford et al., 2019). Climate change adaptation strategies for women through a green economy are significant for increasing welfare (Tridico & Paternesi Meloni, 2018). The framework for this research can be seen in Fig. 12.1.

12.1.1 Human activities cause global warming Global warming, if left unchecked, continues to threaten human life. Earth’s greenhouse gas emissions trap solar heat. This causes global warming and climate change. The world

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is experiencing the fastest global warming caused by human behavior (Trenberth, 2018). Global warming is a phenomenon of drastic climate change due to an increase in the average temperature of the atmosphere, sea, and land. This condition can make the ozone layer thin. The depletion of the ozone layer, the speed of the earth’s air, affects changes in weather, weather, and air sources which are important for the survival of living things. The three main human activities that cause global warming include the generation and use of electricity, deforestation, and transportation. Generating electrical and thermal energy by burning fossil fuels will produce a lot of global emissions (Leonard et al., 2020). Most electric power is still generated from burning coal, oil, or gas. Residential and commercial buildings use more than half of global electrical energy. As coal, oil, and natural gas continue to be used for heating and cooling systems, residential and commercial buildings generate significant greenhouse gas emissions. This burning will produce carbon dioxide and nitrous oxide, harmful greenhouse gases that weigh on Earth and trap the sun’s heat. Deforestation for agriculture, animal husbandry, or other reasons will generate emissions because the trees cut down will release the carbon stored in them (Leite-Filho et al., 2021). About 12 million hectares of forest are destroyed every year. Because forests absorb carbon dioxide, their destruction will also limit nature’s ability to reduce emissions in the atmosphere. Deforestation, agriculture, and other land use changes account for about a quarter of global greenhouse gas emissions. Most cars, trucks, ships, and planes operate on fossil fuels. This makes the transportation sector a significant contributor to greenhouse gases, especially carbon dioxide emissions (4). Land vehicles produce the most emissions due to the combustion of petroleum-based products, such as gasoline, in their internal combustion engines. However, emissions from ships and aircraft continue to rise. Transport accounts for nearly a quarter of global energy-related carbon dioxide emissions. In addition, trends indicate that there will be a significant increase in energy use for transportation in the coming years.

12.1.2 The impact of climate change on women as a vulnerable group Climate change significantly impacts human life (Celik, 2020). Increasing temperatures and natural disasters in several places in Indonesia impact people’s welfare. The adverse effects of climate change include natural disasters such as landslides, floods, and storms. The impact of climate change is felt in various fields of life, including agriculture which causes reduced food security, human health, migration patterns, and poverty. In many ways, women are more vulnerable to climate change’s impacts than men. Especially women from poor populations depend on natural resources for their livelihood. Women face social, economic, and political barriers that limit their abilities. Women in rural areas of developing countries are particularly vulnerable when they depend heavily on local natural resources for their livelihoods. Gender-sensitive strategies must be identified to respond to the environmental and humanitarian crises caused by climate change (Patel et al., 2020). Women are good actors or agents of change related to mitigation and adaptation. Women often possess substantial knowledge and skills that can be used in

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climate change mitigation, disaster reduction, and adaptation strategies. The responsibility of women in the household and society is a strategic position in making changes.

12.1.3 Adaptation of women’s groups to climate change Women empowerment and economic development cannot be separated (Duflo, 2012). The program must have equality between men and women. Gender equality is the basis for building a green economy through social entrepreneurship programs. Women’s views are still underdeveloped in the process of economic growth. Social entrepreneurship reflects that women are more productive in economic engagement to increase incomegenerating jobs, especially in the agricultural and informal sectors (Mehra, 1997). Social entrepreneurs are individuals, groups, organizations, networks, and alliances who seek large-scale sustainable change through problem-solving ideas. Social entrepreneurship addresses social problems, including unemployment, poverty, and education. Social entrepreneurship needs to be considered to solve social issues in Indonesia. Social entrepreneurs develop a system of social entrepreneurship to be applied in Indonesia and many developing countries (Katsushi, 2020). Through social entrepreneurship for women, economic growth will increase, which will impact welfare. Welfare is not just an increase in income. There are three necessary measures of well-being: quantity, quality, and equity (Enflo, 2021). Social work is a field of study that deals with social growth and change, social cohesiveness, and individual empowerment and freedom. Social work is between social justice, human rights, communal responsibility, diversity, and respect. Supported by social work theory, social sciences, humanities, and local wisdom bind society to its structures to face life’s challenges and improve health (IFSW, 2022). There are three essential components in green social work: adaptability, interdisciplinarity, and engagement (Wu & Greig, 2022). Now we have to realize that we have neglected environmental wisdom. We have the right to exist in a healthy and sustainable environment. An environment that allows everyone to use nature to meet their needs. Injustice felt by humans comes from the human treatment itself. Social work practices should help make the earth greener. Protecting the world and maintaining environmental balance are very important. The catastrophe of climate change will impact everyone. Green social work is a new approach that aims to address the inequality of ecological degradation. Green social work is crucial in introducing environmental issues and increasing people’s knowledge (Dominelli & Ku, 2017).

12.2 Material and methods This study uses a mixed method between quantitative and qualitative approaches (Creswell & Hirose, 2019). Quantitative data were obtained using the unit of analysis on the Beneficiary Group of the social entrepreneurship program. Meanwhile, qualitative data will provide comprehensive information about the role of social workers in assisting women in adapting to climate change by developing a green economy through social entrepreneurship.

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12.2.1 Population and sample A survey was conducted in May 2021; the sample was the beneficiaries of the social entrepreneurship program in 2020. The population is 1000 families spread over four regencies (Majalengka, West Bandung, Semarang, Bantul) and one city (DKI Jakarta). The overall sample size draw is based on Cohen Manion and Morrison tables. The number of research samples was taken with a confidence level of 99% and an alpha value of 0.05; the number of pieces was 509 respondents. The selection of respondents’ names was determined by Simple Random Sampling based on the list of beneficiaries of the social entrepreneurship program in 2020. Randomization in selecting respondents used the online application "Random Number Generator" with predetermined proportions.

12.2.2 Data collection The data collection stage is to obtain information to answer research questions. The methods used in data collection include (1) questionnaires: carried out by distributing questionnaires to respondents who meet the criteria. Enumerators were trained before the interview process was carried out by entering respondent data. (2) Researchers conducted in-depth interviews to explore the implementation of social entrepreneurship for beneficiary groups. (3) Focus Group Discussion (FGD): also conducted by researchers to obtain in-depth information from stakeholders involved in the social entrepreneurship program for beneficiary groups. (4) Documentation and literature study are sourced from diaries, reports, and photos. In addition, literature studies from books, journals, websites, and research reports are also essential to support the data. Enumerators assist researchers in data collection. Enumerators came from local communities that were not involved in the program. Each enumerator has the responsibility to interview respondents divided by the proportional calculation. Enumerators interviewed about seven people daily, completing 21 respondents in three days. The proportion of samples and enumerators is adjusted to the total population in each location. The balance of the population, sample, and required enumerator can be presented in Table 12.1. Informants were selected purposively based on predetermined characteristics. Informants will be interviewed to collect qualitative data. The informants include social service officers, business incubators, social workers, beneficiaries who are considered TABLE 12.1 The population, sample, and total number of enumerators. No

Location

Population

Sample

Enumerator

1.

West Bandung

285

145

7

2.

Majalengka

300

153

7

3.

Bantul

106

54

3

4.

Semarang

200

102

5

5.

DKI Jakarta

109

55

3

TOTAL

1000

509

25

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successful, and beneficiaries who are categorized as less productive. Qualitative data was also collected through focus group discussions (FGD) with stakeholders related to the social entrepreneurship program. The staff from social services, trade offices, planning and development offices, small business offices, business incubators, social assistance, financial institutions, community leaders, and beneficiary representatives are involved in FGD activities.

12.2.3 Data processing and analysis The next step is data processing, which has six steps: (1) organizing information, (2) reading all the information and coding, (3) detailing the cases and the contexts, (4) providing the pattern and the relationship between categories, (5) interpreting and develop generalizations from cases, and (6) presenting the narrative. The descriptive statistical analysis used a percentage to examine the study’s variables. The green economy approach aims to create an Indonesian economy that focuses on environmental protection. This program focuses on transforming Indonesia’s economic system to reduce greenhouse gases to maintain economic growth. This green economy program through social entrepreneurship has benefits for women. The program also develops a social work approach to adapting climate change to a green earth. The achievement of the program’s success is measured based on nine predetermined indicators. Indicators to measure these achievements include: (1) Requirements to become a beneficiary, (2) business capital assistance, (3) incubation of business assistance, (4) mentoring, (5) Financial inclusion, (6) management assets, (7) sustainable Livelihoods, (8) social capital and networks, and (9) increased income. The results are categorized into four types: the first category is very good, with a score of 75.26 to 100.00; the second category is good, with a score of 50.51 to 75. 25; the third category is enough, with a score of 25.76 to 50.50; and the fourth category is not good with a value of 01.00 to 25.75.

12.3 Results and discussion Climate change is a severe problem facing the whole world. Low-carbon development is one of the transition strategies toward a green economy and sustainable development. The transformation of the Indonesian economy into a green economy is one of the strategies to improve people’s welfare while maintaining environmental quality. Indonesia still faces many challenges toward a green economy. Collaboration between various stakeholders is needed to ensure the transition to a green economy.

12.3.1 Climate change case studies Global warming increases the average temperature of the Earth’s atmosphere and surface. Global warming causes several changes that can be observed and felt, such as unpredictable seasonal changes such as prolonged droughts, rising sea levels, extreme weather, and melting polar ice caps. Climate change in Indonesia to activities such as

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fossil burning, deforestation, and industrialization. Environmental balance is a system of maintenance and sustainability. This power is strongly influenced by the magnitude of human activities that damage the order of an ecosystem. This attitude is related to the awareness that beneficiaries must have that environmental damage and climate change are important issues. All locations used as research sites use electricity partly produced by burning fossil fuels. Jakarta is the highest electricity consumption compared to the other four areas that are the research locations. Most of the electricity used by the five regions is supplied from the power plant in Situbondo, which is located in East Java Province. There it produces electricity by burning coal, which has many global emissions. The five areas where the research is located use electrical energy. Commercial and residential buildings are being developed. This requires a lot of electrical energy. So indirectly, coal, oil, and natural gas continue to be used. The use of electrical power in housing is for heating and cooling systems which produce significant greenhouse gas emissions. This burning will produce harmful greenhouse gases that burden the Earth and trap the sun’s heat. No deforestation was found in the five study areas. However, the cutting of trees for housing development is widespread. Even the conversion of land from agriculture or plantations to housing. Many trees are cut down so that nature’s ability to reduce emissions in the atmosphere is reduced. The felling of these trees contributes to global greenhouse gas emissions. Most of the greenhouse gas contributors are in the transportation sector. Cars, trucks, ships, and airplanes use fossil fuels. This makes the transportation sector a significant contributor to greenhouse gases, especially carbon dioxide emissions. Jakarta contributes the most carbon emissions; land vehicles produce the most emissions in the combustion of petroleum-based products. Transport accounts for nearly a quarter of global energyrelated carbon dioxide emissions. In addition, the trend indicates that there will be a significant increase in energy use for transportation in the coming years.

12.3.2 Women are a vulnerable group Women are the most vulnerable group when affected by climate change. This vulnerability stems from a lack of understanding of the impacts of climate change. Women are a vulnerable group regarding livelihoods (Neil Adger, 1999). Lack of access to participate in climate change policies causes women to lack the capacity to adapt. The importance of gender mainstreaming in climate change policies aims to ensure that women are not made more vulnerable to climate change policies. Women have limited access to climate change policies. Therefore, women’s empowerment programs are critical to increasing their capacity in economic activities to supplement family income. This program is beneficial for women to manage their time effectively while caring for the family and setting goals for themselves. This program is also essential to help women develop a green economy through social entrepreneurship in self-capacity development. The social entrepreneurship program will bring about social changes in social life. Adaptation to climate at the local level should consider gender mainstreaming to support women and strengthen their resilience (Wrigley-Asante et al., 2019). Women’s

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empowerment is essential in developing countries, especially in Asia and Africa. Zero poverty and gender equality are specific goals of the Sustainable Development Goals (SDGs). For example, in Pakistan, microfinance has a positive impact on women’s empowerment, poverty alleviation, and women’s social life. They increase their income. Therefore, it is concluded that microfinance and MFIs have become effective mechanisms for achieving the SDGs (Niaz & Iqbal, 2019). Empowerment of women is essential to reduce poverty. Patriarchal culture leads to male domination, which places women in the domestic sphere and marginalizes them (Nurasyiah et al., 2021). Providing access to “economic empowerment” is focused on women, families, and communities. Social entrepreneurship is urgently needed to empower women, which can help them to increase their income. This condition will also help them to be equal with men in their household roles. Social entrepreneurship has impacted society by increasing economic access for the poor, promoting peace in conflict zones, and helping farmers escape poverty. Social entrepreneurship also initiates social change. As an initiator, social entrepreneurship encourages social innovation and capacity building for social impact. Social entrepreneurship is a dynamic process created by individuals or teams who seek to use social innovations to create new social values. The innovation ecosystem is essential for transferring the technology model into the social enterprise organization (Gerli et al., 2021).

12.3.3 Adaptation through the green economy Climate change is already happening in our world, so adaptation to climate change must be made. One of the problems faced by the Indonesian people is the declining level of community welfare due to the disruption of their work. A green economy through social entrepreneurship is one solution to improve the welfare of women vulnerable to climate change. In 2020, the government began implementing programs to improve women’s economic well-being in adapting to climate change. Through this program, the government will help low-income groups of women. There are 1000 program beneficiaries spread over five regions: Majalengka Regency, West Bandung Regency, DKI Jakarta, Semarang Regency, and Bantul Regency.

12.3.4 Social entrepreneurship program for women The green economy supports women’s empowerment through social entrepreneurship programs. Before participating in the social entrepreneurship program, all participants must attend training which is held for five meetings. Training is conducted once a week. The guiding business incubator team will visit participants’ locations to provide learning sessions and practice the basics of making the products that will be produced. Participants also received assistance from social workers who played a role in forming an awareness of climate change. Through this awareness, pro-environmental behavior will be created so that economic development does not damage the environment. Entrepreneurship developed by the community with local potential is; culinary, agribusiness, fashion, retail, service, and crafts.

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Beneficiaries receive additional capital of IDR 3,500,000/$237.03 USD in one period of the program, which is distributed through state banks. Capital in the form of money is a person’s strength in running a business. Capital in the form of money is not only needed for entrepreneurs on a large scale, but entrepreneurs on a small scale also really need it to develop. That must be instilled in the beneficiaries that the business capital provided is for business development and is not consumptive. The beneficiaries of this social entrepreneurship program are women categorized as poor and vulnerable to the impacts of climate change. The social entrepreneurship program’s ultimate goal is to improve families’ welfare to adapt to climate change. The reason why most women participate is that most of their husbands are already working. However, her husband’s income is still insufficient to meet basic needs, so women help their husbands to meet the family’s basic needs. The female population is half of Indonesia’s population. Gender equality is morally good and is the right and strategic thing in the economy (Otieno et al., 2021). Women are vital in promoting a green economy as a climate change adaptation. Women can participate in the family economy and, as mothers responsible for their children’s health. This is very important for the survival and sustainability of the nation and state. Social entrepreneurship carried out by women is still classified as a side job because their work status helps their husbands work and increases their husbands’ income. However, the results are significant in helping the family income and meeting the daily economic needs of the household. Usually, her husband works as a seller, fisherman, farmer, and farm labor whose income is also mediocre. Women’s income from their businesses is used to help their husbands meet basic needs and school fees for children and save if there is an excess.

12.3.5 Improved economic welfare Inputs from regulations, guidelines, human resources, and budgets are not measured quantitatively but will be seen in the process qualitatively. An interesting finding is that this study did not consider the entrepreneurial spirit of the beneficiaries (Nawawi et al., 2020). This is very important because some beneficiaries choose to become workers. All locations of social entrepreneurship programs implement digital marketing, but the most prominent is in Bantul Regency because it has developed its platform for digital marketing. Although in practice, it still needs further development. In the era of information and communication technology advances, the seller’s relationship with the market is changing. Information technology and digital tools influence marketing, making it possible to build consumer relationships (Zio´łkowska, 2021). Indicators of social capital and networks that received an assessment from beneficiaries of 10.00 or in the poor category. The elements assessed in this indicator include networking with fellow heirs, networking with mentors, and adding employees. Social capital is a relational concept because people access and mobilize social resources through relationships with others (Shin, 2021). Social capital can promote social entrepreneurship in three main ways: the creation of social capital, its relationship to institutions, and social capital as a form of the group (Hidalgo et al., 2021). Overall program evaluation based on nine indicators can be seen in Table 12.2.

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12.3 Results and discussion

TABLE 12.2

Category average value of each element and dimension.

No

Dimension

Value

Category

1.

Requirements to be a beneficiary

97.97

Very good

2.

Business capital assistance

76.80

Very good

3.

Business mentoring incubation

61.47

Good

4.

Business Assistance

56.83

Good

5.

Financial inclusion

75.62

Very good

6.

Asset Management

56.54

Good

7.

Sustainable Livelihood

79.93

Very good

8.

Social capital and network

10.00

Poor

9.

Increased income

58.58

Good

The social entrepreneurship program for women aims to increase income and welfare. Most beneficiaries reported an increase in revenue, even if the increase was slight. Efforts to empower women through social entrepreneurship have influenced shifts in attitudes toward women workers, women’s authority to make family decisions, and the perspectives of men and women. Women who have participated in this program have other potential sources of income to help support their family finances. Support from family and environment is prioritized because it is proven that women can work at home while still taking care of the family (Enflo, 2021). Social entrepreneurship programs contribute to sustainable development (Al-Qudah et al., 2022). More and more women want to be part of this social enterprise because it has a positive impact. However, due to family obligations, women may share household responsibilities with their husbands to help them support their work. They also want their daughter to attend secondary school to improve their standard of living. This effort is a form of adaptation to climate change to create family resilience.

12.3.6 Green social work approach The green social work model supports a holistic approach to all living things: people, plants, animals, and physical ecosystems. The emphasis is on the natural relationship between all the elements that exist and are responsible for preserving the earth. Although still new, this approach is the key for today’s society to harmonize with the environment (Bhuyan et al., 2019). Knowing that living things are interdependent will also bring all existing systems and institutions into the realm of work. Green social work promotes equity, social inclusion, resource sharing, and a sustainable human rights-based approach. Ecological and social problems have now become problems that can no longer be understood and solved separately at the regional and national levels. Climate change is closely related to the social welfare of society because the environmental crisis will exacerbate economic and social conditions (Dominelli, 2014). This is related to social injustice, misery, and

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human suffering due to the impacts of climate change. The adverse and social effects of climate change must be responded to appropriately. Social workers have an essential role in realizing and developing a social welfare business system to sustain human life. Social workers who are experts in the field of social welfare must be involved and play an active role in discussing climate change issues and finding solutions to problems based on environmental potential. Based on the ecological paradigm, social worker associations in various parts of the world are trying to carry out movements to protect the welfare and lives of environmental communities. Humans must realize that life is part of an ecological ecosystem, and environmental diversity must be preserved to support the social welfare business system. Social workers have an important role related to environmental issues (Dominelli, 2011). Green social workers who play a role in social empowerment through social entrepreneurship can provide beneficiaries with an understanding of climate change in human life. Social workers also encourage sustainable use and consumption of energy. Social workers can raise awareness to protect the environment for future generations. Their involvement can be done through participation in designing solutions using the greenhouse model, namely reducing carbon emissions, advocating for conservation care, and preserving the environment. Social workers change the attitudes of beneficiaries toward their environment. Social workers have challenges because the attitude shown by beneficiaries is to recognize that climate change is essential but refuse to make lifestyle changes. He still likes to live extravagantly and spend various resources to fulfill the pleasures of his life. The next challenge is the attitude of beneficiaries who are indifferent to their surroundings. They live well but do not care about mitigating climate change. Therefore, social workers can increase their awareness of climate change issues related to human life. They think that climate change has nothing to do with their lives. Although it does not harm the environment and society, social workers must be aware of this attitude.

12.4 Limitation of the study This chapter is limited to answering problems in five regions and cannot be generated for all parts of Indonesia. The results can be applied in other areas if the situation and conditions are the same. A preliminary assessment of the condition of the community is critical to determine the type of community. The reliability of this study is difficult to use as a standard measure because of the community’s unique and unstable social situation. The Covid19 pandemic caused their business to experience setbacks due to various physical problems. Indonesia has implemented a lockdown policy to reduce the impact of the spread of the Covid-19 pandemic. Policies made by the government will, of course, also impact small business actors so that their business activities are reduced or even temporarily suspended.

12.5 Recommendations All government, private, and community stakeholders must be aware of the need for mitigation and adaptation. Ways to reduce the impact of climate change, among others, are by saving energy and reducing the waste of electrical power and fossil fuels. This should be a

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movement of all levels of society to save energy. The transfer of environmentally-friendly energy needs to be done to slow global warming. Women are one of the most vulnerable groups affected by climate change. So government programs should be encouraged to accommodate the different needs of each gender group to access, participate, control, and benefit from policies related to climate change. Social entrepreneurship programs should be expanded so that women can actively mitigate and adapt to climate change. Saving the earth with a green economy through social entrepreneurship programs implemented by the Indonesian government can improve the welfare of women affected by climate change. This can be seen from the increase in the income of the beneficiary families. Some things that need to be improved are stronger regulations as a reference for local governments to develop social entrepreneurship programs independently. Social capital and networks significantly enhance beneficiaries’ social mission, which can help alleviate poverty.

12.6 Conclusion The use of fossil fuels, deforestation, and air conditioning are the causes of global warming. These activities co-occur, and the impact accumulates into global warming. Climate change has been felt by people in Indonesia, especially women, as a vulnerable group. Women face declining incomes. Climate change adaptation for women must be a serious concern. Women are a group that is vulnerable to climate change in Indonesia. A green economy is an option because it can increase welfare. Social entrepreneurship can help create jobs and improve economic well-being. Women involved in social entrepreneurship are provided with training and income-generating skills. The results of the evaluation of the green economy program through social entrepreneurship there are still several indicators that must be improved. This program requires additional time for the women’s empowerment program to run effectively. This program involves a selection of potential beneficiaries because not all women have a business spirit. Some women are more interested in becoming workers. Some women feel that they need more means to develop their businesses. It is necessary to classify business capital requirements based on the turnover of business capital that has been carried out. Social workers must be able to enter the daily lives of women who are beneficiaries. The facilitator is the central role of social workers in developing green economy programs through social entrepreneurship. Beneficiaries’ awareness of the green earth’s importance must continue to increase. Understanding climate change’s impact on human life must also be increased. In addition to empowering women, green social workers must change policies by advocating. Through a green social work approach, it aims to realize a sustainable human life.

Acknowledgments We thank the Centre for Social Welfare Research and Development for supporting this research in 2021. We also thank Ms. Yanuar Farida Wismayanti, Ph.D., who guided this paper’s writing. Thanks to Agus Budi Purwanto, Badrun Susantyo, Ita Konita, Muhammad Belanawane Sulubere, and Delfirman, as data contributors from various regions.

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2. Climate change, social response and resilience

C H A P T E R

13 Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala Jayarajan K and Dhanya Punnoli Department of Geography, Govt College Chittur, Palakkad, Kerala, India

13.1 Introduction There is widespread acceptance that climate change is an emergency that affects all economic and social spheres. It disturbs agriculture, economic growth, and human well-being through both short- and long-term adverse impacts (Barrett & Santos, 2014; Hoddinott, 2006; Hsiang & Jina, 2014). These effects are diverse; some groups, such as households, towns, or nations, have a larger likelihood of exposure, while others are better able to withstand shocks and recover. Hazard consequences on agriculture can further exacerbate risk factors and deepen poverty traps. In the absence of markets for insurance and credit, these impacts can lead farmers to choose low-return agricultural technologies to maintain stable livelihoods, often at levels below the poverty line (Barrett & Santos, 2014; Carter et al., 2007; Dercon & Christiaensen, 2011). Repeated exposure to climate hazards can undermine current and future coping capacity (Barrett et al., 2016; Duncan et al., 2017). In this context of growing awareness of the damaging interaction between climate hazards, response, coping capacity, and short- and long-term growth, the policy of “resilience building” has emerged as a possible antidote. Its prevalence is increasing in the development of discourses and policy (Department for International Development (DFID), 2011). In an agrarian nation like India, the majority of farmers heavily rely on temperature and precipitation distribution available during the growing season for agriculture production (Auffhammer et al., 2012). A warmer world may lead not only to lesser agriculture output and crop failures but also to more farmers’ suicides even in a state like Kerala. Rice is one of the most important cereal crops. More than two thousand varieties of rice are commercially grown throughout the world. It is the staple food of more than three

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13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

billion people, mainly in Asia. Apart from that certain varieties are cultivated for medicinal purposes. Rice, a water-intensive crop in general is highly sensitive to climatic aberrations (Agarwal, 2008). It is estimated that the yield of Thai rice is expected to decline by about 18% in the 2020s because of alterations in temperature and rainfall cycle and its cascading negative impacts on soil quality, pests, and diseases due to climate change. Rice cultivation under anaerobic flooded conditions is a major driver for methane emissions (Saseendran et al., 2000). Process-based crop simulation models are used by many researchers to project climate change impacts on rice crops. The yield projections using the CERES-Rice model showed that there will be a decline of 13.1% in the productivity of major ruling rice varieties by the 2050s if adaptation measures are not in place (Ramachandran et al., 2017). The Ayurvedic Treatise (Indian Materia Medical) records show the existence of many medicinal rice varieties in India apart from the common traditional varieties (Das & Oudhia, 2003). Navara is one such ancient rice variety (belonging to the family Oryza) that has been cultivated in Kerala for its medicinal properties for over 2500 years. However, apart from periodic reports on the agronomic evaluation of Navara (Menon, 2004), no further information on its linkages with climate or social adoption and economic significance is available in the literatures. Navara is an upland crop cultivated in a water-stressed environment (Menon, 2004). Navara paddy has comparatively low yields but is found to be resistant to pests and diseases. This research is an exploration of how climate change is perceived as a farm-level risk by the traditional Navara rice farmers in the Palakkad district. Navara is an indigenous rice variety that is famous for its medicinal and nutritional properties. Kerala’s Navara rice has been awarded Geographic Indication certification/Geographical Indication Tag (GI) in 2007, and since then it has received international recognition. This study sheds light on the effects of community resilience and risk appraisal on adaptation behavior within the context of weather and climate vagaries. There may be physiological, morphological, and biological impacts due to climate or weather aberrations (Fig. 13.1).

13.2 Study area Palakkad district is situated at the foot of the Western ghats, which is the gateway to Kerala from the north. Palakkad district is placed between 10 20’ N to 11 14’ N latitude and 76 20’ E to 76 54’ E longitude Fig. 13.2. The district shares borders with Malappuram district in the North and Northwest, Thrissur in the South and Southwest and Coimbatore district of Tamil Nadu in the East. Out of the 14 districts of Kerala, Palakkad is one of the five districts which do not have a coastline. Its geographical position, historical background, rural nature, educational status, tourist attractions, and above all, developmental activities are wide and varied. According to the Kerala Gazetteer, the physiography of Palakkad district is grouped into three divisions, namely, the lowlands (areas having heights lower than 30 meters), midlands (areas having heights between 30 and 300 m), and highlands (areas having heights above 600 m). Accordingly, the district of Palakkad is divided into three physiographic divisions: the lowlands, midlands, and highlands. The Lowland region occupies

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13.2 Study area

FIGURE 13.1

Climate induced farm risks.

Rainfall Aberraons/ Hail storm

Climate induced Farm Risks

Warmer environs

Pest/Insect aacks

Cyclonic storms/ Winds

Soil stress/ degradaon about 48% of the total area, and midlands region is about 33% of the total area and consists of valleys and plains. The remaining 19% of the area is under the highlands consisting of high mountains, extensive ravines, dense forests, and also the Palakkad gap region. In the Palakkad district, soils are dominated by lateritic soils and alluvial loams. Due to other local physiographical conditions, soil types vary. Based on the physicochemical properties and morphological features, soils are classified by the Soil Survey Unit of the Department of Agriculture in Kerala State. The district soils are grouped into four broad groups. These are the Laterite soils, Virgin forest soils, Black cotton soils, and alluvial soils. Laterite soils are seen in major parts of Ottapalam, Alathur, Chittur, and Palakkad taluks. These are the most predominant soil types in the midland and the gap areas. Laterites on high grounds are more compact when compared to low-lying areas. Virgin forest soils are seen in Mannarkkad block and in the forest areas. They are rich in humus and organic matter. Black cotton soil is found in Chittur and Attappady Valley of the Mannarkad Taluk, which is used for cotton cultivation. They exhibit mud cracks and have high water retaining power. Alluvial soils are found along the banks of the Bharathapuzha and its tributaries. The valley portion is filled with deposits composed of talus and scree materials (“Ground Water Information Booklet of Palakkad District,” 2013).

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FIGURE 13.2

Location map of Palakkad district. 2. Climate change, social response and resilience

245

13.2 Study area

13.2.1 Climate profile of the study area As per Koppen’s classification, the climate of Palakkad district experiences a humid type of climate. The district receives maximum rainfall during the southwest monsoon followed by the northeast monsoon. The other months receive considerably low rainfall. The temperature is pleasant from December to February. The annual rainfall varies from 1757.6 to 2849.5 mm based on a long-term normal. The district receives on average 2348 mm of rainfall, annually. Major rainfall is received during June to September in the southwest monsoon (71%). The northeast monsoon contributes about 18% of the rainfall. The western part of the district, around Mannarghat, receives the maximum rainfall (2849 mm) whereas the rain shadow area of Chittur in the eastern part receives the minimum rainfall (1758 mm). The temperature of the district ranges from 25 C to 40 C. The maximum temperature recorded at Palakkad was 43 C in 2009. The district has got two types of climates. Ottapalam, Alathur, and Mannarkkad taluks have a climate similar to that of other districts of Kerala whereas Palakkad and Chittur have rather a dry climate similar to neighboring Tamil Nadu state (“Ground Water Information Booklet of Palakkad District,” 2013).

13.2.2 Administrative divisions of the district Palakkad is one of the largest revenue districts of Kerala. Palakkad district consists of two Revenue divisions: Ottappalam and Palakkad (Table 13.1). Of five taluks, Palakkad, Alathur and Chittur taluks form the Palakkad Revenue division and Ottappalam and Mannarkkad taluks form Ottappalam Revenue division. These five taluks altogether contain 163 villages. There are 13 Development blocks and four Municipalities in the district. Ninety-one Panchayats are grouped to form the thirteen blocks. TABLE 13.1

The administrative divisions within the surveyed areas of the study area.

Revenue divisions

Taluks

Community development blocks

Palakkad

Alathur

Alathur Kuzhalmannam

Chittur

Chittur Kollengode Nenmara

Palakkad

Palakkad Malampuzha

Ottappalam

Ottappalam

Ottappalam Pattambi SrikrishnapuramThrithala

Mannarkkad

Attappady Mannarkkad

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13.3 Methods This research tries to understand the climate change perceptions of traditional Navara rice farmers in the Palakkad district. The present study focused to analyze the spatial distribution of farmers living with Navara cultivation in Palakkad and their livelihood and environmental condition, community resilience, and risk appraisal on adaptation behavior. On the basis of the primary data collected from the farmers in the selected five Taluks in Palakkad district statistical analysis was carried out. The data was gathered from a primary survey of Navara farmers. The data gathering is done according to a schedule. The schedule has four subdivisions and 76 variables, including: (1) Farmer livelihood in Navara; (2) Variability of climate and community resilience; (3) Navara cultivation and marketing issues; (4) Risk appraisal on adaptation behavior (Table 13.2). In addition, the TABLE 13.2 Data set of selected variables of Navara farmers in Palakkad district. S. no Name of variables

S. no

Name of variables

I

Livelihood of Navara farmers

13

Navara cultivation lasted 5 years

1

Age group of Navara cultivators (20
40)

14

Navara cultivation below 5 years

2

Age group of Navara cultivators (above 40)

II

Variability of climate and community resilience

3

Family members: above 4

15

Rainfall decreasing last decades

4

Farmers religion: Hindu

16

Heat stress

5

Farmer religion: Christian

17

Water scarcity

6

Farmers education: up to High school

18

Groundwater decreasing by one meter

7

Farmers education: above higher secondary

19

Temperature is increasing

8

Occupation as Farmer

20

Drought issues affect Navara cultivation

9

Self-employed

21

Flood-affected Navara cultivation

10

Income above 50,000

22

Seasonal flood affect Navara cultivation

11

Marginal Navara Farmers

23

Flood impact on Navara cultivation

12

Main Navara farmers

24

Drought impact on Navara cultivation

III

Issues of Navara cultivation and marketing

25

Production cost increasing Navara cultivation

39

Navara Product Demand in the market

26

Navara Seed cost raised

40

Seasonal demand from the market

27

Application of fertilizer in Navara farm

41

Storage problem of Navara

28

Application of biofertilizer in Navara farm

42

Getting NGO assistance to Navara farmers

29

Weeding

43

SHG assistance to Navara farmers

30

Labor issue in Navara cultivation

44

Loan to Navara farmers

31

Mechanization

45

Crop insurance for Navara farmers (Continued)

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13.3 Methods

TABLE 13.2

(Continued)

S. no Name of variables

S. no

Name of variables

32

Wage level raised in Navara cultivation

46

Mortgaged land

33

Farm insurance

47

Sold livestock

34

Farmers have a social network

48

Diversified household activities engaged Navara farmers

35

Farmers collective group of Navara

49

Government relief to farmers

36

Organic farmers group

50

Reduced food sold market

37

Access to market

51

NGO assistance

38

Navara cultivation is Profitable

IV

Risk appraisal on adaptation behavior

52

Changing agricultural patterns due to rainfall variation

65

Floods affect Navara cultivation

53

Changing biofertilizers in farm

66

Wind Damage to Navara cultivation

54

Using Jeevamrutham in Navara farm

67

Higher Temperature Heat Stress

55

Following traditional practices in Navara farm

68

Pests and Diseases

56

Strategy used for marketing Navara products

69

Hailstorm

57

Availability of Navara seeds

70

Animal impact on Navara cultivation

58

Cost of production/expenses increasing

71

Peacock impact on Navara cultivation

59

Pesticides/fertilizer costs Increased

72

Wild animals’ impacts on Navara cultivation

60

Weeds (rise/fall)

73

Insecticides

61

Drought (low rainfall)

74

Soil fertility loss on the farm

62

Monsoon Onset failure

75

Labor availability

63

Wet conditions (heavy rainfall/cyclones)

76

land use change impact Navara cultivation

64

Rainfall (delayed/fluctuation/untimely)

number of Navara farmers who have been registered or interviewed in the five Taluks of Kerala’s Palakkad district is taken into account. Field research was carried out between June 2020 and December 2021 in the selected Navara-cultivated farmers in the Palakkad district of Kerala. We conducted in-depth interviews and focus group discussions with representative farmers and key stakeholders at different levels. The study area was limited to the five major Navara cultivating taluks in Palakkad districts. Thirty interviews were carried out among the small/marginal and large Navara cultivators in five different taluks in Palakkad in order to understand the Navara cultivation and their resilience in climatic adaptation in the state of Kerala. Primary data was collected to get a detailed picture of the livelihood condition of farmers and to understand the current adaptation status at micro level. Multivariate analysis of variance

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(MANOVA) in IBM SPSS.20 is used to put in order the variables regarding the living environmental condition of the Navara cultivators. The entire interrelated variable in the data matrix of (5x76) is used for statistical analysis. MANOVA in SPSS is concerned with examining the differences between groups and the group differences across multiple dependent variables simultaneously. The statistical results are effectively highlighted to build up the cluster of variables for understanding the resilience of Navara cultivation.

13.4 Results and discussions Principal components analysis is among the oldest and most widely used multivariate techniques. It is originally introduced by Pearson (1901) and independently modified by Hotelling (1933). The basic idea of this method is to describe the variation of a set of multivariate data in terms of a set of uncorrelated variables, each of which shows a particular linear combination of the original variables. The objective of factor analysis is to extract the underlying “factors” that explain correlations among a set of variables. It essentially summarizes a large number of variables with a smaller set of derived key variables. The extraction of Table 13.3 illustrates the total variance explained by the variables included in the analysis. The distribution of Eigenvalues (EV) and the total percentage and cumulative percentage variance of each one of the factor solutions is presented in it. It is pertinent to note that four factors with 6.55 rotated EVs explain 100% of the variance in the data set.

13.4.1 Extraction method: principal component analysis The Eigenvalue of 6.55 is considered as a yardstick to extract four factors and the same are resolved owing to the fact that almost all the variables got loaded with these factors. The four factors explain altogether 100% of the total variance with each one of its values ranging from 9.48% to 37.53%. Although the components were selected as four factors, it is pertinent to note the fact the first two primary factors that have more than 23.71 Eigenvalues alone totally explain 71.26% of the total variance whereas the remaining two factors recorded with more than 6.54 Eigenvalues altogether explain only 9.49% of the total TABLE 13.3 Total variance explained. Extraction sums of squared loadings

Initial eigenvalues

Rotation sums of squared loadings

% of Cumulative Component Variance %

Total

% of Cumulative Variance %

Total

% of Cumulative Variance %

Total

1

33.576

48.660

48.660

33.576

48.660

48.660

25.900

37.536

37.536

2

16.891

24.480

73.141

16.891

24.480

73.141

23.271

33.726

71.262

3

13.200

19.130

92.270

13.200

19.130

92.270

13.276

19.240

90.502

4

5.333

7.730

100.000 5.333

7.730

100.000 6.554

9.498

100.000

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13.4 Results and discussions

variance. In fact, among the first four primary factors, the first component alone with its Eigenvalue of 37.53 explains the highest amount of total variance that is about 25.90% followed by the rest of the factors that explain the total variance of around the second component 23.71% third 13.27% and fourth (6.55%). All these four variables explain 100% of the total variance of the data set.

13.4.2 Major factors and their variable loadings The first component which explains 37.53% of the total variance is significantly loaded with 39 variables (Table 13.4). The factor loading with positive values of 31 variables lies between (0.98 and 0.53) in both directions. While the negative value of 8 variables are ranging from 20.57 to 20.98 EV. TABLE 13.4

Component I: Dimension of climatic and economic problems of Navara cultivators.

S. no

Variables

Eigenvalues

1

Navara cultivation lasted 5 years

.988

2

Cost of production Expenses increasing in Navara cultivation

.982

3

Diversified household activities engaged Navara farmers

.975

4

Drought impacts Navara cultivation

.957

5

Wage level raised in Navara cultivation

.949

6

Using Jeevamrutham in Navara farm

.947

7

Pests and Diseases

.933

8

Hindu Navara cultivator

.932

9

Navara Crop insurance

.910

10

Labor issue in Navara cultivation

.904

11

Animal impact on Navara cultivation

.849

12

Peacock impact on Navara cultivation

.808

13

Twenty to forty age group of cultivators

.799

14

Social network in Navara farmers

.797

15

Navara Seed cost raised

.787

16

Heat stress impact cultivation

.773

17

NGO assistance in Navara cultivation

.742

18

Drought issues in Navara cultivation

.716

19

Navara Product Demand in the market

.696

20

Rainfall (delayed/fluctuation/untimely) impacted Navara cultivation

.655

21

Members of Navara farmers collective group

.644 (Continued)

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13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

TABLE 13.4 (Continued) S. no

Variables

Eigenvalues

22

Following traditional practices of Navara cultivation

.639

23

Flood impact Navara Cultivation

.638

24

Taken Farm insurance in Navara cultivation

.637

25

Monsoon Onset failure impact Navara cultivation

.622

26

Loan is taken for Navara cultivation

.620

27

Weeds (rise/fall)

.613

28

Labor availability in Navara cultivation

.609

29

Drought (low rainfall)

.575

30

Main Navara farmers

.574

31

Wild animals’ impact in farm

.537

32

Marginal Farmers

2 .574

33

Christian

2 .593

34

Navara Damage due to Wind

2 .639

35

Insecticides

2 .732

36

Farmers Income above 50000

2 .840

37

Navara farmers age group above 40

2 .898

38

Wet conditions (heavy rainfall/cyclones)

2 .903

39

Navara cultivation below 5 years

2 .988

13.4.2.1 Component I: Dimension of climatic and economic problems of Navara cultivators The cluster of variables indicates that farmers are engaged in traditional Navarra cultivation mainly for the last 5 years. The cost of Navara production has increased, hence they had to engage themselves in diversified livelihood options. The major threat in Navara cultivation is drought, flood, and monsoon failures. The farm wage level has also increased tremendously. The field surveys revealed that farmers are using organic fertilizers like “Jeevamrutham” on their farm for better production. In recent times wild boar and peacocks cause extensive damage to Navara production. Navara farmers have NGO support and they have a social network to promote the Navara products. The negatively loaded variables from the analysis indicate that more marginal farmers who are in the young age group are engaged in cultivation below 5 years. It focuses that youth and new farmers who have started to practice Navara cultivation. It is a good trend in the future. All the positively loaded factors are mostly confined to the community resilience and risk appraisal on the adaptation behavior of the Navara farmers. Though the association is highly interrelated, all 31 variables really explain the personal status of the farmers, and impact of

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13.4 Results and discussions

climatic change on Navara cultivation. Variables on the role of NGOs and Government toward supporting Navara cultivation are clustered and explained in the multidimension of community resilience and risk appraisal on adaptation behavior of Navara cultivators. 13.4.2.2 Component II: Dimension of strategies to improvise cultivation and marketing of Navara The second component which explains 33.72% of the total variance is significantly loaded with 38 variables. The factor loading positive values of 32 variables lies between 0.98 and 0.50 in both directions as shown in Table 13.5, whereas six variables with negative loadings are added with 2.50 to 2.96. Navara farmers adopt different strategies to sell their by-products. In recent times, these Navara by-products are accessible to not only TABLE 13.5

Component II: Dimension of strategies to improvise cultivation and marketing of Navara.

S. no

Variable

Eigenvalues

1

Strategy for marketing Navara products

.980

2

Navara products are Access to market

.974

3

Soil fertility loss in the farm

.941

4

Mechanization in Navara farm

.932

5

Navara cultivation is Profitable

.921

6

Navara rice Storage is problem

.909

7

Education of Navara farmers is above High school

.882

8

Navara farmers getting assistance from SHG

.872

9

Navara has Seasonal demand from the market

.869

10

Groundwater decreasing one meter in the farm region

.863

11

Navara farmers getting assistance from NGO

.814

12

Main Navara farmers

.813

13

Navara (cultivation) Production cost increasing

.777

14

Navara farmers have Loan for cultivation

.775

15

Navara farmers facing Wild animals problems in the farm

.755

16

Navara farmers used Biofertilizer

.746

17

Navara farmers following traditional practices

.724

18

Flood impact Navara cultivation

.720

19

Navara farmers have collective group

.692

20

Navara has demand in the market

.673

21

Member of organic farmers group

.644

22

Labor availability in Navara cultivation

.616 (Continued)

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13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

TABLE 13.5 (Continued) S. no

Variable

Eigenvalues

23

Navara seed cost raised

.614

24

Monsoon onset failure affects in Navara cultivation

.609

25

Navara cultivators Family members above four

.585

26

Navara cultivators have a social network

.581

27

Rainfall (delayed/fluctuation/untimely) in the farm

.579

28

Availability of Navara seeds

.570

29

Temperature is increasing in the Navara-cultivated areas

.564

30

Navara farmers getting NGO assistance

.546

31

Peacock impact on farms

.502

32

Navara farmers’ Income above 50,000

.498

33

Rainfall decreasing last decades on the farm

2 .509

34

Navara farmers Changing biofertilizers

2 .680

35

land use change impact Navara cultivation

2 .686

36

Marginal Navara farmers

2 .813

37

Above higher secondary education of Navara farmers

2 .882

38

Water scarcity in the Navara farm

2 .960

local markets but also to online markets. Due to this, farmers are finding it difficult to cater to the growing demands from online markets, even after a good harvest. The demands for Navara products are seasonal and it skyrockets during July /August months (“Karkkidaka”), hence farmers need to seek NGO’s support to cater to the seasonal demand. Majority of the Navara farmers are the members of organic farm communities. Farmers had an opinion that complete monsoon failures, sudden rainfall fluctuations, rise in growing season temperature, and animal conflict as the major threats. Decreasing rainfall distribution during the last decade and changing biofertilizers whereas water scarcity in the Navara farm is negatively loaded. A total of 32 variables are included under this dimension explaining the characteristic of the improvising cultivation and marketing of Navara products. Hence, this component is named a dimension of the strategy of improvising cultivation and Marketing of Navara. 13.4.2.3 Component III: Dimension of organic farming for climatic resilience The third component accounts for 19.24% of the total variance with positively loaded nine variables (0.99
0.5) and six variables with negative loadings (20.86 to 20.58) (Table 13.6). It shows that the majority of Navara cultivators’ primary occupation is farmers. The constraints that they are facing, i.e., rising fertilizer costs, climate variabilities, and sudden flash floods, also make problems for cultivation. Farm insurance, weeds, seasonal

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13.4 Results and discussions

TABLE 13.6

Component III: Dimension of organic farming for climatic resilience.

S. no

Variable

Eigenvalues

1

Reduced food sold market

.993

2

Occupation as farmer

.977

3

Pesticides/fertilizer costs increased

.957

4

Higher temperature heat stress

.952

5

Flood-affected Navara cultivation

.872

6

Availability of Navara seeds

.814

7

Farmers’ religion as Christian

.800

8

Navara farmers have organic group

.676

9

Drought (low rainfall) affects Navara cultivation

.491

10

Navara farmers family members are above four

2 .582

11

Farm insurance

2 .689

12

Weeds (rise/fall)

2 .763

13

Floods impact in cultivation

2 .774

14

Seasonal flood impact in cultivation

2 .820

15

Weeding

2 .860

flood impact in cultivation, and weeding are negatively loaded to this component. All of these 15 factors in the variable cluster explain the Navara cultivator’s perception of the impact of climate change on Navara cultivation and organic cultivation. Farms built with healthy soil and crops enable farmers to adapt to a changing climate. Therefore, this component is rightly called a dimension of organic farming for climatic resilience. 13.4.2.4 Component IV: Dimension of vulnerability of Navara farming due to land use and climate change The fourth component exhibits a total variance of 9.49%, contributed by eight variables (Table 13.7). The positive loadings of six variable factors lie between 0.5 and 0.75 in both directions. The remaining two negative variables are the use of insecticides in the Navara and the rise in temperature on Navara farm. This component clearly indicates that Navara cultivation is vulnerable due to various factors such as rainfall variability in last decade, land use changes, wind, drought, and heat waves. Thus, comprehensive planning is required to sustain Navara cultivation for future. In this dimension, application of insecticide is negatively loaded. Whereas the rise in temperature on the farm is also negatively loaded. This indicates that Navara farmers largely engaged in organic farming. For this reason, this component is conveniently stated as dimension of vulnerability in Navara farming due to land use and climate change.

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13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

TABLE 13.7 Component IV: Dimension of vulnerability of Navara farming due to land use and climate change. S. no.

Variable

Eigenvalues

1

Rainfall decreasing last decades

.755

2

land use change impact Navara cultivation

.694

3

Wind damage in Navara cultivation

.688

4

Drought issues in Navara cultivation

.596

5

Heat stress issues in Navara cultivation

.527

6

Age group of Navara farmers is twenty to forty

.507

7

Application of insecticides in the Navara farm

2 .543

8

Temperature is increasing in Navara farm

2 .725

TABLE 13.8 Palakkad: Taluk-wise distribution of the factors and composite index. Taluk

Factor I

Factor II

Factor III

Factor IV

Composite index

Ottapalam

0.86295

2 1.33429

0.75247

0.3298

0.61

Chittur

1.07142

1.37372

0.27501

2 0.29889

2.42

Palakkad

0.0141

2 0.10127

2 1.60353

0.78628

2 0.9

Mannarkkad

2 0.65103

2 0.36932

2 0.26917

2 1.60228

2 2.8

Alathur

2 1.29744

0.43117

0.84522

0.78508

0.76

13.5 The spatial attributes of the multidimensional characteristics of Navara cultivation Integrated Approach for the Sustainable Agriculture Planning This section explains the spatial distribution of factorial scores of the multidimensional Navara cultivation. The component scores derived for each observation areal unit of the selected Taluks in the Palakkad district shows the spatial variation in the respective factor scores. The positive and negative scores pertaining to each component show the levels of sustainable Navara cultivation in the Taluks of Palakkad district. The score value above 1 on the positive side denotes high sustainability of Navara cultivation. Whereas a value of 0 to 1 denotes medium levels of sustainability. The negative value of 2 1 to 0 indicates low range of sustainability.

13.5.1 Spatial pattern of multidimensional factors with Navara cultivators In the present analysis, all the components are selected based on the dimension related to Navara farmers in Palakkad (Table 13.8). All these clustered variables are represented to understand the spatial pattern of the sustainable organic Navara farming (Figs. 13.3
13.6).

2. Climate change, social response and resilience

13.5 The spatial attributes of the multidimensional characteristics

FIGURE 13.3 Dimension of climatic and economic problems of Navara cultivators.

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256

13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

FIGURE 13.4

Dimension of strategy of improvise cultivation and marketing of Navara.

2. Climate change, social response and resilience

13.5 The spatial attributes of the multidimensional characteristics

FIGURE 13.5 Component III: Dimension of organic farming for climatic resilience.

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257

258

FIGURE 13.6

13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

Component IV: Dimension of vulnerability of Navara farming due to land use and climate

change.

2. Climate change, social response and resilience

13.6 Discussion

259

This will be helpful to understand the problematic region for sustainable agriculture. Under the dimension of the climatic and economic conditions of Navara cultivators in Palakkad district shows that Chittur 1.07 ( . 1) ranks the highest. Ottapalam and Palakkad Taluks rank medium level of Navara cultivation (0
1), while Mannarkkad and Alathur secure the lowest concentration. The spatial disparity in the dimension of strategies of improvising cultivation and Marketing of Navara show the high positive factor score reported only in Chittur . 1, while medium concentration is recorded in Alathur Taluks (0
1). Whereas very low concentration of the factor score in the Taluks of Palakkad, Mannarkkad, and Ottapalam ( . 2 1). As far as the spatial disparity in the dimension of organic farming for climatic resilience is concerned, Alathur, Ottapalam, and Chittur scores were highest, while very low scores were represented in Mannarkkad and Palakkad taluk. Spatial disparity in the dimension of vulnerability of Navara farming due to land use and climate change reported high positive scores in the Taluks of Palakkad, Alathur, whereas low range was represented in Ottapalam. At the same time, very low scores are represented in the Taluks of Chittur and Mannarkkad.

13.5.2 Composite index: spatial pattern of resilience to natural hazards among Navara farming communities Four factors have shown vivid descriptions of the spatial pattern of the Navara cultivators in Palakkad. These four factors are generated separately on the basis of the relative importance of their score values obtained from the database. Hence, the overall Navara cultivation factor is explained through the four factors taken into consideration to consolidate the selected taluks in Palakkad district. The sum of factor score value . 11 in positive direction denotes very high sustainability of Navara cultivation. The value of 0
1 indicates medium sustainability of cultivation, while the value of 0 to 21 stands for low sustainability of cultivation. As shown in Table 13.8 and Fig. 13.7, composite score shows that Chittur taluk represents high composite index score. While Alathur and Ottapalam’s taluk scored with medium composite index, whereas low composite score was noted in Palakkad and Mannarkkad taluks.

13.6 Discussion Even though Navara is a climate-smart rice variety, it is on the brink of extinction due to low yields, and high cost of production. Its present use is limited to Ayurvedic medicinal preparations/treatments only. Navara a wild variety of rice cultivated in Kerala since ancient times are used mainly for ayurvedic medicinal purposes. Navara is an highland crop and cultivated in a water-stressed environment. Navara paddy has a low yield and is found to be moderately resistant to pests. This indigenous variety if promoted can lead to support sustainable development goals (SDGs). It is drought and pest tolerant hence its promotion can come under SDG 13. It can be treated as a fortified cereal, which is used as a supplementary diet for the underweight challenges in children. Hence, it can come under SDG 3, good health and well-being. It is a known fact that three of the eight

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260

FIGURE 13.7

13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

Composite index: spatial pattern of resilience to natural hazards among Navara farming

communities.

2. Climate change, social response and resilience

13.6 Discussion

261

Millennium Development Goals were directly focused on health and two others, on nutrition. Hence, promoting Navara cultivation under the purview of climate change has multiple values. It is welcoming to note that over the last few decades, the Navara rice chain has changed as new actors have entered the system, and there has been a change in consumption patterns. Hence, it may be a booster to the livelihood security of the farmers. Though the productivity of this indigenous crop is low compared to the major ruling varieties, the cost-benefit analysis of Navara production reveals the fact that it fetches a comparatively reasonable price and consistently 3
4 times higher price than any other ordinary rice varieties to date. The current market value of l kg of Navara rice is Rs. 220
250 and rice grain is Rs. 100 in many localities of Kerala, whereas ordinary rice fetches a mere value of 50/Kg. Apart from the above, there is an increasing pharmaceutical demand for original Navara rice. Navara is widely used at the household level for a broad range of health concerns. Cultivation of it even under a small patch can fetch better income for the farmers. Hence, it contributes to India’s prime minister’s prestigious scheme’ doubling of farmers’ Income too.

13.6.1 Way forward Conservation and promotion of Navara can be treated as crop-specific/ecosystem-based Adaptation (EbA) that encompasses safeguarding, sustainable management, and restoration of ecosystems. Better cost-effective solutions need to be provided to support the poor and marginal farmers to adapt to the impacts of climate change and weather vagaries. In south India, M S Swaminathan Research Foundation’s (MSSRF) “Rice Seed Village Program” promotes on-farm conservation of promising traditional rice varieties including Navara in the Wayanad district of Kerala. Under this conservation program, awareness, farmers’ training, and capacity building are conducted. Advanced technologies are also introduced to enhance crop yield, and establish market linkage and value addition. The National Medicinal Plants Board (NMPB) was set up to conserve and promote them as medicinal plants form the major resource base of our indigenous health care. Considering it as a climate-smart crop variety and its multiple usage, the Ministry of AYUSH, Government of India under its Centrally Sponsored Scheme of National AYUSH Mission (NAM). Under ‘Medicinal Plants’ component can encourage market-driven cultivation of it in identified cluster/zones in rice growing states. https://dst.gov.in/farmers-turnsaviours-traditional-rice-varieties. Different Ministries/Departments like Environment & Forests, Agriculture, Science & Technology and Commerce (MoEF&CC and DoE&FW, DoC&FT) can come together for its promotion it as currently Indian share of the world herbal trade is less than 1%. Even here, the export of herbal products is largely in the form of raw herbs with 2/3rd of the export basket comprising raw herbs. AYUSH To make cultivation lucrative, it is necessary to support the farmers—both technically and financially. Unless the world draft and carry out local climate change adaptation strategies and build adaptive capacity on the ground, there may not be any sustainable crop production.

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13. Resilience to natural hazards among the Navara rice farming communities in Palakkad, Kerala

13.7 Conclusion In this case study, multivariate statistical techniques were used to evaluate spatial and temporal variations in Navara cultivation in sampling sites. Conserving Navara and the cultural traditions associated with this rice is valuable for a number of reasons. From a geographical and biodiversity perspective, this rice provides potentially useful genetic material for breeding due to its medicinal properties, as well as its pest and disease resistant qualities. The cultivation and use of Navara contain an important cultural component in the study region and it is a solution to climate change-related livelihood issues. The majority of Navara cultivators are farmers by profession. They encounter many difficulties, one of which is the high cost of organic farming. In recent times, flash floods have also caused issues with agriculture. Organic farming for climatic resilience shows how the Navara agriculture is vulnerable because of changes in rainfall over the past ten years. Wind, drought, heat waves, and changes in land use all have an effect on the production of Navara. Therefore, careful planning is necessary to ensure the viability of Navara agriculture in the future. Based on multivariate analysis, composite indices are created, showing that one Taluk has a high composite index (Chittur), followed by two Taluks—Alathur and Ottapalam—with a medium composite index and two Taluks— Palakkad and Mannarkkad—with a low composite index.

Conflict of Interest The authors declare that they have no conflict of interest.

Acknowledgment The authors would like to express their sincere gratitude to the Kerala State Higher Education Council, the Government of Kerala (KSHEC, GoK) for their financial support to conduct the research.

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C H A P T E R

14 Livelihood constraints and socioecological loops: household drought coping survival strategies in rural plateau tracks of eastern India Susmita Sengupta1 and Sanat Kumar Guchhait2 1

Department of Geography, Rabindra Mahavidyalaya, Champadanga, Hooghly, West Bengal, India 2Department of Geography, University of Burdwan, Bardhaman, West Bengal, India

14.1 Introduction Globally, agriculture dominates land use, including significant economic, social, and cultural activities as well as offering a wide range of ecosystem services. However, because of its nature, agriculture is still quite susceptible to changes in the climate. In India, the great majority of smallholder farmers depend on rainfed agriculture for a living, and they are frequently affected by the whims of the weather and climatic conditions (Gautam, 2006). Variability in rainfall is one climatic aspect that significantly affects both the economy and means of subsistence (Gautam, 2006; Hellmuth et al., 2007) of the majority of Indian households. Variability and unpredictability of the climate represent a risk that can severely limit alternatives and opportunities (Hellmuth et al., 2007) for millions of poor people in India. In the last decades, rural households of plateau tracks of the Chota Nagpur plateau of Eastern India experienced severe to extreme droughts with extensive crop failure and food crises. Over 89% of people suffered from high levels of food insecurity as a result of this drought. Lying on the fag-end of the Chota Nagpur plateau Purulia represents an area of particularly low agricultural productivity and a high incidence of severity of poverty among the rain-fed areas in Asia. Purulia is one of the administrative districts of West Bengal chronically affected by meteorological drought conditions (Palchaudhuri & Biswas, 2013). The north-western and south-western portions can be labeled as extreme droughtprone areas of the district. High seasonality of rainfall (82% occurred in monsoon period)

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00014-X

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14. Livelihood constraints and socio-ecological loops

confines the cropping period to only part of the year. Available but not-sufficient moisture over the entire monsoon period limits the time window of opportunity for the various cropping systems practiced by the local farmers. The district is rich in dry-deciduous forest, although excessive use of forest products by the poor local communities leads to the gradual degradation of forest at an alarming rate (Jasper & Gardner, 2015). Primitive subsistence agriculture once a year with off-farm marginal economic activities is the key source of income creating at-risk conditions to sustain livelihood. Therefore, to deal with climate shocks, households have to rely on interregional, rural-urban, and off-farm income streams (Reardon et al., 1988; Reardon & Taylor, 1996). Most of the household labor force is, thus, engaged in a wide variety of non-agricultural income-generating activities during the agricultural off-season expecting assured work (Census of India, 1961). Why are rural remote households of Chota Nagpur plateau track ‘in-lock’ condition in such a gloomy socioeconomic and ecological backdrop? A systematic endeavor to answer the question wants a detailed description of the regional landscapes along with a synthesis of socio-ecological dynamics (Moreau, 2008; Scales, 2014) of the study area. Consistent with many previous pieces of research (Barrett et al., 2008; Carpenter et al., 1999; Carpenter & Brock, 2008; Cinner, 2011; Enfors, 2013; Steneck et al., 2011), the present study adopts Socio-ecological Traps (SET) approach (Folke et al., 2010) to unfold the research questions. The SET conceptualizes the causal interplay of degradation of the environment and shrinkage of livelihood (Boonstra & De Boer, 2014). SET refers to circumstances in which feedback between social and ecological systems strengthens one another and results in negative system states (Cinner, 2011). Specifically, the study concentrates on illustrating several interrelated and partially self-reinforcing SET that are likely to hinder the developmental pathways of the said backward pockets of India. The application of the SET approach is unique in the district of Purulia as no previous research has gone through this approach to explain livelihood constraints and related coping strategies.

14.2 Relevance of the study Additionally, at least 70% of the very poor live in rural regions, with the majority of them relying primarily (or solely) on agriculture for their means of subsistence, according to the International Fund for Agricultural Development (IFAD). Nearly 2 billion people are thought to be supported by 500 million smallholder farms in the developing world, and in Asia and sub-Saharan Africa, these tiny farms provide about 80% of the food consumed there. The progress accomplished in the fight against hunger and malnutrition so far is in danger of being undone by climate change. According to the Intergovernmental Panel on Climate Change’s (IPCC) most recent assessment report, climate change increases and intensifies risks to food security for the most vulnerable populations and countries. Four of the eight major risks associated with climate change listed by IPCC AR5 directly affect food security: • • • •

Loss of income and rural livelihoods; loss of livelihoods and marine and coastal habitats; loss of livelihoods and terrestrial and inland water ecosystems; Lack of access to food and failing food systems

2. Climate change, social response and resilience

14.3 Description of study area

267

The most susceptible nations and people, particularly those living in arid and semi-arid regions, are those who are affected first and most severely due to climate change. Some drought-prone areas of India fall within this category. In India, it is one of the most common natural calamities. About one-third of the country is now more frequently experiencing droughts and has more of them due to greater coverage in recent years. Both in terms of agricultural advancement and general economic expansion, these regions fall behind. Their agricultural output and revenues vary greatly from year to year, and they have a comparatively high rate of poverty. Due to their low and unstable earnings, high debt loads, and low levels of human development, the impoverished in these areas are extremely vulnerable to a range of hazards. Currently, policymakers are faced with a significant challenge: how to assist the poor in escaping poverty and vulnerability while also bringing drought-prone regions into the mainstream of development (Roy & Hirway, 2007).

14.3 Description of study area Physiographically, the district of Purulia is a part of the Chota Nagpur plateau, consisting of “a succession of rolling uplands and intervening hollows” (Banik et al., 2004). With a shield-rim land, the district displays various physical configurations, numerous steep and rocky hills made of metamorphic rocks, and non-perennial streams decorating the physical landscape. The plateau lies up to an altitude of 610 meters above the mean sea level. Mineralogically the rocks are composed of Quartz, Feldspar, Muscovite, Biotite, Illite, and Kaolinite (Dolui et al., 2014) which through extensive weathering have laid to form infertile soil with low nutrient status. Because of the impervious crystalline basement, the district is impoverished in its underground water resources (Bhattacharya et al., 1985). Rainfall is one of the major sources of groundwater recharge (Bharathkumar & Mohammed-Aslam, 2018). The rivers and their tributaries are non-perennial in character, as such they do not serve any purpose for irrigation. The rivers hardly offer large-scale canal irrigation scope, thereby creating obstacles to sustaining agriculture throughout the year. The region is covered by red soil with huge gravels of Chota Nagpur origin and poor organic humus content and high nitrogen deficiency. It is generally poor in physical properties and nutrient content resulting in a poor vegetal cover. The undulating topography and rough terrain determine the kinds of crops grown, the time windows for cropping, and the possible cropping systems in different topo-sequence parts (Fig. 14.1). Purulia has a subtropical and sub-humid climate, with hot wet summers and cool dry winters. Occasional showers with thunderstorms occurring in May welcome relief from the oppressive heat. The wind with low humidity and high temperature together exert a desiccating effect on the soil and vegetation. The Southwest monsoon provides the most rainfall in the district. Purulia experiences low rainfall compared to its adjacent district, as 85% of the average annual rainfall occurs between 15th June and 15th October with 8-20 rainy days on average per month (Indian Meteorological Department, Pune). The high seasonality of rainfall and the continental nature of climate throughout the year confines the cropping period to only part of the year.

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FIGURE 14.1

14. Livelihood constraints and socio-ecological loops

Location of the study area in the fringe of Chota Nagpur Plateau and its physiographic features.

The Purulia district belongs to the most disadvantaged pockets of West Bengal, an economically ‘lagging behind’ district compared to other districts of the state inhabited by scheduled social groups. The district has the second-largest tribal population (18.29%) with a large segment living below the poverty line (35%). The bulk of the rural population still preserves the lifestyles of their tribal forefathers. Being “resource-poor,” the district ranks in the lower rung of the human development ladder (West Bengal Human Development Report, 2004). The district has a huge forest cover of 797 km2 (Census, 2011), but excessive load leads to the degradation of 10.35% of forest cover during the last few decades (1971
2011). Living in nearly unproductive land, local livelihoods depend on the extraction of local forest products and carry out very little agricultural activity such as growing local rice, pulses, millets, and vegetables. The majority of inhabitants are not involved in any paid activity (non-working population: 57.35%, 2011), where a tiny segment has their own land to cultivate (Cultivator: 9.17%, 2011), although a considerable number are a daily wage agricultural labor (16.8%, 2011). Unproductive land, extreme rurality, lack of proper industrialization, limited in-situ employment opportunities, the concentration of tribal population, and social customs and traditions have resulted in a totally stagnated region socially as well as economically. In a nutshell, malnutrition (38.4% among primary students) and hunger (15% of households, of them, 10% go hungry to bed

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14.4 Materials and methods

269

at night) are widespread in the area, especially in the drought-prone western part of the district. Landlessness, meager income, unemployment, physical infirmity, women-headed households, and dispossession of physical assets contribute to poverty and food insecurity for them (Roy & Sen, 2010). The district lags behind in health, education, and other social parameters also. The 5th National Family Health Survey (2019
20) shows only 26.9% of women attaining 10 or more years of schooling; 37% of women aged 20
24 years become married before the age of 18 years. The health situation is alarming; 76.7% women aged 15
49 years are anemic, and only 68.4% receive post-natal care from health personnel within two days of delivery. A total of 994 villages (37%) are identified to be backward in terms of female illiteracy and marginal working population. The present paper opts to analyze the constraints of daily livelihoods in the droughtprone western part of the district in the context of the dismal socio-ecological system stated above, consisting of rural farms and off-farm households based on natural resources mainly. However, the system creates a loop in this unfavorable social and ecological context in which physical setup, society, culture, market, and social network, interact with each other creating specific linkages through feedback processes in the existing system. The overall system analysis helps in understanding several resilience traps that act together to sustain the socio-ecological loops in which poor people are compelled to survive.

14.4 Materials and methods 14.4.1 Approaches and techniques To judge the causal relationship among the environmental factors for predicting several traps prevailing in the existing system, the study has drawn Causal Loop Diagrams (CLDs) on varying socioeconomic interactions in the remote plateau tracks of eastern India. In exploring the dimensions of loops, the study follows an embedded design by triangulating quantitative and qualitative analyses to obtain a complete finding (Bryman, 2016). The phasing of data collection has been simultaneous during the field study.

14.4.2 Selecting target groups and survey procedures The study has selected two westernmost blocks, Jhalda I block and Jhalda II block of Purulia district purposively for micro-level analysis. To assess the economic structure of households, the study conducted a longitudinal survey from January 2018 to December 2018 engaging 200 households systematically selected from the twelve villages of the drought-prone western part of the Purulia district. The participating households were clustered on the basis of the household’s poverty status and dominant livelihood strategies, e.g., cultivator owning irrigated land, agricultural labor, daily wage casual labor, dairy farming and use of natural resources mainly, non-agricultural income and no such

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specific source to maintain household. The parameters of the primary household survey throughout the year consisted of the following: 1 2 3 4 5 6 7 8

Demographic and social status of the households; Sources of farm and off-farm income of the households (main and marginal); Cash expenditures for food, education, health, and others; Use of forest resources to maintain livelihood; Agricultural inputs, crops usage (domestic and then commercial), Food habits in cropping season and hard times; Loan taken and nature of repayment; Coping strategies of households.

14.4.3 Household recall survey The study conducted a recall survey with most of the households (some households missed due to temporary migration) which focused on the coping strategies of the households in hard times, e.g., switching over to alternative temporary occupation, coping with food shortages, dependence on forest resources, lending money with high interest, livestock handling, seasonal migration with children dropping their school, etc.

14.5 Analysis and results 14.5.1 Land use: shadow of coarse physiography The undulating topography is mainly responsible for quick removal of surface soil caused due to rapid run-off resulting in low fertility and high acidity in the soil’s upper surface. The region is covered by red soil with huge gravels of Chota Nagpur origin. It is generally poor in physical properties and nutrient content resulting in a poor vegetal cover. Local people classify the soil as per their usage, described in the figure below. The undulating topography and rough terrain determine the kinds of crops grown, the time windows for cropping, and the possible cropping systems in different topo-sequence parts. Land use is common in the district’s western hilly part, mainly covering Jhalda I and Jhalda II Blocks (Fig. 14.2). Against this coarse physical set-up, the land use of Jhalda blocks also depicts a gloomy pattern. The land use and land cover map displays dispersed settlements around the blocks, where agricultural fallow, dry fallow, and degraded forest at hills predominate in the land use scenario. Besides hills in the north and southern parts, barren land along with dense jungle reveal that most of the lands have remained unutilized in terms of economic productivity. Although Jhalda I Block possesses one municipality, the map hardly shows any patch of urbanization in the land use map. Due to poor urban catchment, accessibility and connectivity have not been developed as well, as a result, the surrounding rural habitation could not emerge as a ‘self-sufficient subsistence unit’ (Thompson, 1967). Furthermore, devoid of natural resources, skilled labor, and enough capital, the habitations could not develop sufficiently as they are physically separated (Fielding, 1974); and continued to be remote and inaccessible.

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FIGURE 14.2 Topographical variation and its influence on soil and agriculture.

In a semi-developed or developing area based on agrarian economy, any settlement should not be beyond the radius of 3 km (Sen, 1975) from any means of transport facility available in that area, the criterion has been considered as the prime factor with due consideration of scarcity of metaled roads and poor socio-cultural status of the block (Fig. 14.3).

14.5.2 Climatic hindrance, drought, and crop calendar Besides coarse physiography, Purulia is one of the districts of the state chronically affected by drought conditions (Drought in India: Challenges and Initiatives, Poorest Areas Civil Society (PACS) Programme, 2001). Several pieces of research on the extremity of climate of Purulia depict

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FIGURE 14.3

14. Livelihood constraints and socio-ecological loops

Land use land cover map of Jhalda Blocks from LISS III Satellite image.

that medium drought occurs once every 3 years and severe type of drought occurs once every 10 years in the District. The northeast part of the district is prone to severe drought, whereas extreme drought occurs in northwest and southwest Purulia (Palchaudhuri & Biswas, 2013).

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The Standardized Precipitation Index (SPI) applies the monthly precipitation data for the period of (1901
2014) of the Purulia district (IMD, Pune) to assess the prolonged drought condition of the district. Fig. 14.4 shows SPI values for three different time scales, viz. 3 months, 6 months, and 12 months of Purulia district. The line graph shows that the maximum SPI values in all-time scale depict 23.04, 23.72, and 23.33 in the year 2003, revealing the very high intensity of drought in that year. Another important year of extreme drought is 1966 crossing the SPI value of 22 in all time scales. The touching of the lines of SPI values of 22 in the year 2001, 2005, and 2010 highlight other important drought years in Purulia. The climatic hindrance and effect of drought also limit the varieties of crops (Fig. 14.5) cultivated in the district. The longitudinal survey along with intense observations and farmers’ interviews reveals that rice is the only dominant crop in the monsoon season, whereas wheat dominates the winter season. In median uplands, rice is cultivated along with millets and maize in the monsoon period only, but vegetables are grown in pre-and post-monsoon seasons. Potato, horse gram, pulses, and oilseed are also common in the Bareidh upland (Fig. 14.5).

14.5.3 Economy: reflection of harsh physical setup Because of the risky environment and the relative difficulty in gaining access to markets, food security is the household’s primary concern. Except for Bahal lands, the rest plots are left fallow during the non-monsoon season. Though agriculture is the mainstay of livelihood for most people, the poor progress of this sector since independence can’t help people escape from poverty and starvation. Most of the workforce is engaged in a wide variety of nonagricultural income-earning activities during the agricultural lean season, causing people’s seasonal migration to sustain alternate livelihood in neighboring districts (pube jaoa) leaving behind the family in most cases. Diversification of income sources is one of the most prevalent household strategies in the occupational western part of Purulia for coping with risk and vulnerability in rural areas with less favorable economic and agro-climatic conditions. Based on the Census data (2011, 2001), it can be said that the occupational structure of Jhalda Blocks (Table 14.1) is traditional and subsistence-oriented. Most households practice agriculture as their main occupation. Recently, a shift in the occupation concentration 3 2 1

SPI

0

Mild

-1

Moderate Severe Extreme

-2 -3 -4 3 months

6 months

12 months

1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

-5

FIGURE 14.4 Assessment of drought condition of Purulia district from 1949 to 2014 by Standardized Precipitation Index.

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FIGURE 14.5

Ergograph of Western Purulia showing extreme seasonality in rainfall and crop production.

TABLE 14.1 Decadal change in occupational structure in Jhalda (I and II) blocks. Block

Census year

Cultivator

Agricultural labor

Household industry worker

Other worker

Jhalda I

2001

29.07

10.95

8.88

16.49

2011

10.73

5.21

17.71

64.56

2001

18.81

7.09

26.54

9.96

2011

12.75

5.87

31.26

13.89

Jhalda II

Source: Based on, Census. (2011). Census of India
2011. Office of the Registrar General and Census Commissioner, India, inistry of Home Affairs, Governent of India. https://censusindia.gov.in/2011-common/censusdata2011.html.

from farm to off-farm activities is pronounced in the area. Low agricultural productivity, high risk, low return, and lack of proper irrigational facilities may be attributed to this change. Simultaneously, the non-working population has increased enough from 59.87% (2001) to 64.56% (2011) in Jhalda I Block and the respective figure is 46.57% (2001) to 54.66% (2011) (Fig. 14.6).

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FIGURE 14.6 Occupational structure of villages of two Blocks by ternary diagrams.

A huge non-working population in both blocks (average 56.83%) almost has stagnated the local economy shown in the ternary diagrams. Obviously, this stagnation has created poverty; around 45% of the total population lives below the poverty line in the western part of the district of Purulia.

14.6 Livelihood constraints and socio-ecological loops Subsistence agriculture with livestock farming of marginal character is the mainstay of local livelihood. The western part of the Purulia district occupied by Jhalda Blocks can be labeled as an extremely drought-prone area. High seasonality of precipitation poses a constraint to agricultural production (Hanisch, 2015). In the hot Summer spells along with the dry months of Winter season, many rural households suffer from severe food shortages. To tackle forced starvation, the bare reality of this hard time, the majority are engaged in non-agricultural incomegenerating activities during this ‘recurrent hunger season’. Some migrate to sustain livelihood in the neighboring districts and states, and the remaining are used to starve. Some perennial questions emerge in this context, ‘How much does the physical setup of this area pull the peoples’ livelihood into risk?’ ‘What are the constraints of diversification in livelihood in this resource-poor area?’ Despite several rural development programs, why are the households compelled to reduce their meals (adult and child) to cope with bad situations that recur every year in this region? Therefore, it is necessary to dig out the socioeconomic lacunae, better to say, the socioeconomic stagnancy of Jhalda Blocks that apparently lock peoples’ livelihood trajectories to a great extent.

14.6.1 Seasonality of household economy—agricultural production: influence of rainfall and lack of ‘technical know-how’ The mono-cropping agriculture of Jhalda Blocks forms the mainstay of the economy, with strong subsistence nature. A large human resource of agro-based households is

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utilized mainly for rice production, particularly in the rainy season, for family’s own consumption (Banik et al., 2004). The survey conducted in the lean-agricultural season has observed that except for Kanali and Bahal, the remaining plots are left fallow in the nonmonsoon periods. Agricultural production is basically restricted to the rainy season. Listening to most of the local farmer’s voice, “Bristie jetuku varsa” (“Only Rainfall brings hope”), the study has measured ‘to what extent rainfall is responsible for harvest?’; for this agricultural production (kg/household) has been regressed on rainfall (mm), the number of dry spells (above 7 days during the monsoon season from June to September), area of production (acre/household), age of the eldest person of the household and educational attainment in terms of years of schooling. Annual rainfall (ß 5 0.12, ρ 5 0.00), as shown in the present study, has become a strong predictor of agricultural production per on-farm household along with less occurrence of dry spells (ß 5 20.63, ρ 5 0.00) (Table 14.2). The regression analysis explores that production will increase with the harvest area, but the case is not statistically significant for the present study (ß 5 0.17, ρ 5 0.35), probably due to the limited or no use of modern technology in agricultural production by the farmers. To avoid greater risk related to the higher cost involved with the agro-system and ‘lack of know-how’ the low-skilled farmers generally invest little capital resulting in low returns from this field. Also, farmers are generally indifferent to purchasing inputs because of the high risk of crop failure (Banik et al., 2004) which may threaten the loss of cost of purchased inputs. A substantial portion of farmers (above 73%) have reported not using proper irrigation and lack of monetary resources for buying a generator, pumping machine, etc. may be the reason for it. Moreover, rough terrain makes agricultural inputs more costly because of high transaction costs.

14.6.2 Alternative livelihood strategy: low diversification Livelihood diversification is a predominant household strategy to cope with the risk and vulnerability in less-favored climatic and socioeconomic conditions. In rural India, agro-based livelihoods followed by small and marginal farmers are becoming unsustainable, since the low productivity of land is no longer able to meet up their food requirements for the family and fodder for the livestock (Hiremath, 2007). Obviously, rural households have to diversify their source of income in the lean season to sustain life, which may help to escape rural households from poverty in South and South-East Asia (World Bank, 2001). TABLE 14.2 Summary output of regression analysis—“to what extent rainfall is responsible for harvest?” Coefficients

P-value

Lower 95%

Upper 95%

Intercept

7.32

.00

3.46

11.18

Area for harvest (hectares)

0.17

.35

2 0.19

0.53

Rainfall

0.12

.00

0.07

0.16

Age of the oldest member of the HH

0.01

.62

2 0.03

0.05

Years of schooling

0.19

.10

2 0.04

0.42

Dry spells (above 7 days)

2 0.63

.00

2 0.86

2 0.41

F (5, 64) 5 167.39, Sig. 0.000

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In Jhalda Blocks, the livelihood scenario is predominantly agricultural and non-agricultural; nearly one-fourth working population (25.13%) does not possess any cultivable land of their own and engaged in agricultural labor on a daily-paid basis whereas 23.11% of rural population is cultivator possessing certain land for cultivation (Census, 2011). Pronounced seasonality in production due to recurrent drought and frequent dry spells and crop failure makes the local farmers highly risk-averse (Ha¨nke et al., 2017) (Fig. 14.7). Besides main work, the most typical off-farm activities in the lean season reported by the respondents include daily wage labor in a construction site at the nearest Jhalda town, a casual worker at brick kiln site, a rickshaw puller, and also small shop employee. Moreover, to find out diversification, the study selects Hensahatu Gram Panchayat from Jhalda I Block having concentration of marginal-work population (57.99%), and Begun Kodar GP from Jhalda II Block for the maximum main-working population (74.72%). It is noteworthy that Hensahatu bears mostly unproductive lands for forest-covered hillside locations. On the other hand, Begun Kodar at the foothills of the Ajodhya range receives the water of several rivulets and streams coming out from hill gaining fertility to continue agriculture in lean season (Table 14.3). Table 14.3 shows that enough dependence has been noticed in agricultural activities at Begun Kodar whereas non-agricultural activities are more pronounced at Hensahatu. Whenever asked the local villagers that “What do you do in lean season?”, they replied, “Kaj kuthae j kaj korbo?” (“Where is the work to do?”) Low occupational diversification is prominent in the two GPs. It is nothing to say that poorer households are engaged in less-

FIGURE 14.7 Conditional mapping for analysis of livelihood diversification.

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TABLE 14.3 Low occupational diversification of sampled households (percent). Gram Panchayat Name

Hensahatu

Begun Kodar

Farming-Households and one occupation

17.75

44.43

Farming-Households and two occupations

5.17

25.35

Farming-Households and three occupations

0.89

3.85

Non-Farm-Households and one occupation

45.28

15.56

Non-Farm-Households and two occupations

19.43

10.04

Primary survey at candidate households (2019).

FIGURE 14.8 Coping strategies adopted in lean season by poor households of the study area.

remunerative jobs while the economically well-off are getting standard wages. The poor remain poor, the richer become richer (Fig. 14.8). The result of the present analysis matches enough with the previous work of Khatun and Roy (2016) and Carter and May (1999) in hilly plateau regions of eastern India. The majority of the households in both spaces lack resources, assets, skills, and education which constrains their diversification toward more remunerative activities, and are forced to engage in low-return activities (Barrett et al., 2001; Khatun & Roy, 2016). The adopted coping strategies in hard times have been highlighted in Fig. 14.8. Besides, off-farm labor, 78% of local people utilize natural forest resources for food, fodder, and fuel. Migration is presently seen as an emerging trend of coping strategy within the present young generation. Around 32% of households have at least one family member who migrates temporarily in the lean period to augment household income. The recall survey of the surveyed participants (73%) showed evidence of reducing meals as one of the coping strategies in

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279

hard times. High debt from money-lenders to sustain livelihood during the lean season with great uncertainty of repayment includes another coping strategy (22%).

14.6.3 Assessment of poverty and food self-sufficiency A longitudinal study has assessed food sufficiency around the year (January 2018 to December 2018) among the agriculture-based as well as non-agro-based sample households of Jhalda blocks. Both households follow a range of non-farm income-generating activities to cope with the seasonality of cash and food flow in the lean period (Fig. 14.9). The main staple food, i.e., rice is produced in the rainy season. Most of the households reported that they receive enough food just after the harvest, though the product is not sufficient they have to buy rice from the local market. On the other, in the rainy season, off-farm works at construction sites or brick kiln sites are also scarce. Thus, a maximum of 60% of the households get enough food only in three to four months. In the rest of the months, cash shortages and food deficits go parallel to each other (R2 5 0.90) making most households (maximum 90%) hungry. Converting the daily food intake into kcal recorded via recall survey, the study revealed that with enough density of sample households (9.98 5.13), a total of 46,590.6 kcal is produced per household every week; but only 43.25% get food sufficiency, a very high skewness (1.32) value indicates that most of the households suffer from food uncertainty around the year. The deficit in food sufficiency is profound very much (2100,115.4 kcal/HH/week) (Table 14.4). Though the picture is quite different (Box 1) for the few Brahmin families in the same village. The bar diagram (Fig. 14.10) displays that except for the general caste households, especially Brahmins, the remaining do not cross the Minimum Dietary Energy Requirement (MDER per FIGURE 14.9 Seasonality in cash availability and food sufficiency.

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TABLE 14.4 Food self-sufficiency: availability and deficit in respect of minimum dietary energy requirement (MDER). Mean (SD)

Skew

Range

Minimum

Maximum

Household size

9.98 (5.13)

0.28

19

2

21

Total Kcal produced/average HH/week

46,590.6 (21,300)

1.55

106,750

15,750

122,500

Total MDER (in Kcal)/average HH/week

146,706 (75,394.97)

0.28

279,300

29,400

308,700

Self-Sufficiency in food (% HH)

43.25 (33.41)

1.32

121.43

9.52

130.95

Deficits (Kcal)/HH/week

2 100,115.4 (11,102.57)

2 0.14

287,000

2 266,000

21,000

Based on sample households based on Recall survey (January 2018 to December 2018).

FIGURE 14.10 Daily calorie intake by varying social groups of the study area.

TABLE 14.5 Poverty incidence, poverty depth, and severity of poverty. Headcount (P0) (standard deviation)

Poverty depth (P1) (standard deviation)

Poverty severity (P2) (standard deviation)

Food poverty

0.73 (0.09)

0.59 (0.04)

0.19 (0.03)

Total poverty

0.56 (0.05)

0.43 (0.07)

0.12 (0.02)

Based on sample households.

person per day), i.e., 2100 kcal. The MDER of a tribal family being far below the average (Mean: 1560.023; SD: 795.423) and high SD also uncovers the gloomy picture. Table 14.5 bares the fact that the case of food uncertainty is correlated with poverty (Debebe & Zekarias, 2020; Ha¨nke et al., 2017). Table 14.5 shows 73% headcount, 59% poverty gap, and 19% severity of food poverty calculated in the study region, while these values exceed the total poverty in all cases of incidence, gap, and severity. The higher value of food poverty indicated vulnerability in food sufficiency in the remote plateau tracks of Purulia district.

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281

14.7 Resilience traps To better investigate the dynamics of livelihood, the study utilizes the SET approach (Boonstra & De Boer, 2014; Enfors, 2013). When social and ecological system feedbacks reinforce one another and produce negative system states, this is referred to as a condition of the synchronous ecological and social transition (SET) (Cinner, 2011). In particular, SET highlights interconnections between people with their surrounding natural environment. Those who have fallen into the socioeconomic trap are automatically grabbed by the ‘Poverty Trap’ as society and ecology influence and, are influenced by its economy. In the system analysis (Sterman, 2000), special emphasis is placed on key system variables, casual feedback loops, repayment, and external barriers. Table 14.6 includes the sources of the database that the study is based on and discusses the traits of the various traps. By putting up a set of hypotheses about (1) how they are produced in remote rural plateau track, (2) how the various traps may interact to affect the dynamics, and (3) how they exhibit a clustering effect in the studied setting, the study opts to build a socio-ecological loop model fitted for the study area. The following Casual Loop Diagrams (CLD) display socio-ecological interactions in the rural corners of Jhalda Blocks. CLDs explain the relationships between chronic poverty and environmental degradation in rural remote base, highlighting the traps and mechanisms that both create and perpetuate them (Sendzimir et al., 2011). Fig. 14.11 consists of such three loops, presenting feedback between the production, risk and variability trap, the concept is inspired and taken from the (Ha¨nke et al., 2017).

14.8 Discussion 14.8.1 Production-consumption traps When the pace of natural resource consumption or extraction is excessively near to or higher than the actual rate of production, a consumption/production trap develops (CPWF, 2014). The district has a significant population density of 468 people per km2 and a steady increase of population increase (Census, 2011). Poor rural people (87.3%) living closer to the forest are significantly more dependent on it for food, fuel, furniture, fertilizer, and medicinal purposes. Within a span of ten years (2002
03 to 2010
11), the local forest has decreased from 87.17 thousand hectares to 75.07 thousand hectares; while the net sown area is decreasing (256.94
226.13 thousand hectares) within the same time period (District Statistical Handbook, 2007). The households that own arable land to cultivate get only 21% of the production to meet their household needs. A significant correlation exists between income generated from the off-farm labor and food-expenditure (Spearman, r 5 0.78, P , .01), and also from the collection of food from forest to meet up food-demand (Spearman, r 5 0.45, P , .05). Even though woody biomass harvested from the forest serves as the only local fuel source of rural people, the tribal people also sell dry wood and charcoal in local market of Begunkodar and Jhalda town as secondary source of fuel (Dirac et al., 2006). As a result, hidden pressure exerts on the amount of forest cover and the health of the forest ecosystem from the perspective of biodiversity preservation.

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TABLE 14.6 Resilience traps at Jhalda blocks. Resilience traps

Features

Source

Production-Consumption Traps

Degradation of forest; change in land utilization over time; Excess use of bioresource (fuel, food, and fodder) from local forest

Qualitative summary of the conversation with villagers; Literature review (Ha¨nke et al., 2017; Hanisch, 2015)

Loss of soil fertility, poor irrigation results, low productivity; Resource consumption exceeds the production creating ‘vicious cycle of resource mining’; High debt from money-lenders to sustain livelihood during lean season with great uncertainty of repayment; Practically no work in the rainy season, those who migrate in winter engage in low-skilled jobs and are unable to improve their standard of living; Variability Traps

Limited/low investment in agriculture/petty business results in low return; Drought, long dry spells in rainy season, livestock illness, illness of family members and subsequent treatment cost, untimely death of head of the family, investment failure, negligible capital accumulation, and declining standard of living;

Risk Traps

Drought along with Long dry spells in rainy season poses additional risks;

Qualitative summary of conversation with villagers

Hanisch (2015) and Hanisch et al. (2013)

Farmers’ indifference in investment, lack of skill, minimum or no technological usage characterize as risk trap; Distance from main district road creating gap in flow of information creates less opportunity for income generation for off-farm casual labor; Policy Traps

Politically less-favored region; Less employment opportunity in Government sector; Lacunae of proper implementation of policies and lack of transparency averts markets and resources from being used effectively;

Summary of interviews with local people (World Bank, 2013)

Based on literature review and intense field investigation.

The loss of forests has a detrimental impact on the security of one’s supply of food (Andriamparany, 2015), therefore, threatening “resource mining cycle” and producing a Production-Consumption Trap.

14.8.2 Variability traps High variability in environmental conditions acts as external drivers in producing variability traps in Chota Nagpur plateau fringe of Eastern India; limited rainfall with long dry spells along with other climatic adversity results in frequent crop failures, especially

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14.8 Discussion

FIGURE 14.11

283

Socio-ecological traps as explored in the study area.

in the case of rice and other food grains production. This prevents most of the small-scale farmers to invest more in agriculture, which returns low improvement of capital or livelihoods leading to a variability trap. Additionally, food and cash sufficiency are seen just after the harvesting periods, most households sell their crops immediately in the market without getting proper price (Manon, 2014). During the summer season, livestock often suffers from various ‘unknown’ diseases; local people are basically ignorant of the nature and treatment of such diseases. Lack of training and proper skill in dairy farming led to livestock undernourishment and thus enhance the trap dynamics.

14.8.3 Risk traps A risk trap develops when uncertainty reduces internal strength to invest production system. Sudden climatic hazards (here occurrence of cyclones) or drought conditions manifest adversely in the regional cropping system, posing additional risks to small-scale farmers. Thus, a high-risk situation further compels farmers to invest high in agriculture,

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reinforcing a risk trap. Farmers’ indifference to investment, lack of skill, and minimum or no technological usage characterize a risk trap. Under these extreme situations, people adopt diversification strategies and become engaged in off-farm activities to secure livelihood; but distance from main district road creating gap in flow of information and creates less opportunity for income generation for off-farm casual labor.

14.8.4 Policy traps Policy traps emerge when ineffective policies and a lack of transparency obstruct investment because markets and resources can’t be utilized efficiently. The district of Purulia, being a politically less-favored region, gets fewer employment opportunities in the Government sector; thus lacunae of proper implementation of policies and lack of transparency averts markets and resources from being used effectively. Most of the poor people, being indifferent regarding government schemes related to agriculture, short-term loan, and others, they miss the opportunity of improving their livelihood.

14.9 Conclusion In a nutshell, the study fulfilled its objectives by portraying livelihood constraints under the lens of SET approach and thereby explored that repeated crop failure, unsuccessful implementation of the Governmental policies, limited skill and knowledge of technical ‘know-how’, illiteracy and ignorance among the farming and non-farming households in the Jhalda blocks have created several interconnecting social-ecological loops. The study established a nested set of traps existing in the study region formed by the risk trap, the variability trap, and the production and consumption traps. The study concludes that investments in the agricultural sector do not seem to be the best option, despite the fact that interventions are undoubtedly necessary to combat and make up for the food and money shortages that a big portion of households endure for many months every year. And in fact, the only "balanced" feedback connected to off-farm employment that the study revealed. The study faced a few problems in data collection during hot summer spell, but it carries ample future prospects for further research by designing a holistic approach for the economic betterment of the poor local people. Against this backdrop, the study has identified some alternatives in order to improve the existing condition: 1 To increase off-farm employment opportunities for the rural households in order to secure food availability, to minimize the pressure of forest and limited agricultural fields; viz. introduction of dairy-farming, and allied sectors and milk industry with proper training along with skilled personnel to avoid huge loss; 2 To ensure job opportunities for the local people at remote plateau track in order to fill the gap in the number of unemployed days by intensifying 100 days of work for at least 100 days per household in a fiscal year through efficient functioning at the grassroots level so that poverty can be slowly reduced;

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References

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3 Increasing awareness levels among the people and proper decentralization by inclusion of scheduled groups of people in the decision-making area must be given ascendancy in employment generation under various schemes. The facilities of access to credit, capital, and insurance provided by the Government to remove poverty and for mainstream the marginalized section of the society is not infiltrated properly into the marginal section of the society. Increasing awareness levels through proper and intensive counseling, advertising, and widespread campaigning about the availability of jobs can cater a remedy to the people of the remote part of the study area. 4 Keeping in mind the issue of food scarcity the present study proposes the continuation of providing mid-day-meal up to Class XII, which is limited up to class VIII till now in the government schools in West Bengal. The study further proposes the upgradation of standard meals along with the distribution of dry food like Soya beans, Cakes, pulses, Dahlia, etcetera during holidays. 5 During severe drought years, special priority is to be given to drought-stricken households to ensure food security, i.e., providing long-term agricultural loans from banks for dry season and distributing dry ration systems from government and other agencies.

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A case study of Puruliya district. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 7(3). Reardon, T., Matlon, P., & Delgado, C. (1988). Coping with household-level food insecurity in drought-affected areas of Burkina Faso. World Development, 16(9), 1065
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12. Available from http://dx.doi.org/10.4314/mcd.v9i1.2. Sen, L. K. (1975). Growth centres in Raichur : an integrated area development plan for a district in Karnataka. Hyderabad : National Institute of Community Development. Sendzimir, J., Reij, C. P., & Magnuszewski, P. (2011). Rebuilding Resilience in the Sahel: Regreening in the Maradi and Zinder Regions of Niger. Ecology and Society, 1708308716(3), 08. Available from https://doi.org/10.5751/ ES-04198-160301. Austria: Resilience Alliance. http://www.ecologyandsociety.org/vol16/iss3/art1/ES-20114198.pdf. Steneck, R. S., Hughes, T. P., Cinner, J. E., Adger, W. N., Arnold, S. N., Berkes, F., Boudreau, S. A., Brown, K., Folke, C., Gunderson, L., Olsson, P., Scheffer, M., Stephenson, E., Walker, B., Wilson, J., & Worm, B. (2011). Creation of a gilded trap by the high economic value of the maine lobster fishery. Conservation Biology, 25(5), 904
912. Available from https://doi.org/10.1111/j.1523-1739.2011.01717.x, 12317395. Sterman, J. D. (2000). Business dynamics: Systems thinking and modelling for a complex world. Thompson, J. D. (1967). Organizations in action. Social science bases of administrative theory. McGraw-Hill. West Bengal Human Development Report. Government of West Bengal. (2004). World Bank. (2001). Farming systems and poverty
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C H A P T E R

15 Ground water depletion and climate change: role of geospatial technology for a mitigation strategy Gouri Sankar Bhunia1 and Uday Chatterjee2 1

Independent Researcher, Paschim Medinipore, West Bengal, India 2Department of Geography, Bhatter College, Dantan (Vidyasagar University), Paschim Medinipur, West Bengal, India

15.1 Introduction Groundwater contains 99% of the world’s liquid freshwater resources and is the most widely used freshwater reservoir. Groundwater is an essential source of freshwater, accounting for approximately 30.1% of all available freshwater on the planet. On the other hand, groundwater supplies are being depleted at an alarming and unsustainable rate in many parts of the world. According to a recent NASA research, 21 of the world’s largest 37 aquifers have reached their sustainability tipping points and are being depleted. Groundwater is sometimes the sole perennial water source in many areas, and it becomes the dominant source when energy and pumping equipment are adequate. Irrigated agriculture improves crop yields significantly, especially in desert places where solar radiation and soil quality are not limiting considerations. As a result, it’s no surprise that governments, corporations, and farmers have embraced groundwater-based irrigation, both with and without government subsidies. Groundwater accounts for more than 40% of irrigation water consumption. The countries with the most land that can be irrigated by groundwater are India, China, and the US. Vast tracts of groundwater extraction from fossil aquifers are commonly found in India, China, North Africa, the Middle East, Central Asia, North America, and Australia (Lall et al., 2020). Groundwater consumption is fast rising in tandem with population growth, while climate change is putting extra strain on water resources and increasing the likelihood of severe drought. As a result, it’s critical to look at how groundwater storage (GWS) changes in relation to both climate and anthropogenic factors (Dai, 2012; Marvel et al., 2019). Groundwater resources are influenced by climate change in a variety of ways. Climate change can impact

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the quantity of soil infiltration, deeper percolation, and groundwater recharge in the hydrological cycle. Furthermore, rising temperatures raise evaporative demand over land, limiting the amount of water available to recharge groundwater. On the other hand, anthropogenic disturbances on groundwater resources are mostly attributable to groundwater extraction and the indirect influence of irrigation and land use alterations (Wu et al., 2020; Lo & Famiglietti, 2013). The majority of the research cited is conducted on a global level. However, due to rapid economic development, population increase, and urbanization, groundwater-related challenges, and their management are probably more critical in developing and emerging nations, as (Zyoud & Fuchs-Hanusch, 2017) also point out. As a result, research concentrating on developing and poor countries is needed, particularly in the South and Southeast Asia region, one of the world’s fastest expanding and densely populated regions, housing over 32% of the global population (Rasul, 2016). Differences in groundwater are primarily influenced by climate change. Variations in recharge owing to chronic dry or rainy periods have a significant impact on the water balance in shallow aquifers, which is facilitated by the degree and form of surface watershallow groundwater flows, as well as vegetation dynamics. Complex recharge paths and intermediary impermeable layers limit climate-induced recharge variability in deeper aquifers. According to current science, climate change will cause oceans to rise by nearly a meter globally by the end of the century. However, according to this analysis, another half-centimeter-per-year rise is projected due to worldwide groundwater recycling back into the ocean. Storm surges exacerbated by climate change will flood coastal communities, jeopardizing groundwater supplies’ quality and usability (Richard et al., 2012). The Intergovernmental Panel on Climate Change, founded in 1988 by the UN Environment Programme and the World Meteorological Organization, evaluates the latest evidence on climate change and its possible environmental and socioeconomic implications regularly. Prior literature used the output data from General Circulation Models (GCMs) to drive offline hydrological model simulations and assess changes in global and regional groundwater supply (Allen et al., 2010; Haddeland et al., 2014). Internal climatic variability, intermodel variances, and scenario uncertainties contribute to the uncertainty of GCM climate projections (Lehner et al., 2020). This chapter focuses on how remote sensing data engaged people in global climate change research.

15.2 Impact of climate system on groundwater Groundwater is an important component of the hydrological cycle and a vital natural resource across many nations, serving as a key source of water for agriculture, residential, and industrial purposes. Groundwater is also a substantial source of water for human consumption, accounting for about half of all global drinking water and roughly 43% of all water used for cultivation. In many countries, groundwater is also critical for the survival of streams, lakes, wetlands, and groundwater-dependent ecosystems (GDE). Human activities are putting global groundwater resources in jeopardy, as are the unpredictable repercussions of climate change. Many scientific research has been conducted to learn more about how water supplies may respond to global change. Because of their visibility, accessibility, and clear recognition of

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surface waters being influenced by global change, recent study has mostly concentrated on surface-water systems. Variations in regional temperature and precipitation impact all components of the hydrologic cycle. Differences in these factors influence how much water enters the surface, evaporates or seeps back into the atmosphere, freezes as snow or ice, infiltrates the groundwater system, runs off the land, and eventually generates base flow in streams and rivers (Kumar, 2014). Since evapotranspiration is a critical element of the water balance, data on variations in evapotranspiration is required for hydrological impact studies of watersheds (and aquifers). Climate-change scenarios are frequently described in terms of temperature and precipitation fluctuations. As a result, estimating the effects of global warming on potential evaporation is challenging. Many worldwide scenarios predict a rise in potential evaporation, but other factors lowering evaporation may exceed these ones locally or regionally. Using data on net radiation, temperature, humidity, wind speed, and plant physiological parameters, numerous types can be utilized to compute prospective evaporation. The effect of a change in climate on potential evaporation is estimated based on the site’s attributes (Fig. 15.1).

FIGURE 15.1 Conceptual diagram illustrating relationships between climate and groundwater.

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While the most visible effects of climate change may be changes in surface water levels and quality, water managers and governments are most concerned about the potential reduction and quality of groundwater resources, which is the world’s primary source of desalinated water for drinking purposes and irrigation of agricultural produce. As groundwater aquifers are replenished mostly by precipitation or by interaction with surface water bodies, climate change’s direct impact on rainfall and surface water will have an impact on groundwater recharge. Groundwater cannot be considered independently from the landscape above, the society with which it ’interacts,’ or the regional hydrological cycle, as it is universally acknowledged, and must be controlled comprehensively. A crucial (but not exclusive) aspect of comprehending the likely repercussions of future scenario (climate and non-climate) variations on groundwater systems and the regional hydrological cycle is the impact that these elements impose on recharge and discharge. Non-climatic variables determine the vulnerability of certain groundwater systems to these distinct climatic effects. Climate change may affect less widespread, volumetrically smaller aquifers more swiftly than bigger aquifers. Aquifers in arid to semi-arid climates may be more susceptible to climate change than those in more humid climates (Earman & Dettinger, 2011). Snowfed aquifers, as previously stated, may be more susceptible to warming effects than other aquifers. When it comes to how climate change will affect groundwater flow networks, there is no single solution. Climate change will affect various systems, and even various components and sites within a single groundwater system, in distinct manners. As a result, there is a need for further observation and experimentation into the historical links between climate and groundwater hydrology in a range of settings. Because no single groundwater model will be suitable for all spatial and hydrologic contexts, a number of modeling and predictive methodologies may be required to translate observations and research into practical forecasts of climate change impacts on groundwater recharge. Groundwater storage decreases (and related lowering groundwater levels) due to reduced recharge and/or increased pumpage are projected to lead to smaller groundwater contributions to streamflow. As groundwater in several situations is colder than water that has traveled over land to (and through) stream channels, increasing stream water temperature is a frequent occurrence of reduced baseflow in streams. Warmer stream temperatures could have a substantial impact on the survivability of some species. Increased groundwater inflows to streams, on the other hand, are unlikely to create temperature stress among existing biota when recharge occurs. If groundwater contributions to streams alter as a result of climate change, the chemical composition of stream water may also fluctuate, albeit the cumulative trend will rely on the relative strengths of groundwater and stream water. During the 20th century, global sea levels rose by around 22 cm. As ocean waters warm and swell, and large ice sheets melt into the seas, sea levels are predicted to continue to rise, most likely at a faster rate, as a result of global warming. Increasing sea levels around the coast are forecast to expand the risk of ocean water intruding into freshwater aquifers, perhaps raising groundwater salinity. Increases in groundwater quantity and quality, on the other hand, have an impact on ocean chemistry, particularly near coasts. Despite the fact that groundwater discharge to the oceans is only approximately 6% of streamflow discharge, groundwater’s yearly salt intake to the oceans is around 30% of the quantity produced by streamflow. Variations in groundwater contamination could, therefore, affect near-coast ocean chemistry and nitrogen

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cycling. Rising sea levels and groundwater levels could modify the size and/or degree of the hydraulic gradient between aquifers and oceans, affecting the proportion of groundwater discharged to the oceans. An initial evaluation of future groundwater temperatures and potential groundwater warming for chosen aquifers on the Central Plateau was conducted as part of the CH2014Impacts (2014) project. Based on the (CH2014-Impacts, 2014) climate forecasts, the study revealed that the primary influence of atmospheric CC on aquifers (not just those in unconsolidated rock formations) will be through groundwater resources. These scenarios are indicative of the overall gamut of precipitation and temperature outcomes in the future, according to the explanation of the climate projection presented in (CH2018, 2018). The emission possibilities RCP 2.6 (four scenarios, constant climate protection, and a threshold of 2 C warming compared to pre-industrial levels), RCP 4.5 (6 scenarios, medium-rise with weak protection of the environment), and RCP 8.5 (7 scenarios, no climate prevention) were investigated. Because the CH2018 scenarios only supply daily data, Epting et al. (2021) established a disaggregation approach to obtain hourly values with a daily cycle for river runoff and temperature modeling. This approach uses a delta integrated approach to that employed by Bosshard et al. (2011). A 365-day time series is created by averaging historical and future daily data for each day of the year. Harmonic functions are used to smooth these time series even more (Epting et al., 2021). The delta between the two time periods is then calculated by subtracting the gap between past and future normalized time series (additive variance for temperature, multiplicative for other elements).

15.3 Role of geospatial technology in groundwater depletion assessment Remote sensing technology is a quick and low-cost tool for gathering valuable data on geology, geomorphology, lineaments slope, and other topics that aid in the study of groundwater resources. The speedy and cost-effective demarcation of groundwater potential zones is made possible by combining this data with the results of hydrogeological investigations. Although it is conceivable to graphically combine this data and designate groundwater, it is time-consuming, complicated, and prone to human error. In recent years, digital techniques have been utilized to combine various data in order to not only outline groundwater potential zones but also to tackle other groundwater problems. Using a geographic information system (GIS) software application, these varied data are compiled into a thematic map. Where traditional alternatives are unavailable, remotely sensed groundwater biomarkers may contribute valuable data. Groundwater heads, variations in groundwater storage, heat signatures, and subsidence data are examples of measurable data (Becker, 2006). Although ground-based RS (geophysics) is more expensive than space and airborne RS, it is still more efficient and less affordable than invasive approaches (boring drilling) (Meijerink, 2007). Surface water from plants, water discharging to the surface containing heat energy, and runoff are examples of these markers. If auxiliary analysis is employed to infer groundwater behavior from surface expressions, satellite data can be utilized. Remotely sensed data is most effective when integrated with mathematical analysis, geographic information systems, and ground-based data (Table 15.1).

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TABLE 15.1 Use of remote sensing sensors in groundwater study. Sensor

Launch year

Ground resolution (m)

Surface Precipitation temperature

Sentinel 2A

2015

10-100

Sentinel 1

2014

5-400

CartoSAT- 1

2005

2.5

OrbView-3

2003

1,4

AMSR-E

2002

5400
56,000

GRACE

2002

3,00,000

ENVISATRA2

2002

1000

SRTM

2000

30,90

AVHRR

1991
2003 1100

ASTER

1999

15,30,90

X

X

Landsat-7

1999

30,60

X

X

MODIS

1999

25,05,01,000

X

X

OrbView-2

1997

1100

x

X

IRS-1D

1997

23.5
142

IRS-1C

1995

23.6
189

RADARSAT-1

1995

8
100

X

Soil Water moisture storage

Snow water

X X

X

X x

x

x

Land cover

Topography

X

X

X

X

X

X

X

X

x X

x

X

X X

X

X

X

X

X

X X

X

Modified after Elbeih, S. F. (2015). An overview of integrated remote sensing and GIS for groundwater mapping in Egypt. Ain Shams Engineering Journal, 6, 1
15. http://dx.doi.org/10.1016/j.asej.2014.08.008.

The knowledge that the shallow groundwater flow is usually influenced by surface forces and regulated by geologic features that may be determined from surface data is crucial to the RS of groundwater. The discovery of lineaments that are assumed to be associated with faults and shattering in hard rock has been an especially effective application of RS to groundwater. Using remotely sensed imagery to identify boundary conditions including streams, lakes, wetlands, seepage areas, recharge zones, or evapotranspiration zones is a simple technique to combine RS with groundwater flow forecasts. The distribution of groundwater is crucial for water supply and pollution control analyses. The selection of possible well sites can be aided by identifying topographic and vegetation characteristics of groundwater, along with determining the groundwater discharge area (seeps and springs). Groundwater recharge zones could be defined in order to safeguard these areas from practices that would damage the groundwater supply (through zoning restrictions). The depth of water in a groundwater system cannot be properly mapped using available image analysis. For mapping paleo-drainage patterns, huge depressions, playas, and catchment areas, Kwarteng (2002) used aerial pictures, Landsat Thematic Mapper (TM) imagery, and Digital

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Elevation Models (DEM). These values are utilized to store significant volumes of water during flash floods and to recharge freshwater lenses. The most conducive regions for the production of fresh groundwater lenses were identified using GIS analysis of these data sets. Oh et al. (2011) provided a probabilistic approach for estimating an area’s prospective groundwater resources that combined satellite images and GIS. The chosen method entails identifying the 15 most critical variables that influence groundwater recharge. A frequency-ratio model was used to map the groundwater potential, which depicts the link between hydrologic data, specific capacity (acquired from pumping tests), and factors. Robinson et al. (2006) employed seven Radarsat-1 data scenes in the Kufra Oasis, Libya, to outline the drainage of two wadi systems. From the south and southeast, these wadis flow into the Kufra Oasis in south-eastern Libya. Individual scenes’ drainage elements are scanned and mosaicked. The assessment of Radarsat-1 sceneries enables the identification of historical river channels’ tunnels and intermediate holding sites. These findings help to explain why so much water has been extracted from the Kufra wells for over four decades. Microwave or radar images, according to Meijerink (2007), have numerous functions in hydrogeological implementations, including geological features and lineaments, and the dynamics of huge marshes (always connected to groundwater and can be analyzed using radar images). For groundwater heads and flow studies near lakes, radar altimetry is critical for identifying lake levels. Radar data is also used to create Digital Elevation Models (DEMs) and precise measurements of land subsidence. Related land elevation can be determined with great precision using phase shifts of two or more radar images of the same location. Remote sensing and GIS were used in a variety of groundwater recharge potential (GRP) investigations, as well as several factors such as geology, geomorphology, lineaments, drainage, land use, slope, and topography. The weighted overlay methodology was used in several research to analyze recharge potential mapping (Table 15.2). The analytical hierarchy technique (Saaty, 1980) was employed in various research for GRP mapping (Rahmati et al., 2015). Furthermore, most prior literature has not confirmed and conducted sensitivity analyses of GRP maps, which revealed that several investigations quantitatively evaluated groundwater aquifer recharge (Selvam et al., 2016). Other studies have compared GRP maps to yield data approaches (Rahmati et al., 2015) and investigated the validity of GRP maps utilizing vertical electrical sounding (VES) techniques (Abdalla, 2012). Several research has proposed additional validation with field data (Magesh et al., 2012), pumping tests in locations with high GRP (Abdalla, 2012), isotopic follow-up approaches (Yeh et al., 2009), and stable baseflow (SBF) approaches for the verification of GRP maps (Senanayake et al., 2016). Both Remote Sensing and GIS have become increasingly essential to the hydrologic community as a whole. They have proven to be more cost-effective than traditional approaches in terms of generating meaningful data for existing groundwater exploration monitoring and management (Thakur et al., 2017). With its first study on the identification of hydromorphic units for groundwater exploration using Landsat Multispectral Scanner (Roy, 1978), research depending on the application of RS data and GIS techniques in groundwater studies had seen light in the late 1970s. However, it was only in the 21st century that these methodologies were used in research. The combined use of the RS and GIS has now become a revolution in groundwater science, allowing for both quality and

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TABLE 15.2 Summary of groundwater potential zone calculation using the GIS platform. Model

Theme used

References

Heuristic method

Land use/land cover, geomorphology, slope, soil, drainage type, geology

Thomas et al. (2009)

Weighted aggregation method

Slope, geology, lineament distance, hydrogeomorphology, depth to water table, drainage channel distance, well yield

Srinivasa Rao and Jugran (2003)

Normalized aggregation method

Lithology, geomorphology, soil, drainage density, slope, net recharge, surface water bodies

Shahid et al. (2000)

Index overlay

Hydrogeomorphology, land use/land cover, slope, soil, drainage density, pre and post-monsoon water table, static water level

Jasrotia et al. (2011)

Spatial Multicriteria Evaluation (SMCE) and overlay analysis

Geology, geomorphology, land use/land cover, drainage density, lineament density, slope

Mohanty and Behera (2009)

Multicriteria decision Making (MCDM) and Weighted linear combination

Slope, geomorphology, elevation, rainfall, surface water bodies, geology, soil, pre and post-monsoon water table, net recharge

Machiwal et al. (2011)

Weighted overlay analysis, Receiver Lithology, land use, lineaments, drainage, slope, and Kaewdum and operating characteristic (ROC) soil Chotpantarat (2021)

quantity evaluation and management, with the publication expanding at a 34% annual rate. Arsenic and fluoride are the most commonly studied groundwater pollutants in South and Southeast Asian countries, according to a growing body of research. Following 2002, the number of studies on arsenic exploded. The overall rising trend in arsenicrelated articles from India, Bangladesh, Pakistan, and Vietnam was oscillating or falling. Water Quality Index (WQI) has been widely utilized by researchers to analyze and evaluate groundwater quality and appropriateness for various purposes. The WQI combines a number of physicochemical and biological characteristics into a single statement. Hydrochemistry and hydrogeochemistry arose as the two key lines of inquiry in groundwater research, based on the emphasis placed on water quality and contamination. Since 1999, the productivity of hydrogeochemistry research has been steadily increasing, whereas the use of hydrochemistry has been wavering and declining. Groundwater vulnerability research conducted in the last decade has primarily focused on using GIS and DRASTIC model techniques to map sensitive zones and identify groundwater pollutants. The constantly rising use of Managed Aquifer Recharge (MAR), also known as Artificial Groundwater Recharge, as a water adaptation and management strategy to improve and protect groundwater systems threatened by climate change, natural disasters, and hydrological variability is associated with an increasing scientific base (Dillon et al., 2019). In order to control coastal groundwater resources, saltwater intrusion research has been conducted for the past 20 years. The relevance of surface and groundwater exchanges, as well as understanding and controlling the consequences of groundwater salinization, is being given more attention. Understanding how they combine has also aided in the creation of successful conjunctive water resource management programs.

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15.4 Role of geospatial technology in climate change assessment Evaluation of the effects of climate change and uncertainty on groundwater resources is crucial in groundwater management since it affects hydrogeological phenomena both intrinsically and extrinsically (Green 2016). Furthermore, only in the last decade have research on how groundwater supplies reacted to climate change and anthropogenic activity picked up, with substantial achievements from India, Thailand, and Bangladesh. The key issues were (1) the influence of climate change on groundwater levels and recharge in current and prospective contexts; (2) agriculture and subsistence resilience and adaptation in the face of climate-related disasters; (3) climate change’s influence on groundwater contamination. Harris et al. (2013) addressed carbon stock estimations as well as carbon emissions from forests around the world as trees degrade or burn. Satellite remote sensing data are an excellent resource for promoting the research of the Earth, its oceans, and atmosphere at all degrees of geoscience study. Although this course was designed for undergraduates and graduates, it can be used to teach climate change to researchers and students in accordance with the new Next Generation Science Standards (NGSS), which place a strong emphasis on this material as a core discipline (Achieve, 2013). These data were used by students to quantify carbon loss and CO2 emissions due to deforestation in the area of study. Recent progress such results to all Brazilian tropical forest areas using a bioregions map. According to the results of this exercise, annual CO2 emissions will be around 1.7 Gt CO2/year21, which is around 30% of total US emissions. Participants use calculations to place these numbers in the context of global emissions and begin to understand the importance of trees in regulating our climate. The Gravitational Recovery and Climate Experiment (GRACE) is a NASA, German DLR, and European Space Agency (ESA) collaboration in which two satellites circle the Earth 500 km (311 miles) above it, studying its gravity field. The distance between the two satellites changes as the gravitational field changes, which is detected using a microwave sensor that shoots a beam between the two satellites. It is possible to detect variations in satellite distance as small as 10 microns (a fraction of the thickness of a human hair). These alterations are utilized to create an Earth gravity map, which can be used for a wide range of purposes such as water resources, tectonic movement, ice and glacial mass, and sea level oscillation. Snow cover is an integral part of the climate process and a climate change marker. Its existence or absence indicates winter temperatures and can be used to assess climate change when projected over time; it also has an essential climatic impact in that it is a good reflector of sunlight and so serves to maintain the Earth’s cold by reducing sunlight absorption (BUDYKO, 1969). The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra and Aqua satellites is part of NASA’s Earth Observing System (EOS), which was created to conduct long-term Earth observations. Although the writers focus on the Sierra Nevada mountains, the same concept can be used in any geographical area throughout the world. In MODIS band 4 (0.55 lm, green), snow has a high reflectance, but in band 6 it has a low reflection (1.64 lm, near-infrared). The Normalized Difference Snow Index (NDSI), which is defined as the difference in reflectances of these two bands divided by their total, or (band 4 2 band 6)/(band 4 1 band 6), uses these characteristics to distinguish snow from similar-looking objects like clouds (Hall et al., 2002).

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Geospatial technology, with advancements in satellite remote sensing and GIS tools, can prove to be an effective tool in analyzing shifting climate patterns. Geospatial technology has been widely utilized by scientists and researchers for climate-related challenges in recent years. Over the last four decades, NASA has deployed dozens of new satellite instrumentation to track global environmental change in the Earth and atmosphere. The Landsat series (NASA Landsat, 2013), the Terra and Aqua satellites carrying the MODIS (NASA MODIS, 2013), ASTER (NASA ASTER, 2013), and AIRS (NASA AIRS, 2013) instruments, the SSMIS instrument (NSIDC, 2010) aboard the Defense Meteorological Satellite Program (DMSP) satellites, and the two GRACE spacecraft (NASA GRACE, 2013) launched in 2002 are just a few examples. There are four levels of satellite data accessible, ranging from 0 to 4. Higher-level data has been processed and organized into completed goods, whereas lower-level data is unstructured raw data (NASA Earth Science, 2013). Meteorological satellites (GOES, MTSAT, Elektro-L, Metop, EUMETSAT, INSAT, etc) are used to determine atmospheric temperature, winds, moisture, and cloud cover by measuring emitted and reflected radiation. Satellite remote sensing can be used to measure the number of contaminants in the atmosphere. The amount of data one works with will be determined by the application. For example, a Level 4 output would be a synthesized worldwide cloud-free map of land cover obtained from many instruments, while a Level 1 product would be recorded light intensity reflected off the Earth’s surface in each particular band (wavelength range). The lessons focused on the processing of Level 1, 2, and 3 goods, allowing students to better understand the variations between them and how higher-level products are created from lower-level ones. It’s useful for making critical decisions and tracking climate change sightings. It’s commonly employed in the agriculture industry, disaster management, and forest fire risk management, among other things. GIS is commonly used to map the spatiotemporal distribution of weather variables such as rainfall, temperature, and wind direction. Geospatial data is also used to track the amount of carbon and nitrogen in the atmosphere. Rising temperatures such as floods, droughts, heat waves, and storms have been connected to a warming climate by scientists (Cox et al., 2013). According to evidence, rising global temperatures may make these disasters more common and severe. Geospatial techniques allow us to understand how climate change influences weather patterns and can alert communities to threats to infrastructures, residents, and property. When severe weather strikes, geospatial intelligence aids first responders and disaster recovery professionals (Politi et al., 2016). Hazard maps based on remote sensing and satellite photography bring government officials up to date on present circumstances and which regions require immediate attention. Response teams give real-time information and images from the scene, allowing for more efficient and effective crisis management. After a significant storm or fire has passed, geographic information aids utility providers in restoring service and in planning the replanting of crops or the reconstruction of facilities. Understanding and mapping susceptibility patterns are aided by geospatial technologies while developing climate adaptation plans. Vulnerability analysis or determining the extent to which people or the environment may be affected. It necessitates the integration of three forms of knowledge on society and environment interactions: patterns of hazard exposure, sensitivity, and resilience. The level of damage projected from a specific occurrence, such as coastal flooding, a hurricane, or an extreme heat wave, is referred to as

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sensitivity, whereas resilience refers to the ability to recover from the effects of climate change. El-Nino and La Nina Ocean warming and cooling; tropical forest depletion; sea glacier meltdown in Antarctica or at the poles; vegetation monitoring through detailed knowledge of soils, erosion rates, nutrient cycles, and local agricultural practices; and water resource management through weather monitoring are all things that can be monitored, mapped, and shared. The global average temperature trend is visualized and communicated with consumers using GIS. Researchers are employing geospatial technologies to estimate the quantity of carbon in biomass and to use that data in carbon sequestration. Researchers at Massachusetts Institute of Technology (MIT) used ESRI software to create a Carbon Management Geographic Information System for the US, which allows them to acquire, integrate, alter, and evaluate data related to CO2 capture and sequestration.

15.5 Conclusion Despite the fact that climate change is widely acknowledged, research on the effects of climate change on the groundwater system is sparse. The explanation for this could be that analyzing the aspects of climate change necessitates a large amount of historical data. This information isn’t always available. Furthermore, the forces that generate such shifts are still unknown. The climate anomaly may occur on a regular basis and last for a long time. Even if all of the necessary data is available, there is inconsistency in the design variables, structure, and driving force of the hydrological cycle. Because of model constraints and the uncertainty of the forces that drive the earth, projecting the long-term influence of a dynamic system is extremely challenging. To avoid future development of regional water shortages, a physically based model of a groundwater system under anticipated climate change based on existing data is critical. Although limitations are unavoidable, new water resource management action tactics based on the model may be beneficial. Addressing the features of different places necessitates investigating the correlation between climate change and the depletion of fresh groundwater resources. Potential climatic change may have a greater influence on developing countries like India, whose economy is heavily reliant on agriculture and is already under strain due to rising population and corresponding needs for energy, freshwater, and food. Despite the uncertainties surrounding the precise amount of climate change and its potential consequences, particularly on regional scales, steps must be done to foresee, avoid, or reduce climate change’s causes and negative consequences. Investigating the impact of possible changes in the hydrologic cycle as a result of climate change is critical for safeguarding the quality and long-term sustainability of our water resources. Although projections are challenging and unpredictable, groundwater is a valuable resource, therefore a better knowledge of how regional and local climatic changes may affect groundwater systems is critical for effective water-resource management. Long-term assumptions of the interplay between climate and groundwater recharge, storage, and discharge, and also the development and testing of models that best reflect both the long- and short-term connections between climate and groundwater, both in terms of the water accounts and water quality, will be required to improve this situation. Under changing climate circumstances, continuous and regular monitoring of groundwater system reactions to climate is becoming

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increasingly important. Groundwater managers should be able to identify and create appropriate adaptation strategies, such as managed aquifer recharge and conjunctive use programmed, based on their improved knowledge of the potential implications of climate change on groundwater. Due to the vast quantities of information needed for the numerical modeling of watershed activities at the regional scale, the adoption of a geographic information system (GIS) and a relational database management system (RDBMS) was critical.

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C H A P T E R

16 Developing methods for building sustainable communities in flooded industrial complex areas Tadashi Nakasu1, Sutpratana Duangkaew2 and Chutaporn Amrapala1 1

College of Population Studies, Chulalongkorn University, Bangkok, Thailand 2 Faculty of Liberal Arts, Mahidol University, Nakhon Pathom, Thailand

16.1 Introduction The Chao Phraya floods of 2011 had a large impact not only in Thailand but also in Japan and worldwide. From an economic viewpoint, the Munich Reinsurance Company (MunichRe) declared it the seventh-largest natural disaster between 1980 and 2014 (NatCatSERVICE Munich Re, 2015). It also highlighted the supply chain’s vulnerability, drawing public attention. Looking at the background of the local community, Ayutthaya Province consists of 16 district levels called Amphoe, with the eastern part of the province consisting mainly of industrial estates and the western part of the province consisting mainly of agricultural areas. Economically, the province is rich in industrial estates in the eastern part and agriculture and tourism, with the province’s gross domestic product ranking 7th in Thailand in 2016. It is also worth mentioning that 451 of the 804 companies in the seven industrial estates in Ayutthaya Pathum Thani that were inundated during the floods were Japanese-related companies. From a long-term perspective, there have been reports of major changes in the social structure in the industrial estate areas of Ayutthaya Province, including an outflow of the working population, as the center of industrial estates in Thailand has shifted to the eastern coastal areas of the country in conjunction with the EEC (Eastern Economic Corridor) policy and changes in industrial patterns. However, despite the magnitude of the disaster, as mentioned above, only fragmentary information has been reported on the changes in the local social structure after the 2011 floods, changes in the risk awareness and social life of residents, and the outflow of companies and the working

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population, but the details have not yet been clarified. Furthermore, long-term studies on changes in the social vulnerability of industrial park communities are very important for the future sustainability of industrial park areas at risk of disasters. Still, researchers in Thailand, Japan, and other countries have not conducted enough research. After the 2011 floods, a 5
6 m wall was built around the main industrial parks/estates, which means that the communities around the industrial estates are at increased risk.We must not forget that Thai workers are the main reason companies do not withdraw from industrial parks. They work for companies, but they are also residents of the community. Strengthening the communities’ resilience around industrial parks from flooding is very important for the region’s sustainability. Based on the above awareness of the problem, the purpose of this study is to focus on the industrial park area affected by the 2011 Chao Phraya River floods and to identify the changes in social vulnerability and risks in the industrial park area before and after the floods, which have not yet been clarified even over 11 years after the disaster, to identify problems, and to propose solutions adjusted to the update situations. The chapter aims to build sustainable industrial area communities in the future by identifying problems and proposing solutions. Specifically, to achieve the above objectives, the study aims to: 1. Analyze and assess changes in social vulnerability (social structure) before and after disasters at the district (Amphoe) level, Ayutthaya Province (mainly based on statistical data); 2. Assess disaster coping capacity at the sub-district (Tambon) level by community leaders; 3. Visualize social vulnerability and risk as well as critical facilities’ locations in the area surrounding the industrial park, to identify disaster risk on the map; 4. Gather experience from the 2011 floods from community leaders and critical facilities’ representatives to apply for help capacity building in the areas.

16.2 Research methodology 16.2.1 Scope: contribution to sustainable development from a social science perspective Disasters illustrate the historical and cultural roots and development of complex and multidimensional natural and social events. Thus, the inquiry encompasses events, historical/cultural processes, and social, economic, and political circumstances. To enhance the region’s sustainability, the target area’s present situation, which is the scope of the study, was approached from a long-term macro perspective to a short-term micro perspective, as mentioned in Fig. 16.1.

FIGURE 16.1

Scope of the research.

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Specifically, the prefectural, district, sub-district, and village levels were considered from a spatial perspective. From a historical perspective, the region’s social, cultural, and economic changes were considered, as well as the response to disasters. Based on the above, this study systematically and in detail examined changes in social vulnerability. Specifically, at the macro level, interviews with officials in Ayutthaya Province and a survey of statistical data were conducted to establish indicators of social vulnerability at the district level and to examine the sustainable future of industrial estate areas. We focused on the changes in the vulnerability of the target areas before and after the 2011 disaster. At the meso- and micro-level, the study focused on field surveys of administrative units below the sub-district level. In particular, to strengthen local sustainability, we focused on disaster coping capacity assessment surveys, collection and visualization of social vulnerability and risk information, and collection of disaster experiences.

16.2.2 Literature Nakasu et al. (2013) and Okazumi and Nakasu (2015) examined the catastrophic worsening of economic damage through a social context and firm interrelationships. Nakasu (2017) clarified why many Japanese firms relocated to potential risk areas in Thailand. Tamada et al. (2013) approached the topic with various authors, mainly from economic, political, hydrological, and technical perspectives. Singkran (2017) reviews the 2011 floods from a disaster management perspective and emphasizes that more non-structural measures and participatory collaboration among stakeholders are needed for effective disaster management. Therefore, although there are research results from industrial, political, and even social perspectives, there has not yet been sufficient research on policy recommendations that are more community-based and feasible for the Thai government, society, and stakeholders. Based on this situation, Nakasu et al. (2019;2022a) examined the social vulnerability change in Ayutthaya Province, investigating the community capacity, social vulnerability, and risk around the industrial park. Duangkaew (2022) investigates the lives of their households, including employees. The disaster coping capacity assessment survey at the sub-district level was conducted mainly through interviews using a questionnaire which is developed using the FDPI (Flood Disaster Preparedness Indices) project outputs, the first international practical effort to measure community capacity in typhoon-prone countries (Nakasu et al., 2012) as shown in Table 16.1. Since the target communities experienced severe flooding in 2011, this chapter also draws on the following literature for its research. For example, Lindell & Prater (2003) describe an assessment methodology for examining the impact of natural disasters on communities; Lee (2019) highlights the inequality of local government capacity in the resilience of communities affected by past disaster experiences. The frequency and magnitude of disasters also impacted disaster response capacity. Jamshed et al. (2019) studied the link between vulnerability and disaster response capacity. Albright and Crow (2021) found that communities learn from past flood catastrophes and investigate how and why local government policies are adjusted to reduce future flood vulnerability. As discussed above, disaster coping capacity tends not to be well measured by statistical data because it is affected by social networks, past experiences, and other changing factors. Concerning the

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TABLE 16.1 Evaluation criteria: indicators and sub-indicators. No. Indicators

Sub-indicators

1.

Hard (physical infrastructure) countermeasures

Schools and health facility safety inspection, the existence of levee construction, construction plans for drainage facilities, drainage facility management organizations, etc.

2.

Flood disaster mitigation plans and standards

Budget, consistency of disaster management plans, the records, land use, and development restrictions, building codes/laws or guidelines, etc.

3.

Flood disaster mitigation systems Government officer education and training, the framework of effective technologies, stockpiling of daily commodities, response measures for illegal settlers, etc.

4.

Evacuation plans and systems

Emergency communication plans, evacuation shelters, shelter capacity, etc.

5.

Emergency and recovery plans and systems

Disaster management personnel responsibilities, disaster management plan procedures, disease control plans, etc.

6.

Leadership and collaboration between organizations

Community leaders’ attitudes towards disaster management, main policy for disaster management, public organizations, residents, and NPOs relationships, etc.

7.

Information and education for residents

Community residents’ education and training flood hazard maps, school education, information tools, etc.

8.

Community strength

Relationships with neighbors, participation in local activities, mutual help culture, etc.

practical applications for the research outcomes, Nakasu et al. (2022b) developed the scenario construction methods using companies’ disaster experiences in the industrial park. The methods will be applied to the communities and other stakeholders using their experiences to create cross-cutting scenarios and each scenario. The paper indicates the process of developing methods for building sustainable communities in industrial complex areas.

16.2.3 Approaches to identifying and changing pre- and post-disaster social vulnerability at the district (amp-) level 16.2.3.1 Background to the social vulnerability index for industrial complex area (SVI-ICA) Fig. 16.2 shows the 16 districts in Ayutthaya Province for developing the SVI-ICA. As mentioned earlier, Okazumi and Nakasu (2015), and Tamada et al. (2013) investigate the sociopolitical background of industrial complex areas. Among them, Nakasu (2017) explored why many Japanese firms relocated to the area. According to an interview with the deputy governor of Ayutthaya conducted in March 2019, the current key strategic measures taken by the government after the 2011 disaster, industrial zones are mainly located on the eastern side of Ayutthaya, protected by a water wall and the main agricultural areas on the west side of Ayutthaya, using the farmlands as reservoirs to hold water,

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16.2 Research methodology

FIGURE 16.2 Target area: Ayutthaya province.

Ban Phraek

Maha Rat Tha Ruea

Bang Pahan Nakhon Luang

Phachi

Phak Hai

Bang Ban Phra Nakhon Si Ayuhaya

Uthai

Bang Sai Sena

Bang Pa–In

Wang Noi

Bang Sai Lat Bua Luang

like dams during the flood season, was reported. However, long-term and practical soft measures are needed for the region’s sustainability. From a macro perspective, this study first examined social vulnerability at the district level in Ayutthaya Province. 16.2.3.2 Exposure, susceptibility, and capacity After referring to the Pressure and Release Model (PAR model) (Wisner et al., 2004) to identify the social vulnerability of districts to natural disasters, three categories of exposure, susceptibility, and disaster coping capacity were created to variables were identified to assess the social vulnerability of the districts. These variables were determined within the limitations of the available data. Population and industrial area densities were used to establish exposure indices. The population density of children, the population density of elderly, household income poverty, the population density of disabled, and the population density of non-Thai migrant workers were considered when calculating susceptibility. Road density, volunteer population density, and school teacher population density were used for disaster coping capacity. First, the analysis was conducted using data from 2018, when the most updated data are commonly available. 16.2.3.3 Development of SVI-ICA To develop the SVI-ICA, we first collected data through field surveys in Ayutthaya Province and visits to government agencies and local universities in Ayutthaya. Then, the variables used to construct the SVI-ICA were examined, and indicators for exposure,

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susceptibility, and disaster coping capacity were developed. Each indicator, the reasons, and the methodologies chosen were based on de Brito et al. (2018), di Girasole and Cannatella (2017), Fatemi et al. (2017), Fekete (2019a), and Fekete (2019b) and a wealth of other recent literature. In particular, this study applies the MOVE framework using the mentioned PAR model (Wisner et al., 2004) regarding Birkmann et al. (2013) and Lianxiao and Morimoto (2019) and considers it from a demographic perspective. The calculation formulas applied the method of the Human Development Index developed by the United Nations Development Programme (UNDP) (Anand & Sen, 1994) as shown in Eqs. (16.1) and (16.2) below. ½LNðxÞ 2 LNðMinÞ=½LNðMaxÞ 2 LNðMinÞ ðfor exposure and susceptibilityÞ

(16.1)

½LNðMaxÞ 2 LNðxÞ=½LNðMaxÞ 2 LNðMinÞ ðfor coping capacityÞ x : Target Value LN : Natural Logarithum

(16.2)

This study used principal component analysis rather than simply combining the individual components to construct the SVI-ICA. In the principal component analysis, all variables related to exposure, susceptibility, and disaster coping capacity were used in the calculations. The results showed that the first principal component was interpreted as “resistance to natural disasters” (45%), the second principal component as “susceptibility to natural disasters” (25%), and the third principal component as “exposure to natural disasters” (18%). The weights of the relative ratios of each component were used as coefficients; the formula for setting the SVI-ICA is Eq. (16.3). SVI-ICA 5 2 0:45TRes 1 0:23TSus 1 0:18TExp:

(16.3)

PC1: Resistance to natural disasters (Res). PC2: Susceptibility to natural disasters (Sus). PC3: Exposure to natural disasters (Exp). While the preliminary SVI-ICA at the district level presented here has been presented as a proceeding of an international conference (Nakasu et al., 2019), the focus is to use this study to integrate and develop the method for strengthening the local community disaster resilience in the area.

16.2.4 Approach to disaster coping capacity at the sub-district (Tambon) level In this chapter, we exemplified the target area, firstly Uthai district as a part of the project, then, focused on a 2-km area around the Rojana industrial park to develop methods by (1) survey disaster coping capacity, (2) identify social vulnerability and risk information, (3) identify critical facilities, and (4) collect experiences of each disaster. 16.2.4.1 Target community This chapter defines the community as “the living people in the smallest administrative unit and their shared assets.” The smallest administrative unit is the smallest administrative organization with personnel and budget, and the residents, individuals, and public assets within that organization are considered the community. Based on this definition,

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311

Thailand’s sub-district (tambon) was considered a community in this study. Based on the above, 2 km around two industrial estates and the four sub-districts are targeted, and the area is shown in Fig. 16.3. 16.2.4.2 Disaster coping capacity Disaster coping capacity assessment (capacity assessment) is “the process of examining the capabilities of groups, organizations, and societies in light of desired goals, identifying existing capabilities to be maintained or strengthened and identifying gaps in capabilities for further action” (UNDRR/Terminology, 2022). This study followed the above definition to develop the methods. 16.2.4.2.1 Methodology for assessing disaster coping capacity

Each question was developed based on the indicators and sub-indicators with the advice of experts from local governments and related organizations and adapted to local communities in Thailand. Specifically, each indicator item and sub-indicator were organized, and those inappropriate for the local community were omitted and weighted. The formula is:   Indicator score 5 1 ðbasic scoreÞ 1 Σ each subindicator score ð0 2 1Þ x coefficient of each subindicator : where a higher score for each major indicator suggests a higher capacity. Each major indicator’s minimum and maximum values are 1 and 10, respectively. The results of these evaluations are visualized in a radar chart to facilitate the extraction and confirmation of bottlenecks. This helps to understand the current situation in the community, and community leaders can understand the evaluation’s meaning in making appropriate outputs. In addition, community leaders can increase their awareness of their capacities. The indicators,

FIGURE 16.3 Target communities.

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contents, and calculation methods are detailed by Nakasu et al. (2012) as a basic reference for this assessment. 16.2.4.2.2 Finalization of indicators and field survey for self-capacity assessment

As mentioned above, the indicators and sub-indicators were carefully reviewed and revalidated, with expert comments, to suit local conditions in Thailand. The field survey was conducted as follows. First, the district heads were contacted through the Ministry of Interior in consultation with the Office of the Governor of Ayutthaya Province. Subsequently, with the cooperation of the district heads, interviews were conducted with the targeted sub-district leaders. The survey was conducted in Ayutthaya from November 2019 to March 2020. Leaders and other staff from the four targeted sub-districts (Khan Ham, Thanu, Nong Nam Som, and Baan Chan), as well as all 35 village heads in the area, participated in the survey. The survey and analysis were conducted in parallel, taking time to adapt to the situation from the effects of the COVID-19 Pandemic.

16.2.5 Identification of social vulnerability and risk information and collection of experiences in areas surrounding industrial parks While conducting the questionnaire survey of community leaders described above, this study also collected social information about the community. Specifically, we collected risk information on vulnerable groups and their locations, houses, and apartments where vulnerable groups were concentrated, especially those severely affected by the 2011 floods, and identified their locations on a map. In addition to obtaining this information, we also asked community leaders about their experiences and lessons. The collection of experiences was also conducted in about 25 critical facilities in the target area.

16.3 Research results 16.3.1 Ayutthaya province, district (amp-) level This section presents the results of the social vulnerability assessment at the district level. Fig. 16.4 maps the results of exposure, susceptibility, and disaster coping capacity indices. Each district’s name and location can be confirmed in Fig. 16.2. FIGURE 16.4 Exposure, susceptibility, and coping capacity of Ayutthaya by district.

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313

16.3.1.1 Exposure, susceptibility, and coping capacity of districts in Ayutthaya The intensity is visualized in the map above so that the darker-colored districts show a stronger tendency for each indicator. As for exposure, the districts with higher exposure tend to be located in the eastern part of Ayutthaya Province. This is because of the presence of industrial estates such as Saha Rattanakorn, Rojana, Hi-Tech, Bang Pa-In, and Factory Land Wang Noi on the east side. The west side is mainly agricultural land, so the exposure indices for these areas are relatively low. As mentioned above, Ayutthaya Province considers this situation as a countermeasure after the 2011 floods. Susceptibility indicates the susceptibility of each district in Ayutthaya Province to external forces from flooding. Ayutthaya (Phra Nakhon Si Ayutthaya (PNSA)) is the most susceptible because of the concentration of susceptible people, children, and low-income people in the area, while Bang Pahan, due to its proximity to the capital and industrial estates, has a higher proportion of children, elderly, disabled people, non-Thai migrants, and workers. Bang Pahan has the second highest percentage of children, elderly, disabled, and non-Thai migrant workers due to its proximity to the capital and industrial park. Nakhon Luang, home to the Saha Rattanakorn Industrial Estate, has a very low percentage of children and the elderly. Except for Nakhon Luang, the most vulnerable districts are concentrated in the eastern and central parts of the country. Regarding Capacity, PNSA has the highest capacity due to its high density of volunteers and teachers and high road density. On the other hand, Wang Noi district, where the factory site is located, has the lowest capacity. Uthai district, where Rojana Industrial Park is located, has the second lowest capacity. Compared to other industrial parks with high capacity, the two districts of Wang Noi and Uthai need to be considered in terms of future disaster response capacity. 16.3.1.2 SVI-ICA by district, Ayutthaya province Fig. 16.5 displays the SVI-ICA results from Table 16.2 on a map. Based on the values shown in Table 16.2 and the SVI-ICA indicators described in Eq. (15.3), the darker the district in the map, the stronger the indicator tends to be. The SVI-ICA shows that PNSA is the most vulnerable, Bangpahan is second, and Bangpa-in, with its high-tech and Bangpa-in Industrial Estates, also shows very high values. On the other hand, Bangsai (1413:West Side) has the lowest social vulnerability. Other low-vulnerability districts are often agricultural areas. In particular, Nakhon Luang, where the Saha Rattanakorn Industrial Estate is located, shows very low social vulnerability. In this paper, the study selects the Bang Pa-in District, with two industrial estates, as the next target area to investigate.

16.3.2 Sub-district level 16.3.2.1 Disaster coping capacity The disaster coping capacity assessment results of the four sub-districts are shown in Fig. 16.6 and Table 16.3 (0
5 Low, 6
7 Moderate, 8
10 High). Despite the high community capacity indicators for all communities, there is a large gap in scores between the

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FIGURE 16.5 SVI-ICA

N

by district, province.

E

W

Ayutthaya

S

10

5

0

10 Kilometers

Legend SVI 1.29

1.02

1.01

0.73

0.72

0.55

0.54

0.01

0.00 0.64 0.65 2.51

west and east sides of the communities around Rojana Industrial Park for indicators such as hardware measures and information and education for residents. Each community’s (sub-district) name and location can be identified in Fig. 16.3. 16.3.2.2 Gap analysis Following the definition mentioned of disaster coping capacity assessment, we first compared the differences in the sub-indicators of “Hardware measures” and “Information and education for residents,” where there is a large gap between the west and east sides

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16.3 Research results

TABLE 16.2

SIV-ICA by district in Ayutthaya province.

District

Resistance

Susceptibility

Exposure

SVI-ICA

0.14288

0.93175

2 0.89323

2 0.011

Uthai

2 0.56026

2 2.10347

2 0.02952

2 0.237

Wang Noi

2 0.31903

2 4.07335

1.3685

2 0.547

Lat Bua Luang

1.80301

2 0.72782

2 0.24985

2 1.024

Maha Rat

1.6499

1.01452

2 1.22834

2 0.730

2 1.75916

2 1.66261

2 0.088

0.69829

2 0.15142

2.514

1.33396

2 0.74251

2 1.56129

2 1.052

2 0.26562

1.79138

2 0.79217

0.389

1.36462

1.74044

0.60006

2 0.106

Sena

Phachi

2 1.36816

Phra Nakhon Si Ayutthaya

2 5.2907

Phak Hai Ban Phraek Bang Sai (1404) Bang Pa-in

2 0.65

0.56183

1.23358

0.644

Bang Pahan

2 1.94537

1.23571

3.14365

1.725

Bang Ban

1.20466

0.52408

2 1.77273

2 0.741

Bang Sai (1413)

3.6791

0.16383

1.82179

2 1.290

Nakhon Luang

2.13417

0.21992

0.76446

2 0.772

2 2.91316

0.52457

2 0.59086

1.325

Tha Ruea Min

2 1.290

Max

2.514

of the area, as shown in Fig. 16.7. For example, different trends were also analyzed for the educational and scientific information sub-indicators between the eastern and western sub-districts. Flood hazard maps, rainfall information, and weather information (IE2, IE6, and IE8) differed between the east and west. As with the hard measures, the eastern subdistricts scored very low compared to the western ones, which implies a lack of important hazard-related scientific information. Regarding the sub-indicators of trends common to the east and west communities, education and training, tools, and operations (IE1, IE4, IE9, IE10, IE11) are relatively high common features.

16.3.3 Social vulnerability and risk information in areas surrounding industrial parks 16.3.3.1 Identification of social vulnerability and risk information Bellows show some of the information on socially vulnerable groups and risks obtained from community leaders and village heads. In reality, a total of approximately 120 sites were identified. Fig. 16.8 and Table 16.4 show examples of the location information, and Table 16.5 shows some of the disaster experiences obtained through the interviews.

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FIGURE 16.6

Overlapped self-capacity assessment results.

TABLE 16.3 Indicator’s points. Item Indicator

Baan Chang

Nong Nam Som

Khan Ham

1.

Hard countermeasures

Low 3.0

Low 3.0

Moderate 6.3 Moderate 5.8

2.

Flood disaster mitigation plans and standards

Moderate 7.6 Low 4.4

High 10

High 9.7

3.

Flood disaster mitigation systems

Moderate 7.6 Moderate 6.0

High 10

High 8.7

4.

Evacuation plans and systems

Moderate 7.8 High 9.2

High 10

High 9.2

5.

Emergency and recovery plans and systems

Low 5.7

Moderate 7.9

High 10

High 9.5

6.

Leaderships and organizations’ collaboration

Moderate 7.0 Moderate 7.9

High 10

High 9.7

7.

Information and education for local residents

Low 5.4

Low 4.1

High 9.6

High 9.2

8.

Community strength

High 8.4

High 8.8

High 10

High 10

Thanu

16.3.3.2 Identification of critical facilities’ locations and information Next, critical facilities located in four sub-districts within 2 km of the target area, Rojana Industrial Park, were investigated. Then, considering the level and balance of limited resources, 25 critical facilities were selected from the total number of 65.

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16.3 Research results

Information and Educaon

Indicator

IE11 Form of information on IE10 Information water levels, facility operation, etc. IE 9 Emergency communication tools IE 8 Weather Information Level IE 7 Water Information IE 6 Rainfall Information IE 5 Flood Fighting/Evacuation Drills IE 4 School Education IE 3 School Disaster Management Drills IE 2 Flood Hazard Map IE 1 Community residents education and training

IE11 IE10 IE9 IE8 IE7 IE6 IE5 IE4 IE3 IE2 IE1 0.0

0.2

0.4 Point

FIGURE 16.7 Information and education.

FIGURE 16.8 Social vulnerability and risk information.

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0.6

0.8

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TABLE 16.4 Vulnerability and risk location. Sub-districts Baan Chang

Vulnerability and risk locations (extracted) Elderly concentration area Sa Dao Canel Khan Han Factory and foreign labor area Low elevation area. Disabled living areas Populated area Factory and foreign workers location

Nong Nam Som Elderlies living area Populated Area Provincial hospital- Taking care of victims Cholpratan Canal

Thanu

Lowest level area Populated area Foreign workers area Food risk area close to canal Chest Level Hight Flood Flood prone area close to a canal. Many workers apartment Many elderly living areas Many migrant Workers Many Disabled residential areas Risk prone areas near the river Many disabled and elderlies

TABLE 16.5 Extracted narratives of communities. Extracted narratives Baan Chang

There were no toilets and animals were all over the place. The elders and disabled people were transferred to provincial hospitals. Transportation was difficult and poisonous animals were out there.

Nong Nam Som The people in a village where the area was low-level resulted in deep water to seek help and assistance. Khan Ham

Many of factory’s equipment were floating around the community and hit the house, causing severe damage.

Thanu

The transportation was completely stuck in some areas. Villagers, especially elderlies tried to stay on the second floor of their houses because they were worried about their assets.

Fig. 16.9 and Table 16.6 show some of the descriptions of experiences obtained from the interviews during 23
25 March 2020 with the surveyed facilities and their representatives. Due to research ethics issues in Thailand, the location of the key facilities and the interviews’ content were not, identifiable. Regarding critical facilities, the local community plays a main role in helping and supporting the victims’ assets and life security.

16.4 Analysis and discussion 16.4.1 Comparison with before the 2011 flood disaster Due to the limited available data, we only used the results of the 2011 pre-disaster exposure and susceptibility indices at the district level to provide insight into changes in social vulnerability. As part of the longitudinal exposure comparisons, we also examined population density and population change in each district. We also examined changes in the number of firms in the industrial parks and the population changes in those districts from the perspective of the sustainability of the industrial areas.

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319

FIGURE 16.9 Critical facilities’ location.

TABLE 16.6

Extracted narratives of critical facilities.

Categories

Extracted narratives

Evacuation Centers

- During floods male staff were assigned to facilitate transportation while female staff took care of victims in the center and coordinated donations. The organization distributed sandbags and more masonry and relief bags (food and drink) and purchased boats to use for helping victims. It cooperated with military agencies to transfer the victims to the evacuation center. - After the flood, the City Hall prepared an annual flood protection plan to financially support victims in repairing their homes, and some roads. - The severest issue was commuting due to the high flood water levels outside the organization, causing difficulty evacuating. When victims evacuated to hear, it was found that not having enough living equipment such as beds, rooms for victims, and information made many people unable to handle the situation on timein transferringwhenever. (A City Hall)

Provincial Housing Authority

- When the surrounding area got flooded, the Provincial Housing Authority publicized that it opened the community flat as an evacuation center for 500 people of capacity. This housing authority provided help to victims with utilities and consumer products. The severest issue was difficulty to transfer the victims into the facility and not having enough food and drinks. It was not able to handle promptly due to not having enough information. - After the flood, the facility did not make any flood protection plan because this prone is not a normal situation and will not happen regularly. (B Provincial Housing Authority) (Continued)

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TABLE 16.6 (Continued) Categories

Extracted narratives

Government Offices

- Flood protection plan and rehearsal are conducted by the Disaster Prevention and Mitigation Department. During the flood, the government officials had responsibility for public relations assistance, planning, and monitoring. It was aware of the flood news from radio broadcasts and the memorandum from relevant government agencies, such as the Provincial Administrative Organization, Irrigation Department, District Office, etc. The officials assigned male staff to facilitate the transportation and female staff to take care of handling documents, cooking, and coordinating donations. Victims had to report to the officers every time they wanted to go home and advised on safety from electricity and poisonous animals. The offices provided help in the evacuation center until all victims moved out. - The problem was insufficient boats and the central government’s insufficient budgetfor purchasing various consumer products. (C Subdistrict Administration Organization)

Schools

- The buildings became an evacuation center for 500
800 victims when the surrounding flooded. However, schools have no objection to monitoring victims’ recovery. - The issues were (1) Not enough toilets and showers, (2) A large amount of solid waste produced a bad smell. (D College)

Temples

- Many stayed at the temple because the temple had a large building that could support the victims and enough consumer products for victims before the local authorities came in to help the victims and coordinated with government offices and/or private agencies to request support for food and drink. The temple was converted into an evacuation center. They help to warn local agencies and build the facility’s reliability the up sandbag barriers. - Not enough toilets and showers, and faced insufficient food and drinks for victims in the long-term. (E Temple)

Hospitals

- The hospital was not an evacuation center during the flood, but the staff was rotating shifts to protect all medical devices. The severest issue was commuting during the flooding because officials still had to come to the office and did not have enough food for everyone. Office buildings, documents, and some office equipment that could not be moved to a higher place got damaged. It took the long time needed for recovery, which affected the reliability of the facility and public relations. (F Hospital)

Police Stations

- Some police cars were submerged. Office equipment was not damaged as they were moved to a higher place. Police stations were not an evacuation centers during the flood, but there were staff stationed to receive complaints or check if there were any complaints. - The issue was getting out to help victims, as the flood water levels were high. Boats were used for commuting, which caused a delay in helping victims (G Police Station)

Fire Stations

-The issues were (1) Transportation - fire trucks and garbage trucks were used for commuting, (2) Budget from central government, and (3) Foods - needing to go out to buy food outside the area and quite far from the center. (H Fire Station)

16.4.1.1 Exposure indices and population density As shown in Table 16.7, the exposure index (Exp. Index) and population density (Pop. Dens) of industrial estates and lands in UThai, Wang Noi, and Bang Pa-in are higher than before the disaster. In particular, the changes in the Bang Pa-in area are more pronounced

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16.4 Analysis and discussion

TABLE 16.7

Exposure index and population density changes. 2018

2010

Gaps

District

Exp. Index

Pop. Dens.

Exp. Index

Pop. Dens

Exp. Index

Pop. Dens

Sena

0.55

153

0.54

151

0.0021

2

Uthai

1.17

247

1.14

231

0.0225

16

Wang Noi

1.56

248

1.51

221

0.6517

27

Lat Bua Luang

0.67

148

0.65

144

0.0155

5

Maha Rat

0.44

114

0.43

114

0.0025

0

Phachi

0.92

244

0.93

238

2 0.0010

6

Phra Nakhon Si Ayutthaya

1.40

530

1.40

498

0.0000

32

Phak Hai

0.15

120

0.17

124

2 0.0167

24

Ban Phraek

0.40

179

0.41

178

2 0.0050

1

Bang Sai (1404)

1.02

145

1.01

143

0.0057

2

Bang Pa-in

1.43

212

1.32

172

0.1142

41

Bang Pahan

1.45

300

1.46

293

2 0.0088

7

Bang Ban

0.75

108

0.75

108

0.0063

0

Bang Sai (1413)

0.45

93

0.45

94

0.0000

21

Nakhon Luang

0.89

109

0.86

104

0.0354

5

Tha Ruea

1.25

296

1.26

292

2 0.0136

4

than in the PNSA area. On the other hand, the exposure index and population density in agricultural areas tend to be lower or negative compared to pre-disaster levels. 16.4.1.2 Exposure gaps and changes There was a statistically significant difference in the pre-and post-flood exposure changes between districts with and without industrial estates (Wilcoxon Rank Sum Test value (w) 5 48, P 5 .004 , .05). It is noteworthy that the exposure index changed negatively in five districts that do not have industrial parks: Phak Hai, Tha Ruea, Bang Pahan, Ban Phraek, and Phachi. This implies a decrease in population and industry. On the other hand, Bang Pa-in is the only district with two industrial parks and a considerable population density, and its exposure index has also increased significantly. In considering sustainable development, these changes in exposure indicate not only the impact of flooding but also the widening gap between industries, indicating the need to consider the issue from a more holistic aspect. Although there is no statistically significant difference in susceptibility between districts with and without industrial parks (w 5 37, P 5 .06..05), Bang Pa-In, Bang Ban, and Wang Noi are more susceptible to flooding after the 2011 disaster. In these districts, the proportion of elderly and children is higher, especially after the disaster. These changes require improved capacity to cope with disasters for sustainable

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development. For SVI-ICA, the 2018 data revealed no statistically significant difference between districts with and without industrial parks (w 5 22, P 5 .86..05). PNSA was found to be the highest district, while districts east of Ayutthaya without industrial estates, such as Bangpahan and Tharua, were found to be the second and third highest districts; Bang Pa-in district with two industrial estates was found to be the fourth highest district. These SVI-ICA values and the changes in exposure and susceptibility indicated above suggest that PNSA, districts on the east side of the province without industrial estates, and the Bang Pa-in district need to focus more on disaster preparedness than other areas. As mentioned earlier, the limitations of the available data in this study did not allow us to measure changes in coping capacity, but the above considerations can be made. 16.4.1.3 Population change and companies in industrial parks The number of occupied companies in the target Rojana Industrial Park has changed as follows: 2010: 213, 2011: 213, 2012: 206, 2013: 206, 2014: 212, 2015: 213, 2016: 213, 2017: 214, 2018: 215, 2011 only decreased for two years after the floods and has been gradually increasing since then. The population of the Uthai area also shows an increasing trend. Furthermore, a rapid population increase can be observed in Bang Pa-in district, which has two industrial complex areas, as shown in Table 16.7. This indicates that the industrial complex areas in at least two districts are attracting both population and workers as well as firms, regardless of the impact of the floods.

16.4.2 Disaster coping capacity Based on the social context, this study reveals a significant gap between the results for the western and eastern sub-districts of the industrial park. In particular, hardware measures, information, and education gaps were identified. Significantly, for eastern and western sub-districts, the high coping capacity features are associated with education, training, tools, and operation. At the same time, the hazard maps, rainfall, and weather information need more attention in the eastern area. For example, in the central region of Thailand, floods are considered a blessing for farmers because they bring fertile soil. Local people have so far lived harmoniously with floods (Nakasu et al., 2020). When the industrial park was established in this area, the sub-districts around the industrial park were urbanized, mainly in the western sub-district of the targeted industrial park. They are generally more flood conscious. On the other hand, the eastern sub-districts, mainly agricultural land, tend not to be prepared for flooding and instead express concern about drought. Geographic and topographic location is also very important, with some of the sub-districts being slightly higher in elevation and tending to be less concerned about flooding. As for commonalities, all sub-districts maintain a strong sense of solidarity, even if the western sub-district is urbanized. As described above, a detailed analysis of the survey results provides a deeper understanding of disaster risk in the target areas. In addition, the disaster coping capacity contributes to the community’s awareness of disaster preparedness. The survey results reveal the strengths and weaknesses of the capacity of local communities from

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16.5 Applying lessons learned for practical use

different social backgrounds around the industrial parks. The process and results can be used to strengthen the capacity to cope with flood disasters through regular dialogue with community leaders.

16.5 Applying lessons learned for practical use As shown in Fig. 16.10, this study collected the narratives along with the timeline and then extracted the words by text mining to identify critical points. Then, question-style scenarios and answer keys, including lessons learned, were created. Therefore, these points provide the lessons learned for continuing business management as scenarios based on experience. The scenarios are for practical use, such as enterprise education and training. This section shows a brief outline of the application of the disaster experience for practical use based on the previously published paper for scenario development of the companies. Through the mentioned process in Fig. 16.11, evidence-based scenario construction will be created for communities and critical facilities. There are common keywords among each stakeholder’s experience. We can use the common keywords to construct cross-cutting scenario questions to enhance regional resilience for the companies and communities, critical facilities, and other stakeholders together before, during, and after the disasters.

FIGURE 16.10 construction scenario.

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of

Overviews of the disaster

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FIGURE 16.11

16. Developing methods for building sustainable communities in flooded industrial complex areas

Procedure for constructing evidence-based scenario questions.

16.6 Challenges and responses In this study, we faced three main challenges. The first is data collection, the second is survey items for self-capacity assessment, and the third is the impact of the COVID-19 pandemic. The following describes the three challenges and our efforts to address them. Regarding the first challenge, there are difficulties in obtaining data; data from government websites are either unavailable or inaccessible. Some data are considered highly sensitive and cannot be shared publicly. For these reasons, it was impossible to show vulnerability changes in the form of indicators. Based on these limitations, this study attempted to infer changes in social vulnerability from available data, such as population at the macro level, and to visualize disaster response capacity and social vulnerability qualitatively at the meso and micro levels. The second challenge is a large number of questions in the disaster coping capacity assessment survey, the fact that different local governments have different priorities and mandates, and that sub-district leaders are not necessarily familiar with the activities within their areas. Therefore, with the advice of the Department of Disaster Prevention and Management (DDPM) staff of the Ministry of Interior, the questions were adapted to the local context. For the interview survey, we consulted with the staff of the DDPM Ayutthaya office. The third issue was to the interviews over more than one year, as mentioned above, because there were times when face-toface interviews were not possible, and there were restrictions on leaving the house. At the same time, we analyzed and discussed the possible results of the interviews, and conducted other tasks. The interviews were conducted through official letters from the heads of

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departments, as well as top-down introductions from the DDPM of the Ministry of Interior, the Ayutthaya Governor’s Office, district heads, sub-district leaders, and village heads, while also making maximum use of online resources. The survey was conducted by making maximum use of the online system. Although the survey activities overlapped with the entire research period, the results were satisfactory.

16.7 Conclusions for a sustainable future Developing countries tend to have rapidly changing societies that are vulnerable to disasters and for which statistical data are difficult to obtain. We first developed a social vulnerability index for industrial complex areas at the district (municipality) level, where some statistical data are available. We identified district-specific vulnerabilities and their changes within that province. This district-level comparison revealed relative regional social vulnerability and its changes showing the industrial complex areas are becoming more vulnerable after the 2011 flood. At the sub-district (community or village) level, where relevant statistical data are almost unavailable, community leaders and disaster management officials were interviewed to collect and analyze information on disaster coping capacity and social vulnerability and risk (and flood experience), including location information. Furthermore, to improve practical disaster coping capacity, the location of critical facilities such as public institutions, schools, temples, hospitals, etc. (evacuation centers, etc.) that the community (district) should prioritize protection was also investigated. Using the findings, for example, before a flood, community leaders can instruct volunteers to quickly evacuate the elderly and vulnerable residents via safer routes to evacuation centers that are not at risk of

FIGURE 16.12

Outline of the methods.

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16. Developing methods for building sustainable communities in flooded industrial complex areas

flooding. In addition, even during regular times, the disaster experience can be used as a regional (district) hazard map and for various other purposes, such as education and training materials and disaster scenario creation indicated in Figs. 16.10
16.12. Thus, this chapter presents an analysis and assessment of social vulnerability and its changes at the district level, the identification of community-level disaster coping capacity and social vulnerability, the pathways to visualize them, and their applications. These processes can be adapted to other regions in Thailand and abroad. Currently, we have started a survey of communities around industrial estates in Bang Pa-In district, Ayutthaya Province, which was selected as the next target area based on the results of this study. As described above, we continue to develop this research theme, not only in this project but also to make the research itself sustainable, produce compelling research results, and give back to the Thai government and Thai communities. The process and findings contribute to building sustainable communities in industrial complex areas in Thailand and worldwide.

Acknowledgments The authors would like to thank the Governor of Ayutthaya Province, DDPM officers, district, sub-district, and village leaders, and representatives of key facilities for their interviews and other valuable assistance, and we also thank the community research members, Dr. Ruttiya Bula-Or, Dr.Sutee Anantsuksomsri, Mr.Kullachart Prathumchai, Mr.Korrakot Positlimpakul, Dr.Akiyuki Kawasaki for their support. We express our sincere gratitude to all of them. This research was supported by a research grant from the Housing Research Foundation JUSOKEN (Grant No. 2016) and the Science and Technology Research Partnership for Sustainable Development (SATREPS) Program “Strengthening Regional Resilience through the Construction of Area-BCM in Industrial Agglomerations” in collaboration with the Japan Science and Technology Agency (JST, JPMJSAI1708) and the Japan International Cooperation Agency (JICA). Strengthening Regional Resilience through the Construction of Area-BCM in Industrial Agglomerations”. We would like to express our gratitude again here.

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C H A P T E R

17 Climate change and agroecosystem: impacts, adaption, and mitigation in South Asia Shobha Poudel1,2, Bhogendra Mishra1,2, Sujan Ghimire1, Nirajan Luintel1, Praseed Thapa3 and Regan Sapkota4 1 3

Science Hub, Kathmandu, Nepal 2Policy Research Institute, Narayanhiti, Kathmandu, Nepal Agriculture and Forestry University, Chitwan, Nepal 4Policy Initiatives Nepal, Lalitpur, Nepal

17.1 Introduction In South Asia, agriculture provides livelihood to over 70% of the population and contributes 22% of the regional Gross Domestic Product (GDP) (Wang et al., 2017). It is estimated that food grain requirements will grow by about 50% by 2050 in South Asia (UNICEF, 2020.) Due to “green revolution” technologies and high-yielding seed varieties, the food grain production has increased since the 1970s (Hazell, 2010). However, green revolution practices attributed to the over-utilization of fertilizers, pesticides, and resources, while diminishing soil quality and biodiversity (Choudhary et al., 2018). Moreover, as the population has continued to rise, it is at risk of being unable to provide sustained food and nutrition security in the future. Therefore, the already fragile food production system is affected due to climate change, and these impacts have direct, detrimental effects on food security (Barbier & Hochard, 2018). Long-term changes in temperature and precipitation patterns are likely to change the seasons for certain crops, as well as increase the prevalence of pests and diseases that impact crop yields (Poudel & Shaw, 2016), production, and market prices (Mishra et al., 2021). Millions of farmers in the region will be affected by these changes, particularly those with low adaptive capacity to climate change (Aryal, Rahut, et al., 2020). Over the past century, a warming trend of about 0.75 C has been observed in annual mean temperatures in South Asia and is projected to increase by 1.56 C
5.44 C in 2080, tremendously increasing the heat stress regions (IPCC, 2007). The models derived by

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(Almazroui et al., 2021) illustrate a continual increase in annual mean temperature over South Asia during the 21st century. On the other hand, precipitation shows larger spatial variability in the projected climates. The projected mean annual precipitation shows an increasing trend over the South Asian region. Climate-induced drought is likely to reduce water availability, while agricultural water usage is expected to rise by 19% by 2050 (Field & Barros, 2014). Hence, South Asia will face the challenges of food security due to climate change in the region. By 2050, South Asia might lose the equivalent of 1.8% of its yearly GDP, and by 2100, that loss could reach up to 8.8%. The average total economic losses are projected to be 9.4% for Bangladesh, 6.6% for Bhutan, 8.7% for India, 12.6% for the Maldives, 9.9% for Nepal, and 6.5% for Sri Lanka respectively (Ahmed & Suphachalasai, 2014). Therefore, adaptation and mitigation measures are required to sustain agricultural productivity, reduce vulnerability, and enhance resilience to climate change. The details of the statistics are illustrated in Tables 17.1 and 17.2. The Himalayan region is the major source of water, share very common topographical and regional attributes (Mukherjee et al., 2022). Additionally, due to the coherent socioeconomic status of the region, a large similarity is observed in this region. The impact of global warming in the region directly affects the downstream region of South Asia (Mishra et al., 2014). Therefore, the nature of the consequences of climate change is comparable across South Asia. The main objective of this research is to stock-take on climate change impacts, adaption and mitigation measures, particularly in agroecosystem of South Asia. This enables us to perform a comparative analysis of the impacts, adaptation options, and mitigation measures among South Asian countries. Thus, the study explores the

TABLE 17.1 Impacts of increased temperature on major crop production in South Asia. Country

¢T(1) Rice

Maize

Wheat

Nepal

4 C

Yield decreased by 24.3% in terai

Yield increased by 8.3% (Malla, 2008)

India

, 2 C Yield decreased by 6% (Mathauda et al., 2000)

Yield decreased by 10%
30% (Kalra et al., 2007)

Yield decreased by 5.2% (Gupta et al., 2017b)

Yield loss by 14.25% (Basak et al., 2009)

Yield loss about 60% (Karim et al., 1996)

Yield decreased by 1.83% in terai

Bangladesh , 3 C Yield decreased by 26.25% (Karim et al., 1996)

TABLE 17.2 Change in rainfall pattern impacts on major crop production in South Asia. Country

Impacts

Crops

Nepal

Yield is correlated with seasonal rainfall data

Paddy, maize, millet, wheat, barley (Bhandari, 2013)

India

Drought and extreme rainfall negatively affected the yield

Rice and wheat (Kumar, 2016)

Bangladesh

Yield positive and significant with rainfall

Rice (Sarker et al., 2012)

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FIGURE 17.1 The study area: South Asian countries.

impact of climate change on agriculture across South Asian countries, the adaptation measures taken by these countries and the possibilities of lessons learned from one country to another in the region (Fig. 17.1).

17.2 Method This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework. We started the revision work after finalizing the research theme, followed by the database. A number of databases such as Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and SciVerse were applied. We focused on various articles and reports from development partners and agencies, with research articles, short notes, policy briefs, and review articles published in scholarly journals. Multiple keywords were used to search the

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review materials, such as “climate change and agriculture in south Asia,” “agroecosystem,” “climate smart agriculture”, “climate change in south Asia,” “climate change adaptation,” “climate change mitigation,” “agricultural impact of climate change” etc. A total of 261 articles and reports were identified at the beginning. We filtered them based on the impact, adaptation, or mitigation of climate change in agriculture in South Asian countries. It ended up with 111 articles and reports subjected to a systematic review.

17.3 Climate change impacts, adaptation and mitigation measures in South Asian countries The impact of climate change is unique to differ based on location, country, community, sector, gender, and age group (Poudel et al., 2020). Similarly, the impact of climate change on agriculture production differs from one country to another. The effects of climate change are already being felt and are especially problematic in low-latitude, lessdeveloped, and vulnerable countries (Abid et al., 2016; Poudel et al., 2017) where heavy reliance on subsistence farming results in livelihoods closely tied to environmental changes. In these regions, farmers face the need to adapt their agricultural practices to cope with climate change. A growing body of literature has identified that farmers in developing countries perceive that environmental conditions have changed and are adopting alternative agricultural strategies to adapt to these changes (Gentle & Maraseni, 2012; Poudel et al., 2017). The adaptation and the mitigation measures also depend on the proper knowledge, advanced measures, technical, technological, and financial resources.

17.3.1 Case 01: Afghanistan There have been significant changes in the climatic conditions of Afghanistan since the 1950s (Aich et al., 2017). The mean annual temperature in Afghanistan has increased by 0.6 C and the mean decadal temperature by 0.13 C and is projected to increase by 1.4 C
4.0 C by the 2060s, and 2.0 C
6.2 C by the 2090s. In addition, the average monthly annual precipitation has decreased by 0.2 mm since 1960 (Savage et al., 2009). The major climatic hazards for the country include floods and landslides, droughts, and its more gradual form of aridification which contribute to the lower levels of water availability. • Numerous adaptation and mitigation interventions, though limited (Jawid & Khadjavi, 2019), have been adopted in Afghanistan for enhancing local resilience, which are: • Drip irrigation technology introduced in rural communities in Badghis Province • Macro-catchments construction for large water catchments to capture and store rainfall, snowmelt, and flood waters to help recharge groundwater used for irrigation and drinking • Check dams for slowing down the spring flood water velocity and allow the water to percolate and recharge groundwater for the purpose of water harvesting • Community-based watershed management programs prioritized by the National Adaptation Program of Action for Climate Change (NAPA) to address the frequent drought conditions for agriculture production

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• Other interventions that specifically target enhancing the adaptive capacity of the farming communities such as the National Solidarity Program of the Ministry of Rural Rehabilitation and Development/the World Bank, adaptation project under Least Developed Countries Fund (Baizayee et al., 2014), climate change projects by World Food Program, Action Aid, and Agha Khan Foundation.

17.3.2 Case 02: Bangladesh The impacts of climate change in the coastal country are no longer an abstract phenomenon (Eckstein et al., 2019; Uddin, 2022). In recent years, rising salinity levels in water sources is making freshwater-based agricultural livelihoods increasingly unsustainable. According to the Asia Development highlights, it is estimated that rice production in Bangladesh will decline by 17% by 2050 because of rising temperatures and CO2 (ADB, 2018). Further, there could be losses of 17% of land surface and 30% of food production due to rising sea levels and coastal erosion (Agarwal et al., 2022). Manga affected Barind Tract familiar for its historical combat of malnutrition, and food and drinking water scarcity generated by drought, unavailability of underground water extract even from 100 feet down, occurrence of cyclones every 3 years, calendar changing from six to four seasons, etc. are some other impacts in Bangladesh. The development of the Bangladesh Delta Plan 2100 (BDP2100) to build climate change resilience has recently started. Promotion of green financing, fostering green banking, and adoption of a fiscal framework to channel more resources towards adaptation investments are other measures targeted for adaptation. Few major projects implemented by the government of Bangladesh targeted for climate resilience programs are as follows. • Coastal Embankment Improvement Project to mitigate large impacts from cyclones and flooding for improvements of agricultural production. • Multipurpose Disaster Shelter Project to reduce the vulnerability of the coastal population through reconstruction and improvement of multipurpose shelters. • Emergency 2007 Cyclone Recovery and Restoration Project to facilitate recovery from the damage to livelihoods and infrastructure in Sidr/Aila-affected areas including others. • Weather and Climate Services Regional Project for Bangladesh to deliver reliable weather, water, and climate information services to farming communities.

17.3.3 Case 03: Bhutan Impacts like cyclone-induced storms, flash floods, glacial lakes outburst floods, landslides, and prolonged droughts-which have become quite common in Bhutan, which threatens the highly vulnerable agricultural sector due to its dependence on monsoon rains, short growing periods, and exposure to large climatic swings found in mountainous regions (Chhogyel & Kumar, 2018). The requirements of a cool environment for the temperate fruits (Ramirez & Kallarackal, 2015), colonization of the pastureland by invasive weed species, and reducing the regenerative capacity of the indigenous grass species (Suberi et al., 2018) are also other effects observed. Some evidence of climate change

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impacts being felt in Bhutan are high-intensity rain in 2016 that damaged .100-acre of rice crops, Northern corn blight in 2007 damaging above 50% of crops in high altitudes (NCHM, 2019), 50% loss due to leaf blight disease of maize in 2007 (Chhogyel & Kumar, 2018), etc. Loss of crops by 10%
20% due to unpredictable weather, drying of water resources, shorter winter, and increased disaster frequency are major impacts of climate change on agriculture in the country (Chhogyel et al., 2020). • The country has introduced and promoted climate-smart technologies as one important priority (Chhogyel & Kumar, 2018). For illustration, numerous high-yielding, droughttolerant, disease-resistant, and short-duration crop varieties have been developed and released for major cereal crops, that is, rice, maize, wheat, and barley. • Crop diversification, dry land cropping, water harvesting, crop insurance, promotion of agroforestry, plantation of windbreaks, and land management have been increasing, including other mitigating strategies such as intercropping, gender-responsive climate actions, and sustainable waste management practices.

17.3.4 Case 04: India According to the Global Climate Risk Index 2021, India is among the top ten countries most affected by climate change. Several impacts of climate change like floods, cyclone damage, persistent drought, rise in sea-level, disease, pest, insects, etc. are major concerns disrupting production influencing both demand and supply, locally and globally (World Bank Group Climate Change Action Plan 2021
2025 South Asia Roadmap, 2021). It has been reported that if the temperature rises by 2.5 C
4.9 C in India, GDP will fall by 1.8%
3.4%. The losses of about 4%
9% of the agricultural economy each year, which is an overall GDP loss of 1.5 % were estimated by a Parliamentary Standing Committee on Agriculture, 2017’s report. Other impacts like reduced water availability coupled with drought reduced the productivity of major crops such as rice, wheat, and maize (Kumari et al., 2021). The current wheat yield was observed to be 5.2% less than it could have been without the observed temperature rise, and the yield could further decrease by 2%
4% with each degree Celsius rise in temperature (Gupta et al., 2017). • National Initiative in Climate Resilience Agriculture was launched to enhance the resilience of the agriculture sector through the demonstration and promotion of improved crop varieties, crop diversification, and other agricultural practices. • Climate Smart Agriculture initiatives of the CGIAR’s Climate Change, Agriculture and Food Security (CCAFS) are working in India to incorporate gender dimensions of agriculture, and climate change to build Climate Smart Village (CSV) approach to build resilience among farming communities. • National Rural Livelihood Mission is supporting innovative sustainable livelihoods and adaptation measures in Madhya Pradesh and Bihar (Mishra et al., 2020). • The micro-drip system in many water-stressed regions of India has been promoted by the Jain Irrigation System to build the adaptive capacities of farmers through technological innovation. • The traditional participatory watershed development approach is promoted.

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• Cyclone risk mitigation infrastructures have been built for reducing the vulnerability in the coastal regions and also facilitated community-based risk management plans and capacity building of line departments (Mishra et al., 2020).

17.3.5 Case 05: Maldives Maldives, with an average height of just 1.5m above sea level, is considered one of the most vulnerable countries to climate change and its long-term survival has been questioned due to coastal inundation and sea level rise. These events are further aggravated by erratic rainfall, tropical cyclones, and hurricanes (Metz et al., 2007). Schleussner & Hare (2015) state that the effects of climate change on the agriculture sector in the Maldives will negatively impact the national income. The options available for adaptation in Maldives are limited. Unfortunately, the only option available for Maldives is protection, which may also be beyond its financial and economic capacity (Shaig, 2006). Nonetheless, some adaptation/mitigation measures have been adopted in Maldives such as: • Use of setbacks and preservation of coastal vegetation, particularly on the oceanward side of an island. • Application of alternate methods of growing fruits, vegetables, and other food crops using hydroponic systems have been practiced (Shaig, 2011). • Seasonal adaptation measures are also common in Maldives. In the dry season, cucumbers, watermelons, and pumpkins are planted on the surface. • During the wet season, people use containers to plant short crops like chilies and use improvised fences to grow creepers. Some farmers raised the bed to plant creepers on the surface (Shafeeqa & Abeyrathne, 2021). • The changing climate and prolonged periods without rain have introduced many pests and diseases, thus pest and disease-resilient crops have been developed.

17.3.6 Case 06: Nepal Nepal is one of the most vulnerable countries to climate change. The profound impacts of climate change are severe due to the country’s diverse topography and corresponding climatic variations, natural resource-based livelihoods, and resource constraints. Agriculture is one of the major sources of income and livelihood for around two-thirds of Nepalese households. Changes in climatic parameters particularly rainfall patterns adversely affect crop production, livelihoods, and food security. Different strategies are implemented in farming to build climate resilience, some of the measures include the following section. • Promote the use of climate-resilient cultivars (Biggs et al., 2018). • Soil conservation through adjustments in the usage of fertilizers, pesticides, and irrigation (Biggs et al., 2018; de Sousa et al., 2018). • Agroforestry is an equally common adaptation/mitigation measure practiced, by protecting crops from extreme weather and leading to a positive effect on yields (Amadu et al., 2020).

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• Changing the cropping patterns specially in the hilly region, for example, paddy fields into maize and finger millet. • Adaptation of relatively more drought-tolerant crops such as ginger and turmeric in the drought-prone area or cultivation of crops such as watermelon, pointed gourd, ginger, and sweet potatoes in the river banks area where massive sand deposition from rivers is frequent after climate extreme events like floods. • Introduction of renewable energy technologies such as solar-powered irrigation which is a popular means of adapting to climate change while mitigating greenhouse gas emissions. • The farmers have started the practice of climate-smart agriculture (CSA) that aims to facilitate actions toward the transformation of agricultural systems (Rosenzweig et al., 2020). The common adaptation practices in the CSA includes optimum utilization of resources such as water, adapted market mechanism, irrigation facility, hazard tolerant cropping system, shift of the cropping calendar, crop diversification, and agroforestry.

17.3.7 Case 07: Pakistan With its mainly arid geographical profile and resource scarcity, Pakistan is among the highly vulnerable countries to climate change (ADB, n.d). To cope with the impacts of climate change the farmers in Pakistan depending on their location and type of climate issue are applying various adaptations and mitigation measures (Ahamad et al., 2013). The major strategies are changing planting dates, changing crop variety, and changing fertilizer types such as urea, diammonium phosphate, Nitro-Phos, and single superphosphate. • Farmers use weather forecasting information, particularly for planting dates of rain-fed crops. In the rain-fed region, farmers mostly changed planting dates according to variations in climate in irrigated regions, as are other primary strategies implemented by farmers (Abid et al., 2016). • Changing crop varieties like switching from traditional wheat varieties to heat and drought-tolerant varieties to protect the crop from increasing temperature and water shortage (Ahamad et al., 2013; Abid et al., 2016). • Adaptation practices include crop diversification, soil conservation, altering the crop variety, varying the crop calendar, changing the fertilizer/pesticide used, cover cropping, and farm insurance (Mabe et al., 2014). • Flood-resistant crops and construction of field boundaries are also some adaptation measures taken during foods events. Besides, plinth elevation and shelterbelts are adaptation measures used by farm households to reduce exposure to foodwaters and associated damages. Installation of tube wells and use of drought-resistant crops are the major adaptations to cope with droughts. • Some other general adaptation measures taken are tree plantation for protection against winds, foods and rising temperatures, taking monetary loans, and agroforestry practices on farmlands (Qazlbash et al., 2021). • Farm households create grain storage facilities to counter the possibility of crop failure from heavy fooding (Ali & Erenstein, 2017).

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17.3.8 Case 08: Sri Lanka Sri Lanka is highly vulnerable to the impacts of climate change and the agriculture sector is among the highly hit by its impacts. The agricultural sector contributes 10.9% to the GDP and 31% of the population is employed and remains the main source of livelihood for rural communities in Sri Lanka. The majority of the population (77.4%) lives in the rural sector where farming is extensively practiced (Menike & Arachchi, 2016). Agricultural measures such as the use of improved crop varieties, planting trees, soil conservation, changing planting dates, and irrigation are the most widely used adaptation/ mitigation strategies. The socioeconomic, environmental, and institutional factors and the economic structure are key drivers influencing farmers to choose specific adaptation methods (Menike & Arachchi, 2016). Few of the major adaptation and mitigation followed by the farmers in Sri Lanka includes: • Farmers living in the dry zone construct small reservoirs, locally known as tanks, to store wet-season water for dry-season cultivation. • The construction of a network of massive irrigation systems is also a major adaptation action taken by Sri Lanka (Withanachchi et al., 2014). • A land distribution system, bethma, is also practiced. Under bethma, permanent field boundaries are temporarily abolished, and land is equally redistributed amongst all farmers who cultivate in the command area (Thiruchelvam, 2005). • Farm households cultivate food and drought-tolerant varieties and use pest-resistant varieties to better adapt to changes in climate (Weerasekara et al., 2021; Zhang & Managi, 2020). • Farmers are also involved in changing planting and harvesting times, cultivation methods, rainwater harvesting, and improving irrigation. • Agricultural interventions to water use efficiencies, such as regular canal maintenance, support for crop diversification, and monitoring of illegal water use are also practiced. • Government subsidies on improved drilling technology and cheaper pumps have increased the prevalence of agro well use for agricultural cultivation and thus the adaptation capacity of the farmers.

17.4 Discussion 17.4.1 Climate change adaptation measures on agriculture in South Asia -Planning for and adapting food production under future climatic conditions requires a thorough understanding of historical production trends as well as the effects of climate change on farming practices. Climate change adaptation encompasses any activity aimed at reducing vulnerability and increasing system resilience, and therefore, the actual impacts of climate change largely depend on the adaptive capacity (IPCC, 2014). Adaptation measures in agriculture depend on the attributes of climate change, farm types, locations, and cost to farmers (Smit & Skinner, 2002). There are many adaptation practices in the production systems that have been proposed and tested for minimizing the effects of climate change. Among the practiced

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TABLE 17.3 Major adaptation options in the agricultural sector in South Asia. S.N

Adaptation measures

Examples

1.

Soil Management

Tree planting, hedgerow planting Water harvesting Zero tillage (Sapkota et al., 2015) Sequestration of Soil Organic Carbon (SOC) (Powlson et al., 2016) Water Use Efficiency (WUE) Biochar

2.

Crop diversification

Suppresses pest outbreak Reduces pathogen transmission Less water-intensive cropping Underutilized crops (Adhikari et al., 2018) Short duration varieties (Lasco et al., 2011) Flood-resistant variety of rice, e.g., Scuba rice, Jalnidhi (Singh et al., 2010) Drought-tolerant variety of rice, e.g., Sahabhagi dhan, Swarna Sub (Mottaleb et al., 2017) Cold and frost tolerant hybrids of maize, e.g., HQPM1 Changing cropping pattern Shifting planting date (Jalota et al., 2013)

3.

Water management

Integrated water management Water harvesting WUE Micro irrigation (Drip and Sprinkler irrigation) (Kumar, 2016) System of Rice Intensification (SRI) (Barah, 2009) Dry land farming Alternate Wetting and Drying (AWD) (Gathala et al., 2017) Direct Seed Rice (DSR)

4.

Sustainable land management

Agroforestry Conservation Agriculture (CA) Sustainable intensification

5.

Crop pest and disease management

Integrated Pest Management (IPM)

6.

Crop insurance

Index insurance (Matsuda & Kurosaki, 2019)

7.

Agro-advisory services

Meteorological information Information and Communication Technology (ICT)

adaptation options are on-farm practices and biophysical measures that include increased soil organic matter, improved cropland management, use of local genetic diversity, improved livestock management, crop-livestock mixed system, multiple cropping, improved grazing land management, sustainable food production, prevention and reversal of soil erosion and agroecological approaches (Alteri & Koohafkhan, 2008). Table 17.3 illustrates the major adaptation options in South Asian countries.

17.4.2 Climate change mitigation measures on agroecosystem in South Asia Agroecosystems play an important role in climate change by both exhibiting climate change consequences and contributing significantly to greenhouse gas emissions. Methane emissions from enteric fermentation and rice farming, as well as nitrous oxide emissions from soil and fertilizer application, are significant contributors to agricultural GHGs emissions. As a result, adaptation-led mitigation strategies are needed to maintain agricultural output and farm revenue, and reduce GHGs emissions (Aryal, Rahut, et al., 2020; Aryal, Sapkota, et al., 2020). GHGs emissions can be reduced by either sequestering CO2 or reducing CH4 and N2O emissions. Soil, water, and fertilizer management, as well as climate-smart technologies, can considerably cut GHGs emissions from crop fields (Ahamad et al., 2013). Zero tillage, direct seeded rice, fertilizer use efficiency, leaf color chart, etc. are the mitigation measures applied in the region. Similarly, feed inputs and

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manure management have significant potential to minimize animal production emissions by improving carbon sequestration. Table 17.3 illustrates the major adaptation options in agriculture sector in South Asia.

17.5 Enabling institutional and policy support The South Asian Agroecosystem is extremely vulnerable to climate change, given its sensitivity to variations in temperature, precipitation, and occurrence of natural events and disasters such as droughts, floods, and inundation. On the other hand, the agriculture system largely contributes to GHG emissions and Global warming. Hence, there is a cause-andeffect relationship between agriculture and climate change that impacts the region’s food security and socioeconomic development. The prevalence of poverty, malnutrition, and increasing populations also exacerbate the region’s vulnerability. The impact of climate change is different based on location, and more than a stand-alone measure for agriculture to adapt to climate change is needed. Agriculture sector resilience should be mainstreamed and incorporated into policies and programs across the scales of governance in the South Asian region. The governments of South Asian countries investing in climate-induced disaster risk reduction still need to do a lot on agroecosystem resilience to climate change. India has a National Water Mission under the National Action plan on climate change; this is a no-regret strategy for promoting water-use efficiency (Pound et al., 2018). Implementing climate-smart agriculture practices (CSA) is ongoing in some South Asian countries as an adaption response in areas of pronounced climate variability. That is one of the entry points for evidence-based agricultural policies to increase food security under climate change. India potentially has a vital role in sharing its learning from its green revolution program with other South Asian countries. Transboundary agroecosystem management is critical in South Asia since it shares the Ganges
Brahmaputra River basin and Hindu Kush Himalayan ranges among the countries. This policy may help restore degraded lands, sequester carbon, adapt to climate change, and sustain agricultural biodiversity while improving agricultural production, rural livelihoods, and food security. Similarly, multi-country collaboration for climate change planning is urgent in the region. The capacity building of the nations in the agriculture sector should also be included in the bilateral and multilateral collaboration. Specific countries’ characteristics should be considered while formulating regional policies and plans. Need to establish a favorable enabling environment for formulating and delivering climate change policy for agriculture. That can promote an understanding of agroecosystem at all levels of governance, including school, college, and university curricula, and foster evidence-based decision-making. Bilateral or multinational funding is needed to strengthen technical, policy, and investment in climate change adaptation in the region. This includes, but not limited to, implementing the national climate change strategies for agriculture, National Adaptation Program of Action (NAPA), National Adaptation Program (NAP), etc. Some relevant policies that encourage long-term farm viability and investment in sustainable land management, reduce the exposure of farming communities to climate-induced risk, and support resilience should be formulated on a regional scale.

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17.6 Conclusion and recommendation South Asia is one of the world’s most densely populated regions, and it is heavily reliant on agriculture, which is vulnerable to the effects of climate change. Extreme climatic phenomena have a direct and detrimental impact on the region’s agroecosystem. These climatic shocks ultimately damage the livelihoods of millions of farmers, particularly those with limited adaptive capacity. Crop losses due to unexpected outbreaks of diseases and pests, irregular rainfalls, windstorms, hailstorms, droughts, flash floods, and landslides are frequent in the region. As such, climate change will certainly result in increased fluctuations in crop productivity, food supply, and market prices, exacerbating food insecurity and poverty in the near future. Understanding how existing agricultural production responds to climate variability and future climate change is critical to ensuring food security and livelihood in South Asia. Furthermore, adaptation strategies are required to maintain agricultural productivity, reduce vulnerability, and strengthen the agricultural system’s resilience in the face of climate change. Therefore, stakeholders must develop policies that assist farmers in dealing with the vulnerabilities to climate change to ensure agricultural sustainability in South Asia.

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C H A P T E R

18 Climate change and flood: vulnerability and community resilience Henny Warsilah1 and Choerunisa Noor Syahid2 1

Rural and Urban Studies, Center for Community and Cultural Research, National Research and Innovation Agency (PMB BRIN), Jakarta, Indonesia 2Research Center for Area Studies, National Research and Innovation Agency (PRW-BRIN), Jakarta, Indonesia

18.1 Introduction: problems about vulnerability and community resilience Indonesia has a high potential for natural disasters such as earthquakes, volcanic eruptions, tsunamis, floods, climate change, and others, especially on the island of Java, the main island where the country’s capital city is located. In the context of ecology, the island of Java is in an ecological disaster position. Environmental conditions in Java are increasingly threatened because of implementing a Java-centric development paradigm, through government policies, especially in the economic development sector and physical infrastructure. This is reflected in the document of Indonesia’s Mid-Term Development Plan and the Master Plan project for accelerating and expanding Indonesia’s economic development, which focuses more on economic development than on the conservation of natural resource ecosystems. The large population of Indonesia also adds to the burden on the balance of the ecosystem. Indonesia’s population reached 271,349,889 people (BPS, 2021). Around 57.13% of the population resides on the island of Java because of the island’s high economic growth. Java Island is one of the most densely populated areas with more than 136 million people living in an area of 129,438.28 km2 or about 6% of the total land area in Indonesia. As recorded by BPS (2021), the population of Central Java, based on the 2020 Population Census, the population is 36,516,035 people. Population density on the island of Java has implications for the magnitude of pressure on natural resources. This pressure mainly occurs in 4614 villages located in and around

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state forests and coastal areas. Based on data (Environmental and Forestry Management Agency (BPKH), 2012), among 98 million hectares of forest area owned by the state, only about 3.38% are recorded on the island of Java. The forest area in Java is 2,429,203 hectares, and its management is handed over to the State Forestry Public Company. Data from the Central Statistics Agency (BPS, 2012) shows that the number of poor rural people in Java is 8,703,350. The poverty of rural communities living in and around Java’s forests is caused by limited land. The Agricultural Census Data and the RACA Institute (Agricultural Census Data and the RACA Institute, 1993) stated that farmers’ land tenure in Java averaged 0.3 hectares per family head. Coastal cities on the island of Java are strategic areas and the most vulnerable to disasters caused by climate change and social-ecological crises. It is said to be a strategic area because almost all coastal areas in Indonesia are the main gates of marine economic activity in their respective regions while being said to be the most vulnerable to changes that occur naturally due to human activities or a combination of both. The facts show that coastal areas in various parts of the country, especially the island of Java, are experiencing very worrying ecosystem damage. The question posed in this paper is, what are the efforts of the Central and Provincial Governments in responding to climate change disasters on the island of Java, particularly in coastal cities? And how does urban planning become a smart and tough city to encourage smart and resilient people? The answer will be explained in the subsequent narration.

18.2 Climate change and social-ecological crisis on the Island of Java Ecological disasters on the island of Java appear in the form of climate change and urban heat, warming that causes droughts, tidal floods, landslides, land subsidence, and forest fires. Climate change and warming disasters in Indonesia are estimated to have reached 90%. Indonesia’s potential for ecological disasters is also caused by rampant deforestation, massive mining practices, and monoculture crops such as plantations. In coastal areas, ecological disasters and climate change are caused by, among others, shoreline shifts, high waves, coastal reclamation, land conversion, building loading, excessive groundwater extraction, and population density. The condition of the area with the worst potential for ecological disasters on the island of Java is Banten Province reaching 62.5% of the total area, then DKI Jakarta at 51.9%, then West Java at 48.0% (WALHI, 2020). Floods accounted for 32.96% of the total disaster events, while landslides accounted for 25.04% of the total disaster events. In the coastal areas of Java, in the period 1996 to 1999 alone, there were at least 1289 villages affected by floods. The number has increased almost three times (covering 2823 villages) until the end of 2003, which is also an implication of the destruction of coastal ecosystems as a result of land conversion, destructive fishing, reclamation, and marine pollution because 80% of industries in Java are located along the north coast of Java (Kartodihardjo, 2006). The average global economic impact due to disasters each year reaches about US$ 250 billion to US$ 300 billion. In Indonesia, it is shown that in the last 10 years, due to the natural disaster, Indonesia lost around IDR 162 trillion. The National Disaster Management

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Agency also released that in 2014 there were 1967 disasters that caused 566 deaths (Disaster Risk Assessment in Indonesia, 2015). The city of Semarang-Central Java is the center point of the main route of the North Coast of Java with a coastline length of 36.63 km. Semarang is growing rapidly as a big city with a variety of industrial, trade, and service activities. The rapid development of this city has led to a higher population level in the coastal areas of the city of Semarang, including Kamijen East Semarang which are generally used as port centers, industrial areas, and residential areas. This condition eventually triggered a classic phenomenon in the city of Semarang, one of which was tidal flooding and land subsidence.

18.3 Initiatives for developing smart and resilient city-based city management The smart city concept is starting to be known and developed in the last decade. This concept aims to provide solutions for various city problems and to develop initiatives to facilitate urban communities in accessing information, and services, and participate in the city’s development with information communications technology (ICT)-based (Mapping Smart Cities in the EU, 2014). Smart city policy has been widely implemented in many cities in the world, including cities in Indonesia. Major cities in Indonesia, which are Surabaya, Jakarta, Bandung, and Semarang have been starting in realizing and introducing the smart city concept since 2015. The local government of Semarang city has undertaken an innovative step. By registering Semarang as the first city in Indonesia that joins the 100 Resilient City (100 RC) program that was initiated by the Rockefeller Foundation. The 100 RC Program in Semarang started in May 2015. There are six main strategies such as water and energy, new economic opportunities, disaster and disease risk reduction, integrated mobility, and transformation of information in policy formulation. In disaster risk reduction, adapting and mitigating tidal floods became an important point in their program. In Indonesia, the smart city component is narrowed down to six aspects: (1) Smart governance, (2) Smart economy, (3) Smart mobility, (4) Smart people, (5) Smart environment, and (6) Smart living (Bappenas, 2015). A smart city is a governance concept that combines two components, hard infrastructure (IT Technology and ICT) and soft infrastructure. Integration between people and technology became a serious issue, but it requires processing continuously to achieve the ideals of a smart city. See Bappenas Criteria, for instance, in a smart economy, the city of Semarang is required to be able to develop the concept of a smart, resilient city and competitive city economy through the integration of productive, creative, and innovative economic activities based on ICT-used. While in a smart environment, cities should be smart and competitive in managing their resources by using technology based. The Semarang government has been integrating technological use and disaster management. Technological use has improved planning, response, decision-making, and disaster response, especially in tidal flood management by assessment and analysis of near-realtime information. The Regional Disaster Management Agency of Semarang also has another hardware system that was built together with Semarang Smart City. This system is an application that can be accessed by a smartphone, which is called flood monitoring. The project plan for the early warning system is one of the supporting programs for

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Semarang City flood prevention. This system is designed not only for mechanisticoriented systems but to engage community participation in mapping the flood at seven urban villages in Semarang. There were also local initiatives being carried out by NGOs and local governments. They tried to build community resilience with community partners, called Kolectif Hysteria and Peka Kota. Coastal cities such as Semarang must be able to integrate the smart city with the resilience concept called “Smart Resilience.” The concept of a Smart Resilience City that has been developed by the City of Semarang, not only includes an early warning system before a disaster occurs, but also after a disaster. Resilience is a system’s ability to deal with a disaster. When cities are facing disaster and pressure (shock and stress), the city’s function must be affected. Through the system and resilience, cities can recover soon and be able to improve their condition from disasters or other threats, such as climate change impact. Resilience is an important concept in the application of disaster risk reduction. With the concept of resilience, disaster risk reduction is no longer only focused on meeting pre-disaster needs and gaps but also focuses on community capacity building to take care of themselves independently (Djalante & Thomalla, 2010). There are four spaces that (Figueiredo, 2018) is concerned with realizing urban resilience, such as (1) economy, (2) society, (3) governance, and (4) environment. Resilience is the key process to overcoming climate change impact, and a resilient city is an answer to solving problems in adapting and mitigating climate change impact. The Organization for Economic Cooperation and Development (OECD) defines Resilience cities as “cities that have the ability to absorb, recover and prepare for future shocks (economic, environmental, social & institutional)” (Figueiredo, 2018). Based on data and figure exposure from OECD, cities have four drivers; economy, society, environment, and institution, which lead them to be resilient. These four drivers are interconnected to each other such as one driver will support the other drivers. The governance driver consists of clear leadership and management, strategic and integrated approaches are taken by leaders, the public sector has the right skills, and the government is open and transparent. Society driver consists of society is inclusive and cohesive, citizen networks in communities are active, and citizens enjoy healthy lives (OECD, 2015), “Resilient Cities, Framework for Resilient Cities,” http://www.oecd.org/gov/regionalpolicy/resilientcities.htm (accessed May 2022). Based on the two concepts, i.e., smart city and resilience city, there are intersections and similar objectives to improve the community’s quality of life in urban areas. The existence of a theoretical relationship between a smart city and a resilient city was expected to be a starting point for further research and reflection on achieving intelligence and resilience as urban development strategies (Arafah & Winarso, 2017). In this concept, community participation is the main point for making cities and also increasing the community’s capacity in disaster management or building smart and resilient communities.

18.4 The concept of social resilience The concept of Social Resilience has become a key concept in science that is oriented toward social analysis in the study of the interaction of the natural environment with humans, or so-called social-ecological systems. Social resilience aims to explore how to

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18.4 The concept of social resilience

Resilience Buffer capacity Endowments: Assets ownership/ Entlements: Access to assets

Self-organisaon Instuons

Human capital

Cooperaon & networks

Natural capital

Network structure

Financial capital

Opportunity for self-organisaon Reliance on own resources

Social capital Physical capital

Capacity for learning Knowledge of threats & opportunies Shared societal (collecve) vision Commitment to learning Knowledge idenficaon capability Knowledge sharing capability Knowledge transfer capability Funconing feedback mechanisms

Diversity

FIGURE 18.1 Resilience concept. Source: Data from Speranza, C.I., Wiesmann, U., Rist, S. (2014). An indicator framework for assessing livelihood resilience in the context of social-ecological dynamics. Global Environmental Change, 28, 1109
1190. 9593780. Elsevier Ltd., Switzerland. http://www.elsevier.com/inca/publications/store/3/0/4/2/510.1016/j. gloenvcha.2014.06.005.

handle successful interactions between humans and climate factors, especially those related to social issues, such as well-being, quality of life, identity, and social and cultural values in relation to transformations to sustainability. Previous researchers have written about ecosystems and socio-ecological resilience, such as Folke et al. (2002), DOI, and Berkes and Seixas (2005). The authors define social resilience as “the capacity of actors to access better livelihoods in urban spaces too—not only cope with and adapt to adverse conditions of climate change (reactive capacity) but also seek and make choices (proactive capacity) against disasters so that they can develop competency improvement in dealing with threats from climate change.” Obrist et al. (2010) more specifically interprets that social resilience means, at the same time, increasing the ability to respond to adverse external conditions and developing collective action aimed at changing the part of the external social structure that limits institutions related to resilience. This means not only being able to cope and adapt to adverse conditions (reactive capacity) but also seeking and making choices (proactive capacity), thereby developing self-improvement competencies (positive outcomes) in dealing with the threat of climate change. The concept of social resilience (Fig. 18.1) from Speranza et al. (2014) can help to understand the factors that enable communities to protect their livelihoods from the negative consequences of climate change and climate variability, which are given in the following sections.

18.4.1 Buffer capacity Buffer capacity is the capacity to change and use emerging opportunities to achieve better livelihood outcomes, such as reducing poverty (Speranza, 2013). The generalization of

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buffer capacity is an organization in social systems that refers to the re-creation of the spontaneity of society through the dialectic of social structure (top-down process) and human action (bottom-up process), without explicit control or constraints from outside the system (Cumming, 2010). Buffer capacity has been described as the number of systems that can undergo and still maintain the same structure, function, identity, and input to function and structure, (Alliance, 2010).

18.4.2 Social self organization Organizational autonomy refers to a state in which actors determine their own rules. Social self-organization connotes autonomy, freedom to act, collective action, self-help, independence, power, and control all of which can foster identity, trust, and confidence and contribute to empowerment. Milestad and Darnhofer (2003) defines social selforganization as a system that demonstrates the ability of groups to form flexible networks, as well as the ability to engage with the social, economic, and institutional environment on a scale other than local. More specifically, self-organization highlights how humans, adaptive capacities, power, and social interactions shape social resilience (Obrist et al., 2010). Social self-organization includes: (1) Institutions refer to social norms and rules (Ostrom, 1999). (2) Cooperation and networks for self-organization refer to the interaction between actors in the social-ecological system which results in the creation of own rules, norms, values, and institutions, builds trust, and reduces dependence on external actors for information, innovation, and capital. Building trust and increasing social capital can sustain livelihoods when shocks or stresses occur (Speranza et al., 2014). (3) The network structure of a socio-ecological system can influence system dynamics and management outcomes, for example, by facilitating or sharing information and impeding access to resources and opportunities (Cumming, 2010). Network structures can suppress livelihoods from larger shocks or transmit shocks more quickly through the system. (4) Reliance on own resources to reduce dependence on external inputs and save time for local-level actions. This refers to the primary, but not exclusively locally available, resources of local knowledge, culture, and leadership, along with openness to integrate external knowledge and practice (Adger, 2000). (5) Capacity for learning The capacity to learn connotes adaptive management, implying that a resilient socioecological system is a learning system that incorporates previous experience into current action and thus has the experience to act. Kim (Kim, 1993) argues that “learning includes two meanings: (1) the acquisition of skills or knowledge, which means the physical ability to produce some action, and (2) the acquisition of knowledge to articulate conceptual understanding. The ability to learn at the livelihood and systems-level Individuals are critical to building resilience through: 1. Knowledge of disaster threats and potential opportunities refers to the extent to which knowledge is an actor ’related to the issue of concern’.

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18.5 Methodology

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2. Shared community vision of life or systems that can contribute to transformation for resilience. The collective vision is demonstrated by the extent to which existing institutions and conditions promote the exchange of information and services, the sharing of knowledge, perceptions, beliefs, and collective actions that empower individual actors (Jerez-Go´mez et al., 2005). Without a shared vision, individual actions do not contribute to learning on a broader scale, and that shared vision is important for life transitions (Kim, 1993). 3. Commitment to “learning and developing a culture that promotes the acquisition, creation, and transfer of knowledge as fundamental values” (Jerez-Go´mez et al., 2005). 4. Knowledge Identification Capacity/KIC is “the ability to identify the external environment for valuable knowledge for survival and development.” Openness and experimentation through learning by doing and not considering one’s own values, beliefs, and experiences to become better (Jerez-Go´mez et al., 2005). 5. Knowledge sharing refers to the extent to which an actor spreads knowledge to others. 6. Knowledge transferability refers to the extent to which one applies one’s own knowledge or internalizes external knowledge to serve agricultural production purposes. 7. Functional mechanism: Feedback can spread knowledge and improve social memory through interaction between actors. Milestad and Darnhofer (2003) notes that feedback mechanisms are very important to study because they allow farmers to monitor signals from the ecosystem and interpret and then respond with relevant changes in livestock management.

18.5 Methodology This study uses a qualitative method that will analyze the concept of a smart and resilient city and social resilience from Speranza et al. (2014) who analyzed this concept through the variables of buffer capacity, self-organization, and capacity for learning in the people of Kemijen
Semarang. These variables are further translated into research indicators, for example, the buffer capacity variable has the following five indicators: human capital, natural capital, financial capital, social capital, and physical capital. The self-organization variable consists of indicators of the institution, cooperation and networks, network structure, the opportunity for self-organization, and reliance on own resources. Then the variable capacity for learning has indicators of knowledge of threats and opportunities, shared societal collective vision, commitment to learning, knowledge identification capability, knowledge transfer capability, and functioning feedback mechanisms. Data were collected through several focused discussions with resource persons in two coastal villages and through in-depth interviews with a number of relevant stakeholders. The qualitative method was chosen because of the depth of the data to be extracted which required several focused discussions with government officials, the community, the railway company, universities, NGOs, and the Kemijen Community. Likewise, the need for deepening data is met through in-depth interviews with a number of stakeholders who understand regional conditions, disasters, and community conditions. Kemijen Village in East Semarang is chosen as a research site because they both experienced climate change disasters and social-ecological crises.

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The objective of this research is: (1) This study is not only about how to understand the implementation of smart resilience in Semarang but also to find out the process of building a resilient community related to climate change disasters and the consequences of the social-ecological crisis. (2) The case of Semarang can be a lesson for other cities in Indonesia in implementing smart city policies that are integrated with the resilience community in disaster management.

18.6 Research findings: potential and condition of social resilience of the Kemijen community, Semarang This research uses the Speranza concept to analyze the findings based on the results of focus group discussions and in-depth interviews. The author considers the Speranza concept appropriate to analyze the findings of the community in Kampong Kemijen
Semarang. The condition of the Kemijen area, which is located in the District of East Semarang, is in a lowland and basin area. This area because it is close to the North Coast of Java Island experiences daily tidal flooding, every afternoon the seawater will rise and flood the village. Therefore, to prevent the tendency of ecological damage in the coastal area of the city of Semarang, a problem-solving approach with a disaster risk management approach is needed to anticipate the impact of the disaster (Henny, 2018). Analysis related to the potential and condition of social resilience of the Kemijen Community. Based on data analysis from focused discussions and in-depth interviews with resource persons who are considered to understand and know about the ecological crisis and disaster problems in Kampong Kemijen-Semarang, it is narrated as follows.

18.6.1 Buffer capacity The buffer capacity in the coastal city of Semarang consists of five elements: human capital, and natural capital. Physical capital, financial capital, and social capital as described below. 18.6.1.1 Human capital An analysis of the Kemijen village case shows that in general the picture of the quality of Human Resources is still low because most of them have low education and earn their living as unskilled laborers in ports or markets. Some are entrepreneurs, educators, civil servants, private employees, and others. Most of the population is of productive age, namely the age of 25
55 years. The community also has a high desire to learn by participating in empowerment carried out by the University, CSR, and NGOs. Therefore, the surrounding community still needs programs for capacity building. Women’s groups are more active in participating in empowerment programs (Source of data for the Kemijen district of Semarang, 2017).

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18.6.1.2 Financial capital The source of funds used by the population is owned by individuals or households. In fact, revolving funds through neighborhood associations and community association schemes can become financial capital. The income of the community is quite low, the average regional minimum wage is 2 million rupiahs, but every 3
5 years they have to provide 15
50 million funds to build houses. To cover the lack of funds to raise houses, many people are in debt. Houses along the coast will be partially evicted to make sea belts and roads commensurate with providing compensation to residents. People, in general, do not have the funds to fill in houses whose land has collapsed, and the condition of their houses is getting lower. 18.6.1.3 Natural capital Natural capital in the form of groundwater is in very bad condition because it is swampland, the air has been polluted due to the operation of several factories and there are no water catchment areas and green plants. The condition of the plains is quite low and close to the sea. When climate change is characterized by high waves, and flooding causing tidal conditions. Tidal floods occur twice a day. In addition, because it is lowland and former swampland, both areas experience land subsidence, 2.5 cm in a year. Since the Banger Polder or pump house was built in 2016 and started operating in 2017, Kemijen Village has been free from tidal flooding, and if it floods because the rainwater recedes quickly. Fig. 18.2 illustrates the restoration of mangroves for coastal forts so that seawater does not directly enter residential areas. 18.6.1.4 Social capital There are many social organizations formed in Kemijen, such as the Family Welfare Association, Kemijen Community, Camar, and other Community Self-Help Organizations. There is but the group of women is much more active in gathering than the male group. The norms that are still maintained by the community include religious norms and politeness norms. Trust between residents is very strong, and very compact when it comes to dealing with outsiders. Trust in outsiders can also be seen in several collaborations with universities, NGOs, the private sector, and the government.

FIGURE 18.2 Mangrove rehabilitation and seawall. Source: Data from Photo Henny (2019).

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18.6.1.5 Physical capital The physical capital observed is a water reservoir (Banger River) which is still functioning today. Initially, Kali Banger was used as a means for washing and bathing. Now, Kali Banger has become the location of the festival which is facilitated by a Peka Kota NGO that actively encourages community participation. The Banger River is now an icon of Kemijen Village and the pride of its citizens. Many plans are top-down from the government to the community, such as the construction of polders, retention ponds, and flooding of the east canal. The community itself also initiated the planting of mangroves in their village (Fig. 18.2). 18.6.1.6 Self organization The potential that exists in self-organization is the variety of social institutions, including Kemijen Community, Village Community Empowerment Institutions, PKK Groups, Youth Organizations, Community Self-Help Agencies for Children, Community Protection, and Disaster Preparedness. The following Table 18.1 is an analysis of self-organization in Kemijen, which was recorded by the research team.

18.6.2 Capacity for learning To strengthen the Capacity for Learning in the locations studied, there are seven influencing factors: (1) Knowledge of threats and opportunities, (2) Shared societal vision, (3) Commitment to learning, (4) Knowledge identification capability, (5) Knowledge sharing capability, (6) Knowledge transfer capability, and (7) Functioning feedback mechanism. From each of these factors, data is obtained that the people in Kemijen have sufficient knowledge about tidal and flood disasters. A resource person, Katno, said that the residents of Kemijen were very used to the tidal waves and floods that they had experienced since the late 1990s or early 2000s. Margo said tidal flooding was overcome by closing the floodgates and normalizing the East Flood Canal last year (2016). The NGO Hysteria, in which there is a platform called Peka Kota, which is very active in fighting for the improvement of villages. The results of the work of NGO Peka Kota appeared in the form of making a Village Map, celebrating arts and music, as well as making a drone video about the Kamijen village area. The discussions that also involved residents and other stakeholders in the festival gave birth to a vision that was actualized in the arts. The social network created fosters a high level of self-confidence. However, knowledge of threats and opportunities is still not well understood. There is a lack of knowledge about the threat of disasters. In contrast, the shared societal collective vision owned by the residents of Kemijen is very good because of the frequent contact with NGOs and universities. The residents hope that the flood canal area and Banger Polder will become river tourism areas, where various shade plants and flowers can be planted along the Polder. This is believed to be able to encourage the development of the local population’s economy, and the opportunity for a better life in the future. The commitment to learning that the residents have is very good. They feel they have to continue to learn through various empowerment programs provided by various NGOs and universities as well as private parties. It is proven that several new businesses have 3. Climate change, ecological impacts and resilience

18.6 Research findings: potential and condition of social resilience of the Kemijen community, Semarang

TABLE 18.1

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Analysis of Kemijen’s self-organization.

Stakeholders Social institution empowerment strategy

Forms of empowerment

Community

Kemijen Local Community (KOMJEN); Community Self-Reliance Agency (BKM); Kemijen Community Consultative Assembly (LPMK); Empowerment and Family Welfare; Community (PKK) Kemijen

Empowerment program socializationCommunication about disasters; Empowerment and Family Welfare; Community

NGO

FORSIBA (Advocacy for democracy after regional autonomy); SIMA (Project Collaboration in flood adaptation impact between NGOs, Community, University, Local Government of Semarang, and Private Sector); PENDIDIKAN by Mercy Corps (Developed Communal Bathroom and Toilet; PATTIRO (Advocacy for public disclosure and transparency); LBH APIK (Advocacy for Women and Children Care); Institute for Business Companion for Labor, Farmer, and Fisherman or LPUBTN (Community-based advocacy for healthy life pharmacy); Hysteria—Peka Kota (art and area mapping)

Advocacy for democracy after regional autonomy; Project Collaboration in flood adaptation impact between NGOs, Community, University, Local Government of Semarang, and Private Sector; Developed Communal Bathroom and Toilet; Advocacy for public disclosure and transparency; Advocacy for Women’s and Children’s Care; life pharmacy; Art and area mapping

University

Empowerment program Urban Planning Faculty Diponegoro University (UNDIP); Public Administration UNDIP as PT Pertamina CSR Consultant; Community Services Organization Semarang University (UNNES) as Former Consultant of PT Pertamina- Community Services Organization Sultan Agung University (UNINSULA); Empowerment community; Community Services Organization Sugiyopranoto Catholic University.

Private Sector

PT Pertamina; PT Indonesia Power; PT Rail Way; OEN Foundation

CSR

Government

Agency for Regional Development Semarang City (Semarang Resilience City Planning); Environmental Agency of Semarang City (related to flood adaptation and mitigation); National Board for Disaster Management for Semarang City; Agency for Mine and Water Sources of Semarang City (related to the normalization of Banger River, East and West Canal, and tidal flood management); Ministry of Public Work for Central Java province; Ministry of Home Affairs (related to the Central Government management)

City Infrastructure Development, Semarang Resilience City Planning; Program-related to flood adaptation and mitigation; National Board for Disasters Management for Semarang City-Agency for Mine and Water Sources of Semarang City (related to the normalization of Banger River, Eastand West Canal, and tidal flood management)

From Henny (2019).

started to emerge, for example, mangrove batik craftsmen, salted egg entrepreneurs with omega, wet waste management whose output is for animal feed, shrimp shell waste business for duck feed, and efforts to increase plastic waste into crafts. Capacity for learning is the third element that supports the realization of resilience. From the results of the research, there is sufficient knowledge about the threats they face, especially related to tidal waves and floods. The vision of a healthy and resilient village has not been well socialized. This of course is influenced by external factors such as limited access. Table 18.2 shows a summary of urban and social resilience strategies in the pilot sites. 3. Climate change, ecological impacts and resilience

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TABLE 18.2 Urban and social resilience strategies in Kampong Kemijen. Factors

Problem

Potential

Strategy

Desire to learn; Positive response to empowerment; Most of the residents are of and productive age 25
55 years old

Enhancement education programs formal and informal; Study groups -Program Family Welfare in the health sector; Give Skills training for improvement capacity

Buffer capacity Human Capital

Education low (Elementary School average); Low health (HIV/AIDS, Malaria, skin diseases); Community work most industrial workers

Natural Capital

Geographical conditions do, Mangroves and Retention Ponds Planting and Maintenance of expanding not support example: mangroves and sea pine trees; Vulnerable marshland, former Retention pool; Climate factors and pond; Mangrove Cultivation in coastal weather which tend to be Tambak Lorok; Retention pool hot and dry; Do not displace construction residents; Build low-rent flats

Financial Capital

Income low; employment of the There is potential to save working population and odd job borrow, collect, cooperatives

Provide alternative work and skills for entrepreneurship; Skills empowerment creative economy, for example, batik, making salted eggs, handicrafts, and tourism.

Social Capital Mutual cooperation; Social cohesion weak

Development of confidence in public; networking with outsiders; There are sociocultural institutions; Local values are still strong; Crime rate low; Still exist organization public; There are informal meetings between citizens

Strengthening social cohesion -Build mutual trust; Activate mutual work exploring Strengthen and expanding the network; Revitalization of social institutions; Activate the meeting of informal citizen

Physical Capital

Public Plant mangroves and sea fir; Built Sea belt; elevate the house; Construction of landfills should be a priority; A floating hall has been built for community gathering

Making the focus on exploring pool retention; Complete development of East Canal Flood; Place Creation Disposal Final Trash-Development Sea belt Beach; water supply clean; Make lamps street lighting

There is no retention pool in the village yet Kemijen; Polder banger not working maximally delayed development East Canal Flood; No garbage disposal

Self-organization Social Institution

There are various organizations that lack coordination and do focus not focus

Social institutions have focused on many various kinds

Revitalizing function and the role of institutions social; There needs to be a synergy Cross institutions and communities

Cooperation & Network

Organizations are partial, reactive and nothing continuity program; Empowerment is partial.

Cooperation woke up because there is trust with outsiders (university, NGOs, private and government)

Build networking; Sharpen networking and focus on the target -There needs to be communication and coordination between the institution

Network structure

Not awake

Open

The network expanded and connected with the government city and BNPB by involving all stakeholders.

(Continued)

3. Climate change, ecological impacts and resilience

18.6 Research findings: potential and condition of social resilience of the Kemijen community, Semarang

TABLE 18.2

357

(Continued)

Factors

Problem

Potential

Strategy

Opportunity for selforganization

Relatively self-Servicing organization. -Still limited range local; Tend to help accepted cause dependency

Evolving through the exploring community in the community (Commissioner General, Hysteria/City Sensitive, Seagulls, LPMK, PKK, Youth organization)

Need to involve the community in exploring useful local values for tourism such as cultural parades, art parties, dance, and music; Reorientation of vision and mission as well as strengthening the organization independently

Reliance on own resources

Often affected by disaster; Because tired, physically weak and feel trust in people and institutions is low.

Optimistic Open Easy to accept novelty Easy to work

Empowerment on policy Disaster management initiation; Institutional strengthening with training; Increasing the participation of community associations in recruiting members; There needs to be the socialization of roles and functions of each institution

Knowledge Knowledge low against disaster; of threats & Development of social conflict opportunities between people with the railway company for the development pool

Optimistic, Open Easy to accept Novelty, Easy to work with all the time eviction in profit form, not compensation.

Socialization about disaster risk reduction; Resolving land conflicts.

Shared societal (collective) vision

Not scattered extensively

Not scattered extensively Have a vision about tourism village

Expand deployment information to all stakeholders and society

Commitment to learning

No time; Insufficient information; Desire to learn tall

Strengthen Empowerment participation in society

Active community participation in programs empowerment

Knowledge identification capability

There are not any yet Mapping knowledge of the network public

Lots of local knowledge has not been identified examples of hall idea float, and house stage.

Mapping comprehensive about history village and the knowledge that is owned by residents.

Knowledge sharing capability

Limitations media

Transfer knowledge goes well through meeting

Open networks on social media like WA, Twitter, and Facebook.

Knowledge transfer capability

Existence gap in internal information between generations the old and young generation

Transfer knowledge goes well via social media (WA, Twitter, Facebook) and face-to-face meetings.

-Involved empowerment by NGOs (mapping the potential of citizens and their wishes contained in dan arts festival culture); Empowerment local potential intensive.

Functioning feedback mechanism

Mechanism communication yet wakes up with good.

No analysis yet feedback from various programs which is executed.

There is a feedback mechanism with a social mechanism already running

Capacity for learning

Data from Henny Warsilah, focus group discussion and interview result, 2019.

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Based on the findings above, it can be concluded that the urban resilience strategy should be an integral part of regional development policies. City resilience policies should not only focus on the development of physical infrastructure but must also pay attention to the concepts of social resilience and social infrastructure which are based on the variable buffer capacity. Independent organizations, learning communities from local communities, the involvement of urban public participation, and social resilience strategies should be taken into account.

18.7 The city government’s response to climate change and the social-ecological crisis The local government of the city of Semarang has responded to the problems of climate change and tidal flooding by making several policies, including: 1. 2. 3. 4. 5. 6.

Regulation of regional disaster management Policy of creating green open space and urban forest development Reforesting mangroves and building a coastal belt Construction of the west flood canal Folder system development Sea belt holder and development of maritime village

What has been done by the local government in Semarang is to develop a disasterresilient city in order to deal with climate change disasters and social and ecological crises in the Semarang area. In addition, the Semarang local government also seeks to build the capacity building and social resilience of the community to participate in reducing the impact of climate change disasters and ecological and social crises by building public awareness about disasters through literacy in collaboration with universities and NGOs.

18.8 Recommendations As a recommendation, this paper proposes the following: (1) The urban resilience strategy should be an integral part of the overall regional development policy, in that context conceptually social security should be an important part of the city’s resilience concept. The city’s resilience policy not only focuses on the development of physical infrastructure but pays attention to the concept of social resilience. (2) City resilience programs in each regional work unit should be integrated with each other leading to comprehensive urban resilience by relying on social resilience that exists in local communities. (3) Increased community participation to accelerate the construction of flood and tidal flood control facilities, especially for the completion of the East Flood Canal and the construction of retention ponds.

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References

359

(4) Increased development of public facilities by the city government, especially the provision of clean water services, street lighting, and landfills, as well as public facilities. (5) Increasing mutual cooperation and social cohesion through increasing the intensity of informal meetings with the substance on the socialization of disaster risk management (DRR), transfer, and sharing of knowledge about DRR. In addition, social networking is open on social media.

18.9 Conclusion Climate change and social-ecological crises have broad and serious impacts on the biogeophysical environment. The impacts of climate change are very diverse, especially the negative implications for the socio-economic activities of the people in Kamijen, including (1) disturbances to the function of coastal areas and coastal cities; (2) disturbances to the function of coastal areas and coastal cities. infrastructure and facilities such as road networks, ports, and airports; (3) disruption to population settlements, disruption to the population’s economic business; (4) reduction of land productivity; (5) increased risk of disease outbreaks, causing disruption to community social resilience. The Semarang city government needs to communicate and coordinate between various institutions so that there is a synergy across institutions/communities in programs related to infrastructure preparation to achieve a disaster-resilient city in the 100 RC corridor. It is important for city development to adopt the concept of smart and resilient city development that includes public participation as public investment, through government programs. In this position, the social resilience of the community can strengthen the resilience of the city. The local government of Semarang and the urban community are able to manage their city from a smart city and resilient city perspective so that they are able to develop smart communities in responding to climate change and social-ecological crises.

References Adger, W. N. (2000). Social and ecological resilience: Are they related? Progress in Human Geography, 24(3), 347
364. Available from https://doi.org/10.1191/030913200701540465. 00913253. Arnold, United Kingdom. https://journals.sagepub.com/home/PHG. Agricultural Census data and the RACA Institute. (1993). Farmers’ land tenure in Java. Alliance, R. (2010). Assessing resilience in social-ecological systems. Workbook for practitioners. Arafah, Y., & Winarso, H. (2017). Redefining smart city concept with resilience approach. IOP Conference Series: Earth and Environmental Science, 70(1). Available from https://doi.org/10.1088/1755-1315/70/1/012065. 75513151. Institute of Physics Publishing, Indonesia. http://www.iop.org/EJ/volume/1755-1315. Bappenas. (2015). The smart city component in Indonesia. Berkes, F., & Seixas, C. S. (2005). Building resilience in lagoon social-ecological systems: A local-level perspective. Ecosystems, 8(8), 967
974. Available from https://doi.org/10.1007/s10021-005-0140-4, 14350629. BPS. (2012). Indonesian Central Bureau of Statistics, 2012. A number of Poor Populations on Java Island. BPS. (2021). Indonesian Central Bureau of Statistics, 2021. Total Population in Indonesia. Cumming, G. S. (2010). Conceptual background on social-ecological systems and resilience (pp. 7
33). Springer Science and Business Media LLC. 98-94-007-0306-3 7. Available from https://doi.org/10.1007/978-94-007-0307-0_2.

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Disaster Risk Assessment in Indonesia, BNPB. (2015). Djalante, R., & Thomalla, F. (2010). Community resistant to natural hazards and climate change impacts: a review of definitions and operational frameworks. Environmental and Forestry Management Agency (BPKH). (2012). Establishment of legal forest areas and Indonesian residents. Figueiredo, L. (2018). Indicators for resilient cities/OECD. Folke, C., Carpenter, S., Elmqvist, T., Gunderson, L., Holling, C. S., & Walker, B. (2002). Resilience and sustainable development: Building adaptive capacity in a world of transformations. Ambio, 31(5), 437
440. Available from https://doi.org/10.1579/0044-7447-31.5.437. 00447447. Royal Swedish Academy of Sciences, Sweden. http:// www.bioone.org/loi/ambi. Henny, W. (2018). Social resilience in a disaster resilient city. OBOR-Indonesia Foundation Publisher. Henny, W. (2019). Photo Collection of PMB BRIN Research, “Social Resilience in the Disaster Resilient City of Semarang”. OBOR Foundation Publisher. Jerez-Go´mez, P., Ce´spedes-Lorente, J., & Valle-Cabrera, R. (2005). Organizational learning capability: A proposal of measurement. Journal of Business Research, 58(6), 715
725. Available from https://doi.org/10.1016/j. jbusres.2003.11.002. Kartodihardjo, J. (2006). Environmental and power politics in Indonesia. Walhi West Java Equinox Publishing. Kim, D. H. (1993). The link between individuals and organizational learning, Sloan management. Mapping Smart Cities in the EU. (2014). Milestad, R., & Darnhofer, I. (2003). Building farm resilience: The prospects and challenges of organic farming. Journal of Sustainable Agriculture, 22(3), 81
97. Available from https://doi.org/10.1300/J064v22n03_09. Obrist, B., Pfeiffer, C., & Henley, R. (2010). Multi-layered social resilience: A new approach in mitigation research. Progress in Development Studies, 10(4), 283
293. Available from https://doi.org/10.1177/146499340901000402. 16499344. SAGE Publications Ltd., Switzerland. https://journals.sagepub.com/home/PDJ. OECD. (2015). Resilient cities, framework for resilient cities. Ostrom, E. (1999). Coping with tragedies of the commons. Annual Review of Political Science, 2, 493
535. Available from https://doi.org/10.1146/annurev.polisci.2.1.493. 10992493. Annual Reviews Inc., United States. http:// www.annualreviews.org/journal/polisci. Source of data for the Kemijen district of Semarang. (2017). Speranza, C. I. (2013). Buffer capacity: Capturing a dimension of resilience to climate change in African smallholder agriculture. Regional Environmental Change, 13(3), 521
535. Available from https://doi.org/10.1007/ s10113-012-0391-5, 146378X3. Speranza, C. I, Wiesmann, U., & Rist, S. (2014a). An indicator framework for assessing livelihood resilience in the context of social-ecological dynamics. Global Environmental Change, 28(1), 109
119. Available from https:// doi.org/10.1016/j.gloenvcha.2014.06.005. 09593780. Elsevier Ltd., Switzerland. http://www.elsevier.com/ inca/publications/store/3/0/4/2/5. WALHI. (2020). WALHI’s resistance to environmental crimes.

3. Climate change, ecological impacts and resilience

C H A P T E R

19 Space technology in solving water crisis-rethinking research collaborative Gouri Sankar Bhunia1 and Uday Chatterjee2 1

Independent Researcher, Paschim Medinipore, West Bengal, India 2Department of Geography, Bhatter College, Dantan (Vidyasagar University), Paschim Medinipur, West Bengal, India

19.1 Introduction The earth has oceans occupying approximately 7% of its total surface. However, 96.5% of the total water mass of our planet is seawater, and an additional 1% has a variable amount of salinity, acidity, and other physiochemical making it unsuitable for human consumption. Only 2.5% of the entire volume of water on earth is drinkable freshwater. Approximately, 70% of freshwater is stored in the polar regions and mountain glaciers as ice. Groundwater, the primary source for human and animal use, accounts for 30% of the total freshwater, so water destined for human consumption can be considered scarce. Water scarcity is further accentuated in some regions due to the uneven distribution of rain and snow, affecting the recharge of groundwater reservoirs. In addition, only 10% of global precipitation penetrates through soil and rocks to feed the water table. Water that cannot return to the normal water cycle remains on the surface, where it can potentially cause floods or stagnate and become a breeding ground for mosquitoes and other pests. Over the next 50
75 years, the worldwide population will grow by three billion or more, with the number of people living in cities more than doubling. The majority of the world’s population expansion will come in emerging countries, where water is already scarce but so many people are poor (Jury & Vaux, 2005). Currently, one billion people do not have access to safe, inexpensive drinking water, and potentially twice that many do not have basic sanitation. In fact, poor water quality is a leading cause of infant death around the universe. Population growth, economic development, accelerated pace of urbanization, increased flood consumption, land clearing, climate change, and pollution

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00019-9

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19. Space technology in solving water crisis-rethinking research collaborative

increase the pressure on an already scarce resource (Sun & Chen, 2012). The artificial environments where most urban populations live also promote behaviors that contribute to water shortage and pollution. The convenience of opening a water tap in our homes removes us from an intuitive understanding of the natural water cycle and the related need to preserve a scarce resource. Economic factors influencing various types of water consumption are one manifestation of scarcity. Water has a much higher value in cities and industries than in agriculture. Environmental uses are frequently much less valuable than urban uses because markets and quasi-markets undervalue them. Market factors will trigger a massive reallocation of water resources from the agricultural and environmental domains to the urban sector as the global population grows by several billion or more over the next 30 years. As a corollary, water-based ecosystems and the world’s food production capability will be put under increased stress. Water management is, therefore, an integral part of urban planning and regional management to ensure the sustainable use of this resource. Globally, we use water mainly for residential use (10%), agriculture (70%) (OECD, Organisation for Economic Co-operation and Development, 2018), and industrial development and energy production (20%) (European Environment Agency EEA, 2019). Agriculture consumption of water is even higher in the developing countries of Asia and Africa, where water scarcity is the most severe. Close to two-thirds of the global population (4.0 billion people) live under conditions of severe water scarcity at least 1 month of the year. Nearly half of those people live in India and China. Half a billion people in the world face severe water scarcity all year round (Mekonnen & Hoekstra, 2016). By 2025, it is expected that worldwide water demand will grow by B3800 km/3 years, with the majority of this water coming from natural systems. This increased consumption will result in significant further depletion of river flows in several locations, with serious environmental repercussions. If the ecological health of a stream and its connected ecosystems is to be preserved, at least 30% of the typical annual flow of the stream must continue in situ. Even today, a large percentage of rivers do not preserve flows at or above this minimum threshold. Climate change and the resulting global warming are also anticipated to have an impact on water supply in the future. Although existing climate models are merely a rough guide to future change, academics increasingly agree that precipitation will increase at higher latitudes and decrease in the subtropics as the world warms. As the average temperature rises, the volume of the snowpack at higher elevations decreases, and snowmelt occurs earlier than in the past, resulting in early water release and increased losses. Despite the ambiguity surrounding the outcomes of climate forecasting models, simulations based on various assumptions have agreed on a number of qualitative elements of climate change’s impact on world water resources. Precipitation will become more unpredictable, resulting in exaggerated changes in runoff and streamflow, according to an almost overwhelming consensus. Extreme events such as floods and droughts will become more common due to this increased variability. Extrapolating existing trends in global water use yields a picture that is cause for concern, if not outright alarm. Despite the fact that population and economic expansion are driving up demand for new water supplies, inefficient water management, and geographical scarcity obtain greater water supplies unlikely to meet new demands. The problem will be exacerbated by continuous and widespread groundwater overdrafts, the

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19.3 Virtual water and water footprint

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threat of climate change, and the salinization of soils in arid and semiarid areas. In this century, developing countries’ growing reliance on food imports might easily reach crisis proportions.

19.2 Methods The Web of Science Core Collection (WOSCC) is regarded as a trustworthy visual analysis database (Hu et al., 2019). The terms “water footprint”, “groundwater crisis,” “water shrinkage,” “virtual water”, and “groundwater sustainability” are frequently used in the titles of studies in this field as fixed phrases. As a result, we looked for all papers from 1993 to 2020 that contained the phrases “virtual water,” “water footprint” or “groundwater” in the title, and then subjectively deleted publications that were unrelated to virtual water or water footprint research. Finally, in 2020, a total of 1339 publications were found.

19.3 Virtual water and water footprint The freshwater utilized by a service or product in its original location that is subsequently traded and transmitted to another region contained in these products or services is referred to as virtual water (VW) (Allan, 1993). Since the turn of the century, the amount of global trade has increased, leading to a rise in virtual water exchange via commodities (Shtull-Trauring & Bernstein, 2018). The VW hypothesis has thus created a solid framework for correctly calculating a country’s or region’s actual water usage. Meanwhile, by viewing water as an internationally traded commodity, it provides an alternate means for water-scarce places to efficiently address their water shortages by importing “freshwater” via international trade (Allan, 1998). There were 1339 papers on virtual water and water footprint as of 2020, indicating a general upward trend. Between 1998 and 2008, the annual average number of publications was only 6.7, but between 2008 and 2020, it increased to 112.56. There were 890 articles, which accounted for 83.10% of the total; 73 proceedings papers, which accounted for 6.82%; 23 reviews, which accounted for 2.15%; and 85 other publications, which accounted for 7.94%. Moreover, recent researchers have highlighted the importance of determining if the virtual water used in production is green or blue water. Blue water is irrigation water extracted from surface sources or extracted from groundwater, whereas green water represents root-zone water in the soil profile (Hanasaki et al., 2010). When green water is used as the source of water, the amount of virtual water in a commodity is substantially lower than when blue water is used as the source via an irrigation system (Aldaya, 2011). In terms of publishing date, the first piece was published in 2001 in the US. Japan (2002), the United Kingdom (2003), Italy (2003), France (2003), Sweden (2004), the Netherlands (2005), China (2005), and India (2005) are among the countries with earlier literatures and studies. China is the most prolific country, contributing 22% of total output, followed by the US (16%), the Netherlands (10%), and Italy (6%). Table 19.1 shows a list of publications derived through a web search. In terms of research institutions, China’s top eight institutions are chosen, along with one each from the Netherlands, Singapore, and Japan. The University of Twente was the

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TABLE 19.1 List of publications derived through web searches. Publications

Number

Percent

Articles

890

66.47

Proceeding papers

341

25.47

Reviews paper

23

1.72

Other’s publication

85

6.35

institution that printed the most papers. The Chinese Academy of Sciences, Beijing Normal University, Hohai University, Northwest Agriculture and Forestry University, University of Chinese Academy of Sciences, National University of Singapore, China Agricultural University, Beijing Forestry University, Shanghai Jiaotong University, and Japan’s National Institute for Environmental Studies are the institutions ranked second to tenth.

19.4 The intensive use of groundwater: a silent revolution Groundwater extraction for urban and industrial use is on the upswing. Its usage in agriculture, in particular, has increased dramatically during the second half of the twentieth century, causing both qualitative and quantitative difficulties. Agriculture accounts for over 70% of global groundwater extraction. Furthermore, groundwater is increasingly being used to irrigate a growing portion of the planet’s irrigated surface area (now around 40%, or more than 100 million hectares). To minimize agricultural extraction for agricultural use, a number of technology solutions are being developed (Bierkens & Wada, 2019). Dam development, hillside reservoirs, and the employment of novel irrigation techniques are among them. However, the consequences of these policies are frequently overlooked. In most arid and semiarid countries, a magnificent increase in groundwater resources for irrigation has occurred over the last half-century: a kind of “silent revolution” driven primarily by the individual effort of millions of small farmers seeking the significant short-term benefits that groundwater tends to bring (Llamas & Martı´nez-Santos, 2005a). Governmental water agencies have traditionally focused on the planning, management, administration, and regulation of surface-water irrigation systems, with so little emphasis made on groundwater management. This mindset, coined by American hydrologist Raymond Nace Llamas in 1975 as “hydro schizophrenia,” has been prevalent in India, Mexico, Spain, and several other arid and semiarid nations around the globe. The Silent Revolution has been aided by developments in hydrogeology and well-drilling processes, as well as the widespread use of the submersible pump, which has greatly decreased abstraction costs over time (Conti, 2013). In most situations, the overall direct cost of groundwater abstraction today, excluding inefficiencies, is a small percentage of the economic worth of the promised yield. As a result, the Silent Revolution is mostly a market occurrence. The adaptability of aquifers during dry seasons is a second and more significant motive. In this sense, most farmers adopt conjunctive use wherever feasible, relying

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19.5 Desalination: potential and limitations

365

on paid surface water when it’s accessible and groundwater when it’s not. Most irrigated income crops, which typically demand significant farmer investment, now rely on groundwater, either entirely or in combination with surface water. Another significant and underappreciated aspect of the Silent Revolution is its good impact on several farmers’ social and economic transitions. Pumping costs are relatively modest, and groundwater offers drought mitigation, allowing poor farmers to progressively climb into the middle class, allowing them to give better education to their children. Those children have been taught as teachers, technologists, and other professionals over the course of one or two decades, benefiting society’s total advancement (Llamas & Martı´nez-Santos, 2005b). One of the most important parts of the Silent Revolution is how farmers transition from low-value crops to cash crops as they get wealthier and more educated. This is owing primarily to groundwater’s inherent reliability: Farmers are attracted to focus on better irrigation technology and, as a result, change to higher-value crops by the prospect of increased earnings. Crop value varies widely due to crop type, climatic and other natural and societal elements at each location, as well as trade limits. Despite the deceptive precision of worldwide irrigation data and the variety of existing estimates, preliminary computations reveal the following: In terms of hydrological efficiency (m3/ha), groundwater-based irrigation appears to be twice as effective as surface-water irrigation, a proportion that climbs between three and ten times in terms of social and economic efficiency (US$/m3 and jobs/m3). Evaluating the consequences of this Silent Revolution should be an important addition to the discussion over world irrigation needs, as several water experts believe. The motto “more crops and employment per drop” is seen as critical in averting a “looming water crisis.” This is due to irrigation’s high share of world water use, as well as irrigation’s generally low efficiency. Few water specialists or decision-makers are cognizant, however, that groundwater irrigation is frequently used to fulfill the purpose behind such a motto. Several groundwater managers have an inadequate awareness of the current groundwater status and its true value due to a lack of data. This causes issues such as well depletion, decreased well yields, water contamination, ground subsidence or collapse, contamination of streams and surface water bodies, and ecological impacts on wetlands and gallery forests (Forne´s et al., 2005). Large groundwater abstractions typically have a substantial impact on the hydrological cycle. This has an impact on springs and river base-flow, as well as increasing recharge, water table depth, piezometric levels, groundwater accumulation, groundwater-dependent wetlands, groundwater quality, river-aquifer relationships, and even land subsidence. Intensive use refers to the fact that groundwater development has a substantial impact on these aquifer water conditions.

19.5 Desalination: potential and limitations Desalination, the method of transforming saltwater to freshwater, has traditionally been too costly and energy-intensive to be used as a broad solution for increasing access. The perforated, hyper-permeable filter is one atom thick and claims to boost water flow by 500% over traditional approaches (Obotey Ezugbe & Rathilal, 2020). While the technique would be especially useful in the oil and gas industry, which is said to produce 18 billion gallons

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of wastewater every year, the company is also looking into other applications, such as food and energy creation. Coastal areas encounter a variety of desalination issues. Within 60 miles of the shore, the overwhelming of the world’s population resides, and this number is expanding. Various coastal regions have different requirements. Furthermore, due to carbonate substrata beneath the earth, this rainfall is unable to reach the groundwater system and offer an appropriate amount of freshwater. Only 10% of the water gets through. Several researchers have continued, although with minimal results, to aggregate and map desalination projects. Cooley et al. (2006), for example, displayed worldwide desalination market dynamics and projected just newly planned desalination plants in California as of spring 2006. The Bureau of Reclamation (2014) only provided single statistics for the most recent desalination projects in each state in 2014, without presenting granular and particular information on those facilities or their geolocation. Ho¨pner and Lattemann (2002), Lattemann and Ho¨pner (2008), and Dawoud and Mulla (Dawoud, 2012) have attempted to map geographical seawater desalination capabilities in the Arabian Gulf at the international level. Furthermore, in terms of mapping desalination plants, such investigations were not as thorough as those undertaken in the US. Computer graphics visualization and imaging have exploded in popularity in recent decades, capturing the attention of governments, scientists, and business leaders (Fox & Hendler, 2011). At the turn of the millennium, the concept of a virtual globe (a computer application that allows consumers to access and search data presented on a geographical representation of the Earth) was born (Bailey, 2010). Interactive digital mapping became feasible in all major virtual glasses with the introduction of the XML-based markup language KML (Keyhole Markup Language)—an open-source, tag-based scripting language (De Paor & Whitmeyer, 2011). Most 3D models nowadays are created using specialist visualization software that is not always routinely available to the public, requires specialized graphic cards or technology, and/or is challenging to use due to a lengthy learning curve. Virtual globe programs have no unique technical requirements in this area and can load and render normal KML files. Table 19.2 shows the summary of the desalinization study using geospatial technology.

19.6 Increasing transparency and participation—role of geospatial technology Monitoring land use patterns is a key element to understanding how the land use changes influence the availability of water resources and how surface water storage (lakes, ponds, rivers) changes over the years. This information might facilitate constructing a time series (seasonal and yearly) about the changes in water utilization and distribution over the past decade because of external factors such as climate change, urbanization, population growth industrial growth, etc., and how the water systems were managed from the past to the present. These observations are well complimented with the existing ground weather stations (e.g., Indian Metrological Department (IMD, http://www.imd.gov.in)). Participatory groundwater management options based on “aquifer-based, common pool resource” strategies are starting to make their way into groundwater practices and policies. Because groundwater development has been “atomistic” in character, participation at all levels is critical in management choices including the construction of a governance structure. In groundwater governance, establishing a legislative framework that supports “resource protection” along with

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TABLE 19.2 Summary of desalinization study using geospatial technology. Aim

Geographic region

Methods

Outcome

References

Impact assessment of desalination Kuwait Bay plants

GIS, Water Quality Index

Due to the discharge of seawater desalination marinades and other polluted effluents, the water quality in Kuwait Bay is very bad to unfit throughout the year.

Hamoda et al. (2015)

Using a comprehensive lifecycle evaluation approach, this research explored the relationship between renewable energy supplies, freshwater demand, and associated environmental implications of desalination plants powered by renewable energy on a worldwide platform.

EES model, optimized solardriven thermal desalination plant

The average CO2 emissions per m3 of desalted water are 4.32 kg CO2 eq/m3, which is 47% less than traditional thermal desalination.

Alhaj and AlGhamdi (2019)

Employing worldwide data to analyze solar-driven, continuousmode AWH (SC-AWH).

Global

Global Land Data Assimilation System (GLDAS), Solar-driven atmospheric water harvesting (AWH) devices, geospatial tool (AWH-Geo), Zonal statistics

The MADP90-t is the average output rate (L/d/m2) of a device that will be exceeded for 90% of periods lasting t days at a given location.

Lord et al. (2021)

This work lays the way for a practical and easy-to-implement solution to this problem. This is performed using a recently built open-source publicly available Geographical Information System Platform.

Mediterranean region

DES2iRES platform, GIS

In a hypothetical scenario for two relevant places in Greece and Tunisia, the benefits of using the DES2iRES platform to build a desalination plant fueled by renewable energy sources are presented.

Petrakis et al. (2020)

This study aims to close that gap by demonstrating a multicriterion, geographically resolved methodology for finding desalination infrastructuresuitable places.

Global

GIS-MCDA, solar-aided SWRO, Natural parameters (solar Grubert et al. global horizontal irradiance (GHI) insolation, ocean salinity, and (2014) ocean temperature) are combined and matched with societal variables (water stress, current water prices, and population) to identify places with the greatest potential for solar-assisted SWRO.

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19. Space technology in solving water crisis-rethinking research collaborative

“good practices of participatory groundwater management” is critical. Instead of continuing with the primarily standard supply-side, infrastructure-based approach to managing groundwater resources, interdisciplinary "science" must serve as the vehicle for advancing both groundwater management and governance. Earth Observation (EO) is particularly suited to monitoring water bodies (Huang et al., 2018). Landsat data has been used for water resource management since 1972 (NASA, 2012), owing to the 30 m/pixel spectral response and spatial resolution of the Landsat sensors (Serbina & Miller, 2014). In the last 10 years, constellations of small satellites from Planet Labs’ Doves and Skybox have been used to monitor the surface water of specific areas with great temporal and spatial resolutions (Cooley et al., 2017). Environment parameters such as temperature, pressure, relative humidity, etc. can also be computed on a global scale using space data products such as Landsat, MODIS, etc. Remote sensing data are true big data, with datasets that are highly comprehensive, varied, and growing in size. All data obtained from ground, aerial, or spaceborne earth observation sensors are classified as remote sensing data. Hundreds of earth observation satellites have been deployed since then, some of which were designed expressly to gather data on Earth’s hydrological systems, such as Landsat or the gravity recovery and climate experiment (GRACE). Earth Explorer (https://earthexplorer.usgs.gov/), Earth Data (https://earthdata.nasa.gov/), and ESA Earth Online (https://earth.esa.int/web/guest/data-access) are some of the web-based systems that provide access to these data centers (Blumenfeld, 2019); for example, most NASA mission data is preserved in live archive centers across the US, which may be accessed via a variety of web-based platforms and software. New remote-sensing missions have been launched and advanced throughout time, resulting in an ever-growing large dataset. NASA’s SMAP missions acquire 458 GB of soil moisture data every day. Table 19.3 shows some of the groundwater-related remote-sensing applications. TABLE 19.3 Remote sensing data used for groundwater assessment. Mission/sensor

Hydrological component

Spatial resolution

Temporal resolution

Launch—End year

Gravity recovery and climate experiment (GRACE)

Terrestrial water storage

110
330 km

monthly

2002
2017

Gravity recovery and climate Experimentfollow on (GRACE-FO)

Terrestrial water storages

110
330 km

monthly

2018—ongoing

Soil moisture active and passive (SMAP)

Soil moisture

3
36 km

1
7 days

2015—ongoing

Soil moisture and ocean salinity (SMOS)

Soil moisture

35
50 km

1
3 days

2009—ongoing

Global precipitation measurement (GPM)

Precipitation

5
15 km

30 min—monthly

2014—ongoing

Tropical rainfall measuring mission (TRMM)

Precipitation

5
550 km

3 h—monthly

1997
2015

Terra/MODIS

Evapotranspiration, LST, NDVI

0.5 km

8 day—annual

2000—ongoing

Sentinel 3 and 3B

LST, NDVI, GVI

Various

Various

2016—ongoing

Modified after Gaffoor Z., Pietersen K., Jovanovic N., Bagula A., Kanyerere T. (2020). Big data analytics and its role to support groundwater management in the southern African development community. Water, 12(10), 2796. https://doi.org/10.3390/w12102796.

3. Climate change, ecological impacts and resilience

19.6 Increasing transparency and participation—role of geospatial technology

369

This is manifested by new low-cost sensors for in situ monitoring (leading to a sprawling “internet of things”), increasingly powerful Earth observations from satellites and drones/unmanned aerial vehicles (UAVs) to provide synoptic views of topography (including high-resolution digital elevation models to identify flood-prone areas and support hydrodynamic modeling), climate, water levels, flows, snow cover, inundated zones, landcover, watershed status, and an expanding “internet of things” (Giustarini et al., 2015). Earth observations with near worldwide continuous coverage are gradually becoming a game-changer for panoramic views in big basins, where the resolution of even the free resources from NASA and ESA are typically suitable for effective water resources insights. Unmanned on-water and underwater vehicles are looking very promising; they can be equipped with sensors and autonomous (single or swarm) ability for exploring large bodies of water (e.g., for bathymetry, hydraulic safety, water quality, or fish stock assessments). Chalh et al. (2015) used a big data open platform to help water resource management in Morocco’s Foum Tillich watershed. To retrieve information from a variety of heterogeneous datasets, the platform uses a variety of tools including stochastic models, simulations, hydraulic and hydrological models, high-performance computing, grid computing, decision support tools, big data analysis systems, communication and diffusion systems, database management, geographic information system (GIS), and knowledgebased expert systems. Innovative ideas for using and conserving water and administering usage caps (e.g., using satellite-derived actual evapotranspiration estimates), implementing a systemic approach to enhance agricultural water productivity, benchmark systems, and incentivize sustainability, have all been made possible by technological advancements. The development and customization of tools for water planning, allocation, and synchronized water infrastructure services in an integrated multi-sectoral systems viewpoint are now possible thanks to new in situ and Earth observation monitoring and analytics (Be´gue´ et al., 2020). Water infrastructure can now be managed as part of a more integrated system for a variety of goals, including service delivery and climate resilience. Assessments based on satellite observations or global models are becoming increasingly equivalent to in situ measurements. Even in data-poor situations, these strategies, when combined with a new generation of artificial intelligence (AI)/machine-learning (ML) algorithms and global models, can transform water resource management (Harshadeep, 2019). This information can be particularly helpful in assessing parts of the water balance, estimating flooding areas, making customized weather/hydrologic/inundation projections, maintaining large water requirements (e.g., agriculture), and power dissipation, as well as strengthening and benchmarking water productivity when retrieved through customized interactive dashboards (Harshadeep, 2018). The underpinning field of public participatory GIS (PPGIS) aims to include citizens’ expertize in urban planning activities and decision-making activities. Governmental organizations employ PPGIS, a type of collaborative spatial decision-making (CSDM), to encourage citizen participation in decision-making (Mansourian et al., 2011). Make GIS and other spatial decision-making tools accessible and available to all individuals with a role in official decisions (Sieber, 2006). The reliability and validity of environmental optimization techniques can be improved significantly by including the spatial dimension in the collective decision-making approach. Web 2.0 (O’Reilly, 2005) was hailed as a major

3. Climate change, ecological impacts and resilience

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19. Space technology in solving water crisis-rethinking research collaborative

catalyst for leveraging web technology to improve public involvement and foster new types of government-citizen collaboration (Brabham, 2009). Governments around the world are requesting web-based public participatory solutions to extend services and increase citizen participation (United Nations, 2016). As a result, web-based PPGIS solutions have been developed to enable citizens to contribute information and expertize about urban problems and potential remedies (Steiniger & Hunter, 2013).

19.7 Data science: potentials and prospects The growth of sensor technologies, including remote sensing, that continuously collects fresh data has pushed the earth sciences discipline, like many other scientific disciplines, into the big data age (Guo, 2017). This has prepared the way for data-driven strategies to be introduced into the earth science profession. It is unsurprising that the prospect for big data to aid knowledge discovery in the hydrogeological field has recently become evident (Adamala, 2017). Big data could be beneficial in the order to promote sustainable groundwater management. Groundwater well field data can be entirely digitized, with real-time data collected from sensors-equipped monitoring systems and other available documents and processed into powerful analytical algorithms to offer well field managers with useful knowledge to help them make decisions. Data from operational equipment, written notes, and user inputs are processed on-the-fly to assist drilling and production activities in the shale gas business (Fig. 19.1). We can turn these data into meaningful information that groundwater operators can use using big data analytics. IBM is at the forefront of this effort, attempting to construct digital aquifers that use IoT-enabled wells, human smartphone data, weather data, and paper records to model aquifer

FIGURE 19.1

Improved groundwater management scenarios.

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19.7 Data science: potentials and prospects

TABLE 19.4

Summary of big data analytical techniques.

Techniques

Description

Computational methods

References

Statistics

Data collection; Data arrangement; Data interpretation

Descriptive statistics; Correlation test; Regression analysis; Hypothesis testing; Factor analysis; Clustering Probabilistic statistics

Tsai et al. (2015)

Data mining

Pattern analysis; Extraction of new insights

SQL queries; Machine learning; Feature selection; Statistics

Hariri et al. (2019), and Uma and Deepa (2018)

Artificial Intelligence

Developing computational systems; Automate intelligent behavior; Amplify system

Statistical learning; Deep learning Optimization method

Najafabadi et al. (2015)

Machine learning

AI subset; Self-learning computer Artificial neural network; Support vector algorithm; Feature recognition; machine; Random forest k-means clustering; Natural language processing Empirical data

Uncertainty analysis

Quantitative analysis Uncertainty Data cleaning; Probability theory; Bayesian Hariri et al. (2019) theory; Shannon’s entropy; Rough set theory; Fuzzy set theory

Visualization Graphical representation 2d 3d

Feature extraction; Geometric modeling; Features Graphs Image

Rahmani et al. (2021)

Berko and Alieksieiev (2018)

activities in the cloud (Fleming, 2020). On the other hand, the coarse spatial resolution of remote-sensing data for groundwater management poses a challenge. Hydrological studies based on remote sensing data have often been conducted on a regional or global scale. This is due to the fact that much of the remote-sensing data is at a spatial scale that makes local or sitespecific assessment impossible. Reanalysis datasets add to the historical data available, making it easier to comprehend past trends in natural earth systems. Original in situ observational datasets that have been amended and altered using statistical downscaling procedures are referred to as reanalysis data, and they are typically by-products of land-surface models and atmospheric models. Interpreting this from the groundwater perspective helps us to understand the crucial interdependency and interaction of different hydrogeological procedures based on current data (descriptive analytics), use this knowledge to predict future groundwater scenarios (predictive analytics), and finally realize what the best actions are going forward (prescriptive analytics). To build groundwater monitoring systems, evaluate groundwater quality, and predict groundwater flow statistical approaches are applied (Table 19.4). Large streams of heterogeneous, highly dimensional, and noisy data are not well-suited to statistical approaches. Standard statistical approaches are best suited to working with samples of population statistics, which are then used to extrapolate across the full population depending on the statistical significance of the findings (Gandomi & Haider, 2015). With the introduction of the Internet, a new route for communication and information transfer was opened up. Almost every industry and people today rely on the Internet in some way. As a result, it’s no wonder that the amount of data generated and communicated through the

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Internet is massive and complicated. All of the hydrological data being communicated over the Internet that isn’t already housed in specialized data repositories is a source of concern for groundwater. This includes data found on websites and social media threads, among other places. The use of IoT devices in groundwater science can yield huge amounts of data on local groundwater conditions much more quickly than traditional or manual data collecting, allowing for better groundwater resource management (Cecchinel et al., 2014). To promote sustainable groundwater management, real-time IoT groundwater monitoring and data management systems have been piloted in several regions, such as California and India. Sensor equipment is becoming more affordable and accurate, which will undoubtedly increase the groundwater domain’s data-collecting capacities in Southern Africa (Malche & Maheshwary, 2017).

19.8 Innovation of emerging technology A number of innovative solutions are being developed to provide access to safe drinking water in poor countries. The reasons for the worldwide water crisis are numerous, necessitating a variety of remedies. To make a genuine difference for the 1.2 billion people who live in water-scarce places, these solutions must encompass policy, technology, and behavior change. Many various filter products can clean water and remove germs, but there must be user demand. It must also be inexpensive or have a credit scheme. Filters are critical in ensuring the safety of the water. WaterSeer extracts water from the atmosphere by employing the local ecosystem. It is buried six feet down, with its lowest chamber enclosed by chilly dirt. Wind spins a turbine above ground, which turns fan blades inside the device. These blades direct the air into an internal condensation chamber, where the vapor condenses on the chamber’s sides as the warm air cools. The water then flows down to the lower chamber, where it may be pumped out using a simple pump and hose. It can gather 37 liters of water each day in optimum conditions. Fog is captured by large mesh nets, which drip into collection trays after condensation. The largest of these projects is located on the slopes of Mount Boutmezguida in Morocco, which has a microclimate that allows for the harvesting of 6,300 liters of water each day. Fog-catching devices were first established in South America and are now found in Chile, Peru, Ghana, Eritrea, South Africa, and California. Fog catching could provide a long-term source of drinking water for tiny settlements in water-scarce areas, but it is unlikely to considerably improve water supplies. Several farmers pump groundwater to cultivate crops in hot and arid locations, and solar-powered pumps are becoming more popular. Farmers that regard solar energy as free have a challenge because it can lead to over-irrigation. Farmers using solar pumps to make money selling electricity back to the grid are incentivized by a part-technological, part-policy, and management solution developed by the CGIAR’s research program on water, land, and ecosystems in collaboration with the International Water Management Institute (IWMI). Farmers earn revenue, the state gets electrical reserves, and the water source is maintained by limiting usage—all while decreasing carbon emissions—thanks to the guaranteed buy-back arrangement. The system is currently being tested in Gujarat, and according to IWMI, solarizing India’s 20 million irrigation wells could reduce carbon emissions by 4%
5% every year.

3. Climate change, ecological impacts and resilience

References

373

19.9 Conclusion Groundwater science must be reinterpreted in an interdisciplinary manner, using the notion of an aquifer as a multipurpose common pool resource. The importance of participation at all levels in management practices and the development of a governance framework cannot be overstated. Considering the fragmented character of India’s groundwater resource development, it’s critical to engage stakeholders at all stages of the process: Development, monitoring, evaluation, synthesis, and decision-making. There is little doubt that science will play an important part in developing an effective solution to the world’s increasing water concerns. Despite this, most of the world’s water resources are still managed in a fragmented manner that (1) causes conflict between upstream and downstream riparians, as well as between numerous water-using sectors; (2) tends to ignore the crucial interconnectedness of ground and surface waters; (3) overlooks the crucial connections between water quality and quantity; (4) motivates groundwater overdraft, despite the fact that enduring credit limit is known to be unaffordable; (5) encourages agricultural wateruse practices that are short-sighted and expensive; (6) recognizes the substantial advantages that well-managed and preserved ecosystems provide. Scientists and policymakers alike must improve their ability to interpret, communicate, and educate water managers, decision-makers, and the general public. Existing research will only be utilized to its highest capacity in the formulation of comprehensive support for tackling the world’s various water challenges if efficient leadership and outreach programs are implemented. Thus, there is a clear need for centered model groundwater governance mechanisms as well as participatory aspects of groundwater management, both of which use aquifer-based strategies that have been launched to make their way into groundwater practices and policies in India, based on relevant factors of health, ecology, and livelihoods. While debates over multiple groundwater management approaches have cropped up, the issue of complementary groundwater governance remains largely unsolved. Regulation must be designed with the goal of “protection” of the resource along with “good practices,” notably methods that encourage equitable and efficient use of groundwater resources, whether through social rules or formal law-making. This regulatory function must be able to complement participatory groundwater management practices that are the result of interdisciplinary studies. Finally, practices that will aid in the integration of science, participation, and regulation will include the development of capacities across a variety of sectors and stakeholders, allowing for healthy collaboration, especially in the maneuvering of groundwater management and governance at multiple levels across the diverse socio-hydrogeological typology.

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20 Community resilience to climate change-induced disasters: the narratives of the cyclone affected communities of Sundarban biosphere reserve Parama Bannerji1 and Uma Chatterjee2 1

Department of Geography, Nababarrackpore Prafulla Chandra Mahavidyalaya, Kolkata, West Bengal, India 2Sanjog India, Kolkata, West Bengal, India

20.1 Introduction With the increasing onslaught of climate-induced natural hazards, the effects of a disaster are magnified among the most vulnerable groups in a population. It is also observed that underdeveloped economies are the worst ends of such catastrophic events where such natural hazards have taken the proportion of disasters due to the socioeconomic deprivation level of the community (Mondal et al., 2022a). The study selects one such climateinduced natural hazard and its impact on the marginalized population, to make the study evidence-based. Although since 2015, landmark UN agreements, such as the Sendai Framework for Disaster Risk Reduction or Paris Agreement, have always focussed on sustainable equitable development to reduce disaster risks to the community, literature revealed that there exists a gap between theory and practice. This article focuses on some of the most vulnerable cyclone-affected villages in the Sundarban coastal regions (North 24 Parganas district) of the Indian Sundarban, a fragile ecosystem with a historically socio-economically backward community taking up a number of challenges to living there. The study is an attempt to understand how coastal communities respond to heightened natural disasters concentrating on cyclones Aila (2009), Amphan in May 2020, and Yaas in May 2021. It focuses on the narratives of the real-life experiences and the reactions of the hazard victims in the selected places of the Sundarban biosphere of North 24 Parganas, West Bengal. The primary interview was collected

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from in-depth interviews of experts and key informants and later three Focus Group Discussions with stakeholders including representatives of victims of the cyclonic disasters, those engaged with relief work, panchayat members, and NGOs working in that area, were conducted. The study concludes by identifying how disaster risk reduction policies must be inclusive and stakeholder-based and should be focussed on reducing the vulnerability and strengthening the coping strategies of the community of Sundarban, under focus.

20.2 Background With the increasing frequency of Catastrophic events, the challenge of underdeveloped or developing countries is on strong warning systems combined with reducing the vulnerability of the exposed population. Since 2015, landmark UN agreements such as the Sendai Framework for Disaster Risk Reduction, Paris Agreement, and Sustainable Development Goals have set goals for risk reduction and the centrality of all these agreements focused on sustainable, equitable, social, and economic development (Pal et al., 2020) However, there exists a gap between what is proposed and the real ground scenario. It is estimated that nearly 10% of the global population are residents of low-lying tropical coastal regions (Neumann et al., 2015) and these communities are vulnerable to storm surges and cyclones. In recent times, climate change has been blamed to cause a sea surface temperature rise and sea-level rise that has consequently led to a higher frequency and more intense Tropical Cyclone, globally (Islam et al., 2021). Because of this climate change and the consequent change-induced disaster, it is a matter of importance that climate change adaptation receives focus. Climate change adaptation has been described by Mondal et al., 2022a) in the paper. Modeling cyclone-induced multi-hazard risk assessment using analytical hierarchical processing and GIS for coastal West Bengal, India as the changes in the human-environment response system to actual or anticipated changes in climatic conditions. This adaptation is required for the purpose of limiting risks (IPCC, 2007). It has also been observed that natural hazards can become disasters in the presence of a vulnerable and exposed population. In fact, India is the third most disaster-prone country in the world and has the highest number of all-time natural hazards between 2000 and 2019. The figure has drastically increased to 6681 climate-related disasters, from 3656 from 1980 to 1999 (Ghosh & Roy, 2021). It has also been pointed out by the same authors that floods accounted for almost 44% of all disasters. Coastal flooding is a magnanimous problem in deltaic Bengal and it has taken the form of river flood, tidal, or even storm floods (Hoque et al., 2016). In fact, Deltaic Bengal has been providing a breeding environment for a number of severely destructive cyclones such as Sidr in 2007, Aila in 2009, Hudhud in 2014, Fani and Bulbul in 2019, Amphan in 2020, and most recently Yaas in 2021. Researchers (Hooijer & Vernimmen, 2021) have pointed out that these hydrometeorological disasters have both social and economic impacts. For example, considering Cyclone Aila, there is a major outmigration from the Sundarban region, which accelerated in the subsequent two to three years. Further, the deltaic region of Bengal also has a high population density covering almost 25% of the population of West Bengal (Census of India, 2011). This makes the region vulnerable to such disasters. It

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is also pointed out by Bhatia et al. (2019) that almost 65% of the population is economically disadvantaged. Hajra et al. (2014) in a study pointed out that cyclonic storm surges damage embankments and lead to saline water intrusion into agricultural fields. This happened during 2019 whereby rice production was reduced to 32
40 quintals per 1.6 hectares from 64 to 80 quintals per 1.6 hectares. This had affected the economically weaker poorer section, with a loss of US $2253.12 million. Alam et al. (2017) pointed out that there is a widespread scarcity of critical infrastructure to deal with such climate-induced disasters in recent times. This situation will grave further if the country is poor. Under deltaic Bengal, mention must be made of the fragile Sundarban biosphere reserve having the largest mangrove stretch. According to a UNEP report (2015), Sunderban is the world’s largest mangrove forest located at the edge of West Bengal in India and Bangladesh covering 9630 km2. This region is a home to almost four million people who are benefitting from the ecosystem services present there. Out of 102 islands, 48 of them are inhabited and the population is mostly engaged in agriculture, fishing, and collecting wood and honey. The same report pointed out that Sundarban is threatened by climate change and its effects are felt in the form of a rise in sea level, coastal flooding, submergence, and increased frequency of flooding. The rising sea levels and temperature increase have intensified the cyclonic storms with increased wind speeds and precipitation levels which are continually increasing. There had been a number of studies on the increasing vulnerability of this biosphere to climate-induced disasters such as cyclones (Raha et al., 2012). As discussed, the Sundarban region of West Bengal is one such area where such hydrometeorological events have had a deep socioeconomic impact. The vulnerability of this region to such disasters is high due to the poor coping strategy of the local community. According to an article in Down to Earth by Ghosh (2015) in the post-Aila cyclone in Sundarban, the region witnessed an outmigration of men due to debt burden. The article pointed out that almost 40% of men had migrated to peri-urban West Bengal and even to other states such as Tamil Nadu, Karnataka, and the Andaman Islands. They were mostly fishermen or farmers and could not find jobs anywhere but in the construction businesses as daily wage earners. Literature and reports have also pointed to the low socioeconomic background of the community (Census of India, 2011). In such a situation, a gap of literature was indicated to understand the degree of suffering of the relied population and the readiness of the community to cope with such disasters. This chapter will hence examine this problem using the narratives of the real-life examples of the community.

20.3 Rationale of the study This research needs to contribute to eliminating the gap in the literature. Although there had been a multitude of studies on the opportunities, as well as challenges for adapting to the frequent cyclonic disasters, induced by climate change, a need was perceived to include specific cases so that adoption strategies are designed keeping in mind the socioeconomic, infrastructural, environmental, and institutional aspects. The study seeks to understand how the poor and vulnerable coastal communities of Sundarbans are coping with the frequent incumbent disasters. This case of specific example can be used to draw

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a general picture of how the changing environmental conditions of the coastal zone are affecting vulnerable communities. At the same time, a need was felt to understand the underpinnings of the readiness of the community for disaster which is only possible by assessing both the actual as well as potential disaster victims’ viewpoints, their knowledge, and culture along with understanding the historical context of the site, its socioeconomic and cultural infrastructure keeping in mind the Notre Dame Global Adaptation Index (ND-GAIN Index).

20.3.1 Objectives Keeping in mind the research gap and rationale behind the study, the objective of the research is identified as follows: • To identify the changing environmental conditions of the coastal zone like sea level rise, more severe storms, or decreasing natural resources and ecosystem services, in general, and that of the study area (the selected Community Blocks of north 24 Pargana districts covering Sundarban) in particular. • To assess, in general how globally the coastal communities are increasingly coping with changing environmental conditions as a result of climate change and that of the study area, in particular. • To identify if disaster resilience working in the study area and if the community is ready to cope with such disasters, in future • To understand if there is any relationship between the community’s socioeconomic and cultural infrastructure with their vulnerability to such climate change-induced disasters.

20.4 Material and methodology 20.4.1 Data collection The study adopts a qualitative approach and collected qualitative primary data through in-depth interviews with experts (Coordinator of the Center for Disaster Management of Jadavpur University) participant observation and three Focus group discussions with 10
12 members with the victims, local body representatives and organizations engaged in relief and rehabilitation (the Keya Organization of Hasnabad, Hingalganj and Teghoria Institute for Social Movement who worked in Sandeshkhali 1 and 2 and Basirhat Block). Secondary data was collected from government documents, reports, and newspaper articles.

20.4.2 Study area Sunderbans is a part of the world’s largest fluviomarine Ganges
Brahmaputra deltaic plain. It is located at the confluence of the Bay of Bengal and is covered with halophytic mangrove forests. The Sundarban estuary is still in the process of formation. The mighty rivers, Ganga and Brahmaputra, drain into the Bay of Bengal after depositing the alluvium

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in the delta carried by them throughout their journey. The dynamics of the Sundarbans are affected by siltation from rivers flowing into the estuary, natural subsidence of the delta, and sea level rise due to global warming. Both aggradation and degradation processes are occurring simultaneously here, and therefore, in the past few decades, not only a few islands have submerged, but new islands have also been formed. The predicted sea level rise will have a marginal impact on these islands, giving a new dimension to the estuary’s profile. The Sundarbans experience high levels of humidity and temperature throughout the year. The region receives heavy rainfall during the monsoon season (Sahana et al., 2021). The minimum temperature ranges between 2 C and 4 C, while the maximum reaches 43 C in March. Mean annual precipitation ranges between 150 and 200 cm. Tropical cyclones, storm surges, and floods are common phenomena during monsoons. According to the Sundarban Affairs Department of West Bengal, the region is composed of 102 islands of which 48 are still under the cover of the forest whereas 54 have been deforested with subsequent conversion to arable land. This region has 19 Community Development Blocks (CDBs) of which 13 are under the South 24 Parganas district while six other CDBs are under the district of North 24 Parganas. The Sundarban region of North 24 Parganas is spread over Sandeshkhali I and II blocks, Hasnabad and Hingalganj under Basirhat Subdivision. North 24 Parganas is a district in southern West Bengal, of eastern India. North 24 Parganas extends from latitude 22 110 6v north to 23 150 2v north and from longitude 88 20’ east to 89 5’ east. The CD blocks (Sub-district named as Community Development block in India) of North 24 Parganas-Hasnabad, Hingalganj, Sandeshkhali 1 and 2 were gravely impacted by cyclones like Aila (in 2009) and Amphan (in 2021), according to the data from Government of West Bengal, India. Thus, these four areas have been chosen as the study region (Fig. 20.1).

FIGURE 20.1 Location of Indian Sundarban Reserve. Source: Google Earth (2022).

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The study area, covering four CDBs of North 24 Parganas, namely Sandeshkhali I and II, Hingalgunj, and Hasnabad, has a combined population of 703,248, as per the 2011 census of India. This makes up 62% of the population of the district, with 51% of that population being female. It also has a high percentage of Schedule caste population of 41% with almost 63% of entire North 24 parganas cultivators and agricultural laborers residing there. In general, it has a historically marginalized population of Scheduled caste. Hingalganj has a literacy of 75.58% with a sex ratio of 963 while the corresponding figure for Hasnabad is 71% with a sex ratio of 954 with almost 50% higher number of women as main workers than Hingalganj Block. Sandeshkhali 1 has a literacy of 71% with a Sex ratio of 960 while Sandeshkhali II has a literacy of 70% with 965. However, the share of females as the main workers is between 4000 and 5000 persons for both Sandeshkhali Blocks making it less than 50% of Hasnabad block. Sil (2016) pointed out that the main occupations are primarily farming and fishing. Here, agriculture depends on rainwater as the river water has high salinity and to protect fields from salty river water high embankments are built around agricultural land. He also pointed out that the outmigration of those in the working age group to urban areas is very high and the worst social problem is human trafficking. The healthcare setup of the study region is also inadequate. It has been pointed out that the size of the population served per primary healthcare center is around 90,000 (De, 2014). This community under study is more or less homogenous with slight variation in terms of social and economic background facing the common problem of the increased onslaught of climate-induced changes.

20.5 Results 20.5.1 Sundarban as a climate hotspot The Indian portion of the Sundarban delta is located in the extreme southern part of coastal West Bengal and covers both South and North 24 Parganas district. It extends from 21 300 to 22 400 N degrees and 88 050 to 89 550 E longitudes. This region is a mangrove area in the delta formed by the confluence of the Ganges, Brahmaputra, and Meghna Rivers in the Bay of Bengal. Climate change has worsened the scenario of Sundarban. In an article by Basu (2021), an interview with OP Singh, former deputy director-general of India Meteorological Department, pointed out that there will be an increase in the frequency of cyclonic disturbances in the Bay of Bengal by almost 50% in the period 2041
60 due to increased greenhouse gas emissions. In the same article, the Council on Energy, Environment, and Water pointed out in a December 2020 study that the Sundarbans were an “extreme climate hotspot” due to cyclone impacts. The region faces serious environmental challenges like submergence and felling of Sundari trees, with the mangroves being severely impacted by rising sea levels, irregular rainfalls, and violent cyclones (Ghosh and Roy, 2021). Rural livelihood has also become prone to such hydrometeorological climate events (Mondal et al., 2022a).

20.5.2 History of cyclone According to the article Sundarban: The Tale of Sahibs and Cyclones by Ghosh (2006), agricultural expansion in East Bengal began in Aurangzeb’s time. The two major industry at

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that time was weaving and salt. During colonial rule, a new port was proposed to be founded in the Matla River which could bring about severe deforestation. A foremost cyclone expert, Henry Piddington pointed out that this could bring about severe cyclones in the region. In fact, Piddington coined the term cyclone. In 1867, a fearful cyclone made landfall in Sundarban as rapid deforestation was taking place. According to the article, the eye of the storm passed through Port Canning. Sir Hamilton, later cleared even more forest cover. There was also an immigration of people from the Midnapore district as there was food scarcity there. West Bengal government still cleared forests later to expand the railway and road network. All these actions made the region vulnerable to cyclone storms.

20.5.3 Sundarban ecosystem and cyclone It was pointed out (Ghimire & Vikas, 2012) that the Sundarban biosphere is under severe threat and is cyclone-prone and low-lying. It was also observed that between 1980 and 2007, the temperature of the waters in the Sundarbans increased at an accelerated rate of 0.5 degrees every decade. An increase in the intensity of this cyclone may be attributed to the sea surface temperature increase. During this study, an interview was taken with the expert and informant Dr. Gupinath Bhandari, the Coordinator of the Center for Disaster Management of Jadavpur University. Dr. Gupinath Bhandari pointed out that Sundarban is of immense value both ecologically and economically. Its ecosystem, particularly mangroves, has the potential to act as a shield of protection from any sort of natural hazards to the entire south Bengal. He also insisted that Mangroves had played a role during Aila in 2009 and also during Amphan and it acted as a shield not only for the people living in Sunderbans but also Kolkata.

20.5.4 Thematic narratives Three focus group discussion, each with 10
12 members, attended by representatives and members of the Keya Organization of Hasnabad, Hingalganj, and Teghoria Institute for Social Movement, will take place in April 2022. These groups will focus on Sandeshkhali 1 and 2 and Basirhat Block. Effectiveness of Warning system: Respondents pointed out that both before Yaas and Amphan, under the initiative of Panchayat announcements were made via mike on small vans asking them to evacuate their houses and gather at the community centers. The attitude of the community during Aila, Amphan, and Yaas: Respondents pointed out that during Aila, people had to be forced to go to Relief camps and evacuation centers. They were not willing to leave their houses. However, during Amphan, they wanted to move to these centers but they could not gauge the intensity of the cyclone. It was a dual debacle as most of the usual evacuation centers or relief camps like schools were used as Covid-19 relief centers. So the community members were apprehensive to go there. However, during Yaas, they voluntarily went to these centers. The impact on social and economic life was significant: livelihoods were affected, and after the Covid pandemic and cyclone, brackish water entered agricultural fields, making cultivation challenging. Moreover, there was a huge outmigration to southern states,

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TABLE 20.1 Table illustrating the perceived impact of cyclone on Sundarban community. Area of concern

Impact

Livelihood impact

Fish farms were damaged, fisherman were unable to fishing, Increased salinity in seawater, standing crop were damaged, Fodder and pasture was not available, and agricultural land showed signs of salinity

Living conditions impact

Damage to the house, water connections, and electricity

Environmental impact

Damage to forest cover, fish farms, tidal surges, Sea level rise, and salinity impact. Inundation due to monsoon rainfall

Social impact

Migration of the male population in search of livelihood, trafficking of women, marital stress, and separation

Others

Theft and pilferage of relief goods and funds, post-cyclone

leaving many families behind. This in turn led to increased cases of trafficking, divorce, and intermarital affairs.

20.5.5 Specific impact and coping techniques The recent impact of cyclones in the Sundarban Region of North 24 Parganas is illustrated in Table 20.1. Respondents pointed out a number of coping techniques that were adopted in general by the victims of such a hazard, while some were food and assetrelated others were finance-related techniques. It was pointed out by most people reduced their food consumption by reducing it to a single meal a day. The women reduced their buying of baby food from the market. Under asset-related techniques, some sold their cattle, spend money from their savings, or migrated to other areas in search of jobs. Under finance-related management, borrowing levels increased from government bodies, NGOs, etc. It is discussed in Table 20.1. Table 20.1 shows the recent impact of cyclones in the Sundarban Region of North 24 Parganas.

20.6 Discussion The findings revealed that the North 24 Parganas covering the Sundarban region are exposed to high socioeconomic vulnerability due to inhabiting a fragile ecosystem, frivolous infrastructure, low socioeconomic background, and inadequate healthcare facilities.

20.6.1 Community readiness model As discussed above, the Sunderbans region, in general, is an extremely challenging area to live in and is prone to natural disasters such as cyclones and flooding, including other problems such as poverty, outmigration, and, in recent times, social trafficking. It is also

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understood that the poorest households are relatively more vulnerable to material and human losses following a natural hazard. And the repeated loss of livelihood makes them more vulnerable to future risk. The previous section also discussed how the researched community was not ready to face the increased onslaught of extreme event namely cyclone which is predicted to increase in the long run. To avoid such kinds of hazards transforming into a disaster for unprepared communities like the Sundarbans region, effort by the community can at least reduce the level of devastation. The increased frequency and onset of climate change-induced disasters had been detrimental to the local community. In such a situation, a socioeconomic vulnerability assessment is imperative to formulate efficient management strategies. Hence, a Community Readiness Model has been designed based on (Edwards et al., 2000) The Community Readiness Model. The original model defines nine stages of community readiness which has be customized according to the need of the study. This model may be used to define research tools as well as be used as a practical tool to serve the community to cope with the upcoming hazards. This model is also based on another report prepared by American Redcross and the Global Disaster Preparedness center (American Redcross Society, 2015). The report pointed out the importance of strengthening community awareness by fostering a systems approach by establishing partnerships between governmental and non-governmental institutions or people, ensuring risk knowledge through awareness training, investing in early warning, integrating inclusion into program design, and using knowledge of successful experiences of inclusion. Once the people living in the vulnerable region of Sundarban achieve a stage of readiness where local efforts can be initiated, advanced-stage community teams may be trained to use the community readiness model (Table 20.2). Table 20.2 shows the community readiness model for the cyclones in the Sundarban Region of North 24 Parganas. The model is developed in the form of a flowchart and one step will lead to the next.

20.6.2 Revisiting theories, conventions, and agreements Disasters may cause communities that were working to come out of poverty to plunge back into the same situation. It is also observed that the effects of a disaster are magnified among the most vulnerable groups in a population. Hence, no development is possible unless sustainable processes are embedded into the development policies. Sustainable development policies are laid out in global frameworks such as the 2030 Agenda for Sustainable Development and the Sendai Framework for Disaster Risk Reduction 2015
30. It is a general opinion that disaster risk reduction policies must be inclusive and stakeholder-based, and should be focussed on reducing vulnerability and strengthening coping strategies.

20.6.3 Socioeconomic background and vulnerability to disasters The backwardness of any area depends both on its environmental shortcoming and the level of infrastructural development (Mondal et al., 2022b). The study has been given

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TABLE 20.2 Community readiness strategy for Indian Sundarban. Community readiness strategy Condition: No Awareness Goal: Raise Awareness to cope before and after cyclone hits Strategy: • One-to-one visits to households with community members and leaders • Visit existing groups to raise awareness on cyclone forecasting, evacuation techniques, financial arrangements, communication techniques, and recovery from post-storm situation Condition: Denial Strategy: • Repeat one-to-one visit • Discuss local incidents and narrate stories of post-cyclone victims • Present information at the local meetings • Use of posters and flyers to raise awareness Condition: Community is aware and the preplanning phase starts Strategy: • Raise awareness with concrete ideas to combat cyclone-related pre and post situation • Review existing efforts of the community and identify the strength • Conduct Community Surveys • Conduct in-service training programs of the government bodies and NGOs working • Begin library or internet search for funding and other relief operation Condition: Expansion of Service and Monitoring Strategy: • Plan community events to support the issue • Maintain a comprehensive database • Diversify fund sources and monitor the disbursement of the previous relief fund • Develop a network with qualified service providers

focused on the increasing vulnerability of the local community in the event of disaster under changing climate scenarios. The findings revealed that the vulnerability of the community to such disasters is high as neither the respondents have economic assets or sociocultural background to adapt or cope with such disasters. In this context, the provision of basic health facilities, improvement of the early warning system, or infrastructural development should be aimed to reduce the socioeconomic vulnerability.

20.7 Limitations of the study Time constraints and limited field visits due to the pandemic had been the main constraints. Another constraint was the lack of previous research on the selected topics. The relevance of previous research was non-existent for the present study because external factors have changed.

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20.8 Recommendation Looking at the case of Sundarban, it may be pointed out that such disaster risk reduction policies must focus on the following: 1. Risk identification: Identify the primary risks as the occurrence of climate-induced natural disasters, unplanned destruction of forest cover, and settlement of a historically marginalized population on a disaster-prone fragile ecosystem 2. Risk reduction: Increase of the mangrove cover, resettlement if required, and various socioeconomic development policies for this extremely vulnerable community. 3. Preparedness: Early warning system and use of community readiness approach 4. Financial protection and resilient recovery 5. Zoning of Sundarban region according to vulnerability 6. Construction of disaster-resistant homes and bolstering existing livelihood pattern. The study further recommends strengthening the social system while generating economic opportunities for minimizing socioeconomic vulnerability. Education among local communities may lead to the development of an understanding of the implications of severe weather events. Better physical infrastructural setup, developing disaster monitoring centers, and stronger transport connectivity to the remote islands may improve the situation. It is urgently needed to improve healthcare facilities. Other measures are constructing embankments along the rivers in the coastal areas, exploring livelihood opportunities such as tourism, minimizing population pressure on resources, and taking up policies for mangrove conservation, and disaster risk management, which can improve the situation.

20.9 Conclusion The Sundarbans delta faces tremendous pressure from an increasing human population who are economically, educationally, and socially backward and who inhabits an area with poor infrastructural facility. Not only the shrinking coastline is a threat here, but the recurring cyclonic storms—Sidr (2007), Aila (2009), Phailin (2013), Hudhud (2014), Bulbul (2019), Amphan (2020) and Yaas (2021)—have also caused immense destruction in this low lying, riverine mangroves. The infiltration of saline water in the adjacent agricultural lands has reduced its productivity, thus depriving the inhabitants of their primary source of income. Moreover, Human activities such as the over-exploitation of natural resources and tidal-based aquaculture have further increased the vulnerability in the region. The residents of Sundarbans are vulnerable to such frequent disastrous meteorological events, and therefore disaster resilient infrastructure is the only solution to help the vulnerable communities from their adverse impacts. Government should also find scopes to enhance livelihood security or provide alternative livelihood measures to avoid the existing communities to convert into environmental refugees in the future. Climate models need to be enhanced and applied after every such episode to predict the future of the regions and consequently, strategies need to be devised to take further actions before future calamities. As discussed in the absence of any industry, the community is dependent on agriculture.

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With the impact of climate change, the Sundarban area is cyclone-prone and low-lying, as a result of which changes in climate have significantly impacted the area. Management of Sundarbans in the event of the challenges faced due to climate change should be the prime focus of the government’s public policy.

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22. District Census Handbook-North 24 Parganas. Office of the Registrar General. (2011). Edwards, R. W., Jumper-Thurman, P., Plested, B. A., Oetting, E. R., & Swanson, L. (2000). Community readiness: Research to practice. Journal of Community Psychology, 28(3), 291
307. 00904923. Wiley-Liss Inc., United States. http://www.interscience.wiley.com/jpages/0090-4392/. Ghimire, K., & Vikas, K. (2012). Climate change—The impact of Sunderban: A case study. International Scientific Journal Environmental Science. Ghosh, A. K. (2015). People in Sundarbans most susceptible to climate change. https://www.downtoearth.org.in/ blog/climate-change/people-in-sundarbans-most-susceptible-to-climate-change-50650. Ghosh, D. (2006). Sundarban: The tale of Sahibs and cyclones. https://www.livehistoryindia.com/story/religiousplaces/sunderbans. Ghosh, S., & Roy, S. (2021). Climate change, ecological stress and livelihood choices in Indian Sundarban. Springer Science and Business Media LLC. Available from https://doi.org/10.1007/978-981-16-0680-9_26. Hajra, R., Szabo, S., Tessler, Z., Ghosh, T., Matthews, Z., & Foufoula-Georgiou, E. (2014). Unravelling the association between the impact of natural hazards and household poverty: evidence from the Indian Sundarban delta. Sustainability Science, 12, 453
464. Hooijer, A., & Vernimmen, R. (2021). Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics. Nature Communications, 12(1). Available from https://doi.org/10.1038/s41467-021-23810-9. 20417231. Nature Research, Netherlands. http://www.nature.com/ncomms/index.html. Hoque, M. A., Scheelbeek, P. F. D., Vineis, P., Khan, A. E., Ahmed, K. M., & Butler, A. P. (2016). Drinking water vulnerability to climate change and alternatives for adaptation in coastal South and South East Asia. Climatic Change, 136(2), 247
263. Available from https://doi.org/10.1007/s10584-016-1617-1. 15731480. Springer Netherlands, United Kingdom. http://www.wkap.nl/journalhome.htm/0165-0009. IPCC. (2007). Climate change: Impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Islam, M. T., Charlesworth, M., Aurangojeb, M., Hemstock, S., Sikder, S. K., Hassan, M. S., Dev, P. K., & Hossain, M. Z. (2021). Revisiting disaster preparedness in coastal communities since 1970s in Bangladesh with an emphasis on the case of tropical cyclone Amphan in May 2020. International Journal of Disaster Risk Reduction, 58. Available from https://doi.org/10.1016/j.ijdrr.2021.102175. 22124209. Elsevier Ltd., United Kingdom. http://www.journals.elsevier.com/international-journal-of-disaster-risk-reduction/. Mondal, M., Haldar, S., Biswas, A., Mandal, S., Bhattacharya, S., & Paul, S. (2022a). Climate change, multihazards and society: An empirical study on the coastal community of Indian Sundarban. Natural Hazards Research, 2(2), 84
96. Mondal, M., Subrata, H., & Paul, S. (2022b). Socio-demographic backwardness in cyclone prone coastal villages: An Insight from Indian Sundarban. Safety in Extreme Environments, 4, 13
33. Available from https://link. springer.com/article/10.1007/s42797-021-00048-8.

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Neumann, B., Vafeidis, A. T., Zimmermann, J., Nicholls, R. J., & Kumar, L. (2015). Future coastal population growth and exposure to sea-level rise and coastal flooding—A global assessment. PLoS One, 10(3), e0118571. Available from https://doi.org/10.1371/journal.pone.0118571. Pal, I., Meding, J., Shreshta, S., Ahmed, A., & Gajendran, T. (2020). An interdisciplinary approach to disaster resilience and vulnerabilty. Springer. Raha, A., Das, S., Banerjee, K., & Mitra, A. (2012). Climate change impacts on Indian Sunderbans: A time series analysis (1924
2008). Biodiversity and Conservation, 21(5), 1289
1307. Available from https://doi.org/10.1007/ s10531-012-0260-z, 17297105. Sahana, M., Sufia, R., & Paul, A. (2021). Assessing socio-economic vulnerability to climate change-induced disasters: Evidence from Sundarban Biosphere Reserve, India. Geology, Ecology, and Landscapes, 5(1). Available from https:// doi.org/10.1080/24749508.2019.1700670, https://www.tandfonline.com/doi/full/10.1080/24749508.2019.1700670. Sil, A. (2016). Reaching the unreached in Sunderbans. Community Eye Health Journal, 29(95), s10
s12. 03683395. International Centre for Eye Health, Nigeria. http://www.cehjournal.org/wp-content/uploads/reaching-theunreached-in-sunderbans.pdf. UNEP. (2015). The Sundarban and climate change. https://www.cms.int/sites/default/files/publication/fact_sheet_sundarbans_climate_change.pdf.

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C H A P T E R

21 Climate change, urban flooding, and community perceptions of vulnerability and resilience: lessons from Diamond Harbor region Sudarshana Sinha Department of Humanities and Social Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

21.1 Introduction Over the past decade, there has been an increase in the frequency and magnitude of climate-induced disasters (CIDs), adversely affecting people’s lifestyles and well-being (Haggag et al., 2022). A double-digit increase in fatalities due to CID is expected over the next 13 years (Mclennan, 2021). A further increase of 250,000 deaths is expected over the next decade. As of 2040, CID-induced damage is predicted to increase by 20% Lo´pez et al. (2020). Climate change is expected to increase several hazards, such as floods, as stated by Jongman (2015). There have been cascading effects on the urban regions as a result of climate change-induced hydro-meteorological disasters, which have rendered them vulnerable (Ingle & Chattopadhyay, 2022). Owing to climate change, urban flooding events have become frequent owing to the seriousness of the calamities caused by them in urban coastal areas (Arnell & Gosling, 2016). The calamities caused by urban flooding, especially in coastal strips are aggravated by the rise in sea levels (Chang et al., 2021), and the increase in the intensity and frequency of precipitation (Kunkel et al., 2020) all of these factors are driven by climate change. Owing to the transformation of blue-green areas into gray regions tends to amplify flood-related losses in various economic and social dimensions (Chen et al., 2020; De Risi et al., 2018). Between 1985 and 2003, one-third of the world’s population has been affected by floods, and among the various nation-states, the Asia-Pacific region has experienced some of the most disastrous ones in the recent past (Mechler et al., 2019). Among the

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various Asian countries, India is exposed to multidimensional flooding activities every year and the estimated loss incurred by the nation due to flooding activities has been estimated to be around 7 billion each year (UNDRR, 2020). As of 2019, according to the Inform Risk Index, India has obtained a rank of 29 out of 191 countries and stands at a high exposure to risks induced by disasters (World Bank, 2021). Vulnerability is a multidimensional construct assessment that is incomplete when a sole variable is given importance (Salazar-Briones et al., 2020; Tanim & Tobin, 2018). In the case of urban flooding, several scholars have found it more apt if its areal extent, frequency, severity, intensity of rainfall, flood duration, the efficiency of the drainage system, infiltration rate, characteristics of various attributes such as built environment and quasipermanent features, natural terrain, characteristics of soil, land use and land cover of the area, exposure of people, physical infrastructure, and its impact on tangible and intangible assets are taken into consideration (Angeon & Bates, 2015; Lee, 2014; Mavhura et al., 2017; Quinta˜o et al., 2017; Wolch et al., 2014; Zhang et al., 2019). Song et al. (2019) stated that although arriving at resilience strategies is particularly an important cornerstone for combatting the challenges of urban flooding, however arriving at a singular social-ecological systems framework might not suffice. Generally, flood assessments tend to focus on geophysical and geomorphological determinants of a flood (Alam & Huq, 2019), landscape damage, damage to physical property, and developing predictions about the areal extent of flooding (Haggag et al., 2022; Oubennaceur et al., 2019; Rehan, 2018). A significant gap between the current levels of adaptation strategies that have been adopted and the necessary levels that need further incorporation can be observed (IPCC, 2022). Climate resilience planning is required to predict the occurrence of climate-induced disasters and assessing flood vulnerabilities is crucial not only for its mitigation but also stands a chance to reduce the potential occurrences of various infrastructural and ecological damages (Nasiri et al., 2016). In addition to the usage of data-driven statistical modeling methods that are required to reduce flood-related household damages (Ganguly et al., 2019), studies focusing on the prevalent socioeconomic conditions, native practices, values, ethics and norms of a region are equally important to make the strategies much relevant and custom fit for the particular region in question (Haggag et al., 2022). If such issues revolving around urban flooding are not distinctly addressed then it would certainly increase the vulnerability of inhabitants staying in urban areas (Waters & Adger, 2017). Scholars suggested that the approach to flood management goes beyond the management of geophysical assessments; however, an assessment of social pros and cons in the case of the built environment, and social disadvantage needs to be conducted (El-Zein & Tonmoy, 2017). Hence, the major objective of this chapter is to analyze the people’s perception of urban flooding and to analyze their socio-ecological vulnerability.

21.2 Theoretical orientation Urban flooding refers to the inundation of a densely populated area due to excess rainfall on a continuous and impervious stretch of land that mostly arises due to an overwhelming capacity of the drainage system and reduced infiltration rate. Han et al. (2020) stated that those areas where leapfrog expansion and edge expansion have taken place

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tend to experience a greater rate of urban flooding (Cutter et al., 2003). The conversion of the natural landscape into impervious gray surfaces tends to reduce the rate of infiltration and as a result, the rate of urban flooding tends to increase (Abebe et al., 2018; Fernandez et al., 2016; Wu et al., 2019). However, the drainage patterns and infrastructure that are needed to mitigate floods are lacking in most urban areas rendering them to be acutely vulnerable (Mohtar et al., 2020; Gimenez-Maranges et al., 2020; Papathoma-Ko¨hle et al., 2019). The impact of floods is unequally distributed across the entire diaspora of social infrastructure (Karagiorgos et al., 2016; Kawasaki et al., 2020). Despite being exposed to urban floods the intensity and impacts of disruption vary. In the case of disaster management studies, vulnerability is a risk-driven concept that is related to the magnitude, duration, and ecological and social attributes of a particular event (Ford et al., 2010). However, as pointed out by Gallopı´n (2006) instead of considering vulnerability to be a penultimate occurrence, it can be better understood if one considers it as a starting point of their analysis. It is difficult to find a standardized definition of vulnerability. However, the International Strategy for Disaster Reduction has stated that vulnerability is dependent upon physical, social, economic, and environmental processes that modify the stance of people’s susceptibility (UNISDR, 2004). Vulnerability is the degree to which the socio-ecological system is likely to be affected by exposure to any sort of hazard (El-Zein et al., 2021). The vulnerability of urban flooding is the outcome of three factors such as hazards, exposure to these circumstances, and the stance of people owing to their social vulnerability (Chakraborty et al., 2020). However, the interpretation and perception of this tend to vary spatial-temporally and according to various socioeconomic conditions of various individuals such as disparity in their age (Ferrari et al., 2018), demographic information (Rufat et al., 2015), household status (Mahapatra et al., 2015), sex, economic status (Kunte et al., 2014), place of residence (Muis et al., 2015), people’s sensitivity (Ingle & Chattopadhyay, 2022), and people’s awareness of risk. Exposure to hazards and vulnerability is a dynamic concept that can be gauged when one takes into account the probability with which the valuable aspects and assets that are owned by an individual or community are likely to be affected (Cheng et al., 2017; Rufat et al., 2015). In addition to an assessment of the frequency of occurrence of hazards (Koks et al., 2015), a social vulnerability assessment (Walkling & Haworth, 2020) is crucial. Therefore, garnering the latest information about the community or region-specific vulnerability characteristics is crucial. The adoption of spatially relevant scales to gauge social vulnerability is advantageous because the intricacies underlying the complex dynamics of an area can be easily traced using a region-specific methodology (ElZein & Tonmoy, 2017). Identification of ideologies and assessment of the perceived notions of vulnerability among people needs to be analyzed when the entire discourse of risk management is being discussed (Van Vliet et al., 2016). Individuals and communities belonging to lower socioeconomic strata have a greater dependency rate and the occurrence of hazards would result in the loss of their livelihood and tends to face significant challenges to adopting resilient strategies (Collins et al., 2019; Cutter et al., 2003; Cutter et al., 2018). Urban flooding tends to take a toll on the physical health of the people (Owrangi et al., 2014) and those having pre-existing medical conditions (Blaikie et al., 2014), women, and children tend to face significant challenges combatting the hazard.

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Indicator-based assessments of social vulnerability have been explored at different levels such as macro, meso, and micro. Various scholars such as (Apotsos, 2019; Tavares et al., 2015) has explored it at a micro level by restricting their analysis among select municipalities, by (Garbutt et al., 2015; Kirby et al., 2019) at a meso levels by contextualizing their case studies of different districts and by scholars such as (Khajehei et al., 2020; Nasiri et al., 2016) who has analyzed the notions of vulnerability at a national level. Social vulnerability of urban flooding has been assessed in the case of Sydney by studies (El-Zein & Tonmoy, 2017; El-Zein et al., 2021), six US cities by Chang (Chang et al., 2021), Washington D.C. by Tanir (Tanir et al., 2021), Ethiopia by Erena (Erena & Worku, 2019), Italy by Ferrari (Ferrari et al., 2018), Pakistan by Rana (Rana & Routray, 2018), Zimbabwe by Mavhura (Mavhura et al., 2017), Boston by Cheng (Cheng et al., 2017). Yet Song et al. (Song et al., 2019) stated that spatial patterns between resilience and vulnerability for deducing risks and its correlation to spatial variation have not been much explored. Most of the vulnerability assessments have been carried out at the macro and meso level and among the micro level analysis most of them have been contextualized in the case of the western countries (Ingle & Chattopadhyay, 2022) and very few studies have investigated the intra-city-level flood vulnerability, by taking into account the heterogeneous composition of people within the urban fabric (Gu et al., 2018).

21.3 Objectives The major objective of this chapter is to analyze the people’s perception of urban flooding and to analyze their socio-ecological vulnerability.

21.4 Materials and methods To analyze the Vulnerability Index (VI) four broad indicators namely Area Vulnerability Index (AVI), Individual Vulnerability Index (IVI), Livelihood Vulnerability Index (LVI), and Technological Vulnerability Index (TVI) were constructed. The details of the four broad indicators and 31 sub-indicators are stated in Fig. 21.1.

21.4.1 Justification for the selection of the indicators In this chapter, the multidimensional characteristic of vulnerability has been taken into account. In this chapter, the authors have borrowed from the theoretical orientation of the pressure and Release model’s approach to looking at vulnerability (Mazumdar & Paul, 2018). Under this theoretical framework, vulnerability is looked upon as a dynamic framework that is highly influenced by various demographic, the economic, and resource base of an area, ecological underpinnings, political scenarios and the ability of the people to cope with the changing dynamics have been considered. The Hazard of Place model by Cutter that takes into account various bio-physical and social factors as major influencers of vulnerability has also been considered (Cutter et al., 2000). The methods for the

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FIGURE 21.1 Showing the brief methodological framework.

improvement of the Vulnerability Assessment (MOVES) framework that has explored the potential of various physical, social, and environmental factors in either reducing or magnifying the impact of catastrophes has also acted as an inspiration for the paper (Birkmann, 2013). Kuhlicke et al. (2011) stated that quantitative assessment of household vulnerability is based on people’s perception of vulnerability at a micro level. In the case of the assessments of social vulnerability assessment, most scholars have taken select factors such as rate and level of dependency, gender, economic conditions, occupational status, accessibility and availability of select resources, and their strengths and weakness into account while assessing the social vulnerability aspect (Fatemi et al., 2017; Frigerio & De Amicis, 2016). For assessing social vulnerability SETS (i.e., social, ecological, and technological) vulnerability assessments have been profoundly used by various scholars (Childers et al., 2019; Kirby et al., 2019; Rufat et al., 2015). In this chapter, we would be borrowing from the theoretical framework of the above-mentioned scholars. The justification for the selection of the various theoretical framework (refer to Table 21.1), indicators, and sub-indicators (refer to Table 21.2) are stated below.

21.4.2 Data sources The data presented in this paper has been obtained from primary as well as secondary sources. In the case of the primary survey select focal areas were chosen to lie in the northern, southern, eastern, western, and central parts of the study region. A purposive random sampling method was opted for choosing the respondents. The focal points were either centering those areas where a higher rate of urban flooding has been reported by the

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TABLE 21.1 Showing the description of the various models and theoretical frameworks. Index

Variables considered

Source

Pressure and Release Model

Demographic, economic, the resource base of an area, ecological underpinnings, political scenarios

Mazumdar and Paul (2018)

Hazard of Place Model

Bio-physical and social factors

Cutter et al. (2000)

MOVES framework

Physical, social, and environmental factors

Birkmann (2013)

SETS

Social, ecological, and technological indicators

Childers et al. (2019), Kirby et al. (2019), Rufat et al. (2015)

Human Housing condition, the standard of living, respondent’s Vulnerability Index take on self-help measures, the population density of the (HVI) area, perception of risk and hazards

Khan and Salman (2012)

Social Income, gender, age, structural or physical vulnerability, Vulnerability Index access to assets (SVI)

Fekete (2009), Rygel et al. (2006), Schmidtlein et al. (2008)

Livelihood Livelihood status, dependency, access to the health care Vulnerability Index facility, water supply, food (LVI)

Hahn et al. (2009), Shah et al. (2013)

No Permission Required.

respondents or the ones that were residing in comparatively low-lying areas as stated by the respondents. The respondents were of the age group of 20
70 years. The responses of only respondents that had fulfilled the above criteria and had completely furnished the responses for the entire questionnaire schedule were selected for the study. The details about the satellite imagery are stated in Tables 21.3 and 21.4.

21.4.3 Statistical analysis The details of various statistical analyses are stated below: NDBI Analysis It can becalculated using:  NDBI 5 ðSWIR1
NIRÞ=ðSWIR1 1 NIRÞ . The value tends to range between 1 and 2 1 where 1 tends to signify a highly built-up area and 2 1 tends to signify a very low density of the built-up area. UII Analysis It explains the degree of concentration of urban area to total geographical area over time. It can be calculated using: UrbanIntensityðUIIÞ 5 ðSWIR2
NIRÞ=ðSWIR2 1 NIRÞ. Higher UII values indicate a higher concentration of urban areas. BAEMOLI It was also used to extract the continuous built-up area image. Higher values in BAEMOLI indicate a higher possibility of those pixels representing the built-up area,

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21.4 Materials and methods

TABLE 21.2

Showing the details of the various indicators and sub-indicators.

ID

Names

AVI

Area Vulnerability Index

NDBI

Built-up area analysis is important to understand the percentage of built-up spaces in the study region as well as to understand the dynamics of infiltration and percolation of water in these regions that might aggravate or subdue the chances of urban flooding.

UII

An analysis of the quantity and quality of vegetation is important to understand the rate of percolation of waterbodies. It would also help to understand the possibility of the occurrence of urban floods.

ARVI

MNDWI

Source

Normalized difference built-up Index is used to analyze the presence of built-up spaces, to form an idea about the concentration of impervious surfaces.

Mohtar et al. (2020), Abebe et al. (2018), Chang et al. (2021), Cutter et al. (2018), El-Zein et al. (2021), Fernandez et al. (2016), Gimenez-Maranges et al. (2020), Hahn et al. (2009), Ingle and Chattopadhyay (2022), Papathoma-Ko¨hle et al. (2019), Wu et al. (2019)

Urban Intensity Index is used to analyze the intensity of urban clusters that would help us in refining our idea about the presence of gray infrastructure in the study region. It is used to analyze the pattern of built-up urban areas (i.e., whether continuous or discontinuous). The increase in the concentration of higher extent of impervious areas would further reduce the rate of infiltration and percolation of water, thus prolonging the time duration of stagnation of water and increasing the chances of urban flooding

BAEMOLI

NDVI

Description and Relvance

Normalized Difference Vegetation Index, is used to measure the vegetation cover of the study area and would help us to understand the regions where a greater extent of percolation of water is possible. The atmospherically resistant vegetation Index would help us to understand the quality of vegetation which is useful to predict the ability of green space to facilitate the percolation of water.

Analysis of the presence of water bodies is important to understand the presence of blue spaces in the area. The presence of such spaces tends to either reduce or increase the duration of stagnation of water on impermeable surfaces thus altering the time duration of stagnation of water.

The modified Normalized Difference Water Index helps us to analyze the presence of waterbodies in the study region.

(Continued)

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TABLE 21.2 (Continued) ID

Names

Description and Relvance

NDBaL

Analysis of the presence of bare soil is an important determinant of the surface runoff rate and the rate of percolation of water. This analysis would help us to predict the nature of urban flooding in that area.

Normalized Difference Bare Land Index is used to understand the presence of bare surfaces in the area. This is useful to predict the runoff and the percolation rate.

DBSI

IVI

Individual Vulnerability Index

Xa

Good condition of houses

Xb

Dwelling rooms

Xc

Availability of good sanitation facility (where running water, bathroom, and latrine are present within the house)

Xd

Age

Xe

Gender

Xf

Educational level

Xg

Number of members in each household

Xh

TV

Xi

Refrigerator

Xj

Personal vehicles

Xk

Have access to baking services

Xl

Have access to medical facilities

Xm

Have access to Internet services

Xn

Stagnation of water in the neighborhood

Xo

Quality of transport and communication

Xp

Availability of emergency services

Xq

Perception of risk and hazard

Xr Xs

Source

Dry Bare Soil Index is used to understand the pattern of bare surfaces in the area. This is useful to predict the runoff and the percolation rate.

The housing conditions of an individual are important to understand their stance in a vulnerable situation and their susceptibility to various sorts of diseases

Muis et al. (2015)

Having a clear idea of the demographic profile would help us to understand their access to information, mobility rate, and stance on disaster preparedness.

Cheng et al. (2017), Ferrari et al. (2018), Mazumdar and Paul (2018), Rufat et al. (2015)

Analysis of the environs and assets owned by the respondents would help us to understand the nature of their loss, vulnerability, and ability to obtain redressal in case of urban flooding.

Blaikie et al. (2014), El-Zein and Tonmoy (2017), Koks et al. (2015), Walkling and Haworth (2020)

Analysis of these factors would Ahsan and Warner (2014), help us understand the Fekete (2009), Rygel et al. (2006), Frequency and intensity of physical perception of individuals toward Schmidtlein et al. (2008) exposure to urban floods floods and the health conditions that they are experiencing Self-reported health conditions (Continued) 3. Climate change, ecological impacts and resilience

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21.4 Materials and methods

TABLE 21.2

(Continued)

ID

Names

LVI

Livelihood Vulnerability Index

Xt

Occupational status

Xu

Dependency of livelihood on climatic conditions

TVI

Adaptability Vulnerability Index

Xv

Dependency

Xw

Coping Ability

TABLE 21.3

Description and Relvance

Source

This would help us understand the occupational vulnerability of an individual and the risks encountered by him due to urban floods

Childers et al. (2019), Hahn et al. (2009), Kirby et al. (2019), Kunte et al. (2014), Shah et al. (2013)

This would help us to understand the risk and the coping ability of an individual towards such disasters.

Khan and Salman (2012), Song et al. (2019)

Showing the details of the satellite imagery.

Study area

Satellite

Sensor

Path

Row

Date of acquisition

Diamond Harbor

Lansat 8

Operational Land Imager

138

045

21/12/2021

TABLE 21.4

Showing the details of the bands. Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)

Bands

Details

Wavelength (micrometers)

Resolution (meters)

1

Coastal aerosol

0.43
0.45

30

2

Blue

0.45
0.51

30

3

Green

0.53
0.59

30

4

Red

0.64
0.67

30

5

Near Infrared (NIR)

0.85
0.88

30

6

SWIR 1

1.57
1.65

30

7

SWIR 2

2.11
2.29

30

8

Panchromatic

0.50
0.68

15

9

Cirrus

1.36
1.38

30

10

Thermal Infrared (TIRS) 1

10.6
11.19

100

11

Thermal Infrared (TIRS) 2

11.50
12.51

100

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whereas the lower values depicted land-cover classes other than the built-up area (Bhatti & Tripathi, 2014). BAEMOLI 5 NDBIOLI
NDVIOLI
MNDWIOLI where NDBI represents the Normalized Difference Built-up Index; NDVI represents the Normalized Difference Vegetation Index; MNDWI represents the Modified Normalized Difference Water Index. NDVI Analysis It is used to estimate the relative biomass of vegetation. It can be calculated using: NDVI 5 ½ðNIR
REDÞ=ðNIR 1 REDÞ. The value tends to range between 1 and 2 1 where 1 tends to signify dense vegetation area and -1 tends to signify sparse vegetation. ARVI Analysis It is an improved index that is used to correct the influence of the atmosphere and it is especially useful in regions with a high content of atmospheric aerosol, especially in tropical areas (Somvanshi & Kumari, 2020). ARVI 5 ðNIR
RBÞ=ðNIR 1 RBÞ. RB 5 Red
γðBlue
RedÞ. MNDWI Analysis It is used to study the quality of water bodies. It can enhance the features of open water by effectively either or at times removing built-up land noise, vegetation noise, and soil noise. MNDWI 5 ðGREEN
SWIRÞ=ðGREEN 1 SWIRÞ. The value tends to range between 1 and 2 1 where 1 tends to signify deep waterbodies. NDBaL It is used to map bare land areas, a higher value represents built-up areas than bare land (Li et al., 2017). This index is based on thermal capacity information between different types of soil and other materials (Stathakis et al., 2012). It can be calculated using: NDBaL 5 ðSWIRI1
TIRS1Þ=ðSWIRI1 1 TIRS1Þ. The value ranges from 2 1 to 1, where 1 signifies a higher density of bare land. DBSI Rasul et al. (2018) stated that although NDBal is frequently used to map bare soil areas, improved techniques should be used; hence, DBSI is used to map bare soil index based on Landsat 8 imageries; the values range between 2 2 and 1 2, and higher numbers represent more bare soil and lower values can be delineated to other classes.  DBSI 5 ðρSWIR1
ρGreenÞ=ðρSWIR1 1 ρGreenÞ
NDVI. where NDVI represents the Normalized Difference Vegetation Index. Principal Component Analysis Principal Component Analysis (PCA) is a variable reduction technique; it aims at reducing a large set of variables into smaller sets by selecting the most important variable which accounts for most of the variance. It takes into its account the variation between the Eigenvalues. The PCA analysis was conducted among the sub-indicators belonging to the three broad domains to bring out the specific sub-indicator which has brought about the maximum variance within each one of the three broad domains and hence this analysis was chosen.

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401

21.4.4 Software Statistical analysis was performed using Statistical Package for Social Science (SPSS v22) and mapping was based on Arc GIS software (v10.5).

21.5 Study area For this chapter, the Diamond Harbor Municipality has been chosen (refer to Fig. 21.2). It is situated at a distance of 48 km (approx.) from Kolkata. According to Census (2011), it has a population of 41,215, among which 50.34% are males and 49.73% are females. This area houses 9777 households and the literacy rate stands at 65% (approx.). This region primarily serves as a trading center for various agricultural products, and household and food processing industries. Out of the total population, 26.53% are employed as main

FIGURE 21.2 Showing the map of the study area.

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21. Climate change, urban flooding, and community perceptions of vulnerability and resilience

workers, 2.47% are employed as marginal workers, and 69.35% are categorized as nonworkers. However, a disparity in the male
female employment rate can be observed. On one hand, 22.61% of the male population and 3.91% of the female population are classified as main workers, whereas 1.68% of the male population and 0.80% of the female population are classified as marginal workers. Other than that, 22.15% of the male population and 39.20% of the female population are classified as non-workers.

21.6 Discussion and results 21.6.1 Demographic details of the respondents For this survey, 1002 respondents were selected. To reduce the biases of a select section of respondents, approximately 20 respondents belonging to each category were selected wherein each member represents a single household that was affected due to urban floods. The demographic details of the respondents are stated in Table 21.5. Out of which, 11% of the respondents were students, 7% were retired, 46% could be classified as working professionals, 31% were housewives, and 5% were unemployed.

21.6.2 Area vulnerability index The vulnerability index was analyzed under four broad subheads that is the presence of built-up areas, vegetation cover, waterbodies, and bare land areas. Different analysis has been conducted to understand the environs of the study area (refer to Fig. 21.3 and Table 21.6). According to NDBI analysis, it can be observed that the value of the highest concentration of built-up areas was 0.347 whereas the lowest value recorded was 2 0.290. However, most of the concentration of built-up areas was concentrated in the northern and southern parts of the study region and the lowest concentration was recorded in the eastern part. According to UII intensity analysis, the highest concentration of urban areas was 0.534 and the lowest value recorded was 2 0.421. The highest and the lowest concentration of urban areas matched the areas complying with the NDBI analysis. This shows that the presence of gray infrastructure and the impermeable surface is restricted to select regions of the study area. According to BAEMOLI analysis, in general, the study region displays a scattering of built-up areas except for select pockets that have clusters of continuous high-intensity built-up regions (the highest clustering was 0.847). These are the regions that are more prone to urban flooding owing to the reduced rate of percolation, increased levels of surface runoff, and higher chances of stagnation of water. According to NDVI analysis, the highest intensity of vegetated areas that has been recorded lies at 0.32, which uniformly covers most of the study region. However, according to ARVI analysis, the entire study region has medium to good-quality vegetation with occasional patches of bad-quality vegetation especially centering the built-up patches. Hence, it can be observed that the region does not display a high vegetation cover, which tends to reduce the percolation rate and the presence of vegetation is further reduced near the built-up areas thus escalating the chances of stagnation of water and urban floods.

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21.6 Discussion and results

TABLE 21.5

Showing the demographic details of the respondents. Gender

Educational qualification

Age

Male

Female

Total

Illiterate

21
30

20

20

40

31
40

20

20

40

41
50

20

20

40

51
60

20

20

40

61
70

20

20

40

Total

100

100

200

21
30

20

20

40

31
40

20

20

40

41
50

20

20

40

51
60

20

20

40

61
70

20

20

40

Total

100

100

200

21
30

20

20

40

31
40

20

20

40

41
50

20

20

40

51
60

20

20

40

61
70

20

20

40

Total

100

100

200

21
30

20

20

40

31
40

20

20

40

41
50

20

20

40

51
60

20

20

40

61
70

20

20

40

Total

100

100

200

21
30

22

18

40

31
40

18

24

42

41
50

22

18

40

51
60

20

20

40

61
70

18

22

40

Total

100

102

202

Secondary Level

Higher Secondary Level

Graduate

Post Graduate

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21. Climate change, urban flooding, and community perceptions of vulnerability and resilience

FIGURE 21.3 Showing the various indices. (A) NDBI analysis; (B) UII analysis; (C) BAEMOLI analysis; (D) NDBal analysis; (E) DBSI analysis; (F) NDVI analysis; (G) ARVI analysis; (H) MNDWI analysis. TABLE 21.6 Showing the details of various indexes. Indexes

High

Low

Net change

Average

NDBI

0.347

2 0.29

0.637

0.029

BAEMOLI

0.847

2 0.777

1.624

0.035

UII

0.534

2 0.421

0.955

0.057

NDVI

0.32

2 0.13

0.45

0.095

MNDWI

0.317

2 0.642

0.959

2 0.163

NDBaL

0.135

2 0.633

0.768

2 0.249

ARVI

0.365

2 0.067

0.432

0.149

DBSI

0.341

2 0.34

0.681

0.001

According to MNDWI analysis, the highest concentration of open water areas could be observed in the (0.317) eastern part of the study region whereas the northern and the southern part of the study region ( 2 0.642) display a lower concentration of such spaces.

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21.6 Discussion and results

The respondents have also stated that overflowing of the ponds and streams and infiltration of the river water tends to increase the stagnation of the water in the study region. The concentration of bare land areas as per NDBal is comparatively low. However, the presence of bare land is well distributed across the entire study region. According to DBSI analysis, a higher concentration of values can be mostly observed near the southern part and northern tip of the study area, however, the central and the eastern part of the map displays lower values. However, the respondents stated that the soil texture also prolongs the subsidence of the flood water. It can be stated that the area is extremely vulnerable to stagnation of water bodies where the percolation rate is very low and select regions have a high vulnerability index.

21.6.3 Individual vulnerability index Nineteen sub-indicators were used to analyze the individual vulnerability index. These sub-indicators were grouped under 7 broad heads refer to Fig. 21.1). It can be observed (Table 21.7) that most of the respondents had a medium family size that ranged between 5 and 8 members. The respondents claimed that most of the households had 3
4 dwelling rooms on an average basis but a widespread disparity could also be observed where 26.1% of the households has just 1
2 dwelling rooms and on the other hand 21.2% of the respondents stated that their houses had over 6 1 dwelling rooms. The respondents were asked to rate the condition of their houses and sanitation facility on a five-point scale (refer to Table 21.8). Most of the respondents stated that the condition of their houses was moderate (27.1%), and a greater percentage share of respondents stayed in comparatively better condition of houses (41.5%). Most of the respondents stated that they have a moderate-quality sanitation facility (29.9%). However, a slight disparity can be noticed among those respondents that have separate bathrooms with select facilities within the house (40.1%) and those having no bathrooms or have bathrooms outside the house (30%). The respondents who had stated that during floods the condition of their houses tends to worsen. There had been several instances when the waters had entered several of their rooms, destroyed their personal belongings, impaired their mobility, and caused a huge financial loss to them. The respondents complained that the frequent occurrence of floods has destroyed the physical infrastructure of the houses and a constant need for maintenance has become difficult for them. Those respondents who did not have TABLE 21.7

Showing select sub-indicators of the housing facility.

Number of members in each household

Dwelling rooms

Range

Frequency

Percent (%)

Range

Frequency

Percent (%)

1
4

256

25.5

1
2

262

26.1

5
8

530

52.9

3
4

318

31.7

9
12

216

21.6

5
6

212

21.2

61

210

21.0

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21. Climate change, urban flooding, and community perceptions of vulnerability and resilience

TABLE 21.8 Showing the condition of houses and sanitation facility. Rank

Condition of houses

Sanitation Facility

Category

Percent (%)

Category

Percent (%)

1

Very Good

15.8

No bathroom

10.0

2

Good

25.7

Bathroom with semi-permanent roof and electricity outside the house

20.0

3

Moderate

27.1

Bathroom with a concrete roof, running water, and electricity outside the house

29.9

4

Bad

19.6

Separate bathroom with concrete roof and electricity within the house

25.1

5

Very Bad

11.8

Separate bathroom with concrete roof, running water, and electricity within the house

15.0

Mode

3.00

Mode

3.00

Minimum

1.00

Maximum

5.00

bathrooms within the houses stated that during the floods they tend to face a lot of problems and maintaining proper hygiene tends to become difficult for them and this situation becomes exceedingly worse for the women who are forced to adapt themselves to the environs. In the case of the standard of living, the respondents were asked whether they possessed select assets (refer to Table 21.9). It can be observed that the standard of living of most of the respondents is not very high. Although television and refrigerator are owned by most people, other assets such as motorized personal vehicle and the availability of internet services at their house is beyond the affordability of a sizeable section of people. According to the PCA analysis, both of these factors have created significant variations in the dataset. Most of the respondents stated that they lacked the affordability to have motorized personal vehicles and internet services (refer to Table 21.10). The respondents stated that it has become very hard for them to maintain any sort of electronic gadgets, and they live in constant fear of power cuts and short circuits during floods. Alongside these issues, several respondents stated that floods tend to impair the smooth functioning of the refrigerators, it tends to become a challenging task for them to access their vehicles during floods and a copious amount of money needs to be invested in repairing these vehicles after the subsidence of the water. The respondents that have internet connection in their homes tend to face several connectivity issues during floods. To analyze the neighborhood characteristics, selected indicators such as stagnation of water in the neighborhood, accessibility of medical centers in the close vicinity of the respondents, accessibility, and availability of good transport and communication facilities, emergency services, and banking services were chosen (refer to Table 21.11). Of the total, 61% of the respondents stated that although banking services are available in their close vicinity, accessing those facilities tends to become very hard for them during floods owing to the stagnation of water for prolonged hours. The respondents were asked to rate on a

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21.6 Discussion and results

TABLE 21.9

Showing the possession of select assets. Percent (%)

Categories

Yes

No

TV

85

15

Refrigerator

74.9

25.1

Motorized Personal Vehicles (Moped/ Scooter/Car)

50.5

49.5

Internet Services

50.5

49.5

TABLE 21.10

Showing PCA among the sub-indicators of PCA. Total variance explained Extraction sums of squared loadings

Initial eigenvalues

Rotation sums of squared loadings

Component

Total

% of Variance

Total

% of Variance

Total

% of Variance

1

2.03

50.72

2.03

50.72

2.00

50.03

2

1.49

37.15

1.49

37.15

1.51

37.83

3

0.49

11.14

4

0.20

1.00

Extraction Method: Principal Component Analysis.

TABLE 21.11

Showing the rank of the sub-indicators while analyzing the neighborhood characteristics. Accessibility of good transport and communication

Accessibility of emergency services (other than medical services)

Rank

Stagnation of water In their neighborhood

Category

Percent (%)

Category

Percent (%)

Category

Percent (%)

Category

Percent (%)

1

Very High

24.95

Very Good

16.17

Very Good

21.96

Very Good

11.23

2

High

29.16

Good

22.36

Good

18.36

Good

15.36

3

Moderate

20.76

Moderate

26.95

Moderate

25.95

Moderate

23.31

4

Low

17.56

Bad

18.36

Bad

13.37

Bad

34.65

5

Very Low

7.57

Very Bad

16.17

Very Bad

20.36

Very Bad

15.45

3. Climate change, ecological impacts and resilience

Accessibility of medical services

408

21. Climate change, urban flooding, and community perceptions of vulnerability and resilience

five-point scale about the stagnation of water in their neighborhood, accessibility of good transport and communication, and emergency, and medical services. A higher percentage share of respondents stated that they tend to experience prolonged hours of stagnation of water in their neighborhood (47.11%) and it has affected their mobility patterns and has reduced the accessibility of various services in their area. However, it can be observed that in select areas the time duration of stagnation of water tends to be lesser as a result mixed responses could be observed when the respondents expressed their opinions about the accessibility of various transport and communication facilities. The respondents also expressed similar views about their accessibility to various emergency services. The places where the transport and communication facilities were slightly better off tended to have an edge over the other regions while accessing various emergency services. However, a greater percentage share of respondents stated that they faced significant problems while accessing various healthcare services and medical services during floods. The respondents stated that often medicine shops did not have a stock of essential medicines, and unavailability of trained doctors and medical professionals during these times. They also complained that, in most cases, respondents had to travel for long hours to reach a medical facility and this situation worsened during the floods. According to the PCA (refer to Table 21.12), only two components that are stagnation of water (0.76) in the neighborhood and availability of medical facilities (0.74) created maximum variance among the respondents. This shows that the context of vulnerability and the intensity with which the respondents felt vulnerable even within the study region were subject to change with a slight alteration in their spatial location. The respondents unanimously agreed to the fact that they have witnessed an increase in the occurrence of floods over a couple of years. On average, they stated that they tend to experience 4 to 6 instances of urban flooding with varying intensity. However, the respondents unanimously agreed that in recent years water tends to take a longer time to percolate and the frequency of occurrence of urban floods have become unpredictable. The respondents stated that each one of them perceived very differently about the risks involved in urban flooding. However, based on the interviews that were conducted, most of their notions tended to revolve along select keywords such as loss of property (81%), TABLE 21.12 Showing the PCA about the environs of the neighborhood. Total variance explained Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

Component

Total

% of Variance

Total

% of Variance

Total

% of Variance

1

1.08

26.90

1.08

26.90

1.07

26.84

2

1.02

25.49

1.02

25.49

1.02

25.55

3

1.00

24.89

4

0.91

22.71

Extraction Method: Principal Component Analysis.

3. Climate change, ecological impacts and resilience

21.6 Discussion and results

409

damage to their houses (69%), damp walls and collapsing of various sorts of physical infrastructure (77%), damage to material assets and personal belongings (79%), posttraumatic stress disorder (21%), financial loss (67%), and loss in the availability of select services (49%). Other than these issues, the respondents stated that they tend to experience several ailments such as dysentery (53%) and diarrhea (54%) owing to the contamination of the water, several vector-borne diseases such as dengue (56%), eczema (41%), and skin irritation, inflammation, and allergies (61%) on multiple occasions.

21.6.4 Livelihood vulnerability index The respondents stated that the dependency of livelihood on climatic conditions ranged from high to moderate category. The respondents stated that the stagnation of the waters tended to affect mobility of the respondents (73%). Among the working professionals respondents who are engaged in trading agricultural produce stated that they tend to incur heavy losses due to floods (54%), and those respondents who are engaged in the transport and communication sector stated that water logging tends to negatively affect their livelihood and tends to impair their vehicle. Those respondents who work in other sectors and have to commute to work are also adversely affected by the urban flood (21%). Among the respondents who are engaged in household industries (77%) have stated that flooding tends to reduce their working hours and also makes it difficult for them to store their raw materials as well as their finished products. Among the respondents who are engaged in the manufacturing sector stated that urban floods tend to destroy their pieces of equipment as well it becomes excruciatingly hard for them to transport their finished products to the trading hubs (81%). Thus, it can be observed that flooding activities tend to increase their financial losses and offer economic setbacks to the respondents.

21.6.5 Adaptability vulnerability index It can be observed that a mixed response could be observed among the respondent owing to the diversity in the perception with which they look at the risk of urban flooding and their stake in the vulnerability (refer to Table 21.13). Most of the respondents stated that since they have to cope with such sort of flooding activities it has become a part and parcel of their life (38.8%). They have tried to adopt various resilient measures to prevent the flood water from entering their premises most people are trying to construct bathrooms within their houses to avoid any sort of hassle. They try to keep their belonging on an elevated platform to reduce the damage. However, 34.8% of respondents stated that they find it very hard to cope with the loss incurred due to flooding owing to the loss of their perishable products and increased expenses of renovation and maintenance. They stated that they tend to feel helpless and experience a significant amount of stress while dealing with this problem. Based on the PCA (refer to Table 21.14), it can be observed that four components can explain 65.63% of the variance in the opinion of the respondents. In the case of the first component condition of their houses (0.95) and ownership of personal vehicles (0.85); in the case of the second component sanitation facility (0.875); in the case of the third component availability of medical facilities in close vicinity of their house (0.68); and in case of

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21. Climate change, urban flooding, and community perceptions of vulnerability and resilience

TABLE 21.13 Showing the coping ability of the respondents. Coping ability Rank

Category

Percent (%)

1

Very High

16.2

2

High

22.4

3

Moderate

26.9

4

Low

18.4

5

Very Low

16.2

TABLE 21.14 Showing PCA. Total variance explained Extraction sums of squared loadings

Initial eigenvalues

Rotation sums of squared loadings

Component

Total

% of Variance

Total

% of Variance

Total

% of Variance

1

2.77

25.22

2.77

25.22

2.73

24.78

2

2.26

20.52

2.26

20.52

2.30

20.94

3

1.12

10.19

1.12

10.19

1.11

10.12

4

1.07

9.70

1.07

9.70

1.08

9.79

5

1.00

9.09

6

0.91

8.29

7

0.90

8.17

8

0.51

4.61

9

0.27

2.42

10

0.20

1.78

Extraction Method: Principal Component Analysis.

the fourth component (0.58) stagnation of the water in the neighborhood creates most of the variances among the respondents.

21.7 Conclusion It can be stated that the perception of vulnerability among people tends to vary from one individual to the other. Such notions of the individuals and their responses to various questions were deeply influenced by their past experiences. Although the responses of the

3. Climate change, ecological impacts and resilience

References

411

respondents were very unique, the presence of select keywords could be traced in the case of most of the interviews. Their experiences with urban flooding were significantly influenced by the presence of several factors such as their economic conditions, possession of select assets, and accessibility of select resources in their area. In this regard, it can be observed that the findings of this paper are in sync with the previous literature review where it was stated that respondents belonging to better economic conditions were able to adapt faster to such occurrences. Loss of valued possessions and damage of assets due to urban flooding tends to negatively impact the urbanities. While conducting the interviews, select phrases such as “Occurrence of post-traumatic traumatic disorder”, and “financial loss” were some of the well-repeated terms. Those respondents hailing from relatively disadvantageous financial backgrounds found it excruciatingly painful and mentally challenging to meet up such unprecedented expenses. It can be observed that even among the respondents who are relatively better off than others tend to feel extremely stressed, anxious, and depressed while dealing with their losses. The respondents also stated that they found it hard to combat the increased occurrence of several vector-borne diseases. The ambiguity enshrouding the intensity and magnitude of urban flooding tends to affect the mental health of the urbanities. Their sheer helplessness in their inability to gauge the damage urban flooding can wreck upon the lives of the urbanities tends to increase their vulnerability quotient. The respondents tend to overstress and agonize over the fact that they do not have sufficient means to prevent the reoccurrence of such a phenomenon. Despair is written all over their faces as they try to gear up each year for a fresh set of losses. It can also be observed that the adoption of resilient strategies while dealing with urban floods is lacking in this study area. The existence of drainage and sewerage systems that could have possibly reduced the water stagnation levels and helped in the surface runoff is also lacking. The respondents also stated that since select regions of the study area are situated at a comparatively higher elevation most of their surface runoff tends to accumulate and waterlog the lowlying regions of the study area, thus, amplifying their problems. Respondents stated that they were also confused about the effectiveness of the prevalent that were commonly used to drain off the excess water. Although the respondents stated that they definitely needed intervention from the local authorities to solve such problems but for the complete eradication of such issues, the respondents stated that implementation of the action plan and envisioning various strategies would only be effective if first, the local authorities are well abreast with the challenges and the notion of the vulnerability of the people; and second, if the urbanities unitedly agree to co-operate with the local authorities and actively support their endeavors.

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C H A P T E R

22 Climate protection in spatial policy instruments, opportunities and barriers: the case study of Poland Maciej Nowak1 and Przemyslaw ´ Sleszy´nski2 1

Faculty of Economics, Department of Real Estate, West Pomeranian University of Technology in Szczecin, Szczecin, Poland 2Polish Academy of Sciences, Institute of Geography and Spatial Organization, Warsaw, Poland

22.1 Introduction The challenges of climate protection require a broad, comprehensive response from a diverse range of development policies. According to the authors, spatial policy deserves particular attention in this context. It is on specific spatial policy instruments, especially those at the local level, that the possibilities of implementing climate-friendly solutions in space depend. On the other hand, an inefficient spatial planning system can be a serious barrier to achieving climate goals. Therefore, the discussion on the response of spatial policies to climate change seems to be very important. However, this discussion needs to consider the major national differences in the various spatial policy frameworks. Spatial planning solutions vary widely from one country to another. This applies both to the specific legislation, the planning practices implemented, and the terminology used (across countries, but also within disciplines) (Nadin et al., 2018). One of the more relevant classifications is the distinction between plan-based spatial planning systems (in which local spatial plans are universally binding legal acts) and development-based systems—with the instructional rather than legal role of local spatial plans (Mun˜oz Gielen & Tasan-Kok, 2010). It is, therefore, important to be aware that even the same or similarly named spatial planning instruments in different countries may have quite different capacities to fulfill their stated tasks. This also applies to the response to climate change. Consideration of the broader, international response to the climate challenge needs to be more broadly based on individual country case studies. It is on the basis of

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00022-9

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such analyses, with a broader knowledge of differences, that one can attempt to point to more general recommendations (Albers & Deppisch, 2013; Broto, 2011; FranceschHuidobro et al., 2017). The above regularities also apply to the case study of Poland, a country that needs to define a multifaceted response to climate change risks. This is all the more justified as the Polish spatial planning system is considered inefficient, and in need of major reforms (Nowak et al., 2022). The chapter aims to identify the main problems and challenges of climate protection from the perspective of the application of Polish spatial planning instruments at the local level. The focus was primarily on spatial planning at the local level, including the identification of patterns occurring at the national level. The results and the ensuing discussion provided a basis for broader recommendations: both on a national and international scale. The chapter also fills a research gap. It addresses the link between spatial planning and climate protection in Poland and how to translate this issue into an international discussion.

22.1.1 Justification of the study In the Polish spatial planning system, the most important role is played by instruments at the local (commune) level. These are: • studies of spatial development conditions and directions. • local spatial development plans. Studies of spatial development conditions and directions are strategic acts. They define the borough s development concept. Local spatial development plans are legally binding acts that contain orders and prohibitions, and which introduce restrictions on land development. They specify, among other things, the intended use of the land and the principles of land development (including development parameters). However, local spatial development plans do not have to be adopted obligatorily for all areas (Nowak, 2020). Consequently, they are only valid for less than 33% of the country’s area (Nowak et al., 2022). The authorities of each municipality (gmina in Polish) may decide independently whether a plan will be adopted for a given area and, if so, what area it will cover. In a situation where no local spatial development plan has been adopted for a given area, its equivalent may be an administrative decision (issued at the request of the investor): decision on land development conditions. In such a case, the municipal authorities may only verify whether the application meets the statutory conditions (it is worth emphasizing that these conditions are insufficient to protect valuable space values). If the application meets the conditions, the municipal authorities must issue a positive decision. The solution described above is one of the major problems of the Polish spatial planning ´ ´ system (Sleszy nski et al., 2020). It contributes to increasing spatial chaos and its economic ´ ´ ´ ´ consequences (Sleszy nski, Nowak, Brelik, et al., 2021; Sleszy nski, Nowak, Sudra, et al., 2021). At this point, it is worth making a detailed reference to the presented solutions to international approaches (which will allow us to better determine the specificity of the Polish system). Studies of spatial development conditions and directions are by definition conceptual documents, but they are not legal acts. In certain respects, they resemble local development strategies, but their thematic scope is different—they are focused exclusively

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on the spatial aspect (one may attempt to compare them with the spatial development strategies existing in some countries). In the Polish reality, this lack of legal dimension combined with the obligation of enactment constitutes one of the main reasons for ignoring the indicated documents. They are not translated into the realm of spatial planning. The situation is different with local spatial plans. They constitute legal acts that are binding for property owners and investors. As indicated above, they are not obligatorily enacted and therefore do not guarantee the implementation of the spatial policy in every area. The limitations of the Polish spatial planning system, as indicated above, significantly hinder the effective implementation of solutions to protect against climate change (Grotholt, 2017). Demands in this regard are made in the international literature. Undoubtedly, climate change is changing the context of spatial planning and redefining its priorities, especially environmental ones (Mehmood, 2009; Yiannakou & Salata, 2017). Hurlimann and March (2012) rightly point out that the spatial configuration of cities and towns and the way land is used and developed will be crucial in responding to climate change. Of course, it also seems necessary to adapt the spatial policy instruments themselves to these objectives. The literature notes that the legal formulation of planning instruments, especially those at the local level, has an important influence in this respect (Wilson & Piper, 2010). The key dilemma boils down to whether planning provisions should be formulated in more detail or be more flexible. It is often pointed out (Becker & Greiving, 2018; van Buuren et al., 2013) that solutions that are more detailed on the formal legal side block adaptive activity and provoke negative practices of interpretation. Hurlimann and March (2012) rightly point out in this context that a central element of spatial planning is a constant focus on seeking future improvements while avoiding potential problems. Overly detailed formulation of regulations can make the aforementioned effect much more difficult. Often, more liberally worded development parameters are also seen as expanding the room for maneuvers in this regard (Seto et al., 2014). However, this must not lead to opportunism in spatial policy, consisting of ad hoc yielding to investors’ expectations. It is precisely such practices that generate spatial chaos. Also, for this reason, the second (broadly defined) important direction is integrated development planning (taking into account the spatial planning perspective). This direction is often advocated in the literature (Yiannakou & Salata, 2017). In this view, the emerging challenges of climate change protection must be directly linked to the work on spatial policy instruments. This is easier in development-based systems, nevertheless possible also in plan-based systems (Lazarevi´c-Bajec, 2011). It is evident from the above summary that the literature pays very close attention to the role of spatial planning instruments in responding to climate change. As indicated above, the scope of this response must be adapted to the legal and planning realities of the specific country. Nevertheless, the following thematic areas can be identified: • the scope of protection by spatial policy instruments of the environmental and natural values of individual areas; • allowing climate-damaging solutions in spatial planning instruments (including limiting potential flexibility of action); • detailed solutions for adapting the spatial structure to new challenges.

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22.1.2 Limitations of the study The research carried out includes data concerning the whole of Poland. Therefore, they constitute a very important point of reference for further scientific discussion. Nevertheless, two types of research limitations need to be distinguished. The first concerns the representativeness of the Polish case and the possibility of translating the results into international discussion. The second category of limitations concerns the data used in the research. As already indicated above, national spatial planning systems are very diverse. A detailed analysis of the problems in one system does not, therefore, allow the simple conclusion that the same problems exist in other systems. Much depends on both the specific legal solutions and the shape of planning practices. It is certain that in other systems, the instruments at the local level are constructed differently, the challenges connected with responding to urban pressure look different, and the mechanism of changing the land use looks different. The above does not change the fact, however, that the indicated problems and challenges will occur to a different extent in other countries. This is confirmed by the literature review. The analysis provides an in-depth case study of the weaknesses of a particular system. It may serve as a certain point of reference for broader international comparisons.

22.2 Material and methods The research uses data collected by the ministry responsible for the government administration department “construction, planning and spatial development and housing” (in 2022 it is the Ministry of Economic Development and Technology), carried out annually by Statistics Poland. For this purpose, a questionnaire with a set of questions and fields to be completed is sent to each municipality, as a result of which detailed quantitative and qualitative information is collected on the planning documents held and administrative decisions issued in the municipalities. On this basis, it is known, inter alia, that there are nearly 57,000 local plans in Poland, what their structure is in terms of land use, etc. The data have been collected since the Act on spatial planning and development was in force, i.e., since 2003. In addition, the data of the Central Office of Geodesy and Cartography on the number of urbanized areas in municipalities were used. For comparative purposes, the study also used the functional classification of munici´ ´ palities, developed specifically for the purpose of spatial planning monitoring (Sleszy nski & Komornicki, 2016). It divides the set of 2477 municipalities in Poland into 10 types in terms of the administrative and settlement hierarchy of cities and economic specificity (predominant functions): A—voivodship capitals, B—their external/suburban zones (functional urban areas), C—sub-regional centers, D—their external/suburban zones (functional urban areas), E— multifunctional sub-regional and local urban centers, F—municipalities in transport corridors, G—municipalities with an intensively developed agricultural function, I—municipalities with moderately developed agricultural functions, J—municipalities with extensively developed functions (forestry and nature conservation).

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22.3 Results

423

22.3 Results As mentioned, municipalities produce two key planning documents: studies of spatial development conditions and directions (municipal studies) and local spatial development plans (local plans). Almost all local governments possess the first document. Municipal studies may—at the municipalities’ discretion—be changed and updated (adjusted to new challenges and needs). This is often done. For example, in 2020, 814 municipal studies were updated (i.e., about 1/3). The very high share of updated municipal studies has been maintained for years. This proves that, if necessary, local governments are able to change provisions in the basic study, related to the territory of the entire municipality. Nevertheless, it should be emphasized once again that studies of spatial development conditions and directions are conceptual documents. They are not directly binding acts but rather acts containing a concept of the spatial development of a given municipality. From the point of view of responding to climate change, one of the key issues is the land use pattern projected in municipal studies. Here, the available data are generally not favorable for two reasons. First, green areas and waters occupy only 22.5% and although this share has increased since 2010 by almost 3.5 percentage points (p.p.), it is still less than the forest cover indicator of Poland (30.2% according to the Head Office of Geodesy and Cartography in Poland; moreover, land under water takes up another 2.1%). This is due to the fact that some wooded areas, smaller in terms of absolute area, are included in agricultural land (59.9%) and even in various types of development. It is worth noting that the differences are significant by type of municipality (Table 22.1). However, it is worth noting that the projected percentages of forests and waters are higher in two very important types of cities: voivodship cores and sub-regional cities, i.e., in the most urbanized areas. This almost balances out in medium-sized and small cities (category E), but already in suburban zones, there is an underestimation. It is the highest (as much as almost 18 p.p.) in the so-called ecological municipalities (forest and nature protection functions). The second reason is potentially more dangerous, as it concerns the anticipated location of the development. Well, for almost 11% of the municipalities’ area, housing development is allowed—in a situation where only 1.21% of their area is, according to data from the geodetic register, “built-up and urbanized land—residential areas.” This means a very large, tenfold overestimation of residential areas in municipal studies. Even if one assumes that some surplus is needed for new development sites, it should not be more than 10%
20% of existing development sites. The total areas for residential development (excluding homestead development) provided for in 1522 municipal studies (in which the document was in force and not updated at the same time) covered 2.0 million ha, of which single-family development accounted for about 90%. Thus, if one were to assume a demographic absorption rate of 40 persons/ha for these single-family development sites, this would give land for single-family settlements of over 70 million persons. The demonstrated oversupply is also related to the policy of land use changes. In 2020, in the communal studies in force, as many as 308,000 ha were projected for deforestation, and this value for the whole country is probably higher by another 150,000 ha, if the areas indicated in the updated documents are taken into account. On the other hand, the expected deforestation amounts to 37 thousand ha (approx. 0.4% of the forest area in Poland).

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TABLE 22.1 Number of answered questions in types of municipalities (gminas) and against the share of areas envisaged as green and water areas in municipal studies in relation to the geodetic area (data for 61.4% of municipalities that did not update the document in 2020). Municipalities (gminas)

Share of land and water areas (%)

Type of municipalities

The total number of respondents

Percent by type Municipalities (representativeness) studies

Land and building registry

Difference

A—regional capitals (cores)

16

48,5

37,5

27,4

2 10,1

B—outer zones of regional capitals (A)

129

48,7

26,0

32,3

6,3

C—sub-regional cities

27

49,1

32,4

21,7

2 10,7

D
outer zones of sub-regional cities (C)

115

57,2

24,6

33,5

8,9

E—multifunctional centers

78

54,9

24,8

27,2

2,5

F—municipalities in the transport corridors

87

63,5

22,4

29,8

7,5

G—municipalities with nonagricultural functions (tourism, industry, mining)

140

63,1

34,2

50,9

16,8

H—municipalities with an intensively developed agricultural function

338

68,1

12,8

17,8

5,0

I—municipalities with a moderately developed agricultural function

427

64,2

19,1

30,4

11,2

J—extensively managed municipalities (forestry and nature protection functions)

165

63,2

31,7

49,6

17,9

Total

1522

61,4

22,5

32,5

9,9

Note: indicative data due to different time sections (municipal studies—2020, geodetic data—2014). Including data from land and building registries changes very little. Based on data from the Ministry of Development and Technology and the Central Office of Geodesy and Cartography. Classification of ´ ´ and Komornicki (2016). nski municipalities described in the work by Sleszy

The second document examined is local spatial development plans. Their adoption by municipalities is not obligatory. Nevertheless, the adoption of a plan for a given area makes the provisions contained in the plan binding for investors and property owners. In 2020, 57.3 thousand plans were in force and the average plan coverage was 31.4% of the municipalities’ area. In 139 municipalities, not a single plan was adopted (from a legal perspective, municipalities do not violate the law—it is up to the discretion of municipal authorities to adopt plans for specific areas). At the same time, the planning coverage ratio in Poland is very uneven. It reaches the highest values in the southern provinces (usually above 50%) and the lowest in the northern provinces (Fig. 22.1). The indicator indicates

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22.3 Results

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FIGURE 22.1 Planning coverage of municipalities (gminas) at the end of 2020.

not only a conscious planning of future use but also securing areas for investments of various activity profiles, including, for example, pro-ecological infrastructure. As a result of the faulty structure of land use in municipal studies, local plans reproduce these errors and even reinforce them. In about 44,000 documents in non-urban (rural and urban-rural) municipalities, 589,000 ha of land were de-landed, which represents 6.5% of the plans and 2.0% of the municipalities’ area. Most such measures were undertaken in the urban environment (Fig. 22.2), which also poses a major challenge to the needs of climate policy. However, it is favorable that in the structure of local plans, relatively many areas are allocated for green areas and waters—25.0% of the area of the enacted documents and 7.4% of the area of municipalities (Fig. 22.3).

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FIGURE 22.2 Changes in the use of agricultural land for non-agricultural purposes (so-called ‘de-agriculturalization’) in non-urban municipalities (gminas) by the end of 2020.

As a result of faulty planning, including overestimation of investment areas, accelerated, uncontrolled, and chaotic urbanization processes reduce the area of natural ecosystems and defragment (fragment) their structure. The reduction of the biologically active area occurs according to four main scenarios (Chmielewski et al., 2018): • perforation of the structure of natural complexes, e.g., cutting subsequent fragments of forest patches, conversion of parts of meadows into field crops, creation of “settlement islands” in hitherto untransformed ecological zones; • dissection of natural complexes by anthropogenic linear structures: roads, railway lines, high-voltage power lines, etc., and a gradual widening of these dissections (e.g., by

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22.4 Discussion

427

FIGURE 22.3 Structure of land use in local plans in non-urban (urban-rural and rural) municipalities (gminas) at the end of 2020. Designations: Hm, multi-family housing; Hs, single-family housing; Hf, farmstead housing (on agricultural land); Sc, commercial services; Sp, public services; TP, technical and production areas; T, transport areas; TI, technical infrastructure areas; A, agricultural areas; GW, greenery and water areas.

development of buildings along roads) until only narrow natural belts are left on the edges of the anthropogenic belt; • densification of the network of the above-mentioned dissections until residual “natural islands” form on the edges of the formerly extensive complex of natural ecosystems; • successive reduction of the area of natural complexes by the development of anthropogenic structures around them and gradual tightening of the ring. However, in the context of the cited international solutions, including a strong emphasis on solutions for the use of RES, it can be concluded that the features resulting from the planning documents are not conducive in Poland to a satisfactory degree for the location of small-scale energy (small hydroelectric power plants, photovoltaic farms, wind turbines, biomass facilities, etc.). Securing land for this type of investment is present in a smaller part of the country, mainly in the southern provinces. Other studies show that only 58.4% of municipal studies dealt with RES issues and that there were only 529 local plans (or amendments to existing local plans) entirely concerning land use for wind power investments (Blaszke et al., 2021).

22.4 Discussion The example of the Polish spatial planning system shows possible planning problems and barriers, concretely felt when trying to implement climate change policies more widely. The main barriers are: • hasty planning arrangements, allowing extensive development in numerous areas;

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• difficulties related to the change in the present factual and legal situation; • inadequacy to meet sectoral challenges. Trends related to the relatively broad inclusion of green areas and waters in studies of spatial development conditions and directions should be regarded as positive. The first of these barriers is the hasty planning arrangements that contribute to the spatial chaos in Poland. As indicated above, Polish municipalities are allocating more and more land for housing development on too broad a scale. This is related to the concept of property rights in the Polish spatial planning system. The right to develop is inextricably linked to the property right, and its restriction must be justified each time and take place only in strictly defined cases (Nowak et al., 2022). This results in the following tendencies: • the reluctance of municipalities to place planning restrictions on development of specific properties; • the inclusion in planning documents of a fairly wide range of development opportunities—also on land that is not suitable for this purpose. As a separate problem should be identified barriers associated with changes in the legal/factual/planning status. This means that in a situation where a municipality designates too many areas for development in its spatial development plan, it will have problems with changing these solutions. First, as already indicated, restrictions on development will have to be justified. From the perspective of Polish legal regulations (e.g., court rulings), climate protection challenges are not in themselves sufficient justification for restrictions. Second, restrictions on the development of a given plot of land entail the possibility for property owners to file compensation claims against the municipality. Any owner of real property whose possibilities of plot development are restricted by a spatial development plan has the right to claim from the municipality compensation equal to the reduction in the value of the real property. The literature has pointed out the particular role of spatial planning in determining responses to climate change (Yiannakou & Salata, 2017). It should be added that in the Polish case, such flexible adaptation of space to new challenges may pose a serious problem (Becker & Greiving, 2018). The realization of this goal will depend on a special, abovestandard determination on the part of the authorities of a particular municipality. A separate issue is the adaptation of the terminology used in spatial planning instruments to climate challenges. It must be stressed that diverse, inconsistent terminology is a general problem in most spatial planning systems (the greater the discrepancies in attempts to harmonize national terminology). Barriers related to the translation of urban planning formulations into legal language are a frequent problem. It is important to point out that similar barriers will occur in attempts to align spatial planning and responses to climate change more broadly. It has already been pointed out above that climate protection objectives do not directly provide a basis for planning restrictions in the Polish system. This creates specific impediments. There may also be other obstacles: for example, in the way renewable energy sources are included in planning acts. There is currently a great deal of terminological diversity in this area (Blaszke et al., 2021). There are also

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22.5 Recommendations

429

different approaches how to relate the implementation of different types of investments in renewable energy sources to the key values declared in the Polish spatial planning system: spatial order and sustainable development. The example of the role of renewable energy sources in the spatial planning system is in many ways relevant and important.

22.5 Recommendations In light of international experience, the Polish discussion on the adaptation of spatial planning systems to climate change is linked to the issues of the integration of development policies and flexibility in planning, which are considered separately. Not only in Poland but generally in the post-communist, including post-Soviet, Central, and Eastern Europe, there are problems especially in the implementation of the latter course of action. Currently, in the Polish system, there are only negligible elements in this respect. This definitely makes it difficult, for example, to achieve the multifunctional use of areas important from the perspective of climate protection as postulated in the literature. On the other hand, in Poland there is also no comprehensive detailed regulation in the sphere of planning practice (which is the opposite of a flexible system). Chaotic solutions (often based on administrative decisions) enforced by individual investors dominate. This is related to the inadequacy of the discussion on public interest in spatial planning. The protection of the public interest cannot, moreover, be understood in a static way, which is best demonstrated by the ongoing challenges of climate change. From the above perspective, the postulate made by Davoudi (2018) needs to be highlighted again. Regulatory interventions should be primarily concerned with how actions are taken. However, these postulates are poorly suited to current spatial planning solutions in Poland. Their possible inclusion would require a comprehensive (unlikely in such a direction in the near term) overall change of the system. Nevertheless, it may be pointed out that regardless of the above, the following more detailed recommendations may be considered: • creating more opportunities for multifunctional land use on the basis of local spatial plans. The aim is not to create universal opportunities in this respect (this would be risky under the current system), but precisely in relation to areas important from the perspective of climate change (e.g., areas at risk of flooding); • a wider role for climate change risk analyses. As indicated in the literature, it would be highly desirable to identify areas of particular importance from the perspective of climate change risks. These are both intensively developed and urbanized areas with a high share of impervious surfaces and areas crucial from the point of view of natural water and retention (the so-called green-blue infrastructure); • in the above context, the role of land use monitoring in areas most vulnerable to climate change is particularly important. Unfortunately, despite various attempts and initiatives, such effective monitoring for the whole country has not been developed; • integration of spatial policy and climate change adaptation plans/policies.

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22.6 Conclusions The Polish example shows that in many national spatial planning systems, adaptation to the challenges of climate change is still a major problem. At the same time, it is clear that weaknesses in a given spatial planning system are a block to the wider implementation of responses to climate change threats. This may relate to how to respond to urban pressures in specific areas, the approach to property ownership (and thus the right to build), as well as the possible efficiency of public administration bodies responsible for spatial planning. A separate issue is that of terminology, in particular the coordination of different formulations in planning acts. It seems that the indicated elements can be noticed to a varying extent in different national systems. Attention should also be paid to issues relating to the protection of areas of natural value—this too has a major bearing on the broader response to climate change. As further postulated research directions we can mention: • International comparisons of selected parts of approaches that integrate spatial planning and climate change responses. As indicated above, such comparisons should be made with caution, taking into account the specific differences that exist between the different systems; • A specific comparison of how climate challenges are responded to in plan-based and development-based systems.

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23 Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence Abd. Rahman As-syakur1, Herlambang Aulia Rachman2, Muhammad Rizki Nandika3, Martiwi Diah Setiawati3, Masita Dwi Mandini Manessa4, Atika Kumala Dewi5 and Rinaldy Terra Pratama1 1

Marine Science Department, Faculty of Marine and Fisheries, Udayana University, Bali, Indonesia 2Department of Marine Science, Trunojoyo Madura University, Bangkalan, East Java, Indonesia 3Research Center for Oceanography (RCO), National Research and Innovation Agency (BRIN), Jakarta, Indonesia 4Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia 5Marine and Coastal Environment Mapping Center, Geospatial Information Agency, Bogor, Indonesia

23.1 Introduction Coastal environments are complex, active systems with various ecological and biological benefits, intense socioeconomic activities, and rich biodiversity. Therefore, these areas are becoming more common for people to live whereas more than 33% of the world population lives within the coastal zone (UNEP, 2006), yet this tendency is projected to grow. However, by nature, coastal areas are vulnerable to climate change and weather extremes like tidal flooding, saltwater intrusion, inundation, storm surges, and erosion. Furthermore, massive anthropogenic pressure such as increasing urbanization and enormous land use changes also put the coastal area at risk. Since coastal areas provide various ecosystem services and

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processes and support the worldwide population, addressing vulnerability to changing environment is the fundamental requirement of scientific studies and unilateral intervention for sustainable environmental control. Meanwhile, coastal vulnerability (CV) has various indicators at different scales in terms of quality and quantity (Thirumurthy et al., 2022). Thus, the assessment of CV is a crucial instrument for helping decision-makers recognize the varied effects of natural components and determine the degree of vulnerability of the areas that tempt them for environment protection and the formulation of policy initiatives. A recent report by the Intergovernmental Panel on Climate Change (IPCC) highlighted the significant threat that coastal areas might experience as a consequence of climate change, including projected sea level rise (SLR) and shoreline changes. At this age, the increased greenhouse gas (GHG) emissions and associated rising temperatures can trigger an SLR from 0.15 to 0.25 m on the global scale (IPCC, 2021) while in the US, it was from 0.3 to 2.5 m (Sweet et al., 2018). Also, the coastlines in different countries around the globe will be threatened by the alterations in SLR, which are far more pronounced now than they were in the late nineteenth century (Anderson et al., 2015). Moreover, the increasing intensity of climate change incidents complexity, and ambiguity also leads to regular ecological consequences. SLR and extreme weather conditions may cause numerous disruptions and submerge vast areas, limiting the options for coastal communities’ means of subsistence (Mehvar et al., 2019). With increased water flow patterns and the SLR, the coastal slope impacts the degree of inundation within the region (Husnayaen et al., 2018). Moreover, lowland and gently sloping areas are also considered vulnerable to risk (Hoque et al., 2018). Hazards seriously threatened all the complex systems along coastlines. Furthermore, local anthropogenic drivers can cause a significant threat to coastal communities, particularly around the coastal cities where urbanization and massive urban development generally occur. This rapid urbanization will drive the excessive demand for water supply, where this insufficient water causes the developer or communities to create water tables under the built-up area (Awasthi et al., 2022). This condition creates severe problems such as urban land deformation and aquifer system consolidation, which further drive land subsidence within the areas (Othman & Abotalib, 2019). A recent study by Wang et al. (2019) revealed that excessive groundwater extraction over a long period is one of the significant drivers of land subsidence. Also, this condition is rarely reported because it takes a slow and gradual process (Wang et al., 2019). Yet, numerous wellknown urban areas, such as Las Vegas (Hoffmann et al., 2001), Mexico City (Cigna & Tapete, 2021), Kolkata (Chatterjee et al., 2006; Brown & Nicholls, 2015; John & Das, 2020), Shanghai (Li et al., 2021; Wang et al., 2014), Dhaka (Brown & Nicholls, 2015), Bangkok (Bagheri-Gavkosh et al., 2021), Hanoi (Nguyen et al., 2022), and Jakarta (Bott et al., 2021) have been experiencing from land subsidence. Although the vulnerability assessment began in the 1970s (Valdemoro et al., 2001), it has only recently gained much attention because of environmental changes and the rising influence of global warming, which has had irregular effects in past decades. Through consolidating multiple criteria, Geographical Information System (GIS) technologies have expanded and added new perspectives to vulnerability analysis. Moreover, the GIS platforms are a widely attractive tool for assessing CVs since spatial approaches provide researchers and policymakers with the necessary infrastructure to study, track, and comprehend by combining various factors to determine the degree of vulnerability at multiple levels.

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23.2 Study area

435

Until now, so many variables have been used to assess the CVs, including slope, elevation, topography, land cover, SLR, tidal variation, precipitation, coastline change, coastal organism, economic status, social structure, demographic data, and infrastructure. Such studies have been carried out to measure the coast’s vulnerability; for instance, in Kannada District, India (Rehman et al., 2022); Semarang city, Indonesia (Husnayaen et al., 2018), in the Eastern Gulf of Finland (Kovaleva et al., 2022); along the mainland China coastline (Cai et al., 2022); Volta Delta, Ghana (Atiglo et al., 2022); Italian Coast (Atiglo et al., 2022) and in Australia (Sano et al., 2015). According to IPPC, 2012, vulnerability is multidimensional, scale-dependent, and has dynamic characteristics (Cardona et al., 2012). In general, vulnerability consists of exposure, sensitivity, and adaptive capacity (Setiawati et al., 2021; Sano et al., 2015), which usually utilize the combination of physical, social, and economic aspects. However, the determinant of vulnerability and exposure are not fixed yet. Therefore, understanding the dynamics of vulnerability determinants is a crucial research component. There were so many methods utilized to integrate various parameters for CV analysis, such as analytic hierarchy process (AHP), Principal Component Analysis (PCA), Fuzzy method, Coastal Vulnerability Index (CVI), etc. In this chapter, we proposed the CVI method, which means we only utilized the physical parameters and assumed that all parameters have equal contributions to an area’s vulnerability. Indonesia, the fourth-most populous country, the largest archipelagic country with a 99,083-kilometer coastline, faces severe climate events such as storm surges and floods during the west monsoon season (October to March). Moreover, the rising SLR, tsunami, salination, and coastal erosion make the country extremely vulnerable to coastal hazards. Furthermore, these coastal disasters will affect tremendous losses in Indonesia, China, India, Japan, the Republic of Korea, and the Russian Federation under the worst-case scenario (Asia Pacific Disaster Report, 2022). Jakarta is the capital of Indonesia and one of the world’s most populous cities, with more than 10.56 million inhabitants in 2020 (BPS DKI Jakarta, 2022). Greater Jakarta is predicted to surpass Tokyo as the largest and most populous metropolises by 2030 (Euromonitor International, 2018). However, this megacity faces severe threats to coastal hazards such as frequent tidal flooding and inundation, affecting the area’s economic and business processes. In addition, this area is also reported to have experienced land subsidence from 1978 until the present, which was indicated by cracks in the buildings and bridges in the city (Djaja et al., 2004; Takagi et al., 2021). In this chapter, we aim to assess the CVI in greater Jakarta by utilizing various physical parameters such as coastal slope, coastline changes, geomorphology, relative sea-level changes, land subsidence, tidal range, and significant wave height. This study will assist policymakers in creating a plan for protection and adaptation through early awareness and spatial definition of CV. Additionally, knowing about vulnerabilities is crucial for preventing and reducing natural disasters. As a result, the research was designed to use various multidimensional contributing variables as a model study to evaluate the coastal vulnerability in densely inhabited susceptible regions.

23.2 Study area The research was conducted in Jakarta, Indonesia, located at 6 50 21.12v
6 50 24.85vS and 106 430 28.87v
106 580 5.87vE. Jakarta is the capital city of Indonesia, located on the island

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23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence

of Java which is the most populous island in the world. Jakarta is one of the largest coastal cities in the world, with a population of more than 10.56 million people and an area of approximately 661.52 km2 (BPS DKI Jakarta, 2022). The study area is located along the coast of North Jakarta, covering 12 villages located in the coastal area (Fig. 23.1). There are several main points in North Jakarta, including Tanjuk Priok and Ancol as the most commercial zone in North Jakarta, Pantai Indah Kapuk as an elite residential area, and Kota Tua as one of the icons of the capital city of Indonesia. Based on the topography, the Jakarta area is categorized as a flat and sloping area. The northern and central areas have relatively flat with a slope between 0 and 2 degrees. This area has about 27 rivers/channels/canals used for various activities and empties into Jakarta Bay. Geologically, the northern part of Jakarta is composed of coastal alluvium, coastal embankments, rivers, and swamps. The alluvium soil has a new age of 5000 years and has not experienced maximum compression. The consequence of these geological conditions is the occurrence of land subsidence. Several studies reported that the subsidence that occurred in Jakarta was caused by a large amount of groundwater extraction (Abidin et al., 2010), soil pressure due to building loads (Abidin et al., 2010), and the geological condition of North Jakarta in the form of an alluvial plain (Rimbaban, 1999).

FIGURE 23.1

The study area of Jakarta Capital City.

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23.3 Data and method

TABLE 23.1

The detailed dataset to calculate CVI.

Parameters

Source data

Period

Significant Wave Height (SWH)

ECMWF ERA-Interim Dataset

2009
2019

Shoreline Changes

Landsat 5 TM (2009) and Landsat 8 OLI (2020)

2009 and 2020

Coastal Slope

Digital Elevation Model Nasional (DEMNAS) from Indonesian Geospatial Information Agency

2021

Coastal Geomorphology

Topographic Maps of Indonesian Geospatial Information Agency

2021

Tidal Ranges

Tidal Dataset from Indonesian Geospatial Information Agency

2008
2022

Sea Level Rise

Global Ocean Physical Multi-Year Product (GLOBAL_MULTIYEAR_PHY_001_030) from Marine Copernicus

1993
2021

Vertical Land Motion Sentinel 1 Single Look Complex (SLC) dataset

2017
2020

23.3 Data and method Data for each of the seven variables were collected to generate a coastal vulnerability index (CVI) (Table 23.1). The coastal vulnerability database presented here is based on the methodology used by Gornitz (1991) with some modifications (Husnayaen et al., 2018). The CVI database was built by creating a buffer along a coastline of about 500 m.

23.3.1 Significant wave height Significant Wave Height (SWH) is a parameter that plays a role in coastal sediment transport (Gornitz, 1991). Wave height is directly related to wave energy, where the greater the SWH, the higher the wave energy produced (Pendleton et al., 2005, 2010). SWH data for the study area was obtained through ERA-Interim Dataset from ECMWF. This data consists of information grids from 2009 to 2019.

23.3.2 Shoreline changes The shoreline was derived from satellite imagery using Landsat 5 (2009) and Landsat 8 (2020). We delineated the coastline based on the threshold between land and water that was generated from false color composite (Infrared, Red, Blue) and other composites. The delineation was converted to the coastline vector of the 2009 and 2020 datasets. To calculate the accretion (erosion) rate, we analyzed using the endpoint rate (EPR) method. The EPR is the simple method to calculate the rate of change using only two shoreline locations. The rates were determined by dividing the distance of shoreline movement by the oldest (2009) and latest data (2020). The baseline and transects to measure shoreline location movement was generated using Digital Software Analysis System (DSAS).

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23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence

23.3.3 Sea level rise Sea level rise (SLR) is an important parameter related to the variability of the coast or small islands. This parameter is defined as the eustatic sea level which is a measure of the total mass or volume of the ocean. SLR was calculated from the linear trend of Sea Surface Height (SSH) data obtained from Marine Copernicus Global Ocean Physics and Reanalysis from 1993 to 2021.

23.3.4 Tidal range Tidal range information is closely related to inundation and erosion hazards. It is based on the concept that large tidal ranges are associated with strong tidal currents capable of eroding and transporting sediment (Gornitz, 1991; Gornitz et al., 1991). Tidal prediction data for 2008 to 2022 obtained from https://srgi.big.go.id/tides was used to determine the tidal range in the study area.

23.3.5 Coastal geomorphology Coastal Geomorphology deals with the sensitivity of geological materials (soil and rocks) to erosion. The higher the erodibility value of geological material, the easier it is to erode. Erodibility is influenced by soil texture, soil structure, organic matter, and permeability (Vrieling, 2006; Vrieling et al., 2008; Teng et al., 2016). This data was obtained from topographic maps published by the Geospatial Information Agency of Indonesia (https:// tanahair.indonesia.go.id/).

23.3.6 Coastal slope The slope is an influential indication that affects shoreline changes because coastal areas with low slopes will retreat faster than steeper areas (Pilkey & Davis, 1987). The slope is calculated from the elevation grid obtained from DEMNAS developed by the Indonesian Geospatial Information Agency, which is built from several data sources, including IFSAR data (5 m resolution), TERRASAR-X (5 m resolution), and ALOS PALSAR (11.25 m resolution), by adding stereo-Masspoint data plotting.

23.3.7 Vertical land motion Vertical Land Motion (VLM), in terms of subsidence (downward VLM) or uplift (upward VLM) of the land surface, data was obtained from 13 pairs of Sentinel 1 Single Look Complex (SLC) IW mode imagery from 2017 to 2020. This data was calculated using the Small Baseline Subset Differential Interferometry Synthetic Aperture Radar (SBASDInSAR) method. SBAS is a time-series SAR technique developed by Berardino et al. (2002) and is commonly used to generate VLM information by processing large sequences of SAR data obtained in the same region of the Earth. This method requires a number of SAR image pairs grouped by slightly different orbital positions and acquisition times. The phase difference of each pair of interferometric SAR data was then extracted and

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23.3 Data and method

calculated to produce VLM. By using this time-series method, it is possible to minimize the decorrelation of the interferogram due to distant temporal and perpendicular differences, resulting in a more accurate mean line of sight (LOS) velocity (Ferretti et al., 2011).

23.3.8 CVI calculation The Coastal Vulnerability Index (CVI) is one of the most commonly used and simple methods to assess coastal vulnerability information. This method provides a simple numerical basis by creating a class along the shoreline that represents the vulnerability level. Values ranging from 1 to 5 were assigned for each parameter to represent the level of vulnerability and its contribution to the analyzed area. Table 23.2 summarizes the criteria used to determine parameter values in the case of Jakarta coastal analysis. The CVI presented here was calculated based on the formula used by the US Geological Survey (USGS) in evaluating the potential vulnerability of the US coastline (Thieler & Hammar-Klose, 1999; Thieler et al., 2002), but with slight modifications, using 7 variables: rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a b c d e f  g (23.1) CVI 5 7 where, a 5 mean significant wave height, b 5 shoreline change, c 5 coastal slope, d 5 coastal geomorphology, e 5 mean tidal range, f 5 sea-level rise rate, and g 5 vertical land motion (VLM). TABLE 23.2 Parameters (Unit)

CVI coastal parameter classifications ranking. Very low (1)

Low (2)

Moderate (3)

High (4)

Very high (5)

Significant Wave Height (m)

, 0.55

0.55
0.85

0.85
1.05

1.05
1.25

. 1.25

Shoreline Changes (m/year)

. 2.0

1.0
2.0

2 1.0
1.0

2 2.0
( 2 1.0)

, 2 2.0

Sea Level Rises (mm/year)

, 1.8

1.8
2.5

2.5
3.0

3.0
3.4

. 3.4

Tidal Range (m) , 0.99

1.0
1.99

2.0
3.99

4.0
6.0

. 0.6

Coastal Cliff, rock, fjords, Geomorphology coastal building

Medium cliffs, indented coast

Low cliffs, alluvial plains

Cobble beaches, estuary, lagoon

Barrier beaches, salt marsh, flats, deltas, mangrove

Coastal Slope (%)

. 1.20

1.20
0.90

0.90
0.60

0.60
0.30

, 0.30

Vertical Land Motion (mm/year)

, 1.1

1.1
2.4

2.5
3.9

4.0
5.0

. 5.0

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23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence

The calculated CVI was then divided into vulnerability classes. Several studies have classified CVI using a different number of classes, using either three classes Gornitz et al. (1991), four classes (Gornitz, 1991; Thieler & Hammar-Klose, 1999), or five classes (Pendleton et al., 2005). In this study, we used five classes of quartile ranges (20%, 40%, 60%, and 80%) as boundaries to generate coastal vulnerability information.

23.4 Results 23.4.1 Physical drivers of coastal vulnerability Fig. 23.2 presents the spatial distribution of the physical drivers for coastal vulnerability. It includes significant wave height, shoreline changes, SLR, tides, beach morphology, slope, and VLM. This chapter calculated the CVI along the Jakarta Bay coastline using the physical-based index as presented in Eq. 23.1. The vulnerability level was divided into five classes (very low, low, medium, high, and very high). The current study indicates that medium to very high vulnerability occurs in most of the coastline along Jakarta Bay, which extends for a length of

FIGURE 23.2 (A) Significant wave height; (B) Tides; (C) Slope; (D) Shoreline Changes; (E) Beach morphology; (F) Sea Level Rise; (G) Vertical land motion.

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23.4 Results

40.14 km. Among the seven parameters used to assess the level of coastal vulnerability, the parameters of sea level rise and subsidence in VLM has a significant effect on coastal vulnerability in the study area. The second prominent driver was shoreline changes, with most of the region being categorized as moderate to very high. The next driver was coastal geomorphology, followed by coastal slope, tides, and significant wave height. 23.4.1.1 Coastal topography In this section, coastal topography was defined by two main parameters: beach morphology Fig. 23.2E and slope Fig. 23.2C. The study area’s geomorphology generally consisted of rock, mangroves, and ruins, whereas the rock was dominant (71.8%). Meanwhile, the slope ranged from 0 to 8.3%, with an average value of 2.8%. Since each parameter has a different unit and data type, we normalized and classified the dataset as previously explained in the method section. As shown in Fig. 23.2E, the study area’s eastern side shows a very high vulnerability regarding beach morphology. Meanwhile, the slope’s vulnerability status varies Fig. 23.2C. Based on the dataset shown in Tables 23.3 and 23.4, the very low vulnerability status is dominant for coastal geomorphology (88.4%) and slope (67.22%) parameters. However, still very high vulnerable area for each parameter was found. For instance, coastal morphology’s very high vulnerability status was mainly found in Tanjung Priok Village, which accounts for 6.9% of the study area (2.8 km). Meanwhile, for slope parameters, the most vulnerable spot was Ancol village (3.1%), followed by Cilincing (1.9%) and Marunda (1.6%), respectively. In general, the Jakarta Bay coastline is vulnerable to coastal disasters since the area is characterized mainly by a low-lying coastal zone where the elevation was ranged from 2 3.3 to 4.5 m above sea level. TABLE 23.3 Jakarta.

The length of each village’s coastal geomorphology vulnerability status (in meters) in North Coastal geomorphology

Sub-district

Village

Very low

Low

Moderate

High

Very high

Cilincing

Cilincing

3538.08

0.00

0.00

0.00

0.00

Kalibaru

3573.14

0.00

0.00

0.00

215.17

Marunda

1745.98

0.00

0.00

0.00

0.00

Koja

Koja

0.00

0.00

0.00

0.00

1341.11

Pademangan

Ancol

11,528.98

0.00

0.00

0.00

0.00

Penjaringan

Kamal Muara

4433.89

0.00

0.00

0.00

0.00

Kapuk Muara

2327.23

0.00

0.00

0.00

0.00

Penjaringan

3202.64

0.00

0.00

0.00

0.00

Pluit

4790.17

0.00

0.00

0.00

332.12

Tanjung Priok

336.90

0.00

0.00

0.00

2776.96

35,477.00

0.00

0.00

0.00

4665.35

Tanjung Priok Total

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23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence

TABLE 23.4 The length of each village’s slope vulnerability status (in meters) in North Jakarta. Slope class Sub-district

Village

Very low

Low

Moderate

High

Very high

Cilincing

Cilincing

1886.03

0.00

0.00

875.65

776.40

Kalibaru

2950.98

0.00

508.47

0.00

328.86

Marunda

1110.96

0.00

0.00

0.00

635.02

Koja

Koja

733.40

49.18

558.52

0.00

0.00

Pademangan

Ancol

8492.06

0.00

1248.42

549.00

1239.50

Penjaringan

Kamal Muara

1932.16

1695.40

0.00

721.36

84.97

Kapuk Muara

1025.30

1040.03

25.38

0.00

236.52

Penjaringan

2227.55

968.16

0.00

0.00

6.93

Pluit

3961.02

332.12

699.37

129.78

0.00

Tanjung Priok

2662.65

451.21

0.00

0.00

0.00

26982.10

4536.09

3040.16

2275.79

3308.21

Tanjung Priok Total

23.4.1.2 Sea level rise (SLR), tides, and wave Although erosion and accretion assessment is important in coastal risk assessment, SLR, wave height, and tidal range are crucial in determining the coastline’s vulnerability. In this study, the SLR was found to rise by 6.69 mm/year, respectively, while the significant height also increased by 0.42 m/year. Furthermore, the tide range is mainly constant at 1.2 m. Fig. 23.2A, F, and B show the study area’s vulnerability status of wave height, SLR, and tide range. Among three parameters, SLR offers significant contributor exposure along Jakarta Bay, where most areas identified a very high vulnerability status. This is because the rate of SLR in the study area is almost double the global SLR rate, which accounts for 3.6 mm/year. Moreover, among 12 villages along the coastline, Ancol was identified as the most affected area in terms of SLR, which account for 28.7% of the study area, followed by Pluit (12.8%), Kamal Muara (11%), Kalibaru (9.4%), Cilincing (8.8%), Penjaringan (8%), and Tanjung Priok (7.7%), respectively. 23.4.1.3 Shoreline dynamics Fig. 23.2D shows the vulnerability status of shoreline changes in the study area. The result found that nearly 23.74 km (59.14%) of coastline has experienced massive erosion. The erosion rate of the study area ranged from 2 64 m/year to 2 0.01 m/year, with an average of 2 11.1 mm/year. Therefore, most of the study area was categorized as moderate to very highly vulnerable regarding shoreline changes. It shows the vulnerability status of each coastal village in the region. The result stated that 65.3% of the area was categorized as moderate vulnerability, followed by very high (31%) and high (3.7%) vulnerability. Furthermore, Cilincing shows the most vulnerable area regarding shoreline changes,

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23.4 Results

443

followed by Ancol and Kalibaru. Moreover, at the sub-district level, the most susceptible area was Cilincing, followed by Penjaringan and Pademangan. 23.4.1.4 Vertical land motion (VLM) Fig. 23.2G shows the VLM vulnerability status in the region. Generally, in urban coastal areas, the VLM process that occurs is land subsidence which also happens in the city of Jakarta. The result revealed that most of the coastline area was identified as a very vulnerable region which nearly 40.14 km having to experience land subsidence with an average of 2 75.4 mm/year. The highest vulnerable area primarily occurred at Ancol (28.7%), followed by Pluit (12.8%) and Kamal Muara (11%), respectively. Moreover, the upper quartile of land subsidence rate mainly occurred at Ancol, followed by Pluit, Cilincing, Kamal Muara, Marunda, Kalibaru, Tanjung Priok, and Koja.

23.4.2 Coastal vulnerability status in Jakarta Fig. 23.3 shows the integrated vulnerability index from the seven parameters above, called as coastal vulnerability index (CVI). The CVI status varied among the village along

FIGURE 23.3 Coastal vulnerability index along Jakarta province’s coast.

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23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence

TABLE 23.5 The length of each village’s CVI status (in meters) in North Jakarta. CVI Sub-district

Village

Very low

Low

Moderate

High

Very high

Cilincing

Cilincing

327.79

57.31

1500.93

0.00

1652.05

Kalibaru

1537.77

0.00

1198.05

723.63

328.86

Marunda

399.43

711.53

0.00

418.55

216.46

Koja

Koja

0.00

0.00

0.00

733.40

607.70

Pademangan

Ancol

5997.28

0.00

1989.28

2418.38

1124.04

Penjaringan

Kamal Muara

1321.91

0.00

2053.58

252.06

806.33

Kapuk Muara

509.53

0.00

515.77

1065.40

236.53

Penjaringan

2227.55

0.00

968.16

6.93

0.00

Pluit

3661.01

0.00

299.96

829.15

332.12

Tanjung Priok

336.90

0.00

0.00

2325.75

451.21

16,319.20

768.84

8525.73

8773.26

5755.30

Tanjung Priok Total

the coastline. The vulnerability status was defined as five classes using the quantile method Fig. 23.3. The result stated that the very low CVI is the most dominant, accounting for 40.6% of the study area, followed by moderate (21.9%), high (21.2%), very high (14.3%), and low (1.9%), respectively (Fig. 23.3 and Table 23.5). As shown in Table 23.5, the very high and high vulnerability area mainly occurred at Ancol village (8.8%), followed by Tanjung Priok (6.9%) and Cilincing (4.1%), respectively. Low elevation, gentle slope, high rate of SLR, VLM (subsidence), and shoreline changes (erosion) were identified as the primary drivers for high and very high vulnerability status in the study area.

23.5 Discussions This chapter calculated the CVI along the Jakarta Bay coastline from Eq. 23.1 using the physical-based index (beach morphology, wave height, SLR, tides, VLM, shoreline changes, and slope). Among those physical drivers, SLR, VLM, and shoreline change significantly contribute to the severe region’s CVI. The CVI also revealed that more than 57% of the Jakarta coastline (Table 23.4; The CVI is from moderate to very high status) is vulnerable to coastal disasters with the cascading effect such as tidal flooding, flash flood, inundation, waterborne diseases, vector-borne diseases, erosion, etc. Furthermore, this chapter also found that North Jakarta is the only regency of the Greater Jakarta Province most affected by the coastal disaster. Moreover, among 34 villages in North Jakarta, 12 villages are under threat. Based on the CVI result, Ancol, Tanjung Priok, and Cilincing village (Table 23.4) are the most severe area of coastal disaster threats.

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As mentioned above, the SLR, VLM, and shoreline changes are the most critical parameters for the study area’s coastal exposure. The SLR increase rate in the study area was 6.7 6 0.1 mm/year. This finding is in line with previous research, which stated that the SLR around Indonesia’s seas would continue at 7 mm/year (Takagi et al., 2016). However, these findings show a discrepancy with the global SLR increase of 3.1 6 0.7 mm/year (IPCC, 2007). These differences may be due to natural climate variabilities, like the discrepancy of thermal expansion rates. According to Takagi et al. (2016), if the SLR continues to increase in Jakarta, the study area will experience a flooded area deeper than 1 m by about 12.9 km2 by 2050. Furthermore, the frequent tidal flooding in Jakarta is not only caused by SLR but also by land subsidence. Our chapter revealed that the land is subsiding by 2 7.5 6 4.8 cm/ year, and according to Takagi et al. (2016) if the land subsidence exceeds 10 cm/year, this parameter will have a noticeable impact on coastal flooding compared with the SLR. Moreover, shoreline changes also play a vital role in coastal flooding exposure. This chapter found that nearly 16 km of the study area’s coastline experienced a massive erosion with an average value of 2.3 m/year, which is in line with previous research by Libriyono et al. (2018), which stated that the erosion rate in Jakarta Bay was 2.24 m/year. Jakarta has experienced frequent flooding annually due to tidal or extreme precipitation. The previous studies by Ward et al. (2011) and Yoo et al. (2014) revealed that North Jakarta is the most affected area by coastal flooding, which had damage exposure of more than 4 billion Euro (Ward et al., 2011). Moreover, according to the Jakarta flooding monitoring system in 2020, the most affected coastal flooding area was located in North Jakarta, particularly in Ancol, Cilincing, Marunda, and Kamal Muara (Jakarta Provincial Government, 2020). This fact is in line with our findings as stated in Fig. 23.3 and Table 23.5 and strictly related to our results for the vulnerable area for VLM (subsidence), where Ancol, Cilincing, Marunda, and Kamal Muara are the top 25% of land subsidence rate in the region. Particularly, Ancol and Cilincing areas have high exposure to tidal flooding, extreme local rainfall, and extreme rainfall in the upper land. According to the CVI, Ancol is the most affected area, followed by Tanjung Priok and Cilincing. However, both Ancol and Tanjung Priok are the most commercial zone in North Jakarta. Ancol is an area with a vast amusement park, resort, and many luxurious apartments. At the same time, Tanjung Priok is the biggest seaport in Indonesia, handling more than half of Indonesia’s transshipment cargo traffic. The frequent flooding, both from tidal or extreme rainfall in these areas, caused a significant disturbance to the business and created a consequential economic loss in the country. Even though Cilincing did not have many commercial zones, this area was well known for the high number of poor households (Firman et al., 2011). People with low incomes are more susceptible than those with high incomes. Suppose there are significant changes in the environment where they live. In that case, the vulnerable group will become even more impoverished since they don’t have anywhere else to go in the event of a flood or rising sea level. According to Cao et al. (2021), the possible future effect of areas hit by coastal flooding in North Jakarta could rise by 110.5 km2 in 2050, primarily due to continuing land subsidence. Also, land-use changes and subsidence would significantly impact future floodplains and play a vital part in floods. In response, the National Capital Integrated Coastal Development (NCICD) master plan was created in 2014 by numerous Indonesian

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23. Coastal vulnerability assessment for the megacity of Jakarta, Indonesia under enhanced sea-level rise and land subsidence

government entities. However, a notable aspect of this project was not addressing the currently underway subsidence, which is the primary reason for flooding but instead closing Jakarta Bay with such a long dike recognized as the Giant Seawall (NCICD, 2018). Thus, the master plan has been heavily criticized for its potential ecological consequences (van der Wulp et al., 2016). The NCICD (2018) approximated that if subsidence could be prevented in the near term, the enhanced coastal dikes could protect against flood events. Therefore, the president of Indonesia declared in 2019 that the national capital would be relocated from Jakarta to East Kalimantan on Borneo Island. Land subsidence and recurring floods have seemed to be one of the reasons for the government’s decision to relocate the headquarters, along with severe road congestion (Lyon, 2019). Major flooding caused by catastrophic events has also prompted governments to develop official adaptation measures, mainly characterized by a rigid infrastructure approach, including tide gates, pump stations, dikes, sluice floods, sea walls, and elevating roads. Even so, there is a current tendency to incorporate hard initiatives and soft measures, like early warning systems, evacuation training, etc. (Cao et al., 2021). Since the local communities have experienced frequent flooding in their area, the local people also did informal adaptation measures, such as raising the floor, building the barrier, and moving the house appliances. However, this informal/reactive adaptation will not solve the problems. Corporating formal adaptation policy into the national/local development planning will benefit local communities, business owners, and local authorities better. However, this kind of implementation faces several challenges. For instance, implementing strict regulations on groundwater extraction is very difficult since the municipal waterworks cannot cover all household water needs. Moreover, the displacement of residents in the vulnerable coastline area and exclusions of residents from the decision-making process are other challenges. Moreover, at the national level, adaptation measures are mandatory, not compulsory, like mitigation efforts. However, this study still contains some uncertainty, including the use of data that has different spatial and temporal resolutions as well as the absence of a validation process. The difference in spatial and temporal resolution causes the details of the resulting data to be different so it needs to be generalized. In this study, we generalize the data into shoreline grid cells that had a size of 500 m by 500 m, and a total of 93 cells are created along the coastal lines. Although the use of shoreline grid cells is common in CVI calculations, in the future it is preferable to use the same or relatively similar data in terms of spatial and temporal resolution to display more helpful results. Furthermore, this study also does not carry out a validation process; therefore, the level of accuracy of the calculations still needs to be questioned. Although it is very rare for studies related to vulnerability to be validated for various reasons, such as the difficulty of finding empirical evidence related to vulnerability. Vulnerability indices are considered an indirect numerical substitute for real phenomena.

23.6 Conclusions This chapter assessed the vulnerability along the coastline in Jakarta Megacities using seven physical parameters. The result revealed that more than 14 km of Jakarta’s coastline

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fall under the high and very high vulnerability groups. Moreover, we found that Cilincing and Ancol is the most vulnerable area within the city. Finally, the study’s main finding is that the SLR, VLM, and shoreline alterations are the key indicators of Jakarta Coastal’s vulnerability. Those three indicators are highly related to global climate change and local anthropogenic pressure. Therefore, reactive adaptation measures are not enough to address these issues in a sustainable way. There is a need to address the adaptation measures in the mid-term and long-term local development planning agenda, where science could be used as a base for policy decision-making processes.

Author contributorship All authors did conceptualization. Abd. Rahman As-syakur developed the original idea. Martiwi Diah Setiawati did data analysis and wrote most of the initial manuscript. Herlambang Aulia Rahman and Muhammad Rizki Nandika did data collection, and data processing and wrote the method section. Abd. Rahman As-syakur, Herlambang Aulia Rachman, Muhammad Rizki Nandika and Martiwi Diah Setiawati have equal contributions. Atika Kumala Dewi contributed to the coastal geomorphology and tidal ranges data preparation. Masita Dwi Mandini Manessa and Rinaldy Terra Pratama contributed to the field survey. All authors contribute to review and editing.

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24 Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones Dhivya Narayanan, Karthi. N, Balamurugan. S and Devaraj Asir Ramesh National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change (MoEF&CC), Integrated Social Sciences and Economics, Chennai, Tamil Nadu, India

24.1 Introduction Gujarat is in the tropical cyclone region, with the country’s longest coastline of 1600 km, and is extremely vulnerable to disasters such as storm surges and floods. Many districts are at risk of cyclones, especially the Gulf of Khambhat is extremely susceptible due to cyclones that often-hit Saurashtra’s southern coast. The eastern coast of the Gulf of Kutch is the most exposed region due to its low-lying flat landscape and high human density. Around 10 million people live in susceptible coastal Talukas. Because of the rapid expansion of ports, saltpans, and energy infrastructure, Gujarat’s coastal population is expanding faster than the rest of the states (GSDMA Gujarat State Disaster Management Plan, 2016). According to the GIDMCRED Training Module on Cyclone Risk Management (2002) estimate, the yearly death toll in Gujarat due to natural hazards is 2000. It is calculated that surges and cyclones are liable for 33% of it. Cyclones, storm surges, and related floods have caused damage to horticulture, agriculture, and animal husbandry in coastal Gujarat, with the cyclone event in Gujarat having an impact from 1910 to 2010. Economic risk cyclones and storm surges are estimated to account for 12% of the state’s risk exposure. For a 100-year return period, the projected aggregate cyclone and storm surge risk to the Gujarat economy is Rs 11,182 cr. for capital assets and Rs 1035 cr for GVA. With 2.5 structural risks to the Gujarat hazard risk and vulnerability atlas, a cyclone with a 100-year return period can affect around 248,000

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24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones

residential buildings out of 675,000 susceptible to cyclonic wind damage (GSDMA Cyclone Preparedness and Response Plan, 2014). It is critical to understand how natural habitats can reduce the pressures that cause coastal erosion and inundation for management activities to best conserve the protective services coastal ecosystems offer. Storms, powerful waves, and the resulting erosion and floods are the norm for Washington’s coastline. Coastal ecosystems play an important role in this challenging environment, stabilizing shorelines, and shielding coastal communities, their life, and livelihoods from negative consequences. However, in an evolving world where ecosystems are changing, and sea levels are increasingly being pushed to their limits by human activity, coastal populations are more vulnerable to coastal hazards (Sharp, Douglass, Wolny, Arkema, Bernhardt, Bierbower, & Wyatt, 2020). Severe weather, rising seas, and damaged coastal ecosystems increase the danger of coastal hazards causing damage to life and livelihoods. Reefs and coastal vegetation, specifically when they surround susceptible communities and assets, may minimize the risk and extent of losses. We compute a hazard index for every 1 km2 of Gujarat’s coastline by applying five shoreline characteristics. We utilize this index to identify the most susceptible individuals and property along the state’s coastline, as shown by being in the top quartile of danger. If existing coastal habitats are adequately protected, the number of individuals, families, the aged, and the monetary value of a residential property that is most susceptible can be drastically reduced. In Florida, New York, and California, coastal ecosystems protect the highest number of people and overall property worth. Our findings provide the first national map of risk reduction due to natural habitats, indicating where reef and vegetation conservation and restoration have the most potential to safeguard coastal populations. The coastal vulnerability model generates a qualitative index of susceptibility to erosion and flood, along with population densities near the shoreline. The approach does not explicitly evaluate any ecosystem service but rather classifies locations as low, moderate, and high risk of erosion and flooding. It is reasonably easy and fasts to execute, so it can be run in most parts of the globe using data that is typically easier to get. These ecosystems on land and in the water work together to produce a living shoreline that benefits humans and the environment (Sharp, Douglass, Wolny, Arkema, Bernhardt, Bierbower, & Wood, 2020). Because of the ecosystem services provided by natural systems, habitat conservation, and restoration generate economic rewards. Moreover, as natural habitats have had the ability to become better with time and adjust as sea levels rise, they are less expensive for shoreline protection than armor shorelines. The Coastal Vulnerability model (Integrated Valuation of Environmental Services and Tradeoffs: InVEST) generates a rank for exposure index at the user-specified interval for each point along a coastline. The exposure index indicates the relative susceptibility of distinct coastal segments to flooding and erosion within the study area. The coastal demographic raster depicts the population density distribution in coastal region boundaries. The index was created by combining seven biogeophysical factors. These factors indicate regional differences in biologic and geomorphologic characteristics, the pace or quantity of rising seas, regional bathymetry and terrain, and the combined wave and wind force associated with the storm. The model compares the exposure from each stretch of shoreline within the zone of study to that exposure of the overall shore. Based on the precision of the input data, model outputs may be meaningful in a range of sizes and extents. Finally, the model aggregates coastal population density at the same scale as the exposure index, which could be used to produce maps that demonstrate the relative susceptibility of human populations to coastal storms (Sharp, Douglass, Wolny, Arkema, Bernhardt, Bierbower, Chaumont, et al., 2020). Kachchh, Rajkot, Jamnagar, Porbandar, Junagadh, Amreli, Bhavnagar, Ahmadabad,

3. Climate change, ecological impacts and resilience

24.2 Materials and methods

453

Anand, Bharuch, Surat, Navsari, and Valsad are among the 13 coastal districts. Mangroves, seaweeds, coral reefs, salt marshes, marine life, and wetlands are among the habitats found along the Gujarat coast (Kankara et al., 2018). It’s also a hub for economic activity. The cost and failure of traditional control structures, as well as the human and environmental damages caused by several storms and tsunamis over the last decade, have prompted a paradigm shift in natural and naturebased methods to shoreline protection is being considered in coastal hazard and climate adaptation planning. Ecosystem contribution, in general, varies greatly depending on the geomorphic context, hazard level, habitat capabilities, socioeconomic variables, and management (Arkema et al., 2017).

24.2 Materials and methods 24.2.1 Study area On the northwestern coast of the Indian Peninsula, the research area is the Gujarat coast. Latitude: 20 100 N to 24 500 N, longitude: 68 400 E to 74 400 E, the state of Gujarat located in India. At its northernmost terminus, Kori Creek lies on the Gujarat coast between the Western Ghats and Valsad (Fig. 24.1; Table 24.1).

FIGURE 24.1 Study area and distribution of coastal ecosystems Gujarat.

3. Climate change, ecological impacts and resilience

TABLE 24.1 Area under different habitats (ecologically sensitive areas) in Gujarat. District

Mangrove (ha)

Mudflat (ha)

Salt Marsh (ha)

Sand Dunes (ha)

Seagrass (ha)

Corals (ha)

Nesting ground of birds (ha)

Turtle nesting ground (ha)

Total ESA area (ha)

5866

14,888

8939.91

-

-

-

-

-

29,694

Dholka

7

-

135.49

-

-

-

-

-

142

Jafrabad

113

-

20.00

-

-

-

-

-

133

Rajula

520

120.9

-

-

-

-

-

-

641

Borsad

-

1181.6

298.51

-

-

-

-

-

1480

Khambhat

2828

13,859.3

3756.17

-

-

-

-

-

20,444

Amod

-

-

826.34

-

-

-

-

-

826

Hansot

1862

9316.0

648.70

-

-

-

-

-

11,827

Jambusar

1101

8784.8

1166.90

-

-

-

-

-

11,052

Vagra

2697

10,620.1

7068.45

-

-

-

-

-

20,386

Bhavnagar

2422

14,764.5

768.61

125

-

-

-

-

18,080

Ghogha

847

751.6

-

-

-

-

-

-

1598

Mahuva

43

-

-

-

-

-

-

-

43

Talaja

204

-

31.99

164

-

-

-

-

400

Kalyanpur

1543

284.2

-

104

647

11,092

-

86.96

13,757

Khambhalia

6520

264.2

15.13

-

746

15,936

-

-

23,481

Okhamandal 849

6134.8

-

412

-

2610

-

91.66

10,097

Kodinar

108

164.5

20.92

681

-

-

-

-

975

PatanVeraval

0.0579

-

-

630

-

-

-

-

630

Una

326

253.3

283.30

-

-

-

-

-

863

Taluk

Ahmadabad Dhandhuka

Amreli

Anand

Bharuch

Bhavnagar

Devbhumi Dwarka

Gir Somnath

Jamnagar

Jamnagar

10,511

5777.9

31.58

-

309

3663

982.49

-

21,275

Jodiya

9607

21,031.2

424.07

-

-

2155

-

-

33,217

Lalpur

256

99.7

-

-

-

1536

-

-

1892

Junagadh

Mangrol

-

-

-

953

-

-

-

71.39

1024

Kachchh

Abdasa

2499

17,807.8

77.94

495

-

-

-

-

20,880

Anjar

14,863

24,099.2

171.09

-

-

-

-

-

39,133

Bhachau

16,651

12,178.4

-

-

-

-

-

-

28,830

Mandvi

73

338.9

114.29

1547

-

-

-

-

2073

Mundra

2250

13,935.2

-

-

-

-

-

-

16,186

Morvi

Maliya

8692

14,238.1

37.73

-

-

-

-

-

22,968

Navsari

Gandevi

503

61.9

30.69

-

-

-

-

-

596

Navsari

1356

260.2

1555.98

268

-

-

-

-

3440

Porbandar

Porbandar

111

-

-

1250

-

-

-

149

1510

Rann of Kachchh

Kachchh

42,841

99,533.3

-

-

-

-

-

-

142,375

Surat

Chorasi

1338

1390.3

1229.67

-

-

-

-

-

3958

Olpad

2151

4758.3

672.77

-

-

-

-

-

7582

Vadodara

Padra

-

321.5

-

-

-

-

-

-

321

Valsad

Pardi

94

-

-

-

-

-

-

-

94

Umbergaon

223

791.9

-

21

-

-

-

-

1035

Valsad

259

50.9

47.09

-

-

-

-

-

357

297,962

28,373

6650

1702

36,992

982.49

399.5

515,295

Grand Total 142,133 (ha)

456

24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones

24.2.2 Approach The InVEST Coastal Vulnerability model from the Natural Capital Project was used for the analysis. The Coastal Vulnerability model calculates the coastal exposure index, a relative index of coastal areas’ exposure to flooding and erosion caused by storms, based on a variety of input factors that influence coastal processes leading to flooding and erosion. It has previously been used for analyses from watershed to national scales (Arkema et al., 2013). Coastal habitats are included in the model as a mitigating influence on coastal hazards (The model is frequently used to examine the preventive properties of coastal habitats since the presence of coastal habitats reduces the coastal exposure index). The InVEST (coastal vulnerability model) (Sharp et al., 2018) generates the exposure index of the coast, a relative assessment of coastal regions’ exposure to storm-related flooding and erosion, based on input parameters that impact coastal processes that contribute to flooding and erosion. It’s already been utilized for assessments ranging from the watershed to the national level. Since coastal habitats are incorporated as a mitigating impact on coastal hazards, the model is frequently used to investigate their protective effects (the presence of coastal habitats decreases the coastal exposure index) (Fig. 24.2). For each input component, including relief, wave exposure, geomorphology, wind exposure, sea level rise, coastal ecosystem, and storm surge intensity, the Gujarat coastline is ranked between 1 and 5 (for this study, each segment was 250 m in length). A greater number indicates more vulnerability to coastal risks in each of these component rankings.

FIGURE 24.2

Gujarat coastal exposure index with and without coastal ecosystem.

3. Climate change, ecological impacts and resilience

24.2 Materials and methods

457

The geometric mean of the factor rankings is used to generate the final coastal exposure index (Sharp, Douglass, Wolny, Arkema, Bernhardt, Bierbower, Chaumont, et al., 2020). The Coastal Vulnerability model, which is further explained below, employs a mix of spatially explicit information on five coastline features. To produce relative maps of shoreline susceptibility to erosion and inundation, one must consider the shoreline’s geomorphology, nearby coastal ecosystems, which may or may not act as a buffer, exposure to wind and waves, surge potential, and surrounding terrain. The work incorporates the susceptibility of coastal systems to climate change hazards (e.g., sea level rise, floods, and erosion) and includes an examination of site-specific environmental and socioeconomic sensitivity to the effect of hazards (e.g., sea level rise, floods, and erosion) and involves an examination of the environmental and socioeconomic susceptibility of coastal systems to the effect of hazards on a site-by-site basis (e.g., land use, geomorphology, vegetation cover, population density). A GIS-based decision support system for assessing the impact of coastal climate change will be created, which will offer information on regional/local vulnerabilities and hazards to help decision-makers design suitable adaptation measures. Index-based techniques measure risk and risk reduction benefits by estimating exposure and vulnerability. For example, InVEST’s Coastal Vulnerability Module (Arkema et al., 2013) applies an index-based method to analyze shorelines either with or without coastal ecosystems that are most vulnerable to flooding. On a scale of 1
5, the index scores seven factors (e.g., winds, wave surge, sea level, and habitat type) to determine coastline exposure. Two habitat scenarios to assess the function of coastal ecosystems in minimizing vulnerability to sea-level rise and storms. In the danger index, “with habitat” covers eight habitats. “Without habitat” implies that those environments are no longer protective. The present condition of the system is believed to be the habitat scenario. The scenario of a world without habitat is not meant to be a realistic depiction of the future. Instead, we utilized it to assess where and to what degree ecosystems are important in safeguarding people and property, as well as how their removal might influence risk from coastal disasters. EI 5 Hazard IndexðRGeomorphology RRelief RHabitats RSLR RWind RWave RSurge Þ1=7

(24.1)

where the exposure index (EI) has been classified as ,1.5 (very low), 1.5
2.5 (low), 2.5
3.5 (Moderate), 3.5
4.5 (High), .4.5 (Very high), where (R) represents the ranking.

24.2.3 Quantifying risk We integrated hazard data with mapping data on population and property in each 1 km2 stretch of the whole coastline to convert hazard to endangered property and human life. We compared our estimates for the population exposed to the highest risk to examine the hazard index’s ability to capture danger. To describe the shorelines of Gujarat coastal zones, we are utilizing the following data as ArcGIS shapefiles in the model. The input is a polyline, with each line segment representing a different type of geomorphology (e.g., rocky cliff, sandy beach). Rankings of shorelines represent the relative degree of susceptibility to erosion and inundation; for instance, stony cliffs are given a rating of “1” indicating that they are less sensitive than

3. Climate change, ecological impacts and resilience

458

24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones

sandy beaches (rank 5 5) (National Centre for Sustainable Coastal Management (NCSCM), 2011) and collected coastal habitats in Gujarat. Based on these habitat layers, the model rates the natural environment near to each coastline length (Table 24.2). Fig. 24.1 depicts habitat strata. As distinct strata, coastal ecosystems such as mangrove, mudflat, salt marsh, sand dunes, seagrass, corals, bird nesting grounds, and turtle nesting grounds are included. Wind and wave exposure is calculated using NOAA Wave Watch III data, which is supplied as a default dataset in the InVEST software package. The point shapefile provides observed storm wind speed and wave power measurements for a certain area of interest (i.e., Gujarat). The model calculates surge potential by calculating the distance from the coastline segment to the edge of the continental shelf. In general, the farther away the shoreline is from the continental shelf at a particular location during a particular storm, the higher the storm surge. Relief is represented by a 30 m resolution topographybathymetry dataset derived from NOAA (Continuously Updated Digital\nElevation Model (CUDEM) - 1/9 Arc-Second Resolution Bathymetric-Topographic Tiles. NOAA National \nCenters for Environmental Information, 2014). To describe the shorelines of Gujarat coastal zones, we are utilizing the following data as ArcGIS shapefiles in the model. The input is a polyline, with each line segment representing a different type of geomorphology (e.g., rocky cliff, sandy beach). Shoreline classifications were examined at a fine scale (1:2000) and graded from 1 to 5 using Esri imagery. The rankings indicate the relative degree of vulnerability to erosion and inundation; for example, stony cliffs are assigned a rank of “1,” indicating that they are less vulnerable than sandy beaches (rank 5 5). Coastal habitats in Gujarat were collected from the National Centre for Sustainable Coastal Management listed in. Based on these habitat layers, the model rates the natural environment near each coastline section. Fig. 24.1 depicts habitat layers. As individual layers, coastal ecosystems such as mangroves, mudflats, salt marsh, sand dunes, seagrass, corals, bird nesting grounds, and turtle nesting grounds are included. Wind and wave exposure is calculated using NOAA Wave Watch TABLE 24.2 Model inputs and parameters for Gujarat. Input name

Data source

Land polygon

(NCSCM) National Centre for sustainable coastal management

Relief and bathymetry

NOAA (Provided with InVEST model)

Shoreline geomorphology

(NCSCM) National Centre for sustainable coastal management

Climatic forcing grid

Wind Watch III (provided with InVEST model)

Sea level rise

(NCSCM) National Centre for sustainable coastal management

Habitats

(NCSCM) National Centre for sustainable coastal management(mangrove, mudflat, salt marsh, sand dunes, seagrass, corals, nesting grounds of birds and turtle nesting grounds)

Model resolution

250 m

Elevation averaging radius

5000 m

Maximum fetch distance

35,000 m

3. Climate change, ecological impacts and resilience

24.3 Results and discussion

459

III data, which is supplied as a default dataset in the InVEST software package. The point shapefile provides observed storm wind speed and wave power measurements for a certain area of interest (i.e., Gujarat). By measuring the distance from the coastal section to the edge of the continental shelf, the model determines surge potential. Generally speaking, during a storm, the farther away the shoreline is from the edge of the continental shelf, the higher the storm surge will be. A topography-bathymetry dataset with a resolution of 30 m, obtained from NOAA, is used to give relief.

24.3 Results and discussion Model outputs include the individual factor rankings as well as the coastal exposure index for each Gujarat shoreline segment. To identify areas where coastal habitats are playing a large role in coastal protection, the coastal exposure index was also calculated with all coastal habitats removed, so as to exclude the effects of their protective influence. Where habitats are protecting us is indicated by the difference between the original coastal exposure index and the coastal exposure index calculated without habitats. A similar analysis can be done for individual coastal habitat types. InVEST Coastal Vulnerability model output maps part of Gujarat. This model calculates the exposure index EI for each shoreline segment as the geometric mean of all the variable ranks. The index is calculated with and without habitats present. Areas with a large difference in the exposure index when habitats are removed are well-protected by existing coastal habitats (Fig. 24.3). The maps above depict the vulnerability of shorelines from low to high with current estuarine and terrestrial habitats present (S1) and then removed (S2). When the coastal habitats are removed, the vulnerability of Gujarat shorelines increases. Based on the coastal exposure value we have classified the index into: ,1.5 (very low), 1.5
2.5 (low), 2.5
3.5 (Moderate), 3.5
4.5 (High), and .4.5 (Very high), as shown in Fig. 24.4. Table 24.3 shows the condition of Gujarat’s coastline vulnerability with and without a natural ecosystem. The result depicts that the vulnerability coastal exposure index in part of Gujarat ranged from 2.0 to 4.8. When all coastal ecosystems are present, it shows the proportion of the coastline in each vulnerability category, ranging from low to high (coastline vulnerable with coastal ecosystems (%)), and when they are absent (coastline vulnerable without coastal ecosystems (%)). When coastal ecosystems are eliminated, the change for each category is displayed in the “Coastal Change” column. A decrease in a “Vulnerability Category” is indicated by a negative “Coastal Change.” A rise in a “Vulnerability Category” is indicated by a positive “Coastal Change.” With current habitats present, a majority of shorelines range from moderate to high in vulnerability (91.33%). The percentage of shorelines with low to moderate vulnerability decreases and the percentage of shorelines with high to very high vulnerability rises when all current ecosystems are excluded from the analysis. For low to moderate classifications, the coastline changes, while the shoreline change for high to very-high categories increases. Thus, a reduction in habitat leads to more vulnerable coastlines.

3. Climate change, ecological impacts and resilience

460

24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones

FIGURE 24.3 The maps above depict the vulnerability of shorelines from low to high with current estuarine and terrestrial habitats present (S1) and then removed (S2).

Using InVEST model estimated the Exposure (assets at risk) index. Asset valuations were estimated duly based on the norms provided by the Government of India (Ministry of Home Affairs) with the assistance of minimum standards of relief for natural calamities (damage avoided cost method). This study demonstrates a coastal vulnerability modeling technique for quantifying and mapping coastal vulnerability in Gujarat coastal zones with and without habitat using InVEST software. Without existing habitats (mangrove, mudflat, salt marsh, sand dunes, seagrass, corals, nesting grounds of birds, and turtle nesting grounds), the susceptibility of the majority of shorelines

3. Climate change, ecological impacts and resilience

461

24.3 Results and discussion

FIGURE 24.4 Gujarat coastal exposure: coastal index with and without coastal ecosystem.

TABLE 24.3

Percentage of Gujarath coastline vulnerability with and without ecosystem.

Vulnerability range

Vulnerability category

Coastline vulnerable with coastal ecosystems (%)

Coastline vulnerable without coastal ecosystems (%)

Coastal change

, 1.5

Very low

0.00

0.00

0

1.5
2.5

Low

7.73

1.55

2 6.18

2.5
3.5

Moderate

65.12

56.18

2 8.94

3.5
4.5

High

26.21

40.89

14.68

. 4.5

Very High

0.94

1.38

0.44

increases from high to very high, which affects human livelihoods. The expected risk avoided for the benefits of coastal protection services. USD 36.83 M/year for protecting people and property accounts (Table 24.4) in the system of the environmental-economic accounting (SEEA) framework.

3. Climate change, ecological impacts and resilience

462

24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones

TABLE 24.4 Estimated assets value of Coastal Vulnerability area protected by coastal ecosystems. S. No.

District

ESA area (ha)

Expected risk-averse assets ($)

1

Ahmadabad

29,836.1

126,965

2

Amreli

773.6

1,910,462

3

Anand

21,923.9

221,695

4

Bharuch

44,090.6

212,835

5

Bhavnagar

20,120.8

4,998,861

6

Devbhumi Dwarka

47,335

2,306,025

7

Gir Somnath

2468

1,288,266

8

Jamnagar

56,384

302,235

9

Junagadh

1024

152,660

10

Kachchh

107,101.8

1,772,034

11

Morvi

22,967.8

546,480

12

Navsari

4035.8

4,583,925

13

Porbandar

1510

0

14

Rann of Kachchh

142,374.7

190,216

15

Surat

11,539.5

13,519,694

16

Vadodara

321.5

0

17

Valsad

1487

4,685,802

Total

515,294.1

36,818,155

24.4 Recommendations The model may be used by planners in conjunction with an environmental ecosystem accounting system; for instance, restoration might be carried out to improve coastal ecosystems to safeguard the coastline in regions on the map where the shoreline is extremely susceptible and the environment is no longer intact. The findings from the model can be used by shoreline planning committees to support the rules and regulations in shoreline master programs. In regions that are indicated as susceptible when habitat is gone, the committee may think about mandating extra habitat protection measures (such as tighter buffers). Predict appropriate green infrastructure measures (e.g., mangroves, coral reefs, oyster reefs, seagrass) to protect the coast with the least negative impact. The site-specific intervention of green coastal infrastructure should be identified for reducing vulnerability by developing scenarios assessment of co-benefits that provide socioeconomic benefits, restoration of ecosystems, increased biodiversity, and fisheries. Benefits to a coastal community, conservation of natural resources, and enhanced fisheries can be identified by implementing an appropriate mitigation strategy.

3. Climate change, ecological impacts and resilience

24.6 Limitations

463

24.5 Rational of the study The executive summary provides a synopsis of the coastal protection role of reefs and mangroves and the recommended approaches for better incorporating these services’ role in coastal decision-making processes (e.g., coastal zoning, development planning, and disaster risk management) and the SEEA. This ecosystem-based risk reduction accounting is vital for the continuous functioning of ecosystems and human well-being, which ensures that Gujarat’s growth, does not take place at the cost of natural habitats and the people who rely on them. Notably, the sustainable development goal target 2020, “integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts.” This is to gain advanced knowledge on the SEEA to integrate biophysical data and link information to accounting with a spatial area. The importance of ecosystem-based risk reduction accounting was also highlighted in many other policies and plans such as the National Environmental Policy, Coastal Zone Management Plan, and the National land utilization policy. Due to the widespread focus on coastal protection services in policy documents in recent years, existing research on coastal protection services valuation in India has been limited to site-specific. This article planned illustrates the modeling technique InVEST software for quantifying and mapping coastal vulnerability in Gujarat coastal zones with coastal ecosystems and without coastal ecosystems. This chapter aims to “integrate ecosystem and biodiversity values into national and local planning, development processes, poverty reduction strategies and accounts” in accordance with the United Nations Sustainable Development Goals.

24.6 Limitations This review has significant gaps, each of which suggests a possible area for further research. Let us first go through our results. The complex mechanisms involved in coastal hazard formation are greatly simplified in the InVEST coastal vulnerability model. It presents an overall picture of a region’s susceptibility to coastal hazards based on the numerous factors mentioned above rather than the potential effects of individual coastal storms. The model does not account for interactions between these variables. There are other particular constraints linked to individual variables, and the model does not include certain important coastal processes. The exposure index includes theoretical constraints in addition to technological restrictions. One of the primary drawbacks is that the dynamic interactions of complex coastal processes that occur in a region are unduly reduced into the geometric mean of seven variables and exposure categories. In nearshore locations, we do not model storm surges or wave fields. More significantly, the model ignores the quantity and quality of habitats, as well as the function of habitats in mitigating coastal risks. Furthermore, the model does not take into account any hydrodynamic or sediment movement processes. It is believed that locations belonging to the same broad geomorphic exposure class act similarly. We believe that natural habitats provide erosion protection to regions regardless of their geomorphology categorization (i.e., rocky cliffs). This constraint

3. Climate change, ecological impacts and resilience

464

24. Integrated ecosystem-based risk reduction into environmental-economic accounting in Gujarat coastal zones

artificially lowers the relative susceptibility of these locations while increasing the relative vulnerability of places with a high geomorphic index. Coastal ecosystems: the model does not take into consideration the quantity and quality of coastal habitats, both of which impact the protective potential of habitats. Coastal processes that are not accounted for in the model sediment movement: Sediment transport has a large impact on the geographical distribution of erosion impacts.

24.7 Conclusion Communities must be able to access and apply pertinent scientific and management tools in order to include natural solutions in coastal planning. One tool that planners and residents might use is the InVEST Coastal Vulnerability model. Communities in Gujarat are educated about the importance of coastal ecosystems through the use of spatial data from the model. This study taught us that shorelines grow more susceptible when ecosystems are lost or degraded. In Gujarat, it seems that coastline protection is mostly dependent on coastal ecosystems, such as mangroves, mudflats, salt marsh, sand dunes, seagrass, corals, and bird and turtle nesting grounds. Terrestrial habitat loss or degradation not only makes people more vulnerable but also has ripple impacts on other areas. Gujarat State has taken steps to slow the growth of hard structure by mandating counties to consider the value of coastal ecosystems while planning shoreline development. The coastal zone management plan for Gujarat (GCZMP) has been implemented. As a state, we are well-prepared to design resilient coastal communities. This is significant because coastal habitats may aid in lowering storm damage and mitigation expenses for landowners. For instance, the benefit of coastal wetlands for storm protection alone has been calculated to be $8240 ha/year on average. Using, restoring, or upgrading coastal ecosystems may be more cost-effective than hardening constructions due to protection services and associated co-benefits. When comparing the costs of restoring marshes versus building hardening structures in Gujarat, the Division of Coastal Management discovered that a network of bulkheads and rip rap costs, on average, $850/m while a marsh planting only costs $70/m. Hardening structures also need maintenance, while natural habitats grow stronger over time. The communities of Pacific County and Grays Harbor have the opportunity to think ahead to prevent the need for hardening structures by using costeffective natural habitats for shoreline protection. Using the InVEST Coastal Vulnerability model and other science tools on sea level rise, Gujarat can become more resilient in the face of a changing coastline.

Acknowledgment The authors would like to cordially thank the anonymous reviewers and the editor for their insightful comments and suggestions. The authors wish to thank the colleagues at various departments of the National center for sustainable coastal management (NCSCM) especially, Dr. Ramesh Ramachandran, Director, Dr. Purvaja Ramachandran, Division Chair, Shri. Tapas Paul and Shri. Ramakrishna (the World Bank), for their guidance and encouragement, and Dr. Rajakumari. S, Scientist - E for sharing GIS data inputs for the valuation exercise. The views presented in this article are entirely of the authors, not of the organizations they are and were affiliated with. Any remaining errors in the article are the entirety of the authors.

3. Climate change, ecological impacts and resilience

References

465

References Arkema, K. K., Griffin, R., Maldonado, S., Silver, J., Suckale, J., & Guerry, A. D. (2017). Linking social, ecological, and physical science to advance natural and nature-based protection for coastal communities. Annals of the New York Academy of Sciences, 1399(1), 5
26. Available from https://doi.org/10.1111/nyas.13322. United States: Blackwell Publishing Inc. http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1749-6632. 17496632. Arkema, K. K., Guannel, G., Verutes, G., Wood, S. A., Guerry, A., Ruckelshaus, M., Kareiva, P., Lacayo, M., & Silver, J. M. (2013). Coastal habitats shield people and property from sea-level rise and storms. Nature Climate Change, 3(10), 913
918. Available from https://doi.org/10.1038/nclimate1944. 17586798. Continuously Updated Digital Elevation Model (CUDEM) - 1/9 Arc-Second Resolution Bathymetric-Topographic Tiles. NOAA National\nCenters for Environmental Information. (2014). GIDMCRED Training module on cyclone risk management. (2002). Gujarat Institute of Disaster Management: Gandhinagar. GSDMA cyclone preparedness and response plan. (2014). Gujarat Institute of Disaster Management: Gandhinagar. Available from http://www.gsdma.org/uploads/Assets/other/cyclonepreparednessresponseplan06072017051948575.pdf. GSDMA Gujarat State Disaster Management Plan. (2016) (1). Government photo litho press: Ahmedabad. Available from http://www.gsdma.org/uploads/Assets/other/gsdmp-2016-17-volume-106072017115412038.pdf. Kankara, R. S., Murthy, M. R., & Rajeevan, M. (2018). National assessment of shoreline changes along Indian coast—A status report for 1990
2016. National Centre for Coastal Research. National Centre for Sustainable Coastal Management (NCSCM). (2011). https://ncscm.res.in/atlas/ 2002. Sharp, R., Tallis, H. T., Ricketts, T., Guerry, A. D., Wood, S. A., Chaplin-Kramer, R., Douglass, J. (2018). InVEST 3.6 User’s Guide. The Natural Capital Project. Sharp, R., Douglass, J., Wolny, S., Arkema, K., Bernhardt, J., Bierbower, W., & Wood. (2020). InVEST 3.8. 5. User’s Guide. The Natural Capital Project. Sharp, R., Douglass, J., Wolny, S., Arkema, K., Bernhardt, J., Bierbower, W., Chaumont, N., Denu, D., Fisher, D., Glowinski, K., Griffin, R., Guannel, G., Guerry, A., Johnson, J., Hamel, P., Kennedy, C., Kim, C. K., Lacayo, M., Lonsdorf, E., Mandle, L., Rogers, L., Silver, J., Toft, J., Verutes, G., Vogl, A. L., Wood., & Wyatt, K. (2020b). The nature conservancy, and world wildlife fund. InVEST 3.11.0. User’s Guide. The Natural Capital Project. Sharp, R., Douglass, J., Wolny, S., Arkema, K., Bernhardt, J., Bierbower, W., Wyatt. (2020). The nature conservancy, and world wildlife fund. InVEST 3.8. 7. User’s Guide. The Natural Capital Project. 1661942730753.

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C H A P T E R

25 Geospatial approach for reducing water stress: case study of Delhi Ishita Singh1 and Vibhore Bakshi2 1

Pursuing MTech in Geomatics, CEPT, Ahmedabad, Gujarat, India 2School of Planning and Architecture, Bhopal, Madhya Pradesh, India

25.1 Introduction 25.1.1 Water as a sensitive issue Water still remains a critical issue for many developing nations across the globe (Bhattacharyya & Prasad, 2020). Even the global initiatives to save water, every drop of the matter is strangled by the overutilization of resources and lack of preservation efforts for the rejuvenation of water bodies in metropolitan cities. SDG 6 focuses on the efforts for ensuring the availability and sustainable management of water and sanitation. The higher urbanization levels have extensively resulted in water shortage and more dependency on water. The emergence of water stress in metropolitan cities is becoming a hurdle in the development process (Ghosh, 2021). However, there are other externalities associated with water quality (van Beek et al., 2010). Water Pollution is also a relatable issue in many cities, further caused by the disposal of untreated wastewater in ponds and lakes (Nalmasri & JKaluarachchi, 2004). The metropolitan lifestyles, faster pace of migration, and overutilization of water have significantly resulted in declining water table levels (Khuller, 2021). As per the UN estimates for 2019, in the last ten decades, the world population has increased three times, in juxtaposition to water demand which has increased up to six times.

25.1.2 Need identification and global approaches The research chapter questions the approach to reducing water exploitation. The literature depicts the major cause of water stress is rapid urbanization, overutilization, pollution of water, concretization, and encroachments around eco-sensitive areas.

Climate Change, Community Response, and Resilience DOI: https://doi.org/10.1016/B978-0-443-18707-0.00025-4

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© 2023 Elsevier Inc. All rights reserved.

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Surface water sources are overutilized and fail to match the rising demand for water supply in urban areas. The multiplication of bore wells for Indian cities for industrial, commercial, and residential needs has resulted in excessive tapping on groundwater reserves, which deteriorates the groundwater quality (Chaturvedi & Bassin, 2009). The research chapter adopts the water-resilient approach for integrating blue and green infrastructure for reducing water stress (Table 25.1). The OECD environmental outlook baseline projection for 2050 highlights an emerging need to focus on the settlements around the river basin since it is under tremendous water stress threat. Globally, the gap between water supply and water demand has reached 40%. The focus is on the TABLE 25.1 Literature matrix pertaining to global approaches for integrating blue and green infrastructure. S. No. Literature

Spatial area

Authors

Concept

1.

Municipal Policies for Managing Stormwater with Green Infrastructure

Alachua, Florida

Environment Protection Agency (2010)

Preservation of natural habitats through a set of regulatory measures of land acquisition, efforts for integrating blue and green infrastructure, furthermore provisions for these approaches in development control regulations.

2.

A Green Sponge for a Water-Resilient City

Harbin, China Oma (2012)

The concept of stormwater park works on the principle of green sponge, cleansing, and storage of urban stormwater. It includes integration of ecosystem services, native species protection, recharge of aquifers, recreation, and esthetics. It depicts the role of water-resilient approaches in resolving water issues.

3.

Green Infrastructure and Water Supply

Los Angeles

Weinstein (2015)

The research identifies favorable areas for increased stormwater infiltration. The study highlights the critical role that green infrastructure can play in augmenting the City’s local water supply.

4.

Green City, Clean Waters

Philadelphia

Focht (2016)

Piped drainage network through integrating bluegreen infrastructure. The concept of green acre is used for water treatment, which is economical and sustainable. Nowadays, many countries are adopting these measures of water utilization.

5.

Improving the Antwerp Antwerp, Green and Blue Belgium Infrastructure

Oppla (2018)

The city has the ambition to become greener. To achieve this purpose, a master plan on green and blue infrastructure is developed with a focus on five “park-regions” thus inculcating large-scale restoration projects and small-scale initiatives.

6.

Reviving a Downtown Village Green Infrastructure

Millbury Connors Massachusetts (2019)

The village has adopted the strategy under Clean Water Act defining a range of measures that use plant or soil systems, permeable pavement, stormwater harvest, and reuse, or landscaping to store, infiltrate, and in turn replenish the groundwater.

Adopted from the specific cases of Florida, China, Los Angeles, Philadelphia, Belgium, and Massachusetts.

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25.1 Introduction

blue-green resilient approaches for water management (Dhyani et al., 2022). It depicts the global cases of blue-green infrastructure that need to be embarked on. The examination of the water-sustainable approaches is useful to contextualize the case of Delhi since the mismanagement of water resources is quite evident in metropolitan cities across the world (Boken, 2016). This research chapter can be a road map for researchers, urban planners, policymakers, and other stakeholders to comprehend the measures for water assessment through spatial layers as depicted in further sections of the chapter.

25.1.3 Water stress in Indian cities To embark on the water issues in India, there is a tremendous need to identify the water losses in the existing municipal supply, and to re-examine the network hierarchy of water supply in Indian cities. Furthermore, there is a need to estimate “how the blue areas have declined over the period of last few years”? In India, 54% of the geographical area in India is under the threat of high to extremely high water stress (Ghosh, 2021). Of the total, 75% of the household do not have portable water for consumption and 70% faces quality issues due to water contamination. Water Quality Index reveals that India ranks 120th out of 122 countries. As per Verisk Maple croft estimates for 2019, India ranks at 46th position. It is alarming to see that 11 out of the 20 largest cities in India fall into the category of the extreme risk of water stress, whereas seven cities fall under high-risk zone (Hofste et al., 2019). Those seven cities are Delhi, Bengaluru, Chennai, Hyderabad, Nashik, Jaipur, and Ahmedabad, as shown in Fig. 25.1. FIGURE 25.1 Alarming water stress situation in India. Source: Data modified from WRI, OECD 2019 datasets WRI, OECD Datasets 2019 depicts that metropolitan cities in India are facing extreme water stress.

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25.2 Research process This chapter aims to formulate planning strategies for the revival of both surface and groundwater resources by adopting the approach of “integrating blue-green infrastructure” (Melissa, 2019). The research framework proposes to undertake objectives to promote waterresilient approaches in Delhi through micro assessment of the Najafgarh area. To embark on research objectives of the chapter, various suitable cases of integrating blue-green infrastructure across different spatial scales are assessed from the intent of deliverables to make an effective water sensitive plan. The second objective examines the existing issues pertaining to the water resources of Delhi through a focus on the identification of factors, that are leading to the contamination of surface and groundwater resources. The third objective includes the spatial-temporal assessment of blue and green variations across the different spatial plans through hydrology analysis, NDVI, and NDWI assessment as mentioned in the later section of the chapter. The last objective of the research attempts to formulate watersensitive strategies to integrate blue-green infrastructure for Delhi (Everett et al., 2021). The detailed methodology of the research is shown in Table 25.2.

25.2.1 Selection of site for research The alarming urbanization and unprecedented water exploitation, water pollution, and water stress require immediate attention for water management in Delhi NCT (i.e., National Capital Territory) (Bhattacharyya & Prasad, 2020). Delhi has the largest population growth and the concretization process has extensively hampered the ecological services thus depleting the water resources. Hence, Delhi is selected for examination, and water sensitive approach to the blue-green infrastructure strategy is proposed. The Delhi Jal Board estimates for 2021 reveal that there is a deficit of 4639 liters per capita per day (Fig. 25.2). The declining groundwater tables are a serious concern, the examination of water zones of Delhi depicts that the South and Southwest zone, the water level has significantly declined to 20
30 m (Dixit, 2022). The quality of underground water has also deteriorated (Nalmasri & JKaluarachchi, 2004). Higher chemical concentrations of Nitrate and Fluoride are found at various locations in Delhi. Even many recent newspaper articles have described the surface water resources in Delhi are witnessing higher concentrations of water pollutants.

25.2.2 Geospatial assessment of national capital territory, Delhi The process of geospatial assessment describes the necessary steps for estimating the current water flow, with a way forward of identifying water streams, and possibilities of integrating blue areas with green spaces (Conant, 2004).

25.2.3 Applicability of stream flow: D8 method For Watershed delineation, the procedure for identification of stream flow using the D8 algorithm is undertaken (Kumar et al., 2017). The tool represents the flow direction in the

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25.2 Research process

TABLE 25.2

Stages of the research process.

Stages of S. No. research

Research process

Current need

1.

Need identification

Alarming water stress, groundwater Assessment of existing issues of water in depletion, surface water bodies metropolitan cities through literature. contamination, over exhaustion of resources by unprecedented growth of population, permissions for construction in eco-sensitive areas require periodic checks.

2.

Literature study

Review of global practices and approaches pertaining to the sustainable flow of water, community participation as one of the tools as stated in “Khare hai Talab”, by Anupam Mishra, takeaways from the literature, models that can be contextualized in the selected site of Delhi.

The applicability of possible solutions requires to be re-looked in the case of Delhi, adopting the approaches from the cases of Florida, China, Los Angeles, Belgium, US, etc. Adoption of sustainable mechanisms to impart solutions for coping with the proliferation of water scarcity issue

3.

Formulation of aim and objectives

Envisioning the research process, creation of research objectives from the problem statement of emerging water issues in Delhi, highlighting the spatial change as depicted in various for Delhi, depiction of sustainable water.

Generation of Map layers for spatial assessment of water issues in Delhi. Prioritization of microzones which requires immediate water attention and proposing suitable interventions

4.

Site selection and OECD Datasets, WRI Reports on water existing scenario issues in India highlight Delhi as one of assessment the cities, struggling with water issues in India.

To estimate the spatial change in Delhi through different maps, and select the microzones which may require immediate attention with a mindset of approaches relatable to global cases.

5.

Data collection and analysis

Meeting different stakeholders, the locals of the identified area, Delhi Development Authority, Collation of information from different stakeholders

Amalgamating the assessment with the literature, stakeholder surveys, and definitely Delhi Development Authority.

6.

Proposed strategies

Water sensitive plan that collates information from literature, and imparts practical solutions to the issues of water stress in identified areas of Delhi

Establishing neighborhood sensitive Plan, which specifies the roles of different stakeholders for efficient implementation of water sensitive plan.

Research Methodology crafted by authors.

hydrology toolset as a part of ArcGIS software. The procedure takes into consideration the task of filling the elevation raster to mark the flow direction. The rationale for flow direction raster selection depicts the direction in which water will flow out of each cell of a filled elevation raster. Furthermore, the procedure of the D8 method assigns a cell’s flow direction to one of its eight surrounding cells that comprises the steepest distance-weighted gradient. The technique of D8 method is used for the assessment of directional flow in all eight directions, i.e., North, South, East, West, Northeast, Southeast, Southwest, and Northwest. The output of the flow direction tool run with the D8 flow direction type is an integer raster whose value is

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25. Geospatial approach for reducing water stress: case study of Delhi

FIGURE 25.2 Enormous gap between water supply and water demand. Source: Estimates from Delhi Jal Board 2019 Widening gap between water demand and water supply for Delhi National Capital Territory.

TABLE 25.3 Interpretation of D8 algorithm application. Cells in first column

Cells in second column

Cells in third column

32

64

128

16

Processing cell

1

8

4

2

Modified from Flow direction (Raster Analysis), ESRI Tool reference.

between 1 and 255 (ESRI, 2022). For example, it depicts, if the direction of the steepest drop was to the left of the current processing cell, its flow direction would be coded as 16 as mentioned in Table 25.3. If a cell is lower than its eight neighbors, that cell is given the value of its lowest neighbor, and flow is defined toward this cell.

25.2.4 Applicability of stream ordering The method of stream ordering assigns a numeric order to links in a stream network which visualizes the natural hydrology streams of the study area (Gu¨lgen, 2015). This order is a method for identifying and classifying types of streams based on their numbers of tributaries (Henderson et al., 2022). The stream order increases when streams of the same order intersect. The stream network is derived from a flow accumulation raster. The derivation is based on a threshold accumulation value, which was assigned as 5000. It highlights that each cell of the drainage network has a maximum of 5000 contributing cells; hence, the fourth-order stream is perennial and flows throughout the year, whereas the ascending order pattern of the streams represents its small tributaries. Finally, the process of delineation is undertaken using the sink points. These points are very essential to understand the water flow in Delhi NCT, and are significant to craft a policy framework and water-sensitive plan for Delhi NCT.

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25.2 Research process

473

25.2.5 Process of watershed delineation In order for a site to collect run-off for infiltration, the site must be located within the flow path of run-off from upgrading areas. If water cannot flow to a site, the site can only infiltrate the water that falls directly on it. Therefore, GIS tools such as ArcGIS Spatial Analyst can be used to estimate the flow accumulation for the drainage area converging at any location within the study area. (Nalmasri & Kaluarachchi, 2004). Watershed delineation process include process of spatial overlays encompassing hydrology and water flow Fig. 25.3 along with the maps depicted in Fig. 25.4. The output of this analysis is a flow accumulation grid layer. The hydrology assessment for Delhi NCT includes overlays of various stages of layers. The first layer includes raster tiff extracted from Bhuvan. The second map showcases the Digital Elevation Model (DEM) for Delhi NCT which depicts the elevation data throughout the surface of Delhi region in meter units. Furthermore, the third map represents the filled DEM using the spatial analyst tool for hydrology. From the methodology of hydrology analysis (Fig. 25.3), it can be examined that flow accumulation points are maximum in the southwestern region of Delhi that highlights the Najafgarh area as one of the areas which require immediate attention for the policymakers, urban planners, and the authorities as evident in Fig. 25.4. Therefore, there is a need to recharge and rejuvenate these flow points for a sustainable future. The drainage basin is evident in the southwestern part of Delhi in proximity to the Najafgarh area; however, the flow of

FIGURE 25.3 Methodology for watershed delineation. The watershed analysis process includes these layers of maps steps for watershed delineation.

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FIGURE 25.4 Natural hydrology depiction for Delhi national capital territory. Spatial layers for determining natural hydrology for Delhi NCT spatial representation for hydrology layers.

aquifers has slowed down, and plenty of construction has hampered the flow of water. Therefore, there is an immediate need to implement resilient approaches (Liao et al., 2017) for integrating blue and green infrastructure in the selected area, the implementation of water sensitive plan, and amalgamation of strategies that may be deciphered as a role model for other cities, if efficiently implemented.

25.3 Spatial detection of change in blue and green areas (1993
2020) The spatial and temporal pattern as highlighted by the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI) depicts alarming challenges for Delhi, there is a need to act on, and make an implementable watersensitive plan. The spatial detection process leads to the selection of the ward, which is identified as a potential area for rejuvenating the blue spaces and interlinking them with greener areas (Du & Li, 2014). For observing the change in blue spaces (surface water bodies) and green spaces (vegetation areas), formal and Informal geospatial techniques are adopted inculcating temporal datasets extracted from USGS (i.e., US

3. Climate change, ecological impacts and resilience

25.3 Spatial detection of change in blue and green areas (1993
2020)

475

FIGURE 25.5 Spatial detection of change in blue and green areas over the past recent years. Source: Data obtained from google imagery as a base layer Emerging need to preserve green and blue spaces.

Geological Survey) data portal (Mishra & Rama Chandra Prasad, 2015). The change detection approach for green areas states that the relationship between the decline in green areas is directly proportional to the decline in blue areas (Henderson et al., 2022). Therefore, for reviving the deteriorating blue spaces in Delhi green areas as shown in Fig. 25.5 should be linked with community participation and enabling waterresilient approaches as described in the previous sections of the chapter. For spatially visualizing the change NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) spectral index has been performed in further sub-section (Bhandari & Kumar, 2015).

25.3.1 Normalized Difference Water Index assessment The Normalized Difference Water Index (NDWI) is used for the examination of open water features in satellites, with the ability of water bodies to stand out in association with soil and other minerals (McFeeters, 1997). Many researchers have undertaken the task of identifying land parcels with the NDWI, it adopts the combination of visible green and infrared, which works on the principle of detection of change in water bodies. The assessment estimates the change in the watercolor depiction over the period of years (Gao, 1996). The downside of the index is that it is sensitive to built structures, which can lead to an overestimation of water bodies. This water index depicts the value of water information within that pixel and enhances it from the surrounding pixels. For calculating the NDWI value, Band 3 (green) and Band 6 (short wave Infrared) for different time periods from 1993 to 2020, as shown in Table 25.4. The NDWI value ranges from 21 to 11 from which a positive value represents a higher water index and a negative value represents a lower water index. From NDWI analysis it can be interpreted that the eastern and southwestern regions of Delhi have lower values which indicated dense built-up areas and diminishing water body rates. The change detection of surface water bodies through NDWI assessment is done and it embarks on the drastic decline in water bodies has been noticed in 1993
2017 (Figs. 25.6
25.10) period in the southwestern region of Delhi. Furthermore, the assessment of NDWI 2020 for NCT highlights Najafgarh drain as one of the critical areas to

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TABLE 25.4 Database sources for NDWI analysis. Database

Data utilized for study Time period Source

LT05_L2SP_146040

Band 3 (Green) and Band 6 (SWIR)

1993/05/06

LT05_L2SP_146040

2013/05/12

LT05_L2SP_146040

2015/11/11

LT05_L2SP_146040

2017/06/11

Landsat 7 ETM & Landsat 8 OLI/TIRS

2020/23/10

Land Processing Distributed Active Archive Centre (USGS) https://lpdaac.usgs.gov/

Adopted from EOS data analytics.

FIGURE 25.6 NDWI assessment for the year 1993. Source: Author generated NDWI assessment 1992 Normalized Difference Water Index for NCT in 1993.

3. Climate change, ecological impacts and resilience

25.3 Spatial detection of change in blue and green areas (1993
2020)

477

FIGURE 25.7 NDWI assessment for the year 2013. Source: Author generated NDWI assessment 2013 Normalized Difference Water Index for NCT in 2013.

be undertaken for the case study, where an attempt for integrating blue-green infrastructure is made from the perspective of literature cases discussed in previous sections of the chapter.

25.3.2 Normalized Difference Vegetation Index assessment The Normalized Difference Vegetation Index deploys the multi-spectral datasets that are used to visualize vegetation, land cover typologies, open areas, and water bodies; therefore, land resources are easily attributes for information extraction (Gandhi et al., 2015). Landsat Normalized Difference Vegetation Index (NDVI) is used to assess greener areas and is useful in understanding the productivity or reduction in greener spaces. The NDVI value ranges from 21 to 11 from which a positive value represents

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FIGURE 25.8 NDWI assessment for the year 2015. Source: Author generated NDWI assessment 2015 Normalized Difference Water Index for NCT in 2015.

higher vegetation index and a negative value represents a lower vegetation index (Bhandari & Kumar, 2015). From NDVI analysis, it can be concluded that the water bodies have diminished in the regions where NDVI has negative values and sustained in the regions where with positive NDVI value. For calculating the NDWI value, Band 4 (Red) and Band 5 (Infrared) are required for different time periods from 1993 to 2020 as shown in Table 25.5. The spatial-temporal change for greener areas can be seen in maps (Figs. 25.11
25.14).

25.3.3 Overall assessment of NDWI and NDVI for NCT (1993
2020) The overall spatial-temporal assessment of NDWI and NDVI (Fig. 25.15) embarks on the immediate attention to the eastern and southwestern regions of Delhi since the analysis identifies the sharp built-up increase in these areas are evident, which has

3. Climate change, ecological impacts and resilience

25.3 Spatial detection of change in blue and green areas (1993
2020)

479

High

Low

FIGURE 25.9 NDWI assessment for year 2017. Source: Author generated NDWI assessment 2017 Normalized Difference Water Index for NCT in 2017.

disrupted the water flow, which caused many green and blue areas to dry up over the last few years. Therefore, there is an immediate need for water sensitive plan that inculcates “convergence of blue and green infrastructure”, and major task is to make it implementable. Fig. 25.15 depicts the spatial change pattern of NDWI and NDVI values.

25.3.4 Weighted overlay analysis: potential zones of recharge In the section of chapter, potential zones of recharge are identified for Delhi NCT with overlays of different spatial layers (Krushna Kadam et al., 2020). The input data for analysis are Soil mapping (Fig. 25.16) Surface temperature (Fig. 25.17), Proximity to water resources, Geological features (Fig. 25.18), and Land use map (Fig. 25.19). All these input layers (Fig. 25.20) are assigned weights with respect to their sensitivity toward water recharge from

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High

Low

FIGURE 25.10 NDWI assessment for year 2020. Source: Author generated NDWI assessment 2020 Normalized Difference Water Index for NCT in 2020.

high or low. After overlaying all these input parameters with respect to their weights assigned, the output generated is a heat map depicting the potential zones for water recharge within Delhi NCT. The importance of each input parameter toward filtration or storage of water has been defined while assigning weights. The capacity of infiltration and run-off of an area is also controlled by the underlying geological structure; therefore, soil and geomorphological maps are crucial. Delhi being the national capital has a high land surface temperature which affects the quantity of water (Bidhuri & Khan, 2020). The surface run-off can be predetermined by the land use and land cover map of the area. More concretization and more built-up areas disrupt the water flow in juxtaposition to the greener areas, which offers better flow, and can be interlinked with the blue areas such as rivers, ponds, etc. Therefore, surface water bodies are another important parameter for analyzing the natural hydrology and flow pattern within Delhi NCT more accurately. The weightage overlay analysis depicts the score values, and more weightages are given to land use, land cover, and geomorphology followed by other layers of soil,

3. Climate change, ecological impacts and resilience

25.3 Spatial detection of change in blue and green areas (1993
2020)

TABLE 25.5

481

Database sources for NDVI analysis.

Database

Data utilized for study Time period Source

LT05_L2SP_146040

Band 4 (Red) and Band 1993/05/06 5 (Infrared)

LT05_L2SP_146040

2013/05/12

LT05_L2SP_146040

2015/11/11

LT05_L2SP_146040

2017/06/11

Landsat 7 ETM & Landsat 8 OLI/TIRS

2020/23/10

Land Processing Distributed Active Archive Centre (USGS) https://lpdaac.usgs.gov/

Adopted from EOS data analytics.

FIGURE 25.11 NDVI assessment for year 1993. Source: Author generated NDVI assessment 1993 Normalized Difference Water Index for NCT in 1993.

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FIGURE 25.12 NDVI assessment for year 2013. Source: Author generated NDVI assessment 2013 Normalized Difference Water Index for NCT in 2013.

geology, proximity to water sources and surface temperature, as depicted in Fig. 25.20. The quantitative parameters, classes of criteria, and corresponding weights used for water retention mappings such as proximity to water resources (m) and surface temperature (degree Celsius) are reclassified into a range from low to moderate to high. On the other hand, qualitative parameters land cover and soil settings were classified according to local conditions. The end result of the weighted overlay analysis, shown in Fig. 25.21, depicts zones for water recharge potential from very poor to moderate and then to very good scales. Nearly 36% of the region has good recharge capacity, whereas the southern and southwestern region of Delhi NCT has very poor to poor water recharge capacity. It can be observed from the potential recharge of water map in Delhi that along the Yamuna River from the northern to the southern part of Delhi the region has potential of water recharge from very good to good.

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25.4 Micro study area selection: pilot project

483

FIGURE 25.13 NDVI assessment for year 2017. Source: Author generated NDVI assessment 2017 Normalized Difference Water Index for NCT in 2017.

25.4 Micro study area selection: pilot project The examination of Delhi NCT through various overlays of maps, NDWI and NDVI assessment, hydrology analysis, weighted overlay analysis, and water recharge zones in previous sections highlights the need for interventions for southwestern region of Delhi. Whereas from NDWI and NDVI analysis, it was concluded that the water resources have been diminished and deteriorated in the regions where the vegetation index was found low. This states that green areas are essential for enhancing the number of water resources (Depietri & McPhearson, 2017). The weighted overlay analysis identifies the locations, as shown in Fig. 25.22, which showcases the higher risk of water stress zones for delineating the micro study area. Critical ecologies such as green and blue areas were identified from the spatialtemporal analysis and the potential water recharge zones. The strategy adopted for the

3. Climate change, ecological impacts and resilience

FIGURE 25.14 NDVI assessment for year 2020. Source: Author generated NDVI assessment 2020 Normalized Difference Water Index for NCT in 2020.

FIGURE 25.15 Spatial temporal pattern of NDWI and NDVI values across the years. Source: Author-generated NDWI and NDVI spatial-temporal change. Overall change in blue and green spaces (1993
2020).

25.4 Micro study area selection: pilot project

FIGURE 25.16

485

Soil map of Delhi. Source: Author-generated soil map of Delhi NCT. Digitized soil Map of Delhi.

ward-level neighborhood area includes the social, hydrological, and ecological interventions for creating a sustainable water-resilient future for the Delhi micro area, and adoption of the same for other areas of Delhi.

25.4.1 Physiographic setting of micro study area Micro study area in ward number 27 is selected for the research which predominantly has a residential character. The impervious cover comprises of majorly the building footprint and

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25. Geospatial approach for reducing water stress: case study of Delhi

FIGURE 25.17 Surface temperature map of Delhi. Source: Author-generated surface temperature map in GIS software Map depicts temperature variation.

the concrete roads (Bidhuri & Khan, 2020). Urbanization has resulted in a more built-up area that has transformed the neighborhood areas into a concrete jungle, thus, adding pressure to the surface and groundwater resources. Fig. 25.23 shows the impervious cover in the form of building footprints and roads. The previous cover is highlighted in the form of green areas, parks, and vegetated areas. Najafgarh drain flows toward the northern portion of the identified micro study area, and a drain flows toward the southern portion. It can be interpreted that the impervious cover is extensive as compared to the previous cover (Everard & McInnes, 2013). Due to the poor stormwater management at different

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25.4 Micro study area selection: pilot project

487

FIGURE 25.18

Geological map of Delhi NCT. Source: Author-generated geological map for Delhi NCT Map depicts geological features of Delhi NCT.

scales, from micro to macro, the city’s major drains and rivers are facing threat to their value. Development of the residential areas along the Najafgarh drain is laying only gray infrastructure without leaving any space for green infrastructure.

25.4.2 Strategies: decentralized cleansing mechanism The strategy adopted for implementation is a decentralized cleansing mechanism for quantity and quality enhancement of water content in the NCT region. Fig. 25.24 shows

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FIGURE 25.19 Land use map of Delhi NCT. Source: Adopted from Master plan of Delhi 2021 Detailed Land use map of the National Capital Territory.

the strategy illustration view for conceptualization of water sensitive plan with water retention measures (Zelenakova & Harstad, 2017). The proposed primary and secondary street grid layout is derived from the slope analysis, existing street networks, and its proximity to the detention area. From a perspective of stormwater infrastructure, the cost of laying street steep slopes is high; therefore, run-off velocity can be achieved by aligning these parallel to the contours. The proposed network aims to bridge the gaps between parcels with vegetation and protect ecosystems using riparian buffers adjacent to the streams. From the lens of the neighborhood water-sensitive plan, the adoption of blue and green infrastructure overlays the built-up layers and other amenities. In this chapter, an attempt is made to create a water-sensitive plan (Fig. 25.25), as a conclusion to all the prior analyses.

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FIGURE 25.20 Thematic parameters for weighted overlay analysis with assigned weights. Source: Author generated overlays for thematic parameters with assigned weightage. Weightage assigned on the basis of vulnerability to water sources.

The proposed map comprises primary routes and secondary routes, which act as green and blue corridors. Green infrastructure is marked on the map, which includes parks and detention areas. The social infrastructure intervention includes the identification of suitable locations for neighborhood blocks and proposed water reservoirs.

25.4.3 Water detention area microanalysis First, the step is to identify the detention or retention areas in the block units of a neighborhood, which will be collecting the stormwater from buildings and streets to them.

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FIGURE 25.21 Potential zones for water recharge within the National Capital Territory. Source: Authorgenerated depicts water zones of recharge. Water recharge potential zones.

For this slope or contour analysis, flow accumulation raster was examined, which identifies the suitable detention areas for any block unit at the neighborhood level. Furthermore, the detention zones are identified (Fig. 25.26) along with the analysis of the vacant lands, urban parks, and green areas. From this, the land pockets that fits best into the location of the detention pond, on the basis of size, location, and proximity to proposed primary and secondary green corridors were selected. Table 25.6 depicts the corridors interlinking the

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FIGURE 25.22 Selection process for micro study area. Source: Site selection process of author Assessment of micro area for site selection.

green and blue infrastructure in the form of primary and secondary routes. The green infrastructure network is shown in the map surrounding the neighborhood area with detention ponds.

25.5 Implementable neighborhood water sensitive plan This section examines the possibilities of making the plan work on the ground. The discourses pertaining to integrating green and blue are discussed in the previous sections; however, the execution of these approaches may require a framework at a neighborhood level. Hence, from Table 25.6, it can be seen how green components like primary and secondary routes of green corridors and urban parks are interlinked with detention areas drains and lastly, the water reservoir. The intent behind integration is to form a decentralized

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FIGURE 25.23 Micro study area base map. Source: Micro area selection process by authors. Micro area assessment of Najafgarh water issues.

FIGURE 25.24 Stages for the decentralized cleansing mechanism. Approach of integrating blue and green through decentralized cleansing mechanism. Approach for Water cleansing from source to sink.

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FIGURE 25.25 Identification of green corridors along street network. Source: Author generated "Water sensitive Plan" for selected micro area. Proposed water-sensitive plan for micro area.

cleansing mechanism for water flow. The proposed primary and secondary street grid layout is derived from the slope analysis, existing street networks, and the proximate detention area overlay.

25.5.1 Interventions: social, hydrological, and ecological The research incorporates three facets of interventions to tackle water stress in Delhi. These facets include social intervention, hydrological intervention, and ecological intervention to foster the current implementation mechanism inculcating efficient and effective mechanisms for coping with the proliferation of water stress in Delhi. The rapid extension of the city and the increase in population creates a rapid demand for public spaces and recreational activities. Linking the site with the rest of the city through major functions and giving a sense of identity for the city dwellers as a social intervention; thus, linking

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FIGURE 25.26 Identification of potential detention areas. Source: Author generated potential detention areas for Delhi NCT. Potential detention areas with flow accumulation. TABLE 25.6 Scale matrix for integrating blue-green infrastructure. S. No. Scale

Element

Function

1.

Building scale

Separate outlet for each building

Water run-off from hard surfaces and streets will From rooftop to be collected along the proposed green corridor. ground streets

2.

Corridor scale

Permeable pavements These corridors will collect and channel the bios wales rain water through permeable pavements, bios gardens wales, and proposed rain gardens

Primary and secondary green corridors

3.

Block scale

Pocket parks, school The channelization of water at various levels grounds, barren land, of detention ponds provides natural cleaning wetlands and serves as purifying mechanism

Detention and retention ponds

4.

Neighborhood scale

Neighborhood scale

Transferring and restoring the water from the canal and forming a reservoir along its wetland thereby serving the needs of residents for secondary purposes.

Canal or drain

5.

Formation of local level water storage

Formation of local level water storage

Replenish groundwater level and serve as a source for potential water source

Water reservoir

Adaption from literature related to linking blue-green infrastructure.

Flow

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the high-density community through educational and recreational functions and enabling open space for the inhabitants. The hydrological intervention focuses on a series of retention and detention.

25.5.2 Integrating stakeholders in implementation process The successful plan implementation requires a water-sensitive plan with the encouragement of more community participation (Redmond, 2019). Integration of all gated community’s participation and awareness regarding the importance of green infrastructure to be integrated with blue infrastructure at the neighborhood level with the cooperation of various stakeholders in the overall process of water-resilient framework. The stakeholders include the Residents Welfare Association (RWA), Delhi Jal Board (DJB), Delhi Municipal Council (DMC), Delhi Development Authority (DDA), Central Public Works Department, Forest Department, and other officials.

25.6 Conclusion The outcome of the research chapter is a water-sensitive spatial strategy and a few recommendations to support the policy interventions for Delhi to impart solutions for water stress in the future. The research outcome reflects on the micro-neighborhood area of Delhi where the Urban or Abadi areas are under severe threat of water shortage. The planned neighborhood areas within the identified water issue zones need to be restored through the process of redevelopment, revised building plans and layouts, and re-densification of older areas. The entire services plan tries to incorporate these elements of existing infrastructure. Therefore, for future neighborhoods to be proposed in Delhi pertaining to the Master plan of Delhi 2041 needs to have provisions for redesign and reorientation of services plans at the neighborhood level. In this chapter, the adoption of spatial analyst tools and techniques identifies the emerging need for micro zonation strategies for areas under tremendous water stress. The process of micro zonation includes spatial detection of change in blue and green areas from 1993 to 2020, NDWI and NDVI assessment for NCT from 1993 to 2020, and weighted overlay analysis for potential zones of recharge. There are many reasons identified for the water stress in Delhi by experts, organizations and agencies, etc. The groundwater table is depleting year by year and the major surface water source that is Yamuna River is overpolluted. This establishes a need of adopting an urban water-resilient approach for coping with water stress. The research undertakes the principles of the blue-green Infrastructure concept and its spatial adaptation strategies that have been implemented around the world. After studying the various components and benefits of the concept, it has been concluded that cities can be developed as resilient to water stress (shortage or excess of water). The green corridor helps to retain water, replenish water resources and reduce the risk of surface run-off. The review of various case studies where green infrastructure is used as a tool for enhancing the water quality and quantity of the area has enabled us to analyze how to develop and implement spatial strategies, plans, and policies for any city to cope with water stress. The implementation framework for coping with water issues of

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water severity, water consumption, and water overutilization requires the joined efforts of different organizations as discussed in the implementation process furthermore, strong interdepartmental and multiple stakeholders coordination with efficient and effective technology and innovation in Delhi is required as need of the hour. This is a time to rethink and embark on the augmentation process of new avenues of water by prioritizing the micro zonation of Delhi zones in terms of water stress severity. Furthermore, Delhi Development Authority can also incorporate these measures in the form of neighborhood standards and ecological corridors, and incentivize this approach for greater community participation. This approach can be further replicated in other neighborhood-level areas in Delhi, mainly the southern and southwestern regions so that the neighborhood areas can become self-sufficient in terms of water availability, and more sustainable and low-impact development (LID) infrastructure can lead Delhi toward a resilient future.

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103. Available from https://doi.org/ 10.1007/s12594-020-1390-7. http://www.springerlink.com/content/121451/. India: Springer. Boken, V. K. (2016). Balanced urban development: Options and strategies for liveable cities. Groundwater Crisis of a Mega City: A Case Study of New Delhi, India. Springer, Cham. Available from https://doi.org/10.1007/978-3319-28112-4_14. Chaturvedi, M. K., & Bassin, J. K. (2009). Assessing the water quality index of water treatment plant and bore wells. Environmental Monitoring and Assessment. Available from https://doi.org/10.1007/s10661-009-0848-2. Conant Jr, B. (2004). Delineating and quantifying ground water discharge zones using streambed temperatures. Ground water, 42(2), 243
250. Available from https://doi.org/10.1111/j.1745-6584.2004.tb02671.x. Denchak, M. (2019). Green infrastructure: How to manage water in a sustainable way. NRDC, Unpublished content. https://www.nrdc.org/stories/green-infrastructure-how-manage-water-sustainable-way 1661495799978. Depietri, Y., & McPhearson, T. (2017). Integrating the grey, green, and blue in cities: Nature-based solutions for climate change adaptation and risk reduction. Springer Science and Business Media LLC. Available from https://doi. org/10.1007/978-3-319-56091-5_6. Dhyani, S., Basu, M., Santhanam, H., & Dasgupta, R. (2022). Blue-green infrastructure across Asian countries improving urban resilience and sustainability. Springer Singapore. Available from https://doi.org/10.1007/978-981-16-7128-9. Du, Z., & Li, W. (2014). Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sensing Letters, 5(7), 672
681. Available from https://doi.org/10.1080/2150704X.2014.960606. ESRI. (2022). Flow direction: Raster analysis. Unpublished content. 1669048000936. Everard, M., & McInnes, R. (2013). Systemic solutions for multi-benefit water and environmental management. Science of the Total Environment, 461-462, 170
175. Available from https://doi.org/10.1016/j.scitotenv.2013.05.010. Everett, G., Adekola, O., & Lamond, J. (2021). Developing a blue-green infrastructure (BGI) community engagement framework template. Urban Design International 14684519. Available from https://doi.org/10.1057/s41289-02100167-5. http://www.palgrave-journals.com/udi/index.html. United Kingdom: Palgrave Macmillan. Gandhi, M., Parthiban, B. S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and gis
A case study of Vellore District. Procedia Computer Science. Gao, B.-C. (1996). NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Elsevier Science. Ghosh, P. (2021). Sustainable climate action and water management. Springer, Singapore. Available from https://doi. org/10.1007/978-981-15-8237-0_11.

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Gu¨lgen, F. (2015). A stream ordering approach based on network analysis operations. Geocarto International. Henderson, H., Bush, J., & Kozak, D. (2022). Mainstreaming blue green infrastructure in cities: Barriers, blind spots, and facilitators. Springer Science and Business Media LLC. Available from https://doi.org/10.1007/978-3-03051812-7_270-1. Hofste, R.W., Reig, P., & Schleifer, L. (2019). Countries, home to one-quarter of the world’s population, face extremely high water stress. p. 17. https://www.wri.org/insights/17-countries-home-one-quarter-worlds-population-faceextremely-high-water-stress. Khuller, A. (2021). Compendium on best practices in water management 2.0, Terragreen. Water crisis in India: The world’s largest groundwater user. Unpublished content. https://www.teriin.org/article/water-crisis-india-worlds-largest-groundwater-user 1661597637432. Kushagra, D. (2022). Groundwater rises in some areas, but Delhi loses overall. Unpublished content. https://timesofindia.indiatimes.com/city/delhi/groundwater-rises-in-some-areas-but-city-loses-overall/articleshow/89858952. cms 1661600638541. Krushna Kadam, A., N. Umrikar, B., & Sankhua, R. N. (2020). Assessment of recharge potential zones for groundwater development and management using geospatial and MCDA technologies in semiarid region of Western India. r Springer Nature Switzerland AG 2020. Kumar, B., Charan Patra, K., & Lakshmi, V. (2017). Error in digital network and basin area delineation using d8 method: A case study in a sub-basin of the Ganga. Journal of the Geological Society of India Volume. Available from https://doi.org/10.1007/s12594-017-0559-1. Liao, K.-H., Deng, S., & Tan, P. Y. (2017). Blue-green infrastructure: New frontier for sustainable urban stormwater management. Springer Science and Business Media LLC. Available from https://doi.org/10.1007/978981-10-4113-6_10. Lisa, R. (2019). ’Hefty’ cost of revitalization project up for vote. Millbury-Sutton Chronicle. Unpublished content. https://www.millburysutton.com/story/news/local/2021/04/01/town-meeting-decide-funding-armory-village-revitalization/4811082001/ 1661595934681. McFeeters, S. K. (1997). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425
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9. Available from https://doi.org/ 10.1155/2015/903465. Nalmasri, M., & Kaluarachchi, J. J (2004). Assessment and management of long-term nitrate pollution of ground water in agriculture-dominated watersheds. Journal of Hydrology, 295(1-4), 225
245. Available from https:// doi.org/10.1016/j.jhydrol.2004.03.013. Available from . Centre for Public Impact: BCG foundation. van Beek, L. P., Wada, Y., van Kempen, C., Reckman, J. W., Vasak, S., & Bierkens, M. F. (2010). A worldwide view of groundwater depletion. Geophysical Research Letters, 37(20). Available from https://doi.org/10.1029/ 2010GL044571. Zelenakova, M., & Harstad, M. (2017). Urban water retention measures. Procedia Engineering, 190, 419
426. Available from https://doi.org/10.1016/j.proeng.2017.05.358.

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26 Statistical analyses of the dependence of tea yield on the land and atmospheric covariates in the Dooars region of West Bengal Piyashee Mallik and Tuhin Ghosh School of Oceanographic Studies, Jadavpur University, Kolkata, West Bengal, India

26.1 Introduction Global climate change is one of the most pressing issues that call for urgent attention especially when the focus is on food security in a world reeling under population pressure. Increasing land temperatures, modifications in the arrival and departure of monsoonal rains, and increased frequency of extreme events are some of the consequences of climate change that agriculture-based developing nations like India are facing for the past few decades and this situation is even worse in case of the regional economies where the livelihood of the common people is entirely dependent upon agricultural crops. As per IPCC 2021 AR6, temperatures are projected to exceed by 1.5 C
2 C during the 21st century along with an increase in the frequency and magnitude of extreme events such as heat extremes, agricultural drought, and flood vis-a`-vis heavy precipitation. The consequences associated with it are projected to reduce agricultural production by 40% in nations like India by the 2080s. This situation harbors a clear indication that agricultural production in developing countries like India is on the verge of devastation which needs urgent attention (Mall et al., 2006; Solomon et al., 2007). Tea is one such kind of plantation crop that is not only significant for foreign income generation but also in sustaining the lives of the population associated with its cultivation and export. Having said that, the cultivation of tea is enormously dependent on climatic conditions and its day-to-day weather variations. The growth of tea bushes is dependent on an average temperature in the range of 18 C
30 C (Carr & Stephens, 1992), evenly

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© 2023 Elsevier Inc. All rights reserved.

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26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

distributed annual rainfall between 2000 and 5000 mm (Jayasinghe et al., 2018), soil temperature between 18 C and 25 C (Carr & Stephens, 1992) and maximum solar radiation up to 600 W/m2 (Willson, 1999). Since tea is a climate-sensitive crop, even slightest but persistent deviations in the required meteorological variables can have widespread repercussions on the production and growth physiology of tea plants. Indian tea as a famous beverage in the world tea market makes this nation secure the second position in world tea production in which the state of West Bengal and the Terai
Dooars region in this state contributes to about 30% and 25% of the national tea yield, respectively (FAO, 2016; Madhumitha, 2020; Sarkar, 2018). The significance of the Dooars region in West Bengal lies in the fact that not only this region is the highest producer of tea in West Bengal (with a yield of 177.85 million kg) but also that this region is close to the Darjeeling Himalayas where the tea production has been badly affected due to global warminginduced climate change (FAO, 2016; Patra et al., 2013). The response of tea production to climate change in India has been analyzed by a handful of research works. Sen et al. (1966) found that increased temperatures from January to March and precipitation up to 18 cm were beneficial to tea production in the Assam Valley. Bhagat et al. (2010) reviewed the changes in important climatic and environmental parameters and their impact on tea cultivation. A study by Patra et al. (2013) concluded that while an increased average maximum temperature was associated with a reduction in tea yield, relative humidity, and rainfall had positive bearings on tea production. Dutta (2014) analyzed the future of North East Indian tea production and predicted that by 2050 tea production will show the highest increase during the monsoon season, modification in the management practices is the need of the hour. A study by Duncan et al. (2016) in Assam indicated that increased monthly average temperature and precipitation intensity were detrimental to tea production in Assam. In a previous study (Mallik & Ghosh, 2022), we analyzed the effects of temperature and precipitation on tea yield in the West Bengal Dooars region where an increase in monthly average maximum and minimum temperatures have been found to be beneficial for tea yield, though with subsequent increase beyond a certain limit this positive impact declines. As for the effect of rainfall, this study showed that excessive rainfall especially during the monsoons hampered tea yield. In another study (Mallik & Ghosh, 2021), we estimated the effect of surface net solar radiation and soil temperature on tea yield in the same region and found that surface net solar radiation had a negative relationship with tea production whereas soil temperature at different levels showed a contrasting relationship with tea yield (while soil temperature at layer 1, 2 and 3 had a positive association with tea production, the case was opposite while considering soil temperature at layer 4). Therefore, it is clear that previous studies have analyzed the effect of climatic variables on tea production in India considering the climatic variables from an atomistic approach, i.e., considering the variables separately. None of the studies focused on the relationship between climatic factors and tea yield from a holistic approach. Moreover, while assessing the impact of climate on tea yield, there is no such extensive and comprehensive study for India where the nonlinear effect of climatic parameters on tea production has been taken into account. Therefore, our present study delves into considering not only the effects of climatic variables on tea yield from an integrated and all-encompassing approach but also digs into the possible presence of nonlinear relationships between the climatic variables

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and tea production in the Dooars region. First, to evaluate the holistic effect of land and atmospheric covariates (including average maximum and minimum temperature, precipitation, surface-net solar radiation, and soil temperature at different soil layers) on monthly tea yield, we estimated multiple panel regression models (linear) where the number of predictor variables in the model was sequentially increased and eventually all covariates were considered as predictor variables. Using the model with the best fit, we further derived the relationship between seasonal tea yield and the seasonal variability of these covariates in a holistic setting which considered all the covariates as the predictor variables. Next, we assessed the presence of nonlinear effects for each of the land and atmospheric covariates by estimating polynomial regression models with varying degrees and determined the optimal order of polynomials for each covariate. Finally, based on the optimal polynomial order determined for each covariate, we estimated multiple multivariate polynomial regression models to determine the nonlinear effects of the considered covariates on monthly tea yield. Seasonal polynomial regression models were also estimated to evaluate how the nonlinear effects varied seasonally.

26.2 Materials and methods 26.2.1 Study area The Dooars region of West Bengal encompasses the tea-producing districts of Jalpaiguri, Alipurduar, and Coochbehar districts, which has the district of Darjeeling to its north, Bangladesh to its north while its eastern portion is bounded by the state of Assam. The maximum and minimum temperatures in this region hover around 33 C and 10 C respectively. The mean annual rainfall in this region is 3653 mm most of which is received during the wet months of May to September. This region has three principal seasons, namely Summer, Monsoon, and Winter (Post-monsoon). Tea bushes find their ideal climatic and environmental conditions in the northern part of this region and therefore most of the tea production comes from this part of the region (Techno-economic survey of dooars tea industry, 1995; Tea garden atlas, 2016). However, our present study focuses only on Jalpaiguri and Alipurduar districts (Fig. 26.1).

26.2.2 Data compilation In this study, we statistically analyzed the effects of different atmospheric variables including maximum and minimum temperature, surface-net solar radiation, and precipitation as well as different land covariates including soil temperature variables on tea production using panel data. The data sources for the different variables are described below. Data on tea production and meteorological variables (namely average maximum and minimum temperature, rainfall, surface net solar radiation, and soil temperature) have been analyzed in this study in order to estimate the combined effects of these variables on tea production in the Dooars region. A total of 44 tea gardens (through random sampling) in Jalpaiguri and Alipurduar districts have been taken into consideration wherein tea gardens having their own meteorological observatories have been selected. The monthly tea

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FIGURE 26.1 Map of the study area (Dooars region) consisting of Jalpaiguri and Alipurduar districts of West Bengal. The green pentagons denote the locations of the tea gardens from which the data were collected.

production per unit area (unit-kg/ha) for each garden was obtained for 10 years, i.e., from 2009 to 2018. Data on monthly average maximum and minimum temperatures ( C) and monthly rainfall (cm) for the aforementioned period have been collected from the meteorological observatories of the selected tea gardens. The historical data on these climatic variables from 1970 onwards have been obtained from the Indian Meteorological Department (IMD). For evaluating the effects of surface net solar radiation and soil temperature, data for the same have been derived from the ERA-Interim dataset (European Centre for Medium-Range Weather Forecasts (ECMWF) re-nalysis), a global atmospheric 4-dimensional variational re-analysis generated by ECMWF (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim) (accessed on 13.04.2022) (Dee et al., 2011). For surface net solar radiation, the total sky quantity (SSR) has been used which considers the presence and effect of clouds. The data for this is accumulated over a time period of 24 hours (unit MJ/m2/day). The monthly average of SSR has been calculated for the period 2009
19 at a spatial resolution of 0.125 degrees. For soil temperature, ECMWF follows a four-layer representation of soil where the surface is at 0cm: layer

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1: 0
7 cm, layer 2: 7
28 cm, layer 3: 28
100 cm, and layer 4: 100
289 cm corresponding to which the soil temperature variables are designated as soil temperature level 1 (stl1), soil temperature level 2 (stl2), soil temperature level 3 (stl3), and soil temperature level 4 (stl4), respectively. In our study, we included soil temperature at level 1 and level 4 (monthly mean for each garden) as the land covariates for the period 2009
18 at a spatial resolution of 0.125 degrees. Soil temperatures at levels 2 and 3 were not considered as they have been found to be highly correlated to soil temperature at level 1 in our previous study (Mallik & Ghosh, 2021).

26.2.3 Linear regression analyses for estimating the holistic effects of land and atmospheric variables on tea yield For estimating the effects of land and atmospheric covariates on monthly tea yield, we used a production function approach while analyzing the panel dataset. First, we analyzed linear panel regression models where the log-production function was the response variable. The independent variables included the atmospheric variables—monthly average maximum temperature, monthly average minimum temperature, total monthly precipitation, and total-sky surface-net solar radiation. Following previous studies (Mallik & Ghosh, 2021), we have included soil temperature layer 1 (stl1) and soil temperature layer 4 (stl4) as the predictor (land) variables. As previous studies (Mallik & Ghosh, 2021, 2022) showed the presence of garden-specific fixed effects, we considered fixed-effect panel regression models that incorporate additional terms corresponding to garden-specific fixed effects. Such fixed-effect terms account for the garden-specific biases due to the tea cultivar and management expertize used in each garden. Moreover, we have minimized the possibility of clonal switching and changes in management practices by constraining our panel dataset to 10 years. The fixed-effect panel regression model is given by   max min ln Ygmy 5 γ 1 αg 1 δm 1 δy 1 β 1 Tgmy 1 β 2 Tgmy 1 β 3 Pgmy 1 β 4 Rgmy 1 β 5 St 1gmy 1 β 6 St 4gmy 1 εgmy : (26.1) In Eq. (26.1), for garden g, Ygmy denotes the tea yield for month m of year y. The response variable is log-transformed following previous studies (Gunathilaka et al., 2017; Mallik & Ghosh, 2021). The variable αg denotes the garden-specific fixed effect. The atmospheric variables that are used as predictor variables in Eq. (26.1) include: Tmax —maximum temperature, Tmin — minimum temperature, P—monthly total rainfall, and R—surface-net solar radiation. The regression model also includes two predictor soil temperature variables S1t and S4t denoting the soil temperature at layer 1 and layer 4, respectively. Corresponding to the six predictor variables, six regression coefficients (β i jiAf1; 2; 3; 4; 5; 6g) are used in Eq. (26.1) and these variables quantify the percentage shift in tea yield with a unit change in the corresponding predictor variable. The models also contain two more fixed-effect terms δm and δy for accounting for month-specific and year-specific fixed effects, respectively. γ and εgmy denote the intercept and error terms, respectively. To account for the fact that tea is harvested sequentially throughout the year, for each year three distinct seasons were considered, namely, summer (March to May), monsoon (June to September), and post-monsoon/winter (October to December).

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26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

Following earlier studies in the Dooars region (Mallik & Ghosh, 2021, 2022), the months of January and February were excluded due to low yields in all the tea gardens under consideration. We estimated two different types of regression models. The pooled model was estimated by pooling all data points together to estimate the overall effects of land and atmospheric covariates on tea yield. In addition, to estimate the seasonal effects of the covariates, three season-specific models (i.e., summer, monsoon, and winter) were also estimated by considering the data points from the months specific to the three seasons, respectively. For both the pooled and seasonal models, we evaluated multiple regression models where the independent variables were progressively added (in the order T max ; Tmin ; P; R; S1t ; S4t ). All regression models were estimated using the ‘lm’ function implemented in R v3.6.3. five-fold cross-validation as described in the study by Mallik and Ghosh (2021) was performed using the ‘caret’ package (Kuhn, 2015) (implemented in R v3.6.3) for validating the regression models.

26.2.4 Determining the order of polynomial for estimating the nonlinear effects of land and atmospheric variables After evaluating the linear regression models, we further wanted to evaluate if the land and atmospheric variables have any nonlinear effect on tea yield. To estimate the presence of nonlinearity, we used polynomial regression models with one independent variable at a time. The polynomial regression model is given by: K X   ln Ygmy 5 γ 1 αg 1 δm 1 δy 1 β i V i 1 εgmy :

(26.2)

i51

In Eq. (26.2), Vis an independent variable, VAfTmax ; T min ; P; R; S1t ; S4t g. K denotes the maximum order of the polynomial, β i denotes the regression coefficient for the ith order term of the independent variable V. The rest of the variables are the same as in Eq. (26.1). As the value of K increases, the complexity of the model also increases. To determine the optimal order of polynomial for each variable V, we estimated the polynomial regression model in Eq. (26.1) by varying K in the range of 1
5. For each model corresponding to a value of K, we evaluated it based on the mean squared error (MSE) in predicting the response variable for test data in a 5-fold cross-validation experiment. The complete dataset was divided into five equal-sized groups, out of which, four groups were used for training the model and the other group was used as a test set for evaluating the model. After fitting the model on the training set, it was used for predicting the response variable for the test set and the MSE of prediction was calculated. The training and testing phases were repeated five times using each of the five groups as the test set and finally, the average of MSE across the five test sets was computed. For a variable V, the value of K (degree of polynomial) for which the average MSE across the test sets was the smallest was chosen as the optimal degree of polynomial for that variable for evaluating its nonlinear effects. The polynomial regression models were estimated using ‘lm’ function with the ‘poly ()’ function used on the respective independent variables. The ‘predict’ function was used for evaluating the trained model on the test set and all functions were implemented in R v3.6.3.

3. Climate change, ecological impacts and resilience

505

26.3 Results and discussion

26.2.5 Multivariate polynomial regression analyses for estimating the nonlinear effects of land and atmospheric variables on tea yield For estimating the nonlinear effects of land and atmospheric covariates on tea yield, we evaluated the following polynomial regression model. 



ln Ygmy 5 γ 1 αg 1 δm 1 δy 1

KX Tmax

max β Ti

KP  i KX  i X T min  i min max min Tgmy 1 β Ti Tgmy 1 β Pi Pgmy

i51

1

KR X i51

β Ri



i

Rgmy 1

K

i51

1

S t X

i51

S1 βi t



St 1gmy

i

K

1

4

S t X

i51

 i S4 β i t St 4gmy 1 εgmy :

(26.3)

i51

In Eq. (26.3), KTmax , KTmin , KP , KR , KS1t , KS4t denote the maximum order of the polynomial max min determined using models in Eq. (26.2) for Tmax ; T min ; P; R; S1t ; S4t respectively. β Ti , β Ti , β Pi , S1

S4

β Ri , β i t , and β i t denote the regression coefficients corresponding to different order polynomial terms for T max ; T min ; P; R; S1t ; S4t respectively. The rest of the variables are the same as in Eq. (26.1). For Eq. (26.3) also, we evaluated the pooled and seasonal models as described earlier. For both the pooled and seasonal models, we evaluated multiple polynomial regression models where the independent variables were progressively added (in the order Tmax ; T min ; P; R; S1t ; S4t ). The polynomial regression models were estimated using ‘lm’ function with the ‘poly ()’ function used on the respective independent variables (implemented in R v3.6.3). After evaluating each polynomial regression model, it was compared against the corresponding linear regression model using the ‘anova ()’ function in R v3.6.3.

26.3 Results and discussion 26.3.1 Holistic effects of land and atmospheric covariates on tea production First, we evaluated the relationship between the climatic variables and tea production through the estimation of linear regression models that holistically accounted for the effects of all land and atmospheric covariates. Second, we considered the pooled model that pooled all data points from March to December for the 10-year period. For the pooled model estimation, we sequentially added the independent variables in our regression models resulting in total of six models wherein model 1 was the simplest as it considered the effect of only maximum temperature, and model 2 considered the effects of maximum and minimum temperature together. We observed that the addition of more predictor variables in the model improved the data fit. Thus, model 3 took into account the effects of maximum temperature, minimum temperature, and rainfall whereas model 4 considered the combined effect of maximum and minimum temperature, rainfall, and surface-net solar radiation. Model 5 explored the effects of maximum and minimum temperature, rainfall, surface-net solar radiation, and soil temperature at level 1 and model 6 involved the coalescence of all the predictor variables. Since model 6 had the best fit for equation (1), we considered model 6 for our analyses.

3. Climate change, ecological impacts and resilience

506

26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

Table 26.1 shows the results of the linear regression analyses which considered pooling all the data of the total growing season (March to December). As per model 6, maximum temperature showed a negative relationship (though not statistically significant) with tea yield while minimum temperature had a positive and statistically significant relationship with the same. These findings are coherent with previous studies which showed that tea plant growth is inhibited by low temperatures (Fordham, 1970) and a minimum air temperature of 13 C
14 C is required for shoot growth (Carr, 1972). A minimum air temperature below 13 C was found to cause foliage damage in tea (Thomas, 1965). Rainfall also was positively correlated with tea yield (although not statistically significant). As for the relationship between SSR and tea yield, the regression coefficient was negative and statistically significant and this is coherent with our previous findings (Mallik & Ghosh, 2021). Similar to our previous study (Mallik & Ghosh, 2021), in our holistic analysis of pooled data, soil temperature at layer 1 and layer 4 showed a contrasting relationship with tea yield, i.e., while soil temperature at layer 1 was positively and statistically significantly associated with tea production, soil temperature at layer 4 had a negative and statistically significant relationship with tea yield. We progressively added the independent variables in our pooled regression models resulting in a total of 6 models wherein model 1 was the simplest as it considered the effect of only maximum temperature, and model 2 considered the effect of maximum and minimum temperature together. Model 3 incorporated the effects of maximum temperature, minimum temperature, and rainfall whereas model 4 considered the combined effects of maximum and minimum temperature, rainfall, and surface net solar radiation. Model 5 explored the effects of maximum and minimum temperature, rainfall, surface net solar radiation, and soil temperature at level 1. Finally, model 6 was considered for the analyses following Eq. (26.1) as it involved the coalescence of all of the climatic and soil variables (maximum and minimum temperature, rainfall, surface net solar radiation, soil temperature at layer 1, and soil temperature at layer 4). For analyzing the seasonal effects of the land and atmospheric variables through linear regression first we considered the pre-monsoon season (March to May) (Table 26.2). As per model 6, the regression coefficient for SSR was negative and statistically significant indicating its negative effect on tea yield. Also, soil temperature at layer 4 had a positive and statistically significant relationship with tea production as is evident from the corresponding regression coefficient. The negative effect of SSR on pre-monsoon tea yield is also coherent with our previous study (Mallik & Ghosh, 2021) and this negative effect could be because of the elevation of near-surface air temperature caused by higher surface-net radiation (Schwingshackl et al., 2018). During summer, a combination of higher irradiance and air temperature above 30 C can result in the reduction of shoot extension rates and net photosynthesis (Hadfield, 1968) which can ultimately lead to reduced dry matter production. Moreover, the long duration of sunshine hours during the summer season can also cause photoinhibition in tea plants (Karunaratne et al., 2003). The positive relationship between soil temperature at layer 4 on pre-monsoon tea yield is also coherent with previous studies (Othieno & Ahn, 1980) that observed that an increase in soil temperature up to 18 C had a positive effect on tea yield in Kenya. For the monsoon season (June to September), only SSR and the soil temperature variables had a statistically significant effect on tea yield. The regression coefficient for SSR

3. Climate change, ecological impacts and resilience

TABLE 26.1 Regression coefficients estimated through multivariate fixed-effect linear regression models to analyze the holistic effects of land and atmospheric covariates on the log monthly tea production (pooled data). Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Maximum Temperature

2 0.002823 (0.002062)

2 0.003337 (0.002070)

2 0.0029854 (0.0020839)

2 0.0023551 (0.0021006)

2 2.313e
03 (2.099e
03)

2 0.0024559 (0.0020960)

Minimum Temperature




0.006195** (0.002396)

0.0062191** (0.0023962)

0.0061898** (0.0023950)

5.779e
03* (2.399e
03)

0.0050006* (0.0024027)

Rainfall







0.0002943 (0.0002014)

0.0001294 (0.0002135)

1.203e
04 (2.134e
04)

0.0001063 (0.0002131)

SSR










2 0.0078048* (0.0033772)

2 7.815e
03* (3.375e
03)

2 0.0103749** (0.0034315)

Soil Temp. (Layer 1)













2.109e
02** (8.127e
03)

0.0415652*** (0.0096423)

Soil Temp. (Layer 4)
















2 0.0687232*** (0.0174851)

Adjusted R2

0.8209

0.8211

0.8212

0.8214

0.8216

0.8222

P-value

, 2.2e
16

, 2.2e
16

, 2.2e
16

, 2.2e
16

, 2.2e
16

, 2.2e
16

Standard errors are in parentheses. Notes: *** P , .001, ** P , .01, * P , .05, ^ P , .1

TABLE 26.2 Regression coefficients estimated through multivariate linear regression models to analyze the seasonal effect of land and atmospheric covariates on the log monthly tea production. Variables

Pre-monsoon Model 6

Monsoon Model 6

Post-monsoon Model 6

Maximum Temperature

5.286e
03 (5.217e
03)

2 0.0011497 (0.0027589)

0.0035211 (0.0040792)

Minimum Temperature

2 1.927e
04 (5.319e
03)

0.0043111 (0.0032655)

0.0059420 (0.0054067)

Rainfall

2 4.612e
05 (1.053e
03)

2 0.0001079 (0.0002011)


0.0010827 (0.0009283)

SSR

2 1.016e
01*** (1.010e
02)

2 0.0069671 (0.0036321)

0.0279462*** (0.0076307)

Soil Temp.(Layer 1)

2 9.951e
03 (1.891e
02)

0.1396672*** (0.0187328)

0.1478253*** (0.0164293)

Soil Temp.(Layer 4) Adjusted R P-value

2

^

^

2.199e
01*** (3.622e-02)

0.0146374 (0.0075116)

0.2606480*** (0.0347151)

0.6721

0.7575

0.8256

, 2.2e
16

, 2.2e
16

, 2.2e
16

Standard errors are in parentheses. Notes: *** P , .001, ** P , .01, * P , .05, ^ P , .1

26.3 Results and discussion

509

was negative and statistically significant, however, the significance level was not so high (P ,.1). Both soil temperature at layer 1 (P ,.05) and layer 4 (P ,.1) showed positive and statistically significant relationships with tea production. The positive effect of the soil temperature at layer 1 is coherent with our previous study (Mallik & Ghosh, 2021) and the range of this variable was appropriate for tea shoot growth (Carr & Stephens, 1992). Finally for the post-monsoon season (October to December), as per model 6, SSR and soil temperature variables were found to have a statistically significant effect on tea yield. SSR had a positive effect on post-monsoon tea production. Since tea requires a minimum solar radiation and winter months receive the lowest solar radiation ( Jayasinghe & Kumar, 2021), an increase in solar radiation should facilitate tea growth during the winter season. Moreover, longer sunshine hours during the winter season can also facilitate avoiding dormancy (Carr, 1972), and surface-net solar radiation was highly correlated (0.82) with sunshine hours. Thus our holistic analysis was able to uncover the positive effect of surface-net solar radiation on post-monsoon tea yield which was not observed in our previous study (Mallik & Ghosh, 2021). Both soil temperatures at layer 1 and layer 4 showed positive and strongly statistically significant relationships with tea production as revealed by the corresponding regression coefficients. While the positive effect of the soil temperature at layer 1 on post-monsoon tea yield was also estimated in our previous study (Mallik & Ghosh, 2021), our holistic analysis revealed the positive effect of soil temperature at layer 4 as well on post-monsoon tea yield.

26.3.2 Order of polynomial for estimating the nonlinear effects of land and atmospheric variables Since varying levels of nonlinear effects for a predictor variable can be explored by varying the order of polynomial of the predictor variable in the regression model, we varied the order of the polynomial, from first to fifth order for each land and atmospheric covariate in the regression model in Eq. (26.2). Polynomials with orders higher than 5 were not used as those can lead to overfitting (Camporeale, 2019). When selecting the optimal order of polynomial based on the average MSE over the test sets, for maximum temperature optimal order of polynomial was 2 resulting in the linear and quadratic term for maximum temperature as the predictor variables. For minimum temperature, the optimal order of the polynomial was 1 indicating the absence of any nonlinear effect. For both rainfall and surface-net solar radiation, the optimal order of polynomial was 3 resulting in the linear, quadratic, and cubic terms of these covariates as the predictor variables. For the land variables, the optimal order of polynomial was 3 for soil temperature at layer 1 and 2 for soil temperature at layer 4. After finding the optimal polynomial order for each variable, the corresponding polynomial regression model was compared against the linear model (K 5 1 in Eq. (26.2)) using ANOVA and for each covariate (with the optimal polynomial order .1) we considered, the polynomial model was significantly better (P-value ,.05) in capturing the data as compared to the linear model.

26.3.3 Nonlinear effects of land and atmospheric covariates on tea production Similar to linear regression analyses, we performed multivariate nonlinear regression analyses through the progressive addition of predictor variables to estimate the nonlinear

3. Climate change, ecological impacts and resilience

510

26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

effects of land and atmospheric covariates on tea production. This resulted in a total of six models where model 6 was the most comprehensive model as it consisted of terms related to all the land and atmospheric covariates as the predictor variables. The sequential introduction of predictor variables resulted in the increase of model fit for Eq. (26.3). The analysis of data pooled for the months of March to December (Table 26.3) showed that for monthly average maximum temperature, the regression coefficient for the linear term was positive and statistically significant and the regression coefficient for the quadratic term was negative and statistically significant. The plot of the marginal effect of maximum temperature on tea yield (Fig. 26.2A) shows a parabolic relationship where the positive effect decreased above a specific temperature and thereafter increase in maximum temperature resulted in the decrease of tea yield. This finding is coherent with our previous study (Mallik & Ghosh, 2022) as well as other studies (Duncan et al., 2016) which observed a decrease in tea yield for maximum temperature above a certain threshold. Since the monthly average minimum temperature only had a linear predictor term, its relationship with the monthly tea yield remained the same albeit with a lowering of statistical significance (.05 ,P-value ,.1) as compared to our linear regression analysis (Table 26.1). However, its positive effect was statistically significant for models 1
5. Monthly total rainfall did not have a statistically significant effect on tea yield as per model 6. According to model 3 (which included maximum and minimum temperature and rainfall as predictors), the linear term of rainfall had a positive and statistically significant effect on tea yield and the quadratic term had a negative and statistically significant effect on tea yield. The relationship between rainfall terms and tea yield as estimated by model 3 is also consistent with our previous study (Mallik & Ghosh, 2022). However, right after the inclusion of surface-net solar radiation as the predictor variable (model 4 onwards), rainfall terms did not have a statistically significant effect on tea yield. According to model 6, all three terms of SSR had a statistically significant effect on tea yield. The regression coefficients for the linear, quadratic, and cubic terms of SSR were positive, negative, and positive, respectively. Fig. 26.2D shows the marginal effect of SSR on tea yield considering the observed range of monthly SSR values. All three terms of soil temperature layer 1 had a statistically significant effect on tea yield. While the linear and cubic terms had a positive effect on tea yield, the quadratic term had a negative effect on tea yield. The marginal effect plot (Fig. 26.2E) for soil temperature layer 1 shows that tea yield increases with an increase in soil temperature, between 17 C and 22 C the rate of increase in tea yield decreases and then increases again as soil temperature increases above 22 C. For soil temperature layer 4, the regression coefficient for the linear term was negative and statistically significant, and that for the quadratic term was positive and statistically significant. The marginal effect plot shows that with an increase in soil temperature layer 4, tea yield steadily decreases, above 20 C, and the rate of decrease in tea yield decreased. Similar to the pooled analyses, for seasonal analyses also, we sequentially added predictor variables, and this sequential addition improved model fit. For all three seasons, model 6 (including maximum and minimum temperature, rainfall, SSR, and soil temperature variables as predictors) had the best fit and we considered that for our parameter estimates (Table 26.4). For the pre-monsoon season, maximum temperature, rainfall, and soil temperature at layer 1 and layer 4 had statistically significant effects on tea yield. The

3. Climate change, ecological impacts and resilience

TABLE 26.3 Regression coefficients estimated through multivariate polynomial regression models to analyze the nonlinear effects of land and atmospheric covariates on the log monthly tea production (pooled data). Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Maximum Temperature

0.0365860** (0.0132683)

0.0367718** (0.0132590)

4.035e
02** (1.330e
02)

3.919e
02** (1.326e
02) 3.747e
02** (1.321e
02) 4.074e
02** (1.316e
02)

Maximum Temperature2

0.0006123** (0.0002036)

2 0.0006233** (0.0002035)

2 6.685e
04** (2.041e
04)

2 6.485e
04** (2.037e
04)

Minimum Temperature




0.0063453** (0.0023946)

Rainfall







Rainfall2




Rainfall3

2 6.135e
04** (2.029e
04)

2 6.564e
04** (2.019e
04)

5.737e
03* (2.402e
03) 5.572e
03* (2.394e
03)

5.541e
03* (2.389e
03)

4.297e
03^ (2.385e
03)

2.674e
03** (9.185e
04)

1.615e
03^ (9.445e
04)

1.690e
03^ (9.424e
04)

1.285e
03 (9.391e
04)




2 2.283e
05* (1.083e
05)

2 1.121e
05 (1.112e
05)

2 1.185e
05 (1.108e
05)

2 8.698e
06 (1.103e
05)







5.579e
08 (3.821e
08)

2.396e
08 (3.891e
08)

2.667e
08 (3.875e
08)

1.972e
08 (3.854e
08)

SSR










2.429e
01*** (5.807e
02)

2.252e
01*** (5.825e
02)

2.017e
01*** (5.806e
02)

SSR2










2 2.076e
02*** (5.352e
03)

2 1.910e
02*** (5.372e
03)

2 1.674e
02** (5.357e
03)

SSR3










5.361e
04*** (1.581e
04)

4.899e
04** (1.588e
04) 4.100e
04** (1.586e
04)

Soil Temp.(Layer 1)













8.348e
01*** (1.403e
01)

8.236e
01*** (1.409e
01)

Soil Temp.(Layer 1)2













2 4.344e
02*** (7.601e
03)

2 3.986e
02*** (7.656e
03)

Soil Temp.(Layer 1)3













7.491e
04*** (1.359e
04)

6.575e
04*** (1.379e
04)

Soil Temp.(Layer 4)

















1.834e
01*** (2.621e
02)

Soil Temp.(Layer 4)2
















3.752e
03*** (7.076e
04)

Adjusted R2

0.8212

0.8215

0.8218

0.823

0.8247

0.8266

P-value

, 2.2e
16

, 2.2e
16

, 2.2e
16

, 2.2e
16

, 2.2e
16

, 2.2e
16

Standard errors are in parentheses. Notes: *** P , .001, ** P , .001, * P , .001, ^p , .01. We performed multivariate nonlinear regression analyses through the progressive addition of predictor variables to estimate the nonlinear effects of land and atmospheric covariates on tea production. This resulted in a total of six models where model 1 consisted of only terms related to maximum temperature as the predictor variables and the most comprehensive model (model 6) consisted of terms related to all the land and atmospheric covariates as the predictor variables. The sequential introduction of predictor variables resulted in the increase of model fit for Eq. (26.3), with model 6 having the highest fit and thus our preferred model for the parameter estimates.

512

26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

FIGURE 26.2 Marginal effect of Average Maximum Temperature on tea yield (logarithmic scale) based on the multivariate polynomial regression model. Marginal effects of Average Minimum Temperature on tea yield (logarithmic scale) based on the multivariate polynomial regression model. Marginal effect of Rainfall on tea yield (logarithmic scale) based on the multivariate polynomial regression model. Marginal effects of Surface net solar radiation (SSR) on tea yield (logarithmic scale) based on the multivariate polynomial regression model. Marginal effect of Soil Temperature at Layer 1 on tea yield (logarithmic scale) based on the multivariate polynomial regression model. The marginal effect of Soil Temperature at Layer 4 on tea yield (logarithmic scale) based on the multivariate polynomial regression model. The Y-axis shows the predicted log yield, while the X-axis on each graph spans the range of values for the above-mentioned variables observed in the pooled dataset. The shaded regions represent a 95% confidence interval.

3. Climate change, ecological impacts and resilience

TABLE 26.4 Regression coefficients were estimated through multivariate polynomial regression models to analyze the nonlinear effect of land and atmospheric covariates on the log monthly tea production (seasonal data). Variables

Pre-monsoon Model 6

Monsoon Model 6

Post-monsoon Model 6

9.127e
02* (4.474e
02)

2.018e
02 (2.510e
02)

7.347e
02** (2.317e
02)

Maximum Temperature

2 1.405e
03* (6.921e
04)

2 2.990e
04 (3.563e
04)

2 1.171e
03** (3.846e
04)

Minimum Temperature

2 2.624e
03 (5.331e
03)

3.992e
03 (3.210e
03)

5.796e
03 (5.363e
03)

Rainfall

Maximum Temperature 2

2 1.269e
02** (4.274e
03)

2.332e
03 (1.231e
03)

2 1.452e
02*** (3.562e 2 03)

Rainfall

2

3.474e
04** (1.234e
04)

2 2.460e
05* (1.232e
05)

3.512e
04*** (9.577e
05)

Rainfall

3

2 2.547e
06* (1.065e
06)

7.556e 2 08* (3.787e
08)

2 1.707e
06** (5.472e
07)

^

2 7.388e
01 (8.228e
01)

2.997e
01** (9.573e
02)

2.029e
01 (4.389e
01)

2

3.433e
02 (6.010e
02)

2 3.052e
02** (1.106e
02)

2 1.882e
02 (4.332e
02)

3

2 5.348e
04 (1.445e
03)

9.624e
04* (4.139e
04)

6.595e
04 (1.394e
03)

7.625e
01* (3.521e
01)

2 7.878e 1 00** (2.486e 1 00)

1.007e 1 00* (4.524e
01)

2 3.599e
02 (2.057e
02)

3.423e
01** (1.137e
01)

2 4.259e
02^ (2.322e
02)

Soil Temp.(Layer 1)3

5.307e
04 (4.013e
04)

2 4.814e
03** (1.731e
03)

7.340e
04^ (3.981e
04)

Soil Temp. (Layer 4)

2 3.343e
01* (1.679e
01)

6.958e
02 (8.850e
02)

2.703e
01 (4.165e
01)

1.824e
02*** (5.439e
03)

2 1.412e
03 (2.222e
03)

2 2.793e
03 (9.976e
03)

0.6824

0.7668

0.8312

, 2.2e
16

, 2.2e
16

, 2.2e
16

SSR SSR SSR

Soil Temp.(Layer 1) Soil Temp.(Layer 1)

Soil Temp.(Layer 4) Adjusted R P-value

2

2

2

^

Standard errors are in parentheses. Notes: *** P , .001, ** P , .001, * P , .001, ^ P , .01

514

26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

FIGURE 26.3 Marginal effects of the predictor variables having a statistically significant effect on premonsoon tea production as estimated by the seasonal multivariate polynomial regression model. (A) Marginal effect of Average Maximum Temperature on tea yield (logarithmic scale). (B) Marginal effect of Rainfall on tea yield (logarithmic scale). (C) Marginal effect of Soil Temperature at Layer 1 on tea yield (logarithmic scale). (D) Marginal effect of Soil Temperature at Layer 4 on tea yield (logarithmic scale). The Y-axis shows the predicted log yield, while the X-axis on each graph spans the range of values for the above-mentioned variables observed in the pre-monsoon dataset. The shaded regions represent a 95% confidence interval.

regression coefficients for the linear and quadratic terms of monthly average maximum temperature were positive and negative respectively indicating that an increase in maximum temperature helped tea yield initially but a further increase resulted in a pre-monsoon tea yield decline. During pre-monsoon, all three terms of rainfall had statistically significant effects on tea yield. The linear and cubic terms had negative effects whereas the quadratic term had a positive effect on tea yield. For soil temperature at layer 1, only the linear term had a positive statistically significant effect on tea yield, while the quadratic term had a negative effect, its statistical significance was low (.05 , P-value , .1). The relationship between the soil temperature layer 4 and tea yield recapitulated that for the pooled analysis, with the linear term having a negative effect and the quadratic term having a positive effect on tea yield. Fig. 26.3 shows the marginal effects of the above variables with statistically significant effects on pre-monsoon tea yield considering the observed ranges for the same variables during the pre-monsoon season.

3. Climate change, ecological impacts and resilience

26.3 Results and discussion

515

FIGURE 26.4 Marginal effects of the predictor variables having a statistically significant effect on monsoon tea production as estimated by the seasonal multivariate polynomial regression model. (A) Marginal effect of Rainfall on tea yield (logarithmic scale). (B) Marginal effect of SSR on tea yield (logarithmic scale). (C) Marginal effect of Soil Temperature at Layer 1 on tea yield (logarithmic scale). The Y-axis shows the predicted log yield, while the X-axis on each graph spans the range of values for the above-mentioned variables observed in the monsoon dataset. The shaded regions represent a 95% confidence interval.

For the monsoon season, rainfall, SSR, and soil temperature layer 1 predictor terms had a statistically significant effect on tea yield. For rainfall, the linear term did not have any statistically significant effect on monsoon tea yield. The regression coefficients for the quadratic and cubic terms of rainfall were negative and positive respectively, indicating a complex relationship between rainfall and monsoon tea yield. For SSR, all three terms had statistically significant effects on tea yield. While the linear and cubic terms had a positive effect on monsoon tea yield, the quadratic term had a negative effect on tea yield. Similarly, for the soil temperature at layer 1, all three terms had statistically significant effects on tea yield. The regression coefficients for the linear and cubic terms were negative indicating a detrimental effect on tea yield. In contrast, the regression coefficient for the quadratic term of soil temperature layer 1 was positive indicating a positive effect on tea yield. Fig. 26.4 shows the marginal effects of the above variables with statistically significant effects on monsoon tea yield considering the observed ranges for the same variables during the monsoon season.

3. Climate change, ecological impacts and resilience

516

26. Statistical analyses of the dependence of tea yield on the land and atmospheric covariates

FIGURE 26.5 Marginal effects of the predictor variables having statistically significant effect on postmonsoon tea production as estimated by the seasonal multivariate polynomial regression model. (A) Marginal effect of Average Maximum Temperature on tea yield (logarithmic scale). (B) Marginal effect of Rainfall on tea yield (logarithmic scale). (C) Marginal effect of Soil Temperature at Layer 1 on tea yield (logarithmic scale).The Y-axis shows the predicted log yield, while the X-axis on each graph spans the range of values for the above-mentioned variables observed in the post-monsoon dataset. The shaded regions represent 95% confidence interval.

For the post-monsoon season, average maximum temperature, rainfall, and soil temperature layer 1 term had statistically significant effects on tea yield. For maximum temperature terms, the relationships again recapitulated that of the pooled analysis, with the linear term having a positive and the quadratic term having a negative effect on tea yield. For rainfall, the linear and cubic terms had a negative effect on tea yield but the quadratic term had a positive effect on tea yield. For soil temperature layer 1, only the linear term had a positive statistically significant effect on post-monsoon tea yield. The quadratic and cubic terms had negative and positive effects respectively but these effects had less significance (.05 ,P-value ,.1). Thus, our polynomial regression models uncovered complex nonlinear relationships between the land and atmospheric covariates and tea yield. Fig. 26.5 shows the marginal effects of the above variables with statistically significant effects on post-monsoon tea yield considering the observed ranges for the same variables during the post-monsoon season.

3. Climate change, ecological impacts and resilience

References

517

26.4 Conclusion In this study, for the first time, we evaluated the relationship between land and atmospheric variables and Dooars region tea production in a holistic setting where we considered four climatic variables (maximum and minimum temperature, rainfall, and surface-net solar radiation) and two soil variables (soil temperature at layer 1 and 4) as the predictors for the monthly tea yield. Our multivariate linear regression analyses suggest that over the total growing season, minimum temperature and soil temperature at layer 1 had a positive effect on tea yield whereas surface-net solar radiation and soil temperature at layer 4 had a negative effect on tea yield. Moreover, our seasonal analyses further uncovered that soil temperature at layer 4 had a positive effect on pre-monsoon and post-monsoon tea yield and surface-net solar radiation had a positive effect on post-monsoon tea yield in the Dooars region. Our analyses also revealed that except for minimum temperature, all other variables had nonlinear effects on tea yield, rainfall, surface-net solar radiation and soil temperature at layer 1 displaying the presence of effect for both quadratic and cubic terms apart from the linear one, whereas for maximum temperature and soil temperature at layer 4, the optimal polynomial order was 2 indicating the presence of nonlinear effect due to quadratic term. Finally, we performed multivariate polynomial regression analyses to explore the holistic effects of the linear and nonlinear terms of the predictor variables on monthly and seasonal tea yield and these analyses uncovered the complex nonlinear relationships between these covariates and tea yield during the total growing season as well as individual seasons. Our analyses suggest that for the total growing season, surface-net solar radiation had a positive effect on tea yield up to B9 MJ/m2/day but beyond that value, it imparted a negative effect on tea yield the rate of which varied polynomially. Similarly, soil temperature at layer 1 had a positive effect and layer 4 had a negative effect both having complex nonlinear variations. While more analyses possibly using more high-resolution data are required for disentangling the nonlinear effects in more detail, our findings will be useful for the tea industry in developing economical strategies for confronting the effects of climatic variability on tea yield in the future.

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800. Available from https://doi.org/10.1175/1520-0450(1966) 005 , 0789:tiocfo . 2.0.co;2. Solomon, S., Manning, M., Marquis, M., & others, Qin D. (2007). Climate change 2007—The physical science basis: Working group I contribution to the fourth assessment report of the IPCC. Tea Garden Atlas. (2016). Tea Board and ISRO. https://bhuvan-app1.nrsc.gov.in/tea/teaPortal/PDF/Jalpaiguri.pdf. Techno-economic Survey of Dooars Tea Industry. (1995). Tea Board of India. Thomas, E. (1965). Tea. Tea. Willson, K. (1999). Coffee, cocoa and tea. CAB International.

3. Climate change, ecological impacts and resilience

Index Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A ACCCRN City Resilience Strategies Model, 91 Accretion process, 101
102 Adaptability vulnerability index, 409
410 coping ability of respondents, 410t PCA, 410t Adaptation, 67
68, 79, 111, 435 to climate change, 227f of climate change on local peoples in Probolinggo, 109
111 through green economy, 233 measures, 95, 330 option, 114 policies, 227 in socio-ecological systems, 57
58 of women’s groups to climate change, 229 Adaptive capability, 58
59 Adaptive capacity, 15, 57
58, 67
68, 193
194 Adhatoda vasica. See Basak (Adhatoda vasica) Afghanistan, South Asian countries, 332
333 Agricultural/Agriculture, 119, 125, 154, 186, 228, 265, 305
306, 329, 451 climate change adaptation measures on agriculture in South Asia, 337
338 consumption, 362 drought, 178 function, 422 household members’ access to agricultural capacity building, 165f land for non-agricultural purposes in non-urban municipalities, 426f production, 275
276, 499 productivity, 158
161, 197
199, 330 sector, 130
131, 154, 339 Agroecosystem, 330
331 change in rainfall pattern impacts on major crop production in South Asia, 330t climate change adaptation measures on agriculture in South Asia, 337
338 climate change impacts, adaptation and mitigation measures in South Asian countries, 332
337 Afghanistan, 332
333 Bangladesh, 333

Bhutan, 333
334 India, 334
335 Maldives, 335 Nepal, 335
336 Pakistan, 336 Sri Lanka, 337 climate change mitigation measures on agroecosystem in South Asia, 338
339 enabling institutional and policy support, 339 impacts of increased temperature on major crop production in South Asia, 330t method, 331
332 study area, 331f Agroforestry, 335 AHP. See Analytic hierarchy process (AHP) AI. See Artificial intelligence (AI) Air conditioners, 78 Air pollution, 82 Air temperature, 94 AIRS (instrument), 300 Alliance of Small Island States (AOSIS), 213 Alluvial soils, 243, 436 Analytic hierarchy process (AHP), 435 Animal husbandry, 451 Annual Exceedance Probability, 30 Anthropogenic pressure, 433
434 Aqua satellites, 299
300 Aquaculture sector, 143 Aquifers, 294, 434 ArcGIS 9.3 software, 32 Area Vulnerability Index (AVI), 394, 402
405. See also Individual vulnerability index details of various indexes, 404t indices, 404f Artificial Groundwater Recharge, 297
298 Artificial intelligence (AI), 369 ASTER (instrument), 300 Atmospheric corrections, 98 Atmospheric covariates holistic effects of atmospheric covariates on tea production, holistic effects of, 505
509 nonlinear effects of atmospheric covariates on tea production, 509
516

519

520 Atmospheric science, 4 Atmospheric variables determining order of polynomial for estimating nonlinear effects of, 504 estimating holistic effects on tea yield, 503
504 estimating nonlinear effects on tea yield, 505 order of polynomial for estimating nonlinear effects of, 509 Attappady Valley of Mannarkad Taluk, 243 AVI. See Area Vulnerability Index (AVI) Ayurvedic Treatise, The, 241
242 Ayutthaya Province, 313 district level, 312
313, 312f exposure, susceptibility, and coping capacity of districts in Ayutthaya, 313 SVI-ICA by district, Ayutthaya province, 313 exposure, susceptibility, and coping capacity of districts in, 313

B Badan Perencanaan dan Pembangunan Nasional (BAPPENAS), 215 Bahal lands, 273 Bangladesh, 333 discharge pattern of river Teesta in study area, 32
35 flood intensity in study area, 36 flood risk assessment in Teesta flood-prone area, 42
48 impacts of flood in study area, 41 indigenous coping strategies, 48
51 inundation area in different flooding years, 39
40 flood-prone area, 40f yearly inundation area of study area, 40f materials and methods, 29
32 objectives of study, 29, 29f recurrence trend of flood in study area, 36
39 Bangladesh FFWC. See Bangladesh Flood Forecasting and Warning Center (Bangladesh FFWC) Bangladesh Flood Forecasting and Warning Center (Bangladesh FFWC), 29 Bangladesh Water Development Board (BWDB), 29, 33 Bankimnagar village, 59
60 Banking services, 406 Bantul Regency, 226 BAPPENAS. See Badan Perencanaan dan Pembangunan Nasional (BAPPENAS) Bartlett’s test, 197, 197t Basak (Adhatoda vasica), 50 Baseline assessment, 155, 158 Battala River, 69 BBC video, 91

Index

Below Poverty Line (BPL), 69 BEST index, 107f Bhutan, South Asian countries, 333
334 BibExcel software, 6
7 Bibliomatrix, 6
7 Bibliometric analysis, 4, 9, 15 analytical techniques for, 5t methodology for, 4
7, 6f Bibliometric data, 4
5 Bibliometric database, 7 Biblomterixdata packages, 6
7 Big data, 368 analytical techniques, 371t Bilok Petung, 154, 159, 163
164 Biodiversity, 15, 329, 462 Black cotton soils, 243 BLC. See Boat License Certificate (BLC) Blue-green areas, 391
392 Blue-green resilient approaches, 467
469 Blue
green infrastructure, 470 Boat License Certificate (BLC), 70 Bonding social capital, 13 Borda rule method, 62
67 BPL. See Below Poverty Line (BPL) Brackish water aquaculture, 143 Brahmaputra River, 28, 121
122 basin, 119, 123
124 renewable energy projects impact on, 127 system, 121
122 Bridging social capital, 13 Bundelkhand, 178
179, 182, 186 average annual rainfall of Bundelkhand region, 184t, 185f region of Madhya Pradesh, 176
177, 183f SPI in districts of Bundelkhand of Madhya Pradesh, 187f Business area, 94 capital, 142
143 management, 323 BWDB. See Bangladesh Water Development Board (BWDB)

C CA. See Correspondence analysis (CA) Carbon dioxide, 228 Carbon emissions, 78, 232 Carbon Management Geographic Information System, 300
301 Carbon sequestration, 338
339 CARE (organization), 67 Cartography, 422 Casual Loop Diagrams (CLD), 269, 281

Index

CCAFS. See Climate Change, Agriculture and Food Security (CCAFS) CDBs. See Community Development Blocks (CDBs) CDM. See Clean Development Mechanism (CDM) Cemara (local NGO), 87
88 Central Plateau, 295 CERES-Rice model, 241
242 Chaotic urbanization processes, 426
427 Chargheri, 70 Chota Nagpur plateau, 267 of Eastern India, 265
266 fringe of Eastern India, 282
283 Purulia, 265
266 track ‘in-lock’ condition, 266 CIDs. See Climate-induced disasters (CIDs) CiteSpace software, 6
7 Cities Climate Change Resilience Network program, 91 City resilience frameworks, 85
86 policy, 358 CLD. See Casual Loop Diagrams (CLD) Clean Development Mechanism (CDM), 217 Climate adaptation, 452
453 Climate change, 3, 15, 57
58, 61, 70, 75
76, 85, 93
94, 119, 121
123, 135
136, 138
139, 144
146, 153
154, 158
159, 176
178, 212
214, 225
226, 231, 235
236, 241, 266
267, 291
292, 330
331, 346, 361
362, 378, 382, 391
392, 419, 429, 433
434, 499
500 adaptation, 79, 114, 227, 331
332 measures in South Asian countries, 332
337 measures on agriculture in South Asia, 337
338 adaptation measures on agriculture in South Asia, 337
338 adaptation of women’s groups to, 229 buffer capacity, 352
354 capacity for learning, 354
358 financial capital, 353 human capital, 352 natural capital, 353 physical capital, 354 self organization, 354 social capital, 353 case studies, 231
232 change in rainfall pattern, impacts of, 330t city government’s response to climate change and social-ecological crisis, 358 climate change and social-ecological crisis on Island of Java, 346
347 climate change-induced disasters, 378
379, 382
386 community readiness model, 384
385 community readiness strategy for Indian Sundarban, 386t

521 data collection, 380 history of cyclone, 382
383 limitations of study, 386 material and methodology, 380
382 objectives, 380 perceived impact of cyclone on Sundarban community, 384t rationale of study, 379
380 recommendation, 387 revisiting theories, conventions, and agreements, 385 socioeconomic background and vulnerability to disasters, 385
386 specific impact and coping techniques, 384 study area, 380
382, 381f Sundarban as climate hotspot, 382 Sundarban ecosystem and cyclone, 383 thematic narratives, 383
384 climate change-induced hydro-meteorological disasters, 391
392 climate-change scenarios, 293 climate-change-induced water management, 124 co-occurrence network, 15
17, 16f concept of social resilience, 348
351 buffer capacity, 349
350 resilience concept, 349f social self organization, 350
351 country collaboration map, 19
20, 20f country wise scientific production, 10, 11f data collection, 7
8 enabling institutional and policy support, 339 engagement, 220 framework to adaptation to, 227f frequent words, 15 global impacts of, 78 globally cited documents, 10
15 cited documents according to number of citations, 11t different adaptation strategies in different social dimensions, 13t different dimensions of community resilience, 12t eminent authors with total number of articles, 10f measures for effective social capital, 14t ground water depletion and impact of climate system on groundwater, 292
295 role of geospatial technology in climate change assessment, 299
301 role of geospatial technology in groundwater depletion assessment, 295
298 hazards, 120, 457 impact, 136 assessment, 97
98

522

Index

Climate change (Continued) on coastal urban areas in Indonesia, 77
78 measures in South Asian countries, 332
337 on women as vulnerable group, 228
229 impacts, adaptation and mitigation measures in South Asian countries, 332
337 Afghanistan, 332
333 Bangladesh, 333 Bhutan, 333
334 India, 334
335 Maldives, 335 Nepal, 335
336 Pakistan, 336 Sri Lanka, 337 indicators, 97 adaptation and resilient of climate change on local peoples in Probolinggo, 109
111 affected land cover by flood inundation, 110t climate change and tidal flood in Indonesia, 94 climate change impacts in Probolinggo, 106
109 climate change indicator in Probolinggo, 105
106 coastal profile, 98
99 detailed dataset and instruments used in study, 97t FGD, 99 flood modeling, 99 foundation in new house construction, 112f geological formation of Probolinggo coastal plain, 99
100 innovative salt production using plastic roofs, 113f land cover and land use of Probolinggo coastal area, 100
101 materials and methods, 96
99 research framework to assess climate change indicator, 98f seawall by local communities, 112f shoreline changes, 98 shoreline dynamic of coast of Probolinggo, 101
104 tidal flood on Northern Coast of Java, 94
95 initiatives for developing smart and resilient citybased city management, 347
348 institutional arrangements and diplomacy towards, 214
219 issues, 209
210 method, 331
332 methodology, 351
352 methodology for bibliometric analysis, 4
7 mitigation, 331
332 measures in South Asian countries, 332
337 measures on agroecosystem in South Asia, 338
339

mitigation and adaptation strategies in coastal city of Tambak Lorok Kampong, 83
86 mitigation measures on agroecosystem in South Asia, 338
339 model of resilience in facing vulnerability of coastal communities in Indonesia, 145
148 negotiations, 209 problems of community adaptation and response toward, 75
76 recommendations, 358
359 relevant authors, 10 relevant sources, 9 research findings, 352
358 resilience, 193
194 on riparian communities, 130t sensitization level study, 196t source dynamics, 9 state agencies and engagement in, 219
220 study area, 331f thematic evolution, 17
19, 17f factorial analysis, 19, 19f thematic map generated with author’s keyword, 18f vulnerability and community resilience, 345
346 vulnerability hotspots, 194 word growth, 15, 16f Climate Change, Agriculture and Food Security (CCAFS), 334 Climate emergency, 4 Climate evacuee, 176
177 Climate events, 3, 175
176 Climate factors, 97
98 Climate hotspot, Sundarban as, 382 Climate migrants, 176
177 Climate mitigation, 227 Climate process, 175
176 Climate profile of study area, 245 Climate protection, 419 in spatial policy instruments, 427
429 agricultural land for non-agricultural purposes in non-urban municipalities, 426f justification of study, 420
421 land use in local plans in non-urban municipalities, 427f limitations of study, 422 material and methods, 422 planning coverage of municipalities at end of 2020, 425f recommendations, 429 results, 423
427, 424t Climate refugees, 176
177 Climate resilience implementation of LAST tool, SLSI, ECVI, 67

Index

livelihood status of economically marginalized people, 69
70 management strategy, 70
71 materials and methods, 59
67 methodology, 61
67 economic and social vulnerability index, 62
67 LAST matrix, 61
62 SLSI, 62 planning, 392 relationship between adaptive capacity and adaptation in light of SLSI and LAST matrix and SLSI and ECVI, 67
68 study area, 59
61, 59f, 60f Climate smart agriculture, 331
332 Climate Smart Village approach (CSV approach), 334 Climate strain, 176 Climate system on groundwater, impact of, 292
295, 293f Climate variability, 13
14 Climate vulnerability hotspots, 193
194 risks, 226 Climate-induced changes of Teesta basin and impact on geopolitics, 126
129 conflicts due to climate-induced changes on Teesta river, 127 renewable energy projects impact on Brahmaputra basin, 127 river basin and community, 126 situational analysis, 128
129 findings, 128t situational analysis of river basin due to climate change, 126
127 study area, 127 Climate-induced disasters (CIDs), 379, 391
392 Climate-related disaster, 112
114 Climate-related exposures, 177
178 Climate-smart agriculture (CSA), 336, 339 Climatic hindrance, 271
273 Climatic resilience, dimension of organic farming for, 252
253 Climatology, 4 Clonal switching, 503 Cluster of variables, 250 Co-occurrence network, 6, 15
17 Co-word analysis, 6 Coarse physiography, 270
271 Coast accretion process, 101
102 Coastal communities, 137 and challenges of vulnerability due to climate change in Indonesia, 142
145

523

evidence of coastal communities vulnerability in Pangkep Regency, East Lombok Regency, & Rembang Regency, Indonesia, 139
142, 140t model of resilience in facing vulnerability of, 145
148 participation, 84
85 participation model, 85f resilience, 138 Coastal disasters, 441 Coastal ecosystems, 452, 457 estimated assets value of coastal vulnerability area protected by, 462t Gujarat coastal exposure index with and without, 456f processes, 93
94 Coastal Embankment Improvement Project, 333 Coastal environment, 108
109, 135, 433
434 Coastal erosion, 93
94 Coastal flooding, 94
95, 113, 378
379 Coastal geomorphology, 438 Coastal natural resource management system, 138 Coastal profile, 98
99 Coastal slope, 438 Coastal systems, susceptibility of, 457 Coastal topography, 441 village’s coastal geomorphology vulnerability status in North Jakarta, 441t village’s slope vulnerability status in North Jakarta, 442t Coastal vegetation, 335, 452 Coastal vulnerability (CV), 433
434 database, 437 estimated assets value of coastal vulnerability area protected by coastal ecosystems, 462t model, 452
453 modeling technique, 460
461 physical drivers of, 440
443, 440f coastal topography, 441 shoreline dynamics, 442
443 SLR, tides, and wave, 442 vertical land motion, 443 village’s coastal geomorphology vulnerability status in North Jakarta, 441t village’s slope vulnerability status in North Jakarta, 442t status in Jakarta, 443
444, 443f length of each village’s CVI status in North Jakarta, 444t Coastal Vulnerability Index (CVI), 435, 437, 439, 443
444. See also Individual Vulnerability Index (IVI); Area vulnerability index calculation, 439
440, 439t detailed dataset to, 437t

524

Index

Coastal water ecosystems, 137
138 Coastal zone management plan for Gujarat (GCZMP), 464 Coastal zone(s), 15, 77
78, 136
137 Coastline angle, 106
108 Coefficient of variability (CV), 36 Collaborative networks, 120 Collaborative spatial decision-making (CSDM), 369
370 Communal integrity, 3
4 Community, 3
4, 15
16, 20
21, 75
76, 99
100, 121
122, 136, 145, 193
194, 205 adaptation, 146 problems of community adaptation and response toward climate change, 75
76 capacity, 307 Community Readiness Model, 384
385 community readiness strategy for Indian Sundarban, 386t community self-help organizations, 353 community-based disaster risk reduction, 77
78 community-based flood risk management, 18 community-based participation, 79 development of poor populations, 78
79 disaster resilience, 310 involvement, 88
90 changing population pressure, 201f climate change sensitization level study, 196t communalities values of Manebhanjyang and Yaksum, 198t Himalayan base stations of popular trek routes, 195f Landuse and Landcover between Manebhanjyang and Yaksum, 202t Manebhanjyang
Sandakaphu
Phalut trekking corridor, 204t materials and methods, 196
197 NDVI status for Manebhanjyang and Yaksum, 200f rotated component matrix of Manebhanjyang, 198t rotated component matrix of Yaksum, 199t study area, 194
195 Yaksum
Goeche La trekking corridor, 205t leaders, 311
312 perception toward tidal floods, 86
91 adaptation and strategies adopted by, 86
91 resilience, 7, 13, 18, 90
91, 146, 345
346 agricultural productivity, 158
161 different dimensions of, 12t irrigation by target village, 160f response, 7, 15, 17 vulnerability, 146, 175
176 coastal areas, 136
137

Community Development Blocks (CDBs), 380
381 Composite index, 259, 260f Computer science, 4 Construction models, 111 Content analysis, 212 Continuously Updated Digital Elevation Model (CUDEM), 457
458 Coping techniques, specific impact and, 384 perceived impact of cyclone on Sundarban community, 384t Coral reefs, 452
453, 462 Corporate Social Responsibility (CSR), 137 Correction technique, 98 Correspondence analysis (CA), 6 Covid-19, 136
137, 154, 236, 312 Crop, 330t, 365 calendar, 271
273 decision maker on crops management, 166f diversification, 337 diversity, 161 impacts of increased temperature on crop production in South Asia, 330t productivity, 339 rotation, 162 Cropping systems, 336 Crystalline basement, 267 CSA. See Climate-smart agriculture (CSA) CSDM. See Collaborative spatial decision-making (CSDM) CSR. See Corporate Social Responsibility (CSR) CSV approach. See Climate Smart Village approach (CSV approach) CUDEM. See Continuously Updated Digital Elevation Model (CUDEM) Cultivation methods, 337 and marketing of Navara, 251
252 Curcuma longa. See Turmeric (Curcuma longa) CV. See Coastal vulnerability (CV); Coefficient of variability (CV) CVI. See Coastal Vulnerability Index (CVI) Cyclone(s), 333, 383, 451 community readiness model, 384
385, 386t cyclone-induced multi-hazard risk assessment, 378 history of, 382
383 limitations of study, 386 perceived impact of cyclone on Sundarban community, 384t recommendation, 387 revisiting theories, conventions, and agreements, 385 risk mitigation, 335 socioeconomic background and vulnerability to disasters, 385
386 specific impact and coping techniques, 384

Index

perceived impact of cyclone on Sundarban community, 384t Cyclonic disasters, 377
378 Cyclonic storm, 378
379, 387
388 Cyclonic wind damage, 451

D Darjeeling
Sikkim Himalayan region, 194
195 Data, 138, 196, 318 collection, 7
8, 230
231, 324
325, 380 and analysis techniques, 139f method, 76
77 population, sample, and total number of enumerators, 230t Scopus database with query, 8t compilation, 501
503 data-driven statistical modeling methods, 392 database management, 369 processing and analysis, 231 science, 370
372 big data analytical techniques, 371t improved groundwater management scenarios, 370f sources, 29, 180, 501 POWER release 8 and 901, 180 DDA. See Delhi Development Authority (DDA) DDPM. See Department of Disaster Prevention and Management (DDPM) Decision-making, 15, 120 activities, 369
370 power, 166
167 process, 446, 463 Defense Meteorological Satellite Program (DMSP), 300 Deforestation, 346, 423 Degradation processes, 380
381 Delhi Development Authority (DDA), 491
493 Delhi Jal Board (DJB), 491
493 Delhi Municipal Council (DMC), 491
493 Deltas, 57
58, 69 DEM. See Digital Elevation Models (DEM) Demak, 94
95 DEMNAS. See Digital Elevation Model Nasional (DEMNAS) Demographic absorption, 423 Demographic details of respondents, 402 Densification, 427 Department for International Development (DFID), 57
58 Department of Disaster Prevention and Management (DDPM), 324
325 Desalination, 365
366, 367t Development-based systems, 421 Dewan Nasional Perubahan Iklim (DNPI), 217

525

DFID. See Department for International Development (DFID) Dietary diversity, 154 Diffusion systems, 369 Digital Elevation Model Nasional (DEMNAS), 437t Digital Elevation Models (DEM), 96, 99, 296
297, 369, 473
474 Digital number (DN), 98 Digital Software Analysis System (DSAS), 437 Disaggregation approach, 295 Disaster coping capacity, 306 analysis, of flooded industrial complex areas, 322
323 approach to disaster coping capacity at subdistrict level, 311
312 assessment, 306
307, 311 finalization of indicators and field survey for selfcapacity assessment, 312 indicator’s points, 316t methodology for assessing disaster coping capacity, 311
312 overlapped self-capacity assessment results, 316f sub-district level highly, 313
314 disaster-related issue, 17
18 management, 348, 352, 393 planning, 15 policies, 15
16 resilience, 380 resilient village, 114 risk management, 82, 359 risks, 377 Disaster resilience of place model (DROP), 12 Disaster risk reduction (DRR), 77
78, 85, 377
378 DRR and resilient city campaign in Indonesia, 79
81 government, stakeholder, and community policies, 79f in Indonesia, 77
81 Diversification of income sources, 273 DJB. See Delhi Jal Board (DJB) DKI Jakarta, 226 DMC. See Delhi Municipal Council (DMC) DMSP. See Defense Meteorological Satellite Program (DMSP) DN. See Digital number (DN) DNPI. See Dewan Nasional Perubahan Iklim (DNPI) Domestic politics, 209
211, 214 Dooars region, 500
501 Double-edged diplomacy, 210 Drainage basin, 473
474 Drainage system, 392
393 DRASTIC model techniques, 297
298 DrinC software, 183

526 Drinking water, 361
362 crisis, 49
50 Drip irrigation, 154 system, 154 technology, 332 DROP. See Disaster resilience of place model (DROP) Drought(s), 182, 271
273, 300 average annual rainfall of Bundelkhand region, 184t drought-prone Bundelkhand region of central India climatic moisture category/drought categories for SPI, 182t data sources, 180 drought induced temporal migration and vulnerabilities, 186
188 methods and materials, 179
183 multiscale pattern of rainfall, 184 rationale and significance of present study, 177
179 research objectives, 179 software, 183 SPI, 180
182 SPI evaluation and characteristics of drought, 184
186 study area, 182
183 temporary migration and local vulnerabilities, 182 drought
prone areas, 267 droughts classified according to intensity, 186t index, 183 induced temporal migration and vulnerabilities, 186
188, 187f probability of recurrence of, 184t severity index, 180
181 SPI evaluation and characteristics of, 184
186 DRR. See Disaster risk reduction (DRR) Dry season, 108 DSAS. See Digital Software Analysis System (DSAS) Durbin
Watson value, 67
68

E Early warning system, 48, 348, 385
386 Earth Observation (EO), 368 Earth Observing System (EOS), 299 East Canal project, 91 East flood canal project, 85 East Lombok Regency, evidence of coastal communities vulnerability in, 139
142 East Lombok Regency vulnerability sensitivity, 140
141 Eastern Economic Corridor policy (EEC policy), 305
306 EbA. See Ecosystem-based Adaptation (EbA) ECMWF. See European Centre for Medium-Range Weather Forecasts (ECMWF)

Index

Ecological municipalities, 423 Ecological vulnerability analysis method, 14 Economic vulnerability index (ECVI), 58
59, 62
67 calculation of, 68t implementation of, 67 relationship between adaptive capacity and adaptation in light of, 67
68 Economic(s), 4 barriers, 228
229 development, 80, 345, 361
362 empowerment, 233 factors, 362 growth, 81
82, 121
122 livelihood status of economically marginalized people, 69
70 security, 62 welfare, 234
235, 235t Ecosystem-based Adaptation (EbA), 110
111, 261 Ecosystem(s), 15, 75
76, 383, 459, 487
488 ecosystem-based risk reduction, 463 process, 433
434 resilience, 15 service, 379, 433
434, 452 ECVI. See Economic vulnerability index (ECVI) EEC policy. See Eastern Economic Corridor policy (EEC policy) EGTT. See Expert Group on Technology Transfer (EGTT) EI. See Exposure Index (EI) Eigen values (EV), 248 El-Nino Ocean warming, 300
301 Electrical energy, 228, 232 Electronic gadgets, 406 Empowerment of women, 233 Endline data collection, 158 Endpoint rate method (EPR method), 437 Energy crisis, 122 production, 362 Enhances Thematic Mapper (ETM 1 ), 197
199 Entrepreneurship, 226 Enumerators, 230, 230t Environmental/Environment balance, 231
232 degradation, 212 ecosystems, 15
16 governance, 120 impacts, 194 injustice, 120
121 as international concern, 212
214 migrants, 57
58 refugees, 120
121 EO. See Earth Observation (EO)

Index

EOS. See Earth Observing System (EOS) EPR method. See Endpoint rate method (EPR method) Erosion, 452
453 assessment, 442 rate, 101
102 ESA. See European Space Agency (ESA) European Centre for Medium-Range Weather Forecasts (ECMWF), 502
503 European Space Agency (ESA), 299 EV. See Eigen values (EV) Evapotranspiration, 293 Expected peak flood, 30
31, 39t Expert Group on Technology Transfer (EGTT), 218 Exposure gaps and changes, 321
322 Exposure Index (EI), 31, 320
321, 457 and population density, 320
321, 321t Exposure to natural disasters (Exp), 310 Extraction method, 248
249 Extreme drought-prone areas, 265
266 Extreme precipitation, 105, 106f

F F-8. See Forestry Eight (F-8) F-11. See Forestry Eleven (F-11) Factorial analysis, 6 Facts, 138 Family Hope Program (PKH), 137 Farmers, 158, 159f, 332 Farmlands, 308
309 Fast Longwave and Shortwave Radiative project (FLASHFlux), 180 FD. See Forest Department (FD) FDPI. See Flood Disaster Preparedness Indices (FDPI) Fertilizer, 329 FGD. See Focus Group Discussion (FGD); Forum Group Discussion (FGD) Field research, 247
248 FII. See Flood Intensity Index (FII) Financial capital, 67, 353 Financial resources, 153
154 Fish Auction Places, 143 Fisherman’s Fuel Filling Stations, 144 Five Year of Development, 214 Flash flooding, 122 Flood Disaster Preparedness Indices (FDPI), 307 Flood Intensity Index (FII), 30 Flood Risk Index (FRI), 31 Flood(s), 27
28, 57
58, 75
76, 81, 126, 228, 300, 346 adaptation, 95 affected land cover by flood inundation, 110t buffer capacity, 352
354 capacity for learning, 354
358 financial capital, 353

527

human capital, 352 natural capital, 353 physical capital, 354 self organization, 354 social capital, 353 city government’s response to climate change and social-ecological crisis, 358 climate change and social-ecological crisis on Island of Java, 346
347 concept of social resilience, 348
351 buffer capacity, 349
350 resilience concept, 349f social self organization, 350
351 consumption, 361
362 control, 119 coping strategies during, 49
50, 49t frequency analysis, 30, 38 hazard, 42
48 maps, 314
315 impacts of flood in study area, 41 flood and flood-induced riverbank erosion in study area, 41t initiatives for developing smart and resilient citybased city management, 347
348 intensity, 29 in study area, 36 management, 347
348, 392 methodology, 351
352 mitigation policies, 95 model, 97
98, 109f modeling, 99 peak, 30
31 recommendations, 358
359 recurrence trend of flood in study area, 36
39 calculation of expected peak flood, 39t calculation of return period of peak flood, 37t reduced variate and flood peak for Teesta river, 38f yearly flood intensity and classification of floods, 36f research findings, 352
358 risk, 29, 31, 42
48 assessment in Teesta flood-prone area, 42
48, 43t management, 95 score, 42
48 vulnerability, 28, 307
308 vulnerability and community resilience, 345
346 water, 51, 332 Flooded industrial complex areas, 324
325 analysis, 318
323 applying lessons learned for practical use, 323, 323f approach to disaster coping capacity at sub-district level, 310
312

528

Index

Flooded industrial complex areas (Continued) disaster coping capacity, 311
312 target community, 310
311, 311f approaches to identifying and changing pre-and post-disaster social vulnerability, 308
310 comparison with before 2011 flood disaster, 318
322 exposure gaps and changes, 321
322 exposure indices and population density, 320
321, 321t population change and companies in industrial parks, 322 disaster coping capacity, 322
323 identification of critical facilities’ locations and information, 316
318 critical facilities’ location, 319f extracted narratives of critical facilities, 319t identification of social vulnerability and risk information and collection of experiences in areas surrounding industrial parks, 312 literature, 307
308, 308t research methodology, 306
312 contribution to sustainable development from social science perspective, 306
307 research results, 312
318 Ayutthaya province, district level, 312
313, 312f social vulnerability and risk information, 315
318 sub-district level, 313
315 disaster coping capacity, 313
314 gap analysis, 314
315 Flooding, 28, 138, 313 duration, 30 intensity, 30 Fluvial process, 102
103 Focus Group Discussion (FGD), 158, 230
231 Fog, 372 Food assessment of food self-sufficiency, 279
280, 280t availability, 154 consumption, 384 crop production, 135
136 grain requirements, 329 production, 333, 337 scarcity, 382
383 security, 154, 156, 175
176, 329
330, 499 supply, 339 Force, 423 Forced migration, 175
177 Foreign ministry, 216
217 Foreign policy-making, 211, 218
219 two-level game of, 211
212 Forest Department (FD), 70 Forestry Eight (F-8), 213 Forestry Eleven (F-11), 213

Forum Group Discussion (FGD), 99 Fragile ecosystem, 377
378, 384 Fragile food production system, 329 Free-to-use approach, 6
7 Freezers, 78 Frequency, 307
308, 391
392 Frequent incumbent disasters, 379
380 Freshwater, 333, 361 FRI. See Flood Risk Index (FRI) Fuel oil, 144 Fuzzy method, 435

G Gamma density function, 180
181 Gangasagar colony, 70 sustainability indicators for block Gangasagar, 64t village, 59
60 Ganges River, 121
122 Ganges-Brahmaputra-Meghna (GBM), 27
28 systems, 119 Ganges
Brahmaputra basin, 127 Ganges
Brahmaputra deltaic plain, 380
381 Ganges
Brahmaputra River basin, 121, 339 GAP. See Good agriculture practices (GAP) Gap analysis, 314
315, 317f Garis Besar Haluan Negara (GBHN), 215 Gasoline, 228 GBHN. See Garis Besar Haluan Negara (GBHN) GBM. See Ganges-Brahmaputra-Meghna (GBM) GCMs. See General Circulation Models (GCMs) GCZMP. See Coastal zone management plan for Gujarat (GCZMP) GDE. See Groundwater-dependent ecosystems (GDE) GDP. See Gross domestic product (GDP) Gender equality, 229, 234 Gender equality and social inclusion (GESI), 155, 164
171 child-care responsibility, 167f decision maker on crops management, 166f decision maker on energy source and usage, 167f household members’ access to agricultural capacity building, 165f post-intervention data on water availability and natural resource management, 164t public consultation with women farmers, 168f social assistance management, 165f solar-powered drilled well at Sembalun sub-district, 163f General Circulation Models (GCMs), 292 Genetic material, 262 Geographic Indication certification/Geographical Indication Tag (GI), 242

Index

Geographic information system (GIS), 295, 369 Geographical area, 396 Geographical Information System (GIS), 297
298, 434 GIS-based decision support system, 457 Geography, 94 Geomorphology, 4, 456
457 Geopolitics, 120 climate-induced changes of Teesta basin and impact on, 126
129 renewable energy projects impact on Brahmaputra basin and impact on, 127 Geospatial data, 300 Geospatial technology, 300 role in climate change assessment, 299
301 role in groundwater depletion assessment, 295
298 groundwater potential zone calculation using GIS platform, 298t use of remote sensing sensors in groundwater study, 296t GESI. See Gender equality and social inclusion (GESI) GEWEX. See Global Energy, Water Exchange Project (GEWEX) GHG. See Greenhouse gas (GHG) Ghoramara Island, 69 GIS. See Geographic information system (GIS); Geographical Information System (GIS) Global approaches, need identification and, 467
469, 468t Global climate change, 499 Global Disaster Preparedness center, 385 Global Energy, Water Exchange Project (GEWEX), 180 Global mean sea level (GSML), 93
94 Global Positioning System (GPS), 96 Global precipitation, 361 Global precipitation measurement (GPM), 368t Global warming, 75
76, 127, 212
213, 226, 231
232, 330
331, 338
339, 380
381, 434, 499
500 human activities and, 227
228 GMAO. See Goddard’s Global Modeling and Assimilation Office (GMAO) Goddard’s Global Modeling and Assimilation Office (GMAO), 180 Good agriculture practices (GAP), 161 Good-quality vegetation, 402 Gorkha Territorial Administration, 196
197 GOs. See Governmental Organizations (GOs) Gosaba block, 59f, 60
61, 60f, 65t, 70 Governmental Organizations (GOs), 29 GPM. See Global precipitation measurement (GPM) GPS. See Global Positioning System (GPS) GRACE. See Gravity recovery and climate experiment (GRACE) GRACE spacecraft, 300

529

GRACE-FO. See Gravity recovery and climate Experiment follow on (GRACE-FO) Graphical user interface (GUI), 183 Gravity recovery and climate experiment (GRACE), 299, 368, 368t Gravity recovery and climate Experiment follow on (GRACE-FO), 368t Green city concept, 80 Green economy, 233 adaptation through, 233 approach, 231 to support women’s empowerment, 225 adaptation of women’s groups to climate change, 229 adaptation through green economy, 233 climate change case studies, 231
232 climate change impact on women as vulnerable group, 228
229 data collection, 230
231 data processing and analysis, 231 framework to adaptation to climate change, 227f green social work approach, 235
236 human activities cause global warming, 227
228 improved economic welfare, 234
235 limitation of study, 236 material and methods, 229
231 population and sample, 230 recommendations, 236
237 social entrepreneurship program for women, 233
234 women vulnerable group, 232
233 Green growth efficiency, 225 Green plants, 353 Green revolution technologies, 329 Green social work, 229, 235
236 Green social workers, 236 Green space, 114, 474
475 Green water, 363 Green-blue infrastructure, 429 Greenhouse emission, 82, 215, 217 Greenhouse gas (GHG), 127, 228, 434 emissions, 78, 158, 338
339 Greenhouse gas contributors, 232 Gross domestic product (GDP), 226, 329 Ground-based RS, 295 Groundwater, 291, 295, 332, 353 availability, 93
94 consumption, 291
292 depletion impact of climate system on groundwater, 292
295 role of geospatial technology in climate change assessment, 299
301

530 Groundwater (Continued) role of geospatial technology in groundwater depletion assessment, 295
298 extraction, 364, 436, 446 hydrology, 294 intensive use of, 364
365 management scenarios, 370f potential zone calculation using GIS platform, 298t recharge zones, 296 reservoirs, 361 resources, 291
292, 294, 470 management, 366
368 science, 297
298, 371
372 sustainability, 363 Groundwater recharge potential (GRP), 297 Groundwater storage (GWS), 291
292, 294
295 Groundwater-dependent ecosystems (GDE), 292
293 Group Discussion data collection, 76
77 GRP. See Groundwater recharge potential (GRP) GSDMA. See Gujarat State Disaster Management Plan (GSDMA) GSDMA Training Module on Cyclone Risk Management (GIDMCRED), 451 GSML. See Global mean sea level (GSML) GUI. See Graphical user interface (GUI) Gujarat coastal zones approach, 456
457, 461f limitations, 463
464 materials and methods, 453
459 quantifying risk, 457
459, 458t rational of study, 463 recommendations, 462 results, 459
461, 460f, 461t, 462t study area, 453
455 area under different habitats in Gujarat, 454t and distribution of coastal ecosystems Gujarat, 453f Gujarat State Disaster Management Plan (GSDMA), 451 Gumbel distribution, 31, 38 functions, 30 Gumbel method, 30
31, 38 GWS. See Groundwater storage (GWS)

H Halophytic mangrove forests, 380
381 Hardware measures, 313
314 Hardware system, 347
348 Hasty planning arrangements, 428 Hazard Index (HI), 31 Hazard maps, 300 Hazard-related scientific information, 314
315 HDI. See Human Development Index (HDI)

Index

Healthcare services, 406 Heat waves, 300 Hensahatu Gram Panchayat, 277 Herbal treatments, 50 Heterogeneous composition, 394 HHLS model. See Household Livelihood Security model (HHLS model) HI. See Hazard Index (HI) Hierarchical processing, 378 Himalayan base stations of popular trek routes, 195f Himalayan mountain system, 203
204 Himalayan rivers, 122
124 Hooghly River, 59
60 Household Livelihood Security model (HHLS model), 57
58 Households, 241, 405 drought coping survival strategies in rural plateau tracks of eastern India alternative livelihood strategy, 276
279 analysis and results, 270
275 approaches and techniques, 269 assessment of poverty and food self-sufficiency, 279
280 climatic hindrance, drought, and crop calendar, 271
273 description of study area, 267
269 economy, 273
275, 274t household recall survey, 270 land use, 270
271 livelihood constraints and socio-ecological loops, 275
280 materials and methods, 269
270 policy traps, 284 production-consumption traps, 281
282 relevance of study, 266
267 resilience traps, 281 risk traps, 283
284 seasonality of household economy, 275
276 selecting target groups and survey procedures, 269
270 variability traps, 282
283 household-level vulnerability, 57
58 recall survey, 270 seasonality of household economy, 275
276 survey, 61 Human activities, and global warming, 227
228 Human capitals, 67, 352
354 Human Development Index (HDI), 62, 309
310 Hydro Power Policy, 127 Hydro schizophrenia, 364
365 Hydro-climatic variables, 48 Hydrodynamic modeling, 369 Hydrodynamic movement processes, 463
464

Index

Hydroelectric power plants, 427 Hydroelectricity energy projects impact on Brahmaputra basin and impact on geopolitics, 127 Hydrological cycle, 365 Hydrological drought, 178 Hydrology, 131, 470 Hydrometeorological disasters, 378
379 Hydroponic systems, 335 Hydropower, 28, 127

I IBM SPSS software, 196
197 ICT. See Information communications technology (ICT) IDP. See Internally displaced person (IDP) IFAD. See International Fund for Agricultural Development (IFAD) IGOs. See International Governmental Organizations (IGOs) IHA. See International Hydropower Association (IHA) IMD. See Indian Meteorological Department (IMD) In-depth qualitative-exploratory method, 138 In-situ data, 97
98 In-situ employment opportunities, 268
269 Index score, 42
48 India, South Asian countries, 334
335 India’s prime minister’s prestigious scheme’ doubling of farmers’, 261 Indian Meteorological Department (IMD), 366
368, 502
503 Indigenous coping strategies, 48
51 during flood, 49
50 in post-flood period, 50
51 during pre-flood period, 48
49 Individual Vulnerability Index (IVI), 394, 405
409. See also Area vulnerability index; Coastal Vulnerability Index (CVI) condition of houses and sanitation facility, 406t PCA about environs of neighbourhood, 408t PCA among sub-indicators of PCA, 407t possession of select assets, 407t rank of sub-indicators while analyzing neighborhood characteristics, 407t select sub-indicators of housing facility, 405t Indonesia climate change and tidal flood in, 94, 96f coastal communities and challenges of vulnerability due to climate change in, 142
145 DRR in, 77
81 climate change impacts on coastal urban areas, 77
78 climate change mitigation and adaptation strategies, 78
79

531

resilient city campaign, 79
81, 79f economy, 94 engagement in climate change negotiations, 209 environment as international concern, 212
214 institutional arrangements and diplomacy toward climate change, 214
219 limitations of study, 210
211 materials and methods, 211
212 methodology, 212 recommendations, 220 state agencies and engagement in climate change, 219
220 two-level game of foreign policy making, 211
212 evidence of coastal communities vulnerability in, 139
142 Indonesia’s Mid-Term Development Plan and Master Plan project, 345 model of resilience in facing vulnerability of coastal communities in, 145
148 vulnerability in Regency of, 141f Indonesian Institute of Sciences, 139
140, 215 Indonesian National Armed Forces, 216 Indus River, 119, 121
122 Industrial complex areas, 307
308 Industrial parks, population change and companies in, 322 Informants, 230
231 Information communications technology (ICT), 347 Infrastructure development in Semarang Municipality, 85
86 INGOs. See International Non-Governmental Organizations (INGOs) Integrated Marine and Fisheries Centers, 144 Integrated Valuation of Environmental Services and Tradeoffs (InVEST), 452
453 Coastal Vulnerability model, 456, 459 Intergovernmental Panel on Climate Change (IPCC), 75
76, 213, 266
267, 292, 434 Internally displaced person (IDP), 176
177 International Fund for Agricultural Development (IFAD), 266
267 International Governmental Organizations (IGOs), 217
218 International Hydropower Association (IHA), 127 International Non-Governmental Organizations (INGOs), 217
218 International Organization for Migration (IOM), 57
58 International Water Management Institute (IWMI), 372 Internet of things, 369 IOM. See International Organization for Migration (IOM) IPCC. See Intergovernmental Panel on Climate Change (IPCC)

532 Irrigated agriculture, 291 Irrigation water, 159 IVI. See Individual Vulnerability Index (IVI) IWMI. See International Water Management Institute (IWMI)

J Jakarta coastal vulnerability assessment for megacity of, 444
446 coastal geomorphology, 438 coastal slope, 438 coastal vulnerability status in Jakarta, 443
444 CVI calculation, 439
440 data and method, 437
440 detailed dataset to calculate CVI, 437t physical drivers of coastal vulnerability, 440
443 results, 440
444 sea level rise, 438 shoreline changes, 437 significant wave height, 437 study area, 435
436 tidal range, 438 vertical land motion, 438
439 flooding monitoring system, 445 Jatibarang embankment project, 85 Java Island, 91, 140
141 tidal flood on Northern Coast of Java, 94
95 Jeevamrutham (organic fertilizer), 250 Jhalda Blocks, 273
275, 277 Jhalda I block of Purulia district, 269
270 Jhalda II block of Purulia district, 269
270 JICA project, 85 Joint Business Group Program (KUBE), 137 Jokowi, 219

K Kaiser-Meyer-Olkin Measure (KMO Measure), 197, 197t Kalibuntu Village, inundation depth at, 108t Kamalpur village, 59
60 KBR. See Khangchendzonga Biosphere Reserve (KBR) KCC. See Khangchendzonga Conservation Committee (KCC) Kelompok Wanita Tani (KWT), 156 Kemijen Local Community (KOMJEN), 355t Kerala Gazetteer, 242
243 Keyhole Markup Language (KML), 366 Khangchendzonga Biosphere Reserve (KBR), 204
205 Khangchendzonga Conservation Committee (KCC), 194 Khangchendzonga National Park, 194, 204
205 Khasimara island, 69

Index

KML. See Keyhole Markup Language (KML) KMO Measure. See Kaiser-Meyer-Olkin Measure (KMO Measure) KOMJEN. See Kemijen Local Community (KOMJEN) Koppen’s classification, 245 KWT. See Kelompok Wanita Tani (KWT) Kyoto Protocol, 209, 217

L La Nina Ocean warming, 300
301 Land, 499 boundary, 108
109 conversion, 346 cover, 201
202, 392, 480
482 and land use of Probolinggo coastal area, 100
101, 100f sub-district in Probolinggo Regency, 101t crop, 154 determining order of polynomial for estimating nonlinear effects of, 504 development, 420 holistic effects of land covariates on tea production, 505
509 linear regression analyses for estimating holistic effects of land on tea yield, 503
504 multivariate polynomial regression analyses for estimating nonlinear effects of land on tea yield, 505 nonlinear effects of land covariates on tea production, 509
516 order of polynomial for estimating nonlinear effects of, 509 pockets, 489
491 resources, 477
478 of rivers, 27
28 Rover, 194
195 subsidence, 84f, 434 in Tambak Lorok Kampong, 81 use, 201
202, 270
271, 392 in local plans in non-urban municipalities, 427f map of Delhi NCT, 488f monitoring, 429 topographical variation and its influence on soil and agriculture, 271f Landsat 4
7 Collection 1 Surface Reflectance Product Guideline (Landsat 4
7 C1 Surface Reflectance Product Guideline), 98 Landsat 4
7 Collection 1 Surface Reflectance Product Guideline (Landsat 4
7 C1 Surface Reflectance Product Guideline) Landsat Ecosystem Disturbance Adaptive Processing System algorithm (LEDAPS), 98 Landsat image dataset, 98

Index

Landsat Multispectral Scanner, 297
298 Landsat series, 300 Landsat Thematic Mapper imagery (Landsat TM imagery), 296
297 Landsat TM imagery. See Landsat Thematic Mapper imagery (Landsat TM imagery) Landscape damage, 392 Landslides, 194, 228 LAST. See Livelihood Asset Status Tracking (LAST) Laterite soils, 243 Layers of resilience, 57
58 Learning capacity for, 354
358 Urban and social resilience strategies in Kampong Kemijen, 356t system, 350
351 LEDAPS. See Landsat Ecosystem Disturbance Adaptive Processing System algorithm (LEDAPS) Legumes, 158 Lembaga Ilmu Pengetahuan Indonesia (LIPI), 215 LENTING, 154 Project, 154
155 Lesser Himalaya, 204 Letter of Intent (LoI), 218 Line graph, 273 Line of sight (LOS), 438
439 Linear equations, 99 Linear regression analyses, 503
504 LIPI. See Lembaga Ilmu Pengetahuan Indonesia (LIPI) Livelihood Asset Status Tracking (LAST), 57
58 matrix, 61
62 computation of, 63t implementation of, 67 relationship between adaptive capacity and adaptation in light of, 67
68 Livelihood constraints, 275
280 Livelihood security, 62, 156 Livelihood strategy, alternative, 276
279, 277f, 278f, 278t Livelihood Vulnerability Index (LVI), 394, 409. See also Coastal Vulnerability Index (CVI); Area vulnerability index; Individual Vulnerability Index (IVI) Local community, 318 resilience, 15
16 Lohachara island, 69 LoI. See Letter of Intent (LoI) LOS. See Line of sight (LOS) Low diversification, 276
279 Lower Subansiri Hydroelectric project, 125
126 Lowland region, The, 242
243 LVI. See Livelihood Vulnerability Index (LVI)

533

M M S Swaminathan Research Foundation (MSSRF), 57
58, 261 Machine-learning (ML), 369 Magnitude, 307
308, 391
393, 499 Mahatma Gandhi National Rural Employment Guarantee Act, 188 Main Central Thrust (MCT), 204 Majalengka Regency, 226 Maldives, South Asian countries, 335 Male migration, 182 Managed Aquifer Recharge (MAR), 297
298 Manebhanjyang communalities values of, 198t Manebhanjyang
Sandakaphu
Phalut trekking corridor, 204t NDVI status for, 200f rotated component matrix of, 198t Mangrove(s), 110
111, 452
453 area, 382 conservation, 387 reforestation, 110, 111f MANOVA. See Multivariate analysis of variance (MANOVA) MAR. See Managed Aquifer Recharge (MAR) Marine environment, 135 and fishery resources, 143 natural resources, 144
145 process, 102
103, 113 resources, 137, 146
148 Marketing high-altitude trekking, 196 Marketing Operation Region (MOR), 142 Massachusetts Institute of Technology (MIT), 300
301 Massive anthropogenic pressure, 433
434 Massive irrigation systems, 337 Material resources, 153
154 Matla River, 382
383 Maximum peak flood, value of, 30 MCA. See Multiple correspondence analysis (MCA); Multivariate categorical data (MCA) MCDM. See Multicriteria decision Making (MCDM) MCT. See Main Central Thrust (MCT) MDER. See Minimum Dietary Energy Requirement (MDER) MDGs. See Millennium Development Goals (MDGs) MDS. See Multidimensional Scaling (MDS) Mean monthly discharge in river Teesta, 32
34, 33f, 34f Mean squared error (MSE), 504 Medical services, 406 Medicinal plants, 261 Medicinal rice varieties, 242

534

Index

MEERA-2. See Modern Era Retrospective-Analysis for Research and Applications-2 (MEERA-2) Memorandum of Understanding (MoU), 142 Meteorological satellites, 300 Methods for improvement of Vulnerability Assessment framework (MOVES framework), 309
310, 394
395 MGREAGA, 182 Micro-drip system, 334 Migration, 176
177, 182 Milkfish ponds, 108, 137
138 Millennium Development Goals (MDGs), 216, 259
261 Minimum Dietary Energy Requirement (MDER), 279
280 Minimum water level discharges, 34
35 MIT. See Massachusetts Institute of Technology (MIT) Mitigation strategy impact of climate system on groundwater, 292
295 measures, 330 role of geospatial technology in climate change assessment, 299
301 role of geospatial technology in groundwater depletion assessment, 295
298 ML. See Machine-learning (ML) MNDWI. See Modified Normalized Difference Water Index (MNDWI) Moderate Resolution Imaging Spectroradiometer (MODIS), 299
300 Modern Era Retrospective-Analysis for Research and Applications-2 (MEERA-2), 180 Modified Normalized Difference Water Index (MNDWI), 400 MODIS. See Moderate Resolution Imaging Spectroradiometer (MODIS) Mono-cropping agriculture of Jhalda Blocks, 275
276 Monoculture crops, 346 Monsoon season, 515 Monsoon tea production, predictor variables statistically significant effect on, 515f Monsoonal rains, 499 MOR. See Marketing Operation Region (MOR) MoU. See Memorandum of Understanding (MoU) Mount Bromo’s eruption, 99
100 Mountain glaciers, 361 MOVES framework. See Methods for improvement of Vulnerability Assessment framework (MOVES framework) MSE. See Mean squared error (MSE) MSSRF. See M S Swaminathan Research Foundation (MSSRF) Multicriteria decision Making (MCDM), 298t Multidimensional Scaling (MDS), 6 Multiple correspondence analysis (MCA), 6

Multivariate analysis of variance (MANOVA), 247
248 Multivariate categorical data (MCA), 6 Multivariate fixed-effect linear regression models, regression coefficients estimated through, 507t Muriganga River, 59
60

N NAM. See National AYUSH Mission (NAM) NAP. See National Adaptation Program (NAP) NAPA. See National Adaptation Program of Action (NAPA) NASA. See National Aeronautics and Space Administration (NASA) National Adaptation Program (NAP), 339 National Adaptation Program of Action (NAPA), 339 for Climate Change, 332 National Aeronautics and Space Administration (NASA), 180 Earth Science, 300 research, 291 National AYUSH Mission (NAM), 261 National Capital Integrated Coastal Development master plan (NCICD master plan), 445
446 National capital territory, Delhi, geospatial assessment of, 470 National Council on Climate Change, 217, 219 National Development Planning Agency, 216 National Disaster Management Agency, 346
347 National Environmental Policy, 463 5th National Family Health Survey, The, 269 National Medicinal Plants Board (NMPB), 261 National Oceanic Atmospheric Administration (NOAA), 106
108 National Research and Innovation Agency (BRIN), 99 Natural capital, 67, 353, 353f Natural climate variabilities, 445 Natural disaster(s), 80, 88
90, 175
176, 228, 305
309, 345, 384
385, 435 Natural drainage system, 93
94 Natural earth systems, 370
371 Natural ecosystems, 15
16, 426
427 Natural environment, 457
458 Natural habitats, 452 Natural hazards, 94
95, 377 Natural hydrology, 472, 474f Natural language processing (NLP), 6 Natural resource(s), 13, 75
76, 145, 215, 269
270, 292
293, 345
346, 380, 462 management, 170
171 post-intervention data on, 164t system, 138 natural resource-based livelihoods, 335
336 sustainable management of, 161
164

Index

Natural retention, 429 Natural systems, 452 Natural terrain, 392 Natural water, 429 cycle, 361
362 Natural wealth in Indonesia, 144
145 Navara, 242 cultivation factor, 259 cultivators dimension of climatic and economic problems of, 250
251 spatial pattern of multidimensional factors with, 254
259 dimension of strategies to improvise cultivation and marketing of, 251
252, 251t dimension of vulnerability of Navara farming, 253, 254t rice chain, 261 spatial attributes of multidimensional characteristics of Navara cultivation, 254
259 composite index, 259 spatial pattern of multidimensional factors with Navara cultivators, 254
259 spatial pattern of resilience to natural hazards among Navara farming communities, 259 NCICD master plan. See National Capital Integrated Coastal Development master plan (NCICD master plan) NCT, overall assessment of NDWI and NDVI for, 478
479 spatial temporal pattern of NDWI and NDVI values across years, 484f ND-GAIN Index. See Notre Dame Global Adaptation Index (ND-GAIN Index) NDBI. See Normalized Difference Built-Up Index (NDBI) NDSI. See Normalized Difference Snow Index (NDSI) NDVI. See Normalized Difference Vegetation Index (NDVI) NDWI. See Normalized Difference Water Index (NDWI) Near Infrared (NIR), 399t Nepal, South Asian countries, 335
336 Network, 350 mapping analysis, 5 New Jalpaiguri railway junction (NJP railway junction), 203
204 Next Generation Science Standards (NGSS), 299 NGOs. See Non-government Organizations (NGOs) NGSS. See Next Generation Science Standards (NGSS) Nijo Bhumi Nijo Griha, 69 NIR. See Near Infrared (NIR) Nitrous oxide, 228

535

NJP railway junction. See New Jalpaiguri railway junction (NJP railway junction) NLP. See Natural language processing (NLP) NMPB. See National Medicinal Plants Board (NMPB) NOAA. See National Oceanic Atmospheric Administration (NOAA) Non-cash Food Assistance, 137 Non-climatic variables, 294 Non-government Organizations (NGOs), 99 Non-traditional security (NTS), 210 Normalized Difference Built-Up Index (NDBI), 196, 200
201 Normalized Difference Snow Index (NDSI), 299 Normalized Difference Vegetation Index (NDVI), 196
199, 400, 474
475 assessment, 477
478 database sources for NDVI analysis, 481t NDVI status for Manebhanjyang and Yaksum, 200f Normalized Difference Water Index (NDWI), 470, 474
475 assessment, 475
477 database sources for NDWI analysis, 476t Notre Dame Global Adaptation Index (ND-GAIN Index), 379
380 NTS. See Non-traditional security (NTS) Nvivo 12 Plus application, 138 Nvivo 12 Pro tool, 138

O Observer Research Foundation (ORF), 127 Oceanography, 99 Ocimum sanctum. See Tulsi (Ocimum sanctum) OECD. See Organization for Economic Cooperation and Development (OECD) OLI. See Operational Land Imager (OLI) Omega, 354
355 Online system, 324
325 Operational Land Imager (OLI), 399t ORF. See Observer Research Foundation (ORF) Organic farming for climatic resilience, dimension of, 252
253, 253t Organic matter, 438 Organization for Economic Cooperation and Development (OECD), 348 Organizational autonomy, 350 Ottappalam, 245 Ozone layer, 227
228

P PACS Programme. See Poorest Areas Civil Society Programme (PACS Programme) Pajurangan Village, 95, 102
103 Pakistan, South Asian countries, 336

536 Palakkad, 242
243, 245 Panel regression models, 500
501, 503 Pangkep Regency, 140 coastal area, 137 evidence of coastal communities vulnerability in, 139
142 Paper, 212, 313 PAR model. See Pressure and Release Model (PAR model) Parganas cultivators, 382 PCA. See Principal Component Analysis (PCA) Peka Kota, 354 Pertamina, 144 Pesticide, 329 Photovoltaic farms, 427 Phra Nakhon Si Ayutthaya (PNSA), 313, 321
322 Physical capitals, 67, 354 Plan coverage, 424
425 Plan-based spatial planning systems, 419
420 Planning coverage of municipalities at end of 2020, 425f Plastic roofs, innovative salt production using, 113f Plastic waste, 354
355 Plastic-roofed room, 111 PMAY. See Pradhan Mantri Abas Yojana (PMAY) PMGSY. See Pradhan Mantri Gram Sadak Yojana (PMGSY) PNSA. See Phra Nakhon Si Ayutthaya (PNSA) Polar regions, 361 Polish spatial planning system, 419
420 Polish system, 429 Political barriers, 228
229 Political refugees, 176 Polynomial order for estimating nonlinear effects of land and atmospheric variables, 509 Polynomial regression models, 500
501, 504
505 Poorest Areas Civil Society Programme (PACS Programme), 271
272 Population change and companies in industrial parks, 322 Population density, 82, 309 exposure indices and, 320
321, 321t Population researcher center for population research LIPI, 142 Porter’s stemming algorithm, 6 Post-disaster recovery, 80 Post-flood period, coping strategies in, 50
51, 51t Post-intervention data collection, 158 on water availability and natural resource management, 164t Post-monsoon season, 516 Post-structuralist approaches, 120

Index

Post-traumatic stress disorder, 408
409 Potassium aluminum sulfate, 49
50 Poverty assessment of, 279
280 daily calorie intake by varying social groups of study area, 280f poverty incidence, poverty depth, and severity of poverty, 280t seasonality in cash availability and food sufficiency, 279f chronic poverty, 188 poverty trap, 281 POWER. See Prediction of worldwide energy resources (POWER) PPGIS. See Public participatory GIS (PPGIS) Pradhan Mantri Abas Yojana (PMAY), 69 Pradhan Mantri Gram Sadak Yojana (PMGSY), 126 Pre-flood coping strategy, 49 Pre-flood period coping strategies during pre-flood period, 48
49 average flood risk index of study area, 47f criteria of assessment, 47t index score of risk components, 47f pre-flood period indigenous coping strategies, 48t Pre-intervention survey, 158 Pre-monsoon season, 510
514 Prediction of worldwide energy resources (POWER), 180 POWER release 8 and 901, 180 Presidential Regulation 7/2005, 210 Pressure and Release Model (PAR model), 309 Primary data, 61 Primary datasets, 31 Primitive subsistence agriculture, 265
266 Principal Component Analysis (PCA), 197, 248
249, 400, 407t, 410t, 435 Probabilistic risk assessment, 31 Probolinggo, 95 adaptation and resilient of climate change on local peoples in, 109
111 climate change impacts in, 106
109 BEST index, 107f flood model, 109f inundation depth at Kalibuntu Village, 108t regional sea level trend, 107f climate change indicator in, 105
106, 106f community, 95 geological formation of Probolinggo coastal plain, 99
100 land cover and land use of Probolinggo coastal area, 100
101 regency, 99
100 regional planning board, 99

Index

sub-district in, 101t sand mining activities in, 105f shoreline dynamic of coast of, 101
104 Process-based crop simulation models, 241
242 Production-consumption traps, 281
282 Public participation, in coastal development, 80 Public participatory GIS (PPGIS), 369 Purulia (part of Chota Nagpur plateau), 267

Q QGIS software, 199
200 Qualitative approach, 351, 380 Qualitative data, 229
231 Quantitative data, 229 Questionnaire survey, 312

R Radar data, 297 Radiometric corrections, 98 Rain-and-snow-fed river, 124 Rainfall, 93
94, 332, 392, 499
500 deficiency, 182 influence of, 275
276 information, 314
315 multiscale pattern of, 184 pattern, 94, 175
176 Rainfed agriculture, 265 Rainwater, 382 harvesting, 337 Rainy season, 108 Randutatah Village, 95 Ranganadi Hydroelectric Project, 125
126, 128
129 RAS. See Recirculating Aquaculture System (RAS) Rathong Chu hydroelectric project, 194
195 Raw materials, 409 sources, 145 RBO. See Rule-based order (RBO) Reactive adaptation, 109 Receiver operating characteristic (ROC), 298t Recirculating Aquaculture System (RAS), 146
148 Recovery policies, 15
16 Recurrence interval, 30 Recurrence trend, 29 of flood in study area, 36
39 Recurrent cyclones, 61 REDD. See Reducing Emission from Deforestation and Degradation (REDD) Reduced variate, 31 Reducing Emission from Deforestation and Degradation (REDD), 218 Reefs, 452 Referring to the local development plan (RPJMD), 114 Refrigerators, 406

537

Refugees, 176
177 Regional development, 354
355 Regional Gross National Product, 82 Regression analysis, 276 Regular cyclones, 57
58 Rembang Regency, evidence of coastal communities vulnerability in, 139
142 Remote sensing technology, 295, 297
298 data, 368, 368t, 370
371 in groundwater study, 296t Rencana Pembangunan Jangka Menengah Nasional (RPJMN), 218 Renewable energy, 154, 168 intervention, 157
158 management, 154 projects impact on Brahmaputra basin and impact on geopolitics, 127 sources, 180, 428
429 Renewable energy management, 154 Research process, 470
474 applicability of stream flow, 470
472, 472t applicability of stream ordering, 472 ethics, 318 gap, 380, 420 geospatial assessment of national capital territory, Delhi, 470 process of watershed delineation, 473
474, 473f, 474f selection of site for research, 470, 472f stages of research process, 471t Reservoirs, 308
309 Reshape2 (software packages), 6
7 Residential areas, 423 Residents Welfare Association (RWA), 491
493 Resilience, 14
16, 42, 210
211, 300
301, 348 community perceptions of adaptability vulnerability index, 409
410 area vulnerability index, 402
405 data sources, 395
396 demographic details of respondents, 402, 403t details of bands, 399t individual vulnerability index, 405
409 justification for selection of indicators, 394
395 livelihood vulnerability index, 409 map of, 401f materials and methods, 394
401 methodological framework, 395f objectives, 394 results, 402
410 satellite imagery, 399t software, 401 statistical analysis, 396
400 study area, 401
402 theoretical orientation, 392
394

538

Index

Resilience (Continued) various indicators and sub-indicators, 397t model in facing vulnerability of coastal communities in Indonesia, 145
148 to natural hazards administrative divisions of district, 245 climate induced farm risks, 243f climate profile of study area, 245 data set of selected variables of Navara farmers in Palakkad district, 246t dimension of climatic and economic problems of Navara cultivators, 250
251 dimension of organic farming for climatic resilience, 252
253 dimension of strategies to improvise cultivation and marketing of Navara, 251
252 dimension of vulnerability of Navara farming due to land use and climate change, 253 extraction method, 248
249 location map of Palakkad district, 244f major factors and variable loadings, 249
253, 249t methods, 246
248 results, 248
253 spatial attributes of multidimensional characteristics of Navara cultivation, 254
259 spatial pattern of resilience to natural hazards among Navara farming communities, 259 study area, 242
245 total variance, 248t traps, 281 at Jhalda blocks, 282t socio-ecological traps as explored in study area, 283f Resilience Index (RI), 31 Resilient city, 75
76 community perception toward tidal floods, 86
91 DRR and resilient city campaign in Indonesia, 79
81 DRR in Indonesia, 77
81 implementation, 76
77 materials and methods, 76
77 network, 75
76, 81 problems of community adaptation and response toward climate change, 75
76 recommendation, 91 to resilient society, 89f social cultural adaptation process of urban coastal community in Tambak Lorok toward rob flood, 81
86 of climate change on local peoples in Probolinggo, 109
111 community, 76
77

data collection and analysis techniques, 139f evidence of coastal communities vulnerability in Pangkep Regency, East Lombok Regency, & Rembang Regency, Indonesia, 139
142 identification and analysis of research problems, 136f level of welfare of coastal communities and challenges of vulnerability due to climate change in Indonesia, 142
145 methods, 138 model of resilience in facing vulnerability of coastal communities in Indonesia, 145
148 strategic model for fisheries management, 147f recovery, 387 society, 88
90, 89f system, 75
76 Resistance to natural disasters (Res), 310 Resource mining cycle, 281
282 resource-poor economy, 176
177 resource-poor smallholder farmers, 154 Return period, 30 RI. See Resilience Index (RI) Rice, 241
242 productivity, 158 Rio 1 10 Conference, 217 Risk information, 306
307, 310 in areas surrounding industrial parks, 315
318 extracted narratives of communities, 318t identification of, 315 identification of risk information and collection of experiences in areas surrounding industrial parks, 312 social vulnerability and risk information, 317f vulnerability and risk location, 318t management, 352, 393 River basins, 121 and community, 126 situational analysis of river basin due to climate change, 126
127 Bidya, 61 discharge pattern of river Teesta in study area, 32
35 mean monthly discharge in, 32
34 monthly variation in maximum and minimum water level of, 35f temporal variation in water levels of river Teesta, 34
35 yearly recorded maximum and minimum water level of, 35f flood hazards, 31
32

Index

network, 27
28 river-aquifer relationships, 365 Riverine communities, 120 climate-induced changes of Teesta basin and impact on geopolitics, 126
129 limitation of study, 130 macrolevel impact, 129 material and methodology, 123
126 profile of case sites, 124
126 research methodology framework, 124f Teesta river basin, 125f microlevel impact, 129
130 climate change on, 130t rationale of study, 122
123 objectives, 123 recommendation, 130
131 research gap, 122 Rob flood in Tambak Lorok Kampong, 81 ROC. See Receiver operating characteristic (ROC) Rojana Industrial Park for indicators, 313
314 Rondoningo River, 99
100 RPJMN. See Rencana Pembangunan Jangka Menengah Nasional (RPJMN) Rule-based order (RBO), 211 Run-off velocity, 487
488 RWA. See Residents Welfare Association (RWA)

S Sagar block, 61
62, 67 Sagar island, 59
60, 69 Sajang villages, 157f, 163
164 Saline water intrusion, 378
379 Salinity in soil, 135
136 of water, 77
78 Salt marshes, 452
453 Salt ponds, 137
138 business, 108 Salt production innovating in, 111 innovative salt production using plastic roofs, 113f Salted egg, 354
355 Saltwater intrusion, 433
434 Salty river water, 382 Sample households, 61 Sand deposition, 336 materials, 103
104 Sanitation, 361
362 facility, 405
406 Satellite data, 97
98 Satellite imagery, 395
396, 399t Satellite remote sensing, 300

539

data, 299 SBE. See Sea-Bird Electronics (SBE) SBF approaches. See Stable baseflow approaches (SBF approaches) Scare irrigation, 182 Scenario development, 323 Science mapping, 5 Scientometrics method, 4 Scopus, 7 database, 7
8 with query, 8t Score interval, 61
62 SD. See Standard deviation (SD) SDGs. See Sustainable Development Goals (SDGs) Sea level rise (SLR), 61, 93
94, 380
381, 434, 438 tides, and wave, 442 Sea Surface Height (SSH), 438 Sea-Bird Electronics (SBE), 96 Sea
level changes, 435 Seasonal effect of land and atmospheric covariates on log monthly tea production, 508t Seasonal polynomial regression models, 500
501 Seawall, 110
111, 112f Seawater, 135
136, 353 desalination, 366 Seaweeds, 452
453 Sediment movement processes, 463
464 Sediment transport, 463
464 SEEA framework. See System of environmentaleconomic accounting framework (SEEA framework) Self organization, 354 analysis of Kemijen’s self-organization, 355t Semarang City, 81
83, 83f, 91 Semarang municipality, 85 Semarang Regency, 226 Sensitivity, 146, 300
301, 438 Sensors-equipped monitoring systems, 370
371 SES. See Socio-ecological system (SES) SET. See Synchronous ecological and social transition (SET) Shallow aquifers, 292 Shoreline changes, 98, 437 Shoreline dynamics, 442
443 of coast of Probolinggo, 101
104 sand mining activities in Probolinggo, 105f Sumberasih sub-district shoreline and rice fields, 104f by using multi-temporal Landsat image data, 103f Shuttle Radar Topography Mission (SRTM), 96, 99 Sierra Nevada mountains, 299 Significant Wave Height (SWH), 437, 437t Sikkim
West Bengal border, 124

540

Index

Silent Revolution, 364
365 Singalila National Park, 204 Single Look Complex (SLC), 437t, 438
439 SL. See Sustainable livelihoods (SL) SLC. See Single Look Complex (SLC) SLF. See Sustainable Livelihood Framework (SLF) Slow-onset events, 177
178 SLR. See Sea level rise (SLR) SLSI. See Sustainable livelihood security index (SLSI) Slum areas in coastal cities of Semarang, urbanization and development of, 81
83 Small Baseline Subset Differential Interferometry Synthetic Aperture Radar method (SBASDInSAR method), 438
439 Small Baseline Subset Differential Interferometry Synthetic Aperture Radar method (SBASDInSAR method) SMAP. See Soil moisture active and passive (SMAP) Smart and resilient city-based city management, initiatives for developing, 347
348 Smart resilience, 352 SMCE. See Spatial Multicriteria Evaluation (SMCE) SMOS. See Soil moisture and ocean salinity (SMOS) Snowfed aquifers, 294 Social adaptation strategies, 15
16 Social barriers, 228
229 Social business, 226 Social capital, 15, 18, 67, 353 and networks, 13, 234 Social cultural adaptation process of urban coastal community in Tambak Lorok toward rob flood, 81
86 Social entrepreneurs, 229 Social entrepreneurship, 226, 229, 233 model, 226 program, 226, 234
235 for women, 233
234 Social issues, 348
349 Social networks, 307
308, 354 Social protection programs, 226 Social resilience, 84
85, 348
351 Social self organization, 350
351 Social vulnerability, 57
58, 136, 146, 305
306 approaches to identifying and changing pre-and post-disaster social vulnerability at district level, 308
310 development of SVI-ICA, 309
310 exposure, susceptibility, and capacity, 309 social vulnerability index for industrial complex area, 308
309 in areas surrounding industrial parks, 315
318 assessment, 393 identification of, 315

in areas surrounding industrial parks, 312 extracted narratives of communities, 318t social vulnerability and risk information, 317f vulnerability and risk location, 318t index, 62
67 calculation of, 68t Social vulnerability index, 58
59 Social vulnerability index for industrial complex area (SVI-ICA), 308
309 development of, 309
310 SVI-ICA by district, Ayutthaya province, 313, 314f, 315t Social work, 229 Social workers, 229, 236 Social-ecological crisis on Island of Java, 346
347 Social-ecological systems, 13
14, 348
349 framework, 392 Social
ecological crises, 346 Societal change impacts, 194 Socio-cultural adaptation, 87 Socio-ecological loops, 269, 275
280 Socio-ecological system (SES), 57
58, 193
194, 203, 393 Socio-ecological vulnerability, 62 Socio-economic vulnerabilities, 137 Socioeconomic aspect, 122
123 Socioeconomic systems, 177
178 Sociology, 4 Software, 6
7, 183 Soil, 162, 362
363, 392, 400 conservation, 335 degradation and risks, 86 erosion, 82, 337
338 map of Delhi, 485f mapping, 479
480 moisture data, 368 salinity, 135
136 variables, 506 Soil moisture active and passive (SMAP), 368t Soil moisture and ocean salinity (SMOS), 368t Soil Survey Unit of the Department of Agriculture, 243 Soil temperature level 1 (stl1), 502
503 Soil temperature level 2 (stl2), 502
503 Soil temperature level 3 (stl3), 502
503 Soil temperature level 4 (stl4), 502
503 Sokhiza B2C levelling, 98
99 Solar drip irrigation, 160 Solar energy, 154 Solar powered irrigation, 336 Solar radiation, 500, 509 Solar-power technology, 154 Solar-powered drip irrigation, 154
155, 169 agricultural productivity, 158
161

Index

GESI, 164
170 limitations of study, 155 materials and methods, 156
158 population in target villages, 156t research procedure, 157
158 research questions, 156 sample, 156 women farmers group at Sajang village, 157f recommendations, 170 related to technical approach in agriculture and natural resource management, 170
171 sustainable management of natural resources, 161
164 South Asia, 329 Afghanistan, 332
333 Bangladesh, 333 Bhutan, 333
334 change in rainfall pattern impacts on major crop production in, 330t climate change adaptation measures on agriculture in, 337
338 climate change impacts, adaptation and mitigation measures in, 332
337 climate change mitigation measures on agroecosystem in, 338
339 impacts of increased temperature on major crop production in, 330t India, 334
335 Maldives, 335 Nepal, 335
336 Pakistan, 336 Sri Lanka, 337 Southern Singalila trekking corridor, 203
204 Space technology data science, 370
372, 370f, 371t desalination, 365
366, 367t increasing transparency and participation, 366
370, 368t innovation of emerging technology, 372 intensive use of groundwater, 364
365 methods, 363 virtual water and water footprint, 363
364, 364t Spatial decision-making tools, 369
370 Spatial Multicriteria Evaluation (SMCE), 298t Spatial pattern of multidimensional factors with Navara cultivators, 254
259 dimension of climatic and economic problems of Navara cultivators, 255f dimension of organic farming for climatic resilience, 257f dimension of strategy of improvise cultivation and marketing of Navara, 256f

541

dimension of vulnerability of Navara farming due to land use and climate change, 258f Palakkad, 254t of resilience to natural hazards among Navara farming communities, 259 Spatial planning solutions, 419
420 system, 419 Spatial plans, 419
420 Spatial policy frameworks, 419
420 SPI. See Standard precipitation Index (SPI) SPSS. See Statistical Package for Social Science (SPSS) SRB. See Surface Radiation Budget (SRB) SRF. See Sundarban Reserve Forest (SRF) Sri Lanka, South Asian countries, 337 SRTM. See Shuttle Radar Topography Mission (SRTM) SSH. See Sea Surface Height (SSH) SSR. See Surface net solar radiation (SSR) Stable baseflow approaches (SBF approaches), 297 Stagnation of water, 406 Stakeholders, 196
199, 307
308 Standard deviation (SD), 36 Standard precipitation Index (SPI), 178
182, 273 climatic moisture category/drought categories for, 182t data analysis and resulting methods, 181f in districts of Bundelkhand of Madhya Pradesh, 187f evaluation and characteristics of drought, 184
186 Statistical Package for Social Science (SPSS), 401 stl1. See Soil temperature level 1 (stl1) stl2. See Soil temperature level 2 (stl2) stl3. See Soil temperature level 3 (stl3) stl4. See Soil temperature level 4 (stl4) Storm surges, 106
108, 433
434 culminate in capacity, 57
58 Storms, 228, 292, 300, 452
453 Stormwater management, 485
487 Study area selection, 29 Sub-units, 212 Subsistence agriculture, 275 Sudden-onset events, 177
178 Summer season, 506 Sundarban (mangrove forest), 57
58 biosphere, 377
378 as climate hotspot, 382 community readiness model, 384
385, 386t ecosystem, 383 limitations of study, 386 perceived impact of cyclone on Sundarban community, 384t recommendation, 387 revisiting theories, conventions, and agreements, 385

542

Index

Sundarban (mangrove forest) (Continued) socioeconomic background and vulnerability to disasters, 385
386 specific impact and coping techniques, 384, 384t study area showing surveyed villages of Sagar Block of Sundarban, 59f surveyed villages of Gosaba Block of Sundarban, 60f Sundarban Reserve Forest (SRF), 60 Support system, 145
146 Surface net solar radiation (SSR), 502
503 Surface Radiation Budget (SRB), 180 Surface water, 295 bodies, 365 irrigation systems, 364
365 sources, 467
469 Susceptibility patterns, understanding and mapping, 300
301 Susceptibility to natural disasters (Sus), 310 Sustainability, 318, 348
349 indicators for block Gangasagar, 64t indicators for block Gosaba, 65t Sustainable agriculture, 161 Sustainable communities, 307
308 Sustainable development, 15, 75
76, 80, 212, 226, 306
307, 321
322, 378, 428
429 Sustainable Development Goals (SDGs), 226, 232
233, 259
261 Sustainable economic development, 225
226 Sustainable economy, 225 Sustainable environmental control, 433
434 Sustainable fish resources, 143 Sustainable food production, 337
338 Sustainable Livelihood Framework (SLF), 203 Sustainable livelihood security index (SLSI), 61
62 implementation of, 67 relationship between adaptive capacity and adaptation in light of, 67
68 techniques, 57
58 Sustainable livelihoods (SL), 57
58 Sustainable management, 467 of natural resources, 161
164 demonstration plot, 162f harvest of commodities in three villages, 160t women farmer groups’ harvest, 161f Sustainable processes, 385 Sustainable urban development, 80 Sustainable waste management, 205 SVI-ICA. See Social vulnerability index for industrial complex area (SVI-ICA) Swell waves, 110
111 SWH. See Significant Wave Height (SWH) Synchronous ecological and social transition (SET), 281

System of environmental-economic accounting framework (SEEA framework), 460
461

T Tambak Lorok community, 90
91 population, 87
88 rob flood, social cultural adaptation process of urban coastal community in, 81
86 coastal community participation model, 85f identified impacts of mitigation and adaptation, 86 land subsidence, 84f mitigation and adaptation to climate change strategies, 83
86 overview of Semarang city, 81
83 residential village, 84f rob flood and land subsidence in Tambak Lorok Kampong, 81 TCs. See Tropical cyclones (TCs) Tea gardens, 501
502 Tea production, 500 average maximum temperature on tea yield, 512f holistic effects of land and atmospheric covariates on, 505
509 nonlinear effects of land and atmospheric covariates on, 509
516, 511t predictor variables having statistically significant effect on monsoon tea production, 515f post-monsoon tea production, 516f pre-monsoon tea production, 514f regression coefficients estimated through multivariate fixed-effect linear regression models, 507t seasonal effect of land and atmospheric covariates on log monthly tea production, 508t Tea yield materials and methods, 501
505 data compilation, 501
503 determining order of polynomial for estimating nonlinear effects of land and atmospheric variables, 504 linear regression analyses for estimating holistic effects of land and atmospheric variables on tea yield, 503
504 multivariate polynomial regression analyses for estimating nonlinear effects of land and atmospheric variables on tea yield, 505 study area, 501, 502f results, 505
516 holistic effects of land and atmospheric covariates on tea production, 505
509

Index

nonlinear effects of land and atmospheric covariates on tea production, 509
516 order of polynomial for estimating nonlinear effects of land and atmospheric variables, 509 Technical approach in agriculture, 170
171 Technological Vulnerability Index (TVI), 394 Teesta basin, 28, 39, 126
129 Teesta Basin of West Bengal, 119 Teesta flood-prone area, flood risk assessment in, 42
48, 43t Teesta Kangse Glacier, 28 Teesta River, 28, 41 basin, 124, 125f conflicts due to climate-induced changes on, 127 reduced variate and flood peak for, 38f Temporal migration and vulnerabilities, drought induced, 186
188 Temporal variation in water levels of river Teesta, 34
35 Temporary migration, 176 and local vulnerabilities, 182 Tentara Nasional Indonesia (TNI), 216 Terai
Dooars region, 499
500 Terminological diversity, 428
429 Terra satellites, 299
300 Terrestrial habitats, 459 Territorial container, 120 Thematic Mapper (TM), 197
199 Thematic scope, 420
421 Thermal capacity information, 400 Thermal energy, 228 Thermal Infrared Sensor (TIRS), 399t Tibetan Plateau, 121, 124 Tidal disaster, 105
106 Tidal floods, 75
77, 81
82 community perception toward, 86
91 attitudes of Tambak Lorok and Kamijen community, 88f causes of, 87f frequency, 108 in Indonesia, 94 map location of study area, 96f Tidal force, 77
78 Tidal range, 438 Tidal rising, 77
78 Tidal wave, 105
106 Tidyverse packages, 6
7 TIRS. See Thermal Infrared Sensor (TIRS) TM. See Thematic Mapper (TM) TNI. See Tentara Nasional Indonesia (TNI) Topography, 94, 435
436 of coast of Probolinggo, 99
100 topography-bathymetry dataset, 458
459

543

Tourism, 193
194, 305
306 business, 197
199, 201
202, 205 growth, 203 Tourist mobility, mitigation policies impacts on, 194 Tourist supply, 197 Towns, 241 Traffic jams, 82 Transboundary rivers, 121
122 Transdisciplinary knowledge, 4 Transparency and participation increasing, 366
370 remote sensing data used for groundwater assessment, 368t Tree plantation, 336 Trekking routes, 194
195, 204 TRMM. See Tropical rainfall measuring mission (TRMM) Tropical cyclones (TCs), 106
108, 380
381, 451 Tropical rainfall measuring mission (TRMM), 368t TsoLamo Lake, 28 Tulsi (Ocimum sanctum), 50 Turmeric (Curcuma longa), 50 TVI. See Technological Vulnerability Index (TVI) Two-level game approach, 210

U UAVs. See Unmanned aerial vehicles (UAVs) UGM. See University of Gadjah Mada (UGM) UI. See University of Indonesia (UI) UNCHE. See United Nations Conference on Human Environment (UNCHE) UNDIP. See Urban Planning Faculty Diponegoro University (UNDIP) UNDP. See United Nations Development Programme (UNDP) UNEP. See United Nations Environment Program (UNEP) UNFCCC. See United Nations Framework Convention on Climate Change (UNFCCC) UNGA. See United Nations General Assembly (UNGA) Unit of analysis, 212 United Nations Conference on Human Environment (UNCHE), 213 United Nations Development Programme (UNDP), 57
58, 62
67, 309
310 United Nations Environment Program (UNEP), 218, 292 United Nations Framework Convention on Climate Change (UNFCCC), 3, 215, 217 United Nations General Assembly (UNGA), 217
218 United Nations system, 213 University of Gadjah Mada (UGM), 139
140 University of Indonesia (UI), 139
140

544 Unmanned aerial vehicles (UAVs), 369 Urban agglomeration, 82 Urban coastal community in Tambak Lorok toward rob flood, social cultural adaptation process of, 81
86 Urban development, 75
76 Urban ecosystems, 77
78 Urban flooding, 391
393 Urban land deformation, 434 Urban planners, 467
469 Urban planning, 346 in Semarang Municipality, 85
86 Urban Planning Faculty Diponegoro University (UNDIP), 355t Urban population, 82, 94
95 Urban pressure, 422 Urban resilience strategy, 358 Urban sprawl, 78
79 Urbanization, 361
362, 433
434, 485
487 US Geological Survey (USGS), 197
199, 439 USGS. See US Geological Survey (USGS)

V Validation process, 446 Variability traps, 282
283 Varimax-rotated Principal Component Analysis, 196
197 Vector-borne diseases, 130
131, 408
409, 444 Vegetation conservation, 452 Vegetation index, 483 Vertical electrical sounding techniques (VES techniques), 297 Vertical Land Motion (VLM), 438
439, 443 VES techniques. See Vertical electrical sounding techniques (VES techniques) VI. See Vulnerability Index (VI) Village community levels, 164 Village Index, 62 Village-wise capital-based scenario, 67 Virgin forest soils, 243 Virtual water (VW), 363
364, 364t Visualize disaster response capacity, 324
325 VLM. See Vertical Land Motion (VLM) Voltage power lines, 426
427 VosViewer software, 6
7 Vulnerability, 14
15, 31
32, 70, 130, 146, 153
155, 177
178, 196, 333, 345
346, 392 analysis, 300
301 assessments, 57
58, 61, 394 of coastal communities, 136
137, 140
141 coastal communities and challenges of vulnerability due to climate change in Indonesia, 142
145 community perceptions of

Index

adaptability vulnerability index, 409
410 area vulnerability index, 402
405 data sources, 395
396 demographic details of respondents, 402, 403t details of bands, 399t individual vulnerability index, 405
409 justification for selection of indicators, 394
395 livelihood vulnerability index, 409 map of, 401f materials and methods, 394
401 methodological framework, 395f objectives, 394 results, 402
410 satellite imagery, 399t software, 401 statistical analysis, 396
400 study area, 401
402 theoretical orientation, 392
394 various indicators and sub-indicators, 397t dimension of vulnerability of Navara farming due to land use and climate change, 253 model of resilience in facing vulnerability of coastal communities in Indonesia, 145
148 in Regency of Indonesia, 141f socioeconomic background and vulnerability to disasters, 385
386 Vulnerability Index (VI), 31, 394 Vulnerable communities, 94 VW. See Virtual water (VW)

W WAI. See Weighted average index (WAI) Warmer stream temperatures, 294
295 Waste, 82 Wastewater, 467 Water Quality Index (WQI), 297
298 Water stress implementable neighborhood water sensitive plan, 491
495 integrating stakeholders in implementation process, 495 interventions, 493
495 in Indian cities, 469, 469f micro study area selection, 483
491, 491f need identification and global approaches, 467
469, 468t physiographic setting of micro study area, 485
487, 492f research process, 470
474 applicability of stream flow, 470
472 applicability of stream ordering, 472 geospatial assessment of national capital territory, Delhi, 470

Index

process of watershed delineation, 473
474 selection of site for research, 470, 471t spatial detection of change in blue and green areas, 474
482 normalized difference vegetation index assessment, 477
478 normalized difference water index assessment, 475
477 overall assessment of NDWI and NDVI for NCT, 478
479 strategies, 487
489 identification of green corridors along street network, 493f stages for decentralized cleansing mechanism, 492f water as sensitive issue, 467 water detention area microanalysis, 489
491 identification of potential detention areas, 494f weighted overlay analysis, 479
482 geological map of Delhi NCT, 487f land use map of Delhi NCT, 488f potential zones for water recharge within National Capital Territory, 490f soil map of Delhi, 485f surface temperature map of Delhi, 486f thematic parameters for weighted overlay analysis with assigned weights, 489f Waterbodies, 402 Waterborne diseases, 444 Waters, 119
121, 361, 467 adaptation and management strategy, 297
298 availability of, 33
34 conservation, 163 crisis, 365 cycle, 361 deficit, 178 detention area microanalysis, 489
491 identification of potential detention areas, 494f scale matrix for integrating blue-green infrastructure, 494t footprint, 363
364 harvesting, 332 index depicts, 475 management, 362
363, 467
469 pollution, 467 post-intervention data on water availability, 164t pressure, 96 quality, 361
362 reservoir, 354, 491
493 resources, 119
121, 127 management, 120
121, 368 scarcity, 175
176, 362 as sensitive issue, 467 supply, 467
469

545

surface temperature, 122 systems, 366
368 temporal variation in water levels of river Teesta, 34
35 wall, 308
309 wars, 129 water-based ecosystems, 362 water-related natural hazards, 178 water-resilient approach, 467
469 water-sensitive plan, 472 water-sensitive spatial strategy, 495 WaterSeer extracts water, 372 Watershed delineation methodology for watershed delineation, 473f natural hydrology depiction for Delhi national capital territory, 474f process of, 473
474 Wave energy, 437 Wave power measurements, 457
458 Waves of adversity, 57
58 WCED. See World Commission on Environment and Development (WCED) Weather information, 314
315 Weather variables, 300 Web of Science Core Collection (WOSCC), 363 Weibull distribution functions, 30 Weibull equation, 30 Weibull’s method, 36 Weighted average index (WAI), 31
32 Well-drilling processes, 364
365 West Bandung Regency, 226 West flood canal project, 85 West monsoon season, 435 Western Ghats, 242 Wet waste management, 354
355 Wetlands, 452
453 Wild Life Protection Act, 71 Winds, 77
78 exposure, 456
459 power investments, 427 turbines, 427 waves, 110
111 Women, 225
226, 228
229, 237 adaptation of women’s groups to climate change, 229 climate change impact on women as vulnerable group, 228
229 farmers, 169 group at Sajang village, 157f groups harvest, 161f managed solar-powered drip irrigation, 168
170 public consultation with, 168f social entrepreneurship program for, 233
234

546

Index

Women (Continued) vulnerable group, 232
233 well-being of, 169 World Commission on Environment and Development (WCED), 213 World Meteorological Organization, 292 World Summit on Sustainable Development (WSSD), 213, 217 World Wildlife Fund (WWF), 204
205 WOSCC. See Web of Science Core Collection (WOSCC) WQI. See Water Quality Index (WQI) WSSD. See World Summit on Sustainable Development (WSSD)

WVI, 154 WWF. See World Wildlife Fund (WWF)

Y Yaksum, 194 communalities values of, 198t NDVI status for, 200f rotated component matrix of, 199t Yaksum
Goeche La trekking corridor, 205t Yudhoyono’s foreign policy, 217

Z Zero poverty, 226