Climate Vulnerability and Resilience in the Global South: Human Adaptations for Sustainable Futures (Climate Change Management) 3030772586, 9783030772581

Table of contents :
Preface
Contents
1 Climate Risks, Adaptation and Vulnerability in Sub-Saharan Africa and South Asia
Introduction
Climate Risks and Factors Contributing to Vulnerability in Sub-Saharan Africa and South Asia
Study Area, Data, and Methodology
Study Area and Data
Methodology
Results and Discussion
Climate Risks Experienced in Past 10 Years
Descriptive Statistics
Factors Determining Climate Adaptation by Farmers
Conclusion and Policy Recommendations
References
2 Climate Modeling, Drought Risk Assessment and Adaptation Strategies in the Western Part of Bangladesh
Introduction
Data and Methods
Selection of Study Area
Data Sources
Missing Data Estimation
Trend Analysis Methods
Prediction of Climate Variables
Modeling Agricultural Drought Using Markov Chain
Drought Proneness Index
Drought Hazard Index (DHI) Calculation
Calculation and Classification of Drought Vulnerability Index (DVI)
Index of Drought Risk (DRI)
Interpolation with GIS
Results and Discussions
Descriptive Statistics of Rainfall and Temperature
Analysis of Mean Annual Rainfall
Analysis of Mean Annual Rainfall in the Cropping Season
Analysis of Mean Annual Temperature
Analysis of Mean Annual Temperature in the Cropping Season
Prediction of Annual Mean Temperature Using the ARIMA Model
Agricultural Drought Modeling Using Markov Chain
Spatial Distribution of the Seasonal Probability of Dry and Wet Weeks
Seasonal Characteristics of DI Indices
The Trend in Drought Index (DI) in Kharif Season
The Trend in Drought Index (DI) in Pre-Kharif Season
Annual Occurrences of Agricultural Drought
Drought Hazard, Risk and Vulnerability Analysis
Drought Hazard Index and Hazard Map
Drought Susceptibility of Socio-economic and Physical Indicators
Population Density
Female to Male Ratio
Literacy Rate
People Reliant on Agriculture
Irrigated Land to Cultivable Land
Crop Production
Drought Susceptibility
Drought Risk
Conclusions and Policy Recommendations
References
3 Contextualizing Resilience Amidst Rapid Urbanization in Kenya Through Rural-Urban Linkages
Introduction
Rural-Urban Linkages and Climate Change
Linear and Non-linear Linkages in the Rural-Urban Context
Climate Change as a Threat-Multiplier
Methods
The Study Area
Data Collection and Analysis
Results and Discussion
Anchorage of Rural-Urban Linkages in Kenya
Status of the Road to Resilience in Kenya
Conclusions and Policy Implications
Pathways to Improved Resilience
Policy Implications
References
4 Modeling and Forecasting Climate Change Impact on Groundwater Fluctuations in Northwest Bangladesh
Introduction
Research Problem
Objective of the Study
Methods and Materials
Study Area and Data Collection
NARX Model Architecture
Normalization
Training Algorithms
Data Preprocessing
Evaluation of Performance
Data Analysis and Discussion
Trend of Annual Average Maximum Temperature
Yearly Average Min Temperature Trend
Yearly Mean Temperature Trend
Yearly Mean Rainfall Trend
Trend of Yearly Average Relative Humidity (%)
Interaction of Climatic Indicators in Northwest Bangladesh
Groundwater Table Depth: Temporal Analysis
Modeling Results
Major Findings
Conclusions
Recommended Policy
References
5 Modeling Household Socio-Economic Vulnerability to Natural Disaster in Teesta Basin, Bangladesh
Introduction
Literature Review
Methodology
Study Area Description
Techniques of Data Collection
Indicators and Factors of the Study for SeVI
Calculation Procedure of SeVI Model
Calculation Procedure of IPCC-SeVI
Results
Adaptive Capacity Index of the Study Area
Exposure Index of the Study Area
Sensitivity Index of the Study Area
IPCC-SeVI of the Study Area
Overall and HHs SeVI Score
Benefits of SeVI and IPCC-SeVI Approaches
Discussion
Conclusion
Appendix
References
6 Post-cyclone Occupational Vulnerabilities of Farmers in South-West Coastal Region of Bangladesh
Introduction
Methods
Design
Study Area
Sampling and Participants
Data Collection Tools and Data Collection
Data Analysis
Ethical Consideration
Results and Discussion
Theme 1: Pre-Cyclone Occupational Situation and Impact of Cyclone
Theme 2: Post-Cyclone Occupational Vulnerabilities by Waterlogging and Salinity Intrusion
Theme 3: Post-Cyclone Occupational Vulnerabilities: Sand Falling, Crisis of Irrigation Water and Loan Debt
Theme 4: Impact of Occupational Vulnerabilities on Life and Adaptation to Climate Change
Theme 5: Solution to Reduce Vulnerabilities
Discussion
Conclusion and Policy Recommendation
References
7 Modeling of Greenhouse Gas Emission and Its Impact on Economic Growth of SAARC Countries
Introduction
Objectives of the Research
Data and Method
Data Source
Time Series Models and Methods
Results and Discussions
Conclusions
References
8 Agriculture and Climate Change in Nepal: GHG Emissions, Mitigation, Indications of Climate Change, Impact on Agriculture, Adaptation, and Co-benefits
Introduction
Methodology
Results and Discussion
Emissions of Greenhouse Gases
Climate Change Mitigation from Agriculture and Forestry in Nepal
Climate Change Indications and Impact on Agriculture
Climate Change Adaptation in Agriculture Sector in Nepal
Co-benefits Leading to Resilient Agriculture System
Conclusion
References
9 Vulnerability, Food Security and Adaptation to Climate Change of Coastal Rice Farmers in Bangladesh
Introduction
Methodology
Description of the Study Area
Sampling and Data Collection
Measurement of Variables and Data Analysis
Results and Discussion
Livelihood Status and Institutional Accessibility
Farmers’ Perception About Vulnerabilities
Food Security Status
Coping Strategies
Adaptation Strategies of Farmers
Conclusion and Policy Implication
References
10 What Influence Evacuation Decisions at Cyclone Shelters? Empirical Evidence from Bangladesh
Introduction
Methodology
Study Area
Data Collection
Data Analysis
Results and Discussion
Socio-demographic Profile of the Respondents
Changes in Settlement Pattern and the Cyclone Shelters in the Last 20 years
Establishment of a Cyclone Shelter
Spatial Distribution of Cyclone Shelters
Factors that Influence Evacuation Decisions
Impact of Factors on Evacuation Decisions
Conclusion
References
11 Assessment of Structural Weakness of Government Response to Natural Hazards
Introduction
Literature Review
Methodology
Geographical Features of the Study Areas
Population and Sample
Sampling Technique
Data Collection
Approaches to Analysis
Data Processing
Results and Discussion
Governance Issues in Char Areas
Status of Institutional Response
Public Perception and Institutional Response to Flood Control
Conclusion
References
12 Ensemble Technique for Predicting Rainfall of Drought Vulnerability of Barind Track in Bangladesh
Introduction
Review of Literature
Methodology
Study Area
Framework of the Study
Sources of Data
Data Preparation
Modeling
Model Evaluation
Calculate Drought Index Based on Forecasted Data
Results and Discussions
Conclusions
References
13 Riverbank Erosions, Coping Strategies, and Resilience Thinking of the Lower-Meghna River Basin Community, Bangladesh
Introduction
Resilience to Natural Hazards and Disasters
Resilience Thinking
Community Resilience
Methods
Study Area
Results
Impacts of Riverbank Erosions
Coping Strategies and Responses to Riverbank Erosions
Discussion and Conclusions
References
14 Understanding the Climatic and Non-climatic Drivers of Livelihood Vulnerability in the Tigray Region of Ethiopia
Introduction
Methodology
Study Area
Data Collection Approach and Methods
Data Analysis Techniques
Results and Discussion
Identifying the Factors that Influence Livelihood Vulnerability
Comparing the Importance of Stressors that Influence Livelihood Vulnerability
Comparing the Importance of Livelihood Vulnerability Factors Across Villages
The Impacts of Climatic Factors on Farmers’ Lives and Livelihoods
The Impacts of Non-climatic Factors on Farmers’ Lives and Livelihoods
Conclusion and Policy Recommendations
References
15 Drivers of Vulnerability and Its Socio-economic Consequences: An Example of River Erosion Affected People in Bangladesh
Introduction
Research Context
Review of Literature
Methodology
Research Context and Location
Research Approach
Research Method
Data Collection Methods and Data Collection Instruments
Sampling and Respondents
Respondents’ Profile
Data Analysis Techniques
Research Ethics
Results
Physical Vulnerability
Economic Vulnerability
Social Vulnerability
Psychological Vulnerability
Discussion
Conclusions
References
16 Analyzing Influential Factors to Flood Resilience in the Northern Flood- Prone Rural Areas of Bangladesh
Introduction
Methodology
Study Area
Data Collection
Data Analysis and Visualization
Results and Discussion
Flood Resilience Capacity of the Unions
Influential Factors for Vulnerability to Flood
Influential Factors for Resilience to Flood
Comparison of Resilience Capacity
Conclusion
References
17 Climate Change and Livelihood Vulnerabilities: The Forest Resource-Dependent Communities of the Sundarbans of Bangladesh
Introduction
The Sundarbans Forest of Bangladesh and the Dependent Communities Amidst Climate Change
Livelihood Vulnerabilities of FRDCs of the Sundarbans
Indicators of Livelihood Vulnerability of the FRDCs Due to Climate Change
Adaptation and Mitigation Strategy of the FRDCs in the Sundarbans
Conclusion
Recommendation
References
18 Socio-political Distancing Amid Disaster: Empirical Evidence from Bangladesh
Introduction
Review of Literature
Methodology
Study Area
Data Collection
Data Characteristics and Analysis
Results and Discussions
Demographic and Socio-economic Profile of the Respondents
Knowledge of Local People in Disaster Management and Climate Change Adaptation
Socio-political Context
Disaster Management and Climate Change Adaptation: How the Local Political Leaders Deal
How Do the Disaster Victims Evaluate Various Factors Affecting Disaster Management, and How Does It Differ Amongst the Respondents?
To What Extent Does the Individual Level Political Engagement Contribute to Assessing Political Leaders’ Role in Disaster Management?
Conclusion and Policy Recommendation
References
19 Climate Change and Health Care Vulnerability in South East Asia: A Review
Introduction
Data and Method
Vulnerability and Adaptation to Climate Change
Strength of Existing Health Care Service in SEA Region
Health Care Vulnerability in SEA Region
Essential Health Service and Maternal and Neonatal Mortality
Conclusion
References
20 Understanding Climate Change Perception of Teachers and Students: An Overview
Introduction
Climate Change and Academic Institutions
Variations in Perception of Climate Change Among Teachers and Students
Teachers’ Perception of Climate Change
Students’ Perception of Climate Change
Variations in Teachers’ and Students’ Perceptions of Climate Change
Conclusion and Policy Recommendation
References
21 Assessing the Role of Organizations for Health Amenities of Flood Affected People in Char Areas of Bangladesh
Introduction
Materials and Methods
Description of the Study Area
Data Collection, Analysis and Interpretation
Measuring Intervention of Organizations on Health Amenities
Results and Discussion
Health Impacts Due to Flood Disaster
Health Amenities to the Affected People: Measures Taken by GOs and NGOs
Sufficiency and Quality of Medical Facilities (People’s Experience)
Problems and Challenges in Receiving Health Facilities
Conclusion and Recommendations
References
22 Resilience for Disaster Management: Opportunities and Challenges
Introduction
Methodology
Search Approach and Scholarly Resources
Inclusion and Exclusion Criteria
Results
Discussions
Conceptualization of Resilience
Dimensions of Disaster Resilience
Challenges of Resilience for Disaster Management
Opportunities of Resilience for Disaster Management
Conclusion
References
23 Hailstorms in Northern Bangladesh: Investigating Hazard Prioritization by and Perceived Risks for Farmers
Introduction
Methodology
Study Area
Data Collection
Data Analyses
Result and Findings
Identification of Existing Hazards and Their Seasonal Variation in the Study Area
Natural Hazard Mapping of the Study Area
Hazard Categorization, Risk Levels, and Existing Mitigation/institutional Approaches
Community Level Risk Assessment of Hailstorms and Other Natural Hazards
Prioritization of the Hazards Based on Perceived Risks for Farmers
Frequency and Distribution of Hailstorms in Bangladesh
Discussion
Conclusion and Implications
References
24 Assessing and Mapping Spatial Variations in Climate Change and Climatic Hazards in Bangladesh
Introduction
Climate Change and Climatic Hazards in Bangladesh
Methodology
Sources of Data
Calculation of Hazard Scores
Results
Spatial Variations in Climate Change for Bangladesh
District-Wise Scores for Specific Climatic Hazards in Bangladesh
Spatial Variations and a Multi-Hazard Map of Bangladesh
Discussion
Conclusion and Recommendation
References
25 Climate Change Vulnerability and its Impacts on Live and Livelihood Patterns in the South-Middle Coastal Areas of Bangladesh
Introduction
Materials and Methods
Study Area
Sample Size Determination
Data Collection
Data Analysis and Processing
Measurement of Climate Change Vulnerability
Results and Discussion
People's Perception of Climatic Variability
Assessment of Climate Change Vulnerability
Vulnerability Ranking at Community Levels
Climate Vulnerability and Influences on Livelihoods
Vulnerability Contexts Versus Well-Being at Community Level
Adaptation Capacity and Strategy at Community Level
Identification of Local Adaptation Strategies
Mitigation Measures
Conclusion
References
26 Temporal Evaluation of Climate Change on Land Use and Land Cover Changes in the Southeastern Region of Bangladesh from 2001 to 2016
Introduction
Materials and Methods
Features of the Study Area
Datasets
Determination of Land Use Classification System
Image Classification and Change Detection
Data Integration
Results and Discussions
Land Use and Land Cover Classification
Land Use and Land Cover Changing Transfer Matrix
Land Surface Temperature (LST) Changes
Climatic Data and Evidence of Climate Change
Conclusion
Competing Interest
References
27 Assessing Farm-Households’ Vulnerability to Climate Change Risks in Semi-arid Ghana
Introduction
Theoretical Review
Vulnerability-Resilience Nexus
Contextual Vulnerability
Materials and Methods
Profile of Study Area
Sampling and Data Collection
Data Analysis
Results
Spatial Variation in Vulnerability of Farm Households
Linking Type of Farm-Household Headship to Vulnerability Status
Discussion
How Does a Farm-Household’s Locational Context Influence Its Vulnerability Status?
Gender of Farm-Household Head as a Determinant of Climate Vulnerability
Conclusions
Appendices
Appendix A: Design of Farm-Household Livelihood Vulnerability Index (FLVI)
Appendix B: Sub-component Values and Minimum and Maximum Values by Household Type
Appendix C: Indexed Major Components, Sub Components, and Overall FLVI by Household Type
References

Citation preview

Climate Change Management

G. M. Monirul Alam Michael O. Erdiaw-Kwasie Gustavo J. Nagy Walter Leal Filho   Editors

Climate Vulnerability and Resilience in the Global South Human Adaptations for Sustainable Futures

Climate Change Management Series Editor Walter Leal Filho, International Climate Change Information and Research Programme, Hamburg University of Applied Sciences, Hamburg, Germany

The aim of this book series is to provide an authoritative source of information on climate change management, with an emphasis on projects, case studies and practical initiatives – all of which may help to address a problem with a global scope, but the impacts of which are mostly local. As the world actively seeks ways to cope with the effects of climate change and global warming, such as floods, droughts, rising sea levels and landscape changes, there is a vital need for reliable information and data to support the efforts pursued by local governments, NGOs and other organizations to address the problems associated with climate change. This series welcomes monographs and contributed volumes written for an academic and professional audience, as well as peer-reviewed conference proceedings. Relevant topics include but are not limited to water conservation, disaster prevention and management, and agriculture, as well as regional studies and documentation of trends. Thanks to its interdisciplinary focus, the series aims to concretely contribute to a better understanding of the state-of-the-art of climate change adaptation, and of the tools with which it can be implemented on the ground. Notes on the quality assurance and peer review of this publication Prior to publication, the quality of the works published in this series is double blind reviewed by external referees appointed by the editor. The referees are not aware of the author’s name when performing the review; the referees’ names are not disclosed.

More information about this series at http://www.springer.com/series/8740

G. M. Monirul Alam · Michael O. Erdiaw-Kwasie · Gustavo J. Nagy · Walter Leal Filho Editors

Climate Vulnerability and Resilience in the Global South Human Adaptations for Sustainable Futures

Editors G. M. Monirul Alam Bangabandhu Sheikh Mujibur Rahman Agricultural University Gazipur, Bangladesh University of Southern Queensland Toowoomba, QLD, Australia Gustavo J. Nagy Universidad de la Republica Montevideo, Uruguay

Michael O. Erdiaw-Kwasie Sustainable Enterprise Division Asia Pacific College of Business and Law Charles Darwin University Darwin, NT, Australia Urban & Regional Planning Discipline Faculty of Health, Engineering and Sciences, USQ, Australia Walter Leal Filho Hamburg University of Applied Sciences Hamburg, Germany

ISSN 1610-2002 ISSN 1610-2010 (electronic) Climate Change Management ISBN 978-3-030-77258-1 ISBN 978-3-030-77259-8 (eBook) https://doi.org/10.1007/978-3-030-77259-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Climate change vulnerability is a problem especially acute in developing countries, which often do not have access to the resources and technologies needed to reduce it. As a result of this trends, efforts to increase their resilience are also made more difficult. Based on the need to shed some light on the relations between climate change vulnerability and resilience, this book has been prepared. It has been designed with a view to providing a hands-on approach, which entails conceptual, theoretical and case study discussions on vulnerability and resilience in the global south. It covers the core of adaptation strategies in a developing country context, in an easyto-follow set of theoretical and empirical examples. This book shares contemporary approaches on vulnerability, adaptation strategies and resilience to climate change, which aim to assist its targeted audience (academics, policy-makers and practitioners) to understand and make better informed decisions in a wide variety of real-world resilience situations. We would like to thank all authors and reviewers for making available their experience in their chapters and the willingness to share their ideas. Much can be gained by offering a platform for the debate on climate change vulnerability and resilience in developing countries, in a pragmatic way. By providing their inputs, the authors have made a positive contribution towards a debate which needs to be continued and reach a depth far beyond what conferences, workshops or seminars may be able to offer. Gazipur, Bangladesh/Toowoomba, Australia Darwin, Australia/USQ, Australia Montevideo, Uruguay Hamburg, Germany Summer 2021

G. M. Monirul Alam Michael O. Erdiaw-Kwasie Gustavo J. Nagy Walter Leal Filho

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Contents

1

2

3

4

Climate Risks, Adaptation and Vulnerability in Sub-Saharan Africa and South Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jeetendra Prakash Aryal, Dil Bahadur Rahut, and Paswel Marenya Climate Modeling, Drought Risk Assessment and Adaptation Strategies in the Western Part of Bangladesh . . . . . . . . . . . . . . . . . . . . Md. Kamruzzaman, Tapash Mandal, A. T. M. Sakiur Rahman, Md. Abdul Khalek, G. M. Monirul Alam, and M. Sayedur Rahman Contextualizing Resilience Amidst Rapid Urbanization in Kenya Through Rural-Urban Linkages . . . . . . . . . . . . . . . . . . . . . . . Risper Nyairo, Ruth Onkangi, and Merceline Ojwala Modeling and Forecasting Climate Change Impact on Groundwater Fluctuations in Northwest Bangladesh . . . . . . . . . . Md. Abdul Khalek, Md. Mostafizur Rahman, Md. Kamruzzaman, Zubair Ahmed Shimon, M. Sayedur Rahman, and Md. Ayub Ali

1

21

55

75

5

Modeling Household Socio-Economic Vulnerability to Natural Disaster in Teesta Basin, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Sosimohan Pal, Abu Reza Md. Towfiqul Islam, Masum Ahmed Patwary, and G. M. Monirul Alam

6

Post-cyclone Occupational Vulnerabilities of Farmers in South-West Coastal Region of Bangladesh . . . . . . . . . . . . . . . . . . . . . 131 Lubaba Khan, Tuhin Roy, and G. M. Monirul Alam

7

Modeling of Greenhouse Gas Emission and Its Impact on Economic Growth of SAARC Countries . . . . . . . . . . . . . . . . . . . . . . 145 Rocky Rahman, M. Sayedur Rahman, and Md. Sabiruzzaman

vii

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Contents

8

Agriculture and Climate Change in Nepal: GHG Emissions, Mitigation, Indications of Climate Change, Impact on Agriculture, Adaptation, and Co-benefits . . . . . . . . . . . . . . . . . . . . . 163 Niraj Prakash Joshi, Luni Piya, and Deepak Aryal

9

Vulnerability, Food Security and Adaptation to Climate Change of Coastal Rice Farmers in Bangladesh . . . . . . . . . . . . . . . . . . 187 Faijul Islam, G. M. Monirul Alam, Rokeya Begum, Md Nazirul Islam Sarker, and Humnath Bhandari

10 What Influence Evacuation Decisions at Cyclone Shelters? Empirical Evidence from Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Nafisa Nuari Islam and Bishawjit Mallick 11 Assessment of Structural Weakness of Government Response to Natural Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 Md Nazirul Islam Sarker, G. M. Monirul Alam, Abu Reza Md. Towfiqul Islam, Md. Enamul Huq, Md Lamiur Raihan, Ram Proshad, and Babul Hossain 12 Ensemble Technique for Predicting Rainfall of Drought Vulnerability of Barind Track in Bangladesh . . . . . . . . . . . . . . . . . . . . . 239 Md. Mostafizur Rahman, Md. Abdul Khalek, and M. Sayedur Rahman 13 Riverbank Erosions, Coping Strategies, and Resilience Thinking of the Lower-Meghna River Basin Community, Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Munshi Khaledur Rahman, Thomas W. Crawford, Bimal Kanti Paul, Md. Sariful Islam, Scott Curtis, Md. Giashuddin Miah, and Md. Rafiqul Islam 14 Understanding the Climatic and Non-climatic Drivers of Livelihood Vulnerability in the Tigray Region of Ethiopia . . . . . . 279 Rahwa Kidane, Thomas Wanner, and Melissa Nursey-Bray 15 Drivers of Vulnerability and Its Socio-economic Consequences: An Example of River Erosion Affected People in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 M. Rezaul Islam 16 Analyzing Influential Factors to Flood Resilience in the Northern Flood- Prone Rural Areas of Bangladesh . . . . . . . . . 327 Zannatul Nayem, Md. Sahadat Hossain, Abdullah Al Nayeem, and Muhammad Abdur Rahaman

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17 Climate Change and Livelihood Vulnerabilities: The Forest Resource-Dependent Communities of the Sundarbans of Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341 Mahfuza Zaman Ela, Taposhi Rabya, Lubaba Khan, Md. Habibur Rahman, Taufiq-E-Ahmed Shovo, Nusrat Jahan, Md. Tanvir Hossain, and Md. Nazrul Islam 18 Socio-political Distancing Amid Disaster: Empirical Evidence from Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353 Zakia Sultana, Pali Mondal, Tuhin Roy, Bangkim Biswas, and Bishawjit Mallick 19 Climate Change and Health Care Vulnerability in South East Asia: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371 Md. Sabiruzzaman, Md. Golam Hossain, and M. Sayedur Rahman 20 Understanding Climate Change Perception of Teachers and Students: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Khandaker Jafor Ahmed, Mohammed Thanvir Ahmed Chowdhury, Mufti Nadimul Quamar Ahmed, and Shah Md. Atiqul Haq 21 Assessing the Role of Organizations for Health Amenities of Flood Affected People in Char Areas of Bangladesh . . . . . . . . . . . . 409 Babul Hossain, Md Nazirul Islam Sarker, Md. Salman Sohel, Md. Abdus Salam, and Sajjad Hossain Shozib 22 Resilience for Disaster Management: Opportunities and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Md. Enamul Huq, Md Nazirul Islam Sarker, Ram Prasad, Tapos Kormoker, Mallik Akram Hossain, Md. Mijanur Rahman, and Ahmed Abdullah Al Dughairi 23 Hailstorms in Northern Bangladesh: Investigating Hazard Prioritization by and Perceived Risks for Farmers . . . . . . . . . . . . . . . . 443 Md Lamiur Raihan, Corinthias P. M. Sianipar, Mrittika Basu, Kenichiro Onitsuka, Tahmina Chumky, Md Nazirul Islam Sarker, and Satoshi Hoshino 24 Assessing and Mapping Spatial Variations in Climate Change and Climatic Hazards in Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465 Khandaker Jafor Ahmed and Yan Tan 25 Climate Change Vulnerability and its Impacts on Live and Livelihood Patterns in the South-Middle Coastal Areas of Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Md. Shahzada Mohiuddin, Md. Nuralam Hossain, Md Nazirul Islam Sarker, Md. Abdur Rakib Nayeem, Shahidul Islam, and Fayjus Salehin

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26 Temporal Evaluation of Climate Change on Land Use and Land Cover Changes in the Southeastern Region of Bangladesh from 2001 to 2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 Shahidul Islam, Mingguo Ma, Md. Nuralam Hossain, Sumon Ganguli, and Md Nazirul Islam Sarker 27 Assessing Farm-Households’ Vulnerability to Climate Change Risks in Semi-arid Ghana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 Gerald Atampugre, Melissa Nursey-Bray, Md. Masud-All-Kamal, and Benjamin Kofi Nyarko

Chapter 1

Climate Risks, Adaptation and Vulnerability in Sub-Saharan Africa and South Asia Jeetendra Prakash Aryal , Dil Bahadur Rahut , and Paswel Marenya

Abstract Climate risk is increasingly affecting the livelihood of the majority of the population in sub-Saharan Africa (SSA) and South Asia (SA), making the majority of people vulnerable to it. Therefore, enhancing adaptation to climate change is inevitable to reduce the vulnerability of people in these regions. This chapter investigated the major climate risks faced by the farm households and also examined the factors affecting the adoption of climate adaptation measures by them, using primary data from SSA (Ethiopia, Kenya, Tanzania, Malawi, and Mozambique) and SA (Bangladesh, India, Nepal). Additionally, we reviewed the level of climate risks and the factors that affect the capability of these countries to climate adaptation at the macro level. We found variation in the climate risks experienced by the farmers. One of the most common climate risks faced by the farmers was drought except in Bangladesh. The econometric model shows that economic status, membership in farm organization, and training are the important factors influencing the adoption of climate adaptation. Our review of macro-level indicators reveals that though the countries under study experience different levels of exposure to climate risks, they are highly vulnerable to climate risks because they all have a low adaptive capacity due to the prevalence of high levels of poverty and corruption in the public institutions. Hence, to enhance climate adaptation, the government and other relevant stakeholders should focus on improving the farmers’ resilience capacity by investing in improving the economic status, enhancing the network and knowledge through training, and building better and cleaner institutions.

J. P. Aryal (B) International Maize and Wheat Improvement Centre (CIMMYT), El Batan, Mexico D. B. Rahut Asian Development Bank Institute (ADBI), Tokyo, Japan P. Marenya International Maize and Wheat Improvement Centre (CIMMYT), Nairobi, Kenya © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_1

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J. P. Aryal et al.

Introduction Climate change, which is adversely affecting the lives and livelihoods of the global population, is expected to be one of the major threats to sustain the economic growth in developing countries (FAO 2012; IPCC 2014; Porter et al. 2014; Tol 2018). As the majority of the population in the global south rely on climate-sensitive sectors such as agriculture, livestock, and fisheries, they are more likely to be vulnerable to increasing climate change (Adhikari et al. 2015; IPCC 2019; Muchuru and Nhamo 2019; Alam et al. 2020; Abid et al. 2020; Aryal et al. 2020b). Climate change has an overall negative effect on agricultural production and food security in sub-Saharan Africa (SSA) (Schlenker and Lobell 2010; Adhikari et al. 2015; Muchuru and Nhamo 2019) as well as in South Asia (SA) (INCCA 2010; Lal 2011; Knox et al. 2012; Lobell et al. 2012; Asseng et al. 2015; Timsina et al. 2018; Alam et al. 2020). Heat-stress caused by climatic variability is expected to substantially affect the production of wheat crop in the Indo-Gangetic Plains (IGP) of SA by the mid-twenty-first century (Ortiz et al. 2008). Data analysis between 1999 and 2018 demonstrates that India falls among the top ten countries suffering from prolonged heat waves (Eckstein et al. 2020). Further, farmers in SSA extensively depend on rain-fed agriculture for food production and thus, suffer more from rainfall variability, inter-seasonal climate variability, recurrent droughts and floods, and other weather extremes (Cooper et al. 2008; Niang et al. 2014). Hence, vulnerability to climate risks of farmers in SSA and SA is critical, as they have limited resources and capacity for adaptation (Shivamurthy et al. 2015; Smucker et al. 2015; Adenle et al. 2017; Alam et al. 2018). While the poverty rate in SSA, defined as the percentage of people living below the international poverty line of $1.90 per person per day, fell between 1990 and 2015, rapid population growth in this region resulted in the rise of the absolute number of poor people over the same period (World Bank 2018). Owing to the slow rate of decline in poverty in SSA compared to other regions of the world between 1990 and 2015, this region had more extreme poor than in the rest of the world combined in 2015 (World Bank 2018). As of 2015, the average poverty rate for SSA is about 41%, while it is 13.5% for SA (World Bank 2018). The persistent poverty combined with vulnerable food production system largely limits the capacity of farmers in SSA to climate change adaptation (Niang et al. 2014; Azzarri and Signorelli 2020). Correspondingly, exposure to climate risks such as floods can reduce household consumption by 35% and increase extreme poverty by 17% in SSA (Azzarri and Signorelli 2020). In SA, almost half of the region’s population was affected by at least one of the climate-related disasters, and such events are expected to increase further in future (Fallesen et al. 2019). Climate change is expected to affect the food security of the millions of people in SA by the middle of the twenty-first century (Hijioka et al. 2014). Due to climate change, the average total economic losses are projected to be 9.4% for Bangladesh, 8.7% for India, and 9.9% for Nepal. Therefore, in the absence of adaptation to climate change, SA is projected to lose almost 2% of its annual gross domestic product (GDP) by 2050 and approximately 9% by 2100 (Ahmed and Suphachalasai 2014). In SSA, economic losses due to climate change

1 Climate Risks, Adaptation and Vulnerability …

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are expected to be higher than in any other region in the world. For example, when defined as a percentage of GDP, the economic losses due to climate change in Africa will be more than 10% higher than that of India and more than twice as high as in the US (African Development Bank 2011). In view of ever-increasing, devastating, and covariate nature of climate risks, this chapter has the following objectives: to investigate the major climate risks experienced by the farmers in SSA and SA, to examine the factors determining the likelihood to adopt climate adaptation, and to assess how several factors add to their vulnerability to climate change. In order to investigate the major climate risks faced by farmers and to examine the factors facilitating or hindering the likelihood to adopt climate adaptation measures, we used the primary data collected from 1900 farm households in three countries of SA (Nepal, India, and Bangladesh) and 4351 farm households in five countries of SSA (Ethiopia, Kenya, Malawi, Mozambique, and Tanzania). Our study is primarily based on a review of literature and the analysis of the primary and secondary datasets to investigate how several factors are contributing to vulnerability to climate change. With a focus on farmers in SSA and SA, the major home for about 75% of the total global poor, the study contributes to our understanding of climate risks, factors contributing to exacerbate climate vulnerability, and adaptation measures applied to reduce the impacts of climate risks in developing countries. Further, it contributes to understanding the cross-regional variations in climate risks and adaptation strategies.

Climate Risks and Factors Contributing to Vulnerability in Sub-Saharan Africa and South Asia Temperature in SSA is projected to exceed 2 °C by 2050 and 4 °C by 2100, and thus, it is more likely to increase climate extremes in the region (Niang et al. 2014). The probability of occurrence of droughts and rainfall variability is expected to rise further in Africa by 2050 (IPCC 2007). Though climate change affects all sectors of the economy, it hurts more to rain-fed agriculture (Ochieng et al. 2016, 2017) and is expected to reduce the production of major crops in the African region by 8 to 22% by 2050 (Schlenker and Lobell 2010). Despite some variations, drought and floods are considered to be the two major climate risks in SSA, which caused severe economic and human losses. For example, in Kenya, eleven droughts were recorded between 1964 and 2004, which affected approximately 1.5 million people against the seventeen major floods, affecting more than 70,000 people (Parry et al. 2012). In Malawi, increased climate variability resulted in droughts, floods, and mid-season dry spells (Katengeza et al. 2019). In Malawi, about 2.8 million people were affected by floods in 2015, and in 2016, nearly 6.5 million people were affected by drought (Government of Malawi 2019). In Ethiopia, twelve extreme droughts, which occurred between 1900 and 2010, affected over 54 million people and killed more than 4 million (You and Ringler 2010). With

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J. P. Aryal et al.

about 60% of the total population living in the coastal areas, Mozambique is one of the most exposed countries in SSA to climate risks, primarily cyclones, floods, and droughts (Baez et al. 2019). Floods and droughts are the main climate risks leading to economic loss in Tanzania. Flood in 2011 in Dar es Salaam (Tanzania) damaged the properties equivalent to 7.5 million Tanzanian Shillings, which required a budget of 1.3 billion Tanzanian Shillings to rescue and relocate the affected people (Anande and Luhunga 2019). For the South Asian region, it is projected that temperature will rise by 0.88– 3.16 °C by mid-twenty-first century and 1.56–5.44 °C by 2080 (IPCC 2007). According to Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, Asia suffered from the highest number of weather and climate-related disasters in the world between 2000 and 2008, which resulted in enormous economic loss—around 28% of the total global economic loss (IPCC 2014). Because of increased monsoon variability and glacial melting, even a modest warming of 1.5– 2 °C can harshly affect the availability and stability of water resources in SA (Vinke et al. 2017). Though the impacts of climate change on food production vary across the regions, a large part of SA is expected to experience a decline in crop productivity (IPCC 2014; Vinke et al. 2017). For example, a rise in temperature has negatively affected crop yield in India—wheat yield by 5–30%, rice yield by 6–8%, and maize yield by 10–30% (Aryal et al. 2020b). Thus, without appropriate climate adaptation measures, SA is projected to lose approximately 2% of its annual gross domestic product (GDP) by 2050 and about 9% by twenty-first century (Ahmed and Suphachalasai 2014). Given that agriculture provides livelihoods to more than 70% of the people, and contributes about 22% of the GDP in SA, any loss of GDP will hit hard to the farmers (Ahmed and Suphachalasai 2014; Wang et al. 2017; Aryal et al. 2020b). Though the exposure to climate risks is higher in both of these regions, countries in SA are found to have suffered more due to past climate extremes and are more vulnerable (Table 1.1). When we compared the long-term Climate Risk Index (CRI) (considering most affected from 1999 to 2018), Bangladesh and Nepal are found to be the hardest hits among the countries under study (Table 1.1). However, the vulnerability to climate risks varies across countries due to differences in the factors contributing to the ability to climate adaptation such as per capita income, level of poverty and food insecurity, the share of the population relied on climate-sensitive sectors, and the quality of public institutions (Adger et al. 2009; Aryal et al. 2020c). Approximately 85% of the world’s poor (measured in terms of income poverty line of US$ 1.90 per capita per day) reside in SSA and SA (Barne and Wadhwa 2019). Almost a similar situation is observed even if we measure the multidimensional poverty—about 84.3% of multidimensionally poor people live in SSA (558 million) and SA (530 million) (OPHI and UNDP 2020). Although Nepal, India, and Bangladesh were among the 16 fastest countries in reducing multidimensional poverty, this situation may change due to COVID-19 (OPHI and UNDP 2020). Almost 72% of people in rural areas in SSA are multidimensionally poor, while it is about 38% in rural areas of SA (OPHI and UNDP 2020). About 84% of multidimensionally poor people live in rural areas and predominantly rely on

7.0

CRI rank

38.8 14.8 1856.0 26.0

Poverty headcount ratio at $1.90 a day (2011 PPP) (% of population)e

Overall economic development (GDP per capita)f

Corruption perceptions index (score) in 2019g

29.0

Employment in agriculture (% of total employment)d

People residing in coastal area

1265.0

Population density (people per square km)b

(%)c

164.0

Population (in millions)b

Factors contributing to climate vulnerability

30.0

Bangladesh

CRI score

Global climate risks index a for the period 1999–2018

Climate risks index

Table 1.1 Climate risk index and factors contributing to climate vulnerability

41.0

2104.0

21.2

42.4

14.2

464.0

1380.0

17.0

38.7

India

34.0

1071.0

15.0

65.0

0.0

203.0

29.0

9.0

31.5

Nepal

37.0

858.0

30.8

66.1

0.0

115.0

114.0

56.0

64.7

Ethiopia

28.0

1817.0

36.8

54.4

8.1

94.0

53.8

37.0

53.7

Kenya

31.0

412.0

70.3

43.6

0.0

203.0

19.2

80.0

77.8

Malawi

26.0

492.0

62.9

70.3

43

40.0

31.3

14.0

37.5

Mozambique

(continued)

37.0

1122.0

49.1

65.3

25.0

67.0

59.7

130.0

114.3

Tanzania

1 Climate Risks, Adaptation and Vulnerability … 5

Global hunger index in the year 2020 scoreh

India 30.3

Nepal 20.8

Ethiopia 28.9

Kenya 25.2

Malawi 23.0

Mozambique 28.8

Tanzania 28.6

Notes a CRI indicates a level of exposure and vulnerability to extreme events, which countries should understand as warnings in order to be prepared for more frequent and/or more severe events in the future. The index focuses on extreme weather events but does not take into account important slow-onset processes such as rising sea levels, glacier melting or more acidic and warmer seas. It is based on past data and should not be used as a basis for a linear projection of future climate impacts. For more details on CRI, we refer to Eckstein et al. (2020) b Taken from https://www.worldometers.info/world-population/population-by-country/ (accessed on September 21, 2020) c Multiple sources such as Centre for Coastal Zone Management and Coastal Shelter Belt (http://iomenvis.nic.in/index2.aspx?slid=758&sublinkid=119&langid=1& mid=1) d Data for 2019 from World Bank Development Indicator https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS?locations=NP e Bangladesh 2016, Ethiopia 2015, India 2011, Kenya 2015, Mozambique 2014, Malawi 2016, Nepal 2010, and Tanzania 2017 f GDP per capita—current US$ for 2019 from World Bank Development Indicators g CPI measures the perceived levels of public sector corruption in 180 countries/territories around the world. The score ranges from 0 to 100, where zero indicates highly corrupt, and 100 is very clean (for details, see: https://www.transparency.org/en/cpi/2019) h The global hunger index (GHI) score incorporates four major indicators: undernourishment, child wasting, child stunting, and child mortality. A low GHI score (i.e., closer to zero) refers to a better situation. The GHI score is categorized in four major groups as follows: Low (less or equal to 9.9), moderate (10.0–19.9), serious (20.0–34.9), alarming (35.0–49.9), and extremely alarming (more than 50.0) (for details see: www.globalhungerindex.org)

Bangladesh 25.8

Climate risks index

Table 1.1 (continued)

6 J. P. Aryal et al.

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the agriculture sector, a highly vulnerable sector to climate risks (OPHI and UNDP 2020). Besides, all of the countries that we consider for this study are suffering from poverty, low income, and ‘serious level of hunger’ measured in terms of the global hunger index (Table 1.1). Reviews in the gaps in adaptation policy and practice in Africa and Asia exhibited a limited evidence of adaptation initiatives being targeted at vulnerable populations (Ford et al. 2015). Macro-level indicators for all countries under study (Table 1.1) reveal that they have high exposure to climate risks due to increasing frequency and severity of climate shocks, while their capabilities for adaptation at the national level is extremely low, implying critical vulnerability to climate change. Another crucial point is that even the existing capacity of the government institutions of these countries is not expected to get fully utilized for reducing the climate vulnerability of the poor people as the government institutions in these countries are highly corrupt (Table 1.1) (Kumar and Mohanty 2012; Alam et al. 2017; Asadullah and Chakravorty 2019; George and McKay 2019; Habib et al. 2020; Jarvis 2020).

Study Area, Data, and Methodology Study Area and Data The primary data used in this study were collected from a survey of 1902 farm households residing along the Indo-Gangetic plains of three countries in SA (Bangladesh: 630, India: 641, and Nepal: 631) in 2013, and a survey of 4351 farm households from five countries in SSA (Ethiopia: 873, Kenya: 851, Malawi: 730, Mozambique: 877, and Tanzania: 1020) in 2018 (Fig. 1.1). In both regions, the data were collected through multistage sampling methods. In SSA, five countries were selected purposefully in the first stage, and then, in the second stage, two maize growing regions from each country were selected: Ethiopia (Oromia region—422 and SNNP region—451), Kenya (Western region—476 and Eastern region—375), Tanzania (Northern region—541 and Eastern region—479), Malawi (Low altitude region—208 and High altitude region—522) and Mozambique (Central region—614 and Northern region—263). In the third stage, study villages were selected, acknowledging the regional variation, and finally, farm households were randomly chosen from the selected villages (Fig. 1.2). In SA, three countries, i.e., Bangladesh, Nepal, and India, were chosen purposefully, at the first stage. Then, in the second stage, three districts in Bangladesh (Bagerhat, Jhalokhati, and Satkhira), one district in Nepal (Rupandehi), and one district in Bihar state of India (Vaishali) were chosen depending on the climate risks and cereal-based cropping system. Finally, individual sample households were selected randomly from 38 selected villages from these countries (Fig. 1.2).

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J. P. Aryal et al.

Fig. 1.1 Map of the study area

Sample size (number)

1200 1000 800 600 400 200 0

Countries

Fig. 1.2 Distribution of sample households in the selected countries of South Asia and Sub-Saharan Africa

Methodology Given that our variable of interest is binary, i.e., apply adaptation measure (i.e., y = 1) or not (i.e., y = 0), we applied a probit model to examine the factors affecting the farmers’ decision to apply climate risk adaptation measures. Hence, the model estimates the likelihood of climate adaptation by farm households. Although there

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are other methods such as linear probability, logit, probit, and complementary loglog models, which can be applied in the case of a binary dependent variable, we applied a probit model assuming that the error term follows the normal distribution (Wooldridge 2010). In the probit model, we use the index function assuming the existence of a continuous (i.e., latent variable) index y∗ related to xi (explanatory variables), rather than stating the binary variable yi as a linear function of xi . Hence, we estimate the following probit model: yi∗ = βx + εi

(1.1)

where β and x refer to vectors of unknown parameters and explanatory variables and εi refers to the stochastic error term, which is independently and identically distributed. In Eq. (1.1), the index variable (yi∗ ), is not directly observed and is generated as: 

yi = 1 iff yi∗ > 0 yi = 0 iff yi∗ < 0

(1.2)

This means, the value of a binary outcome is one if yi∗ exceeds a threshold value (zero), otherwise it assumes value zero. Application of the index function approach allows us to assume that xi can have a linear effect yi∗ on, i.e., can take any value, but the outcome variable yi still takes only two values 0 and 1, and remains within the limits of the probability range. The log-likelihood function of an individual observation in the probit model is given by:       log(L i ) = yi log 1 −  −βxi + (1 − yi ) log  −βxi

(1.3)

Hence, the probability of climate adaptation is given by: βx pi ≡ Prob(y = 1 |x) = F(βx) = (βx) =

φ(Z )dz

(1.4)

−∞

In Eq. (1.4), to ensure that probability falls within the range of [0 ≤ p ≤ 1], we specified F(βx) as a parameter function of βx. The expressions (.) and φ(.) represent standard normal cumulative probability function and probability distribution function, respectively. As the coefficients of the probit model cannot be interpreted directly, we estimated marginal effects from it, which is given by: ∂p = φ(βx)β j ∂ xi

(1.5)

Equation (1.5) reports the marginal effects of each of the explanatory variables on the outcome variable.

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Table 1.2 Major climate risks faced by farm households in the last ten years Climate risks

Percentage of sample households facing climate risks

Drought

NA

Bangladesh India Nepal Ethiopia Kenya Malawi Mozambique Tanzania 92

80

99.8

99.5

100

99.9

93.7

Flood and 26 excessive rains

27

43

100

98.9

100

97.4

67.1

Cyclone

90

NA

NA

NA

NA

NA

NA

NA

Salinity

45

NA

NA

NA

NA

NA

NA

NA

Crop pests 22 and diseases

31

32

99.7

100.0

100

99.4

97.0

Livestock 23 diseases and epidemics

8

10

NA

NA

NA

NA

NA

Hailstorm NA

NA

NA

99.2

98.8

100

99.5

44.9

Note NA and—refer to not applicable and no data, respectively. Multiple responses are possible

Results and Discussion Climate Risks Experienced in Past 10 Years Table 1.2 shows the percentage of farm households who experienced climate risks at least once over the last 10 years. Excessive rains and flooding, and crop pest and disease were commonly experienced in all the countries in both SSA and SA; however, substantial variation was observed in the percentage of farm households experiencing these climate risks. In Bangladesh, cyclone and salinity were the major climate risks experienced by 90% and 45% of the farmers, respectively. In other countries under study, majority of the farmers faced drought in the past 10 years (India-92%, Nepal-80%, Ethiopia-99.8%, Kenya-99.5%, Tanzania-93.7%, Malawi100%, and Mozambique-99.9%). Compared to SA, farmers facing climate risks are considerably higher in SSA.

Descriptive Statistics Table 1.3 portrays the descriptive statistics of the variables used in the econometric model. Although farmers in SSA and SA have limited capacity to adopt climate adaptation strategies, a large number of farmers in the study area adopted climate adaptation strategies. Nearly 80% of the sampled farmers in Bangladesh, India, Nepal,

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Table 1.3 Descriptive statistics of the variables used in the analysis Variables

Bangladesh India

Nepal

Ethiopia Kenya

Tanzania Malawi Mozambique

0.85

0.79

0.87

0.89

0.86

50.19

44.37

53.93

48.16

Dependent variable Adaptation measures used (%)

0.88

0.80

0.68

45.55

49.06

Explanatory variables Age of household head

46.93

50.51

(13.30)

(13.63) (13.69) (11.90)

Female headed household

0.11

0.09

0.22

0.15

0.26

0.15

0.18

0.15

(0.31)

(0.29)

(0.41)

(0.36)

(0.44)

(0.35)

(0.38)

(0.36)

Marital status: married household head

0.90

0.92

0.87

0.87

0.83

0.85

0.82

0.84

(0.29)

(0.27)

(0.33)

(0.34)

(0.38)

(0.36)

(0.38)

(0.37)

Education of head

3.35

3.59

2.91

3.76

9.31

6.98

6.01

4.90

(1.53)

(1.99)

(1.71)

(3.72)

(3.59)

(2.38)

(3.54)

(3.43)

Household size

4.67

6.05

6.39

6.01

5.52

5.95

5.46

6.53

(1.68)

(2.65)

(3.08)

(2.07)

(2.40)

(2.28)

(2.06)

(3.22)

29.52

61.44

63.97

38.90

90.29

91.12

41.16

33.75)

(109.92) (81.30) (40.80)

Distance to 42.48 trading (48.02) center (min)

(17.89) (49.08) (44.54)

(13.07) (11.13)

(13.89) (13.57)

Membership 0.39 in farm (0.49) association

0.11

0.46

0.33

0.28

0.06

0.21

0.34

(0.31)

(0.51)

(0.47)

(0.45)

(0.24)

(0.41)

(0.47)

Training

0.07

0.24

0.04

0.13

0.44

0.11

0.37

0.27 (0.45)

(0.44)

(0.31)

(0.30)

(0.34)

(0.50)

(0.32)

(0.48)

Access to formal credit

0.69

0.39

0.44

0.25

0.59

0.32

0.39

0.15

(0.46)

(0.49)

(0.49)

(0.43)

(0.49)

(0.47)

(0.49)

(0.35)

Tropical livestock unit

0.92

0.7

1.25

3.96

2.10

3.69

0.53

1.93

(1.19)

(0.82)

(1.56)

(4.12)

(1.99)

(6.22)

(1.51)

(3.41)

Land owned 0.44 (hectare) (0.48)

0.51

0.49

1.12

2.32

3.78

2.47

4.20

(0.52)

(0.78)

(0.86)

(2.20)

(4.07)

(2.26)

(3.60)

Economic status: higher

0.27

0.35

0.38

0.36

0.33

0.36

0.15

0.39

(0.41)

(0.39)

(0.44)

(0.48)

(0.47)

(0.48)

(0.36)

(0.49)

0.25

0.15

0.17

0.42

0.21

0.21

0.27

If household 0.19 are engaged in non-farm sector

(continued)

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J. P. Aryal et al.

Table 1.3 (continued) Variables

Bangladesh India

Nepal

Ethiopia Kenya

Tanzania Malawi Mozambique

(0.38)

(0.34)

(0.37)

(0.41)

(0.43)

(0.49)

(0.40)

(0.44)

Note Standard deviations are given in the parentheses

Ethiopia, Kenya, Tanzania, and Malawi applied climate adaptation measures, while only 68% of the sampled households in Mozambique did so. The average age of the heads of the households ranges between 44 and 54 years across the countries under study. Although women are more likely to apply climate adaptation measures due to their concern for family food security (Aryal et al. 2020a), they usually have limited capacity/resources (Mersha and van Laerhoven 2016; Aryal et al. 2020c). Hence, gender plays a crucial role in climate adaptation. Compared to SSA, the percentage of female-headed households (FHHs) is lower in SA except for Nepal (22%). The higher proportion of FHHs in Nepal could be linked with more women-friendly society and large out-migration of male members. Low percentage of FHHs in Bangladesh (11%) and India (9%) may represent conservative gender norms existing in their society. In SSA, the percentage of FHHs is highest for Kenya (26%), followed by Malawi (18%), Ethiopia, Tanzania, and Mozambique (15% each). Majority of the household heads in both regions were married. The average years of schooling of the household heads in SA are fairly low—Bangladesh (3.4), India (3.6), and Nepal (2.9). In SSA, the average year of schooling of the household heads is comparatively higher: 9.3 years for Kenya, followed by Tanzania (7), Malawi (6), Mozambique (4.9), and Ethiopia (3.8). The average family size ranges between 5 and 7 in these countries. Accessibility, as measured by the time taken to reach the trading center, is lower for SA compared to SSA (Table 1.3). In SA, the average time to reach the trading center is least for India (29.5 min) and highest for Nepal (61.4 min), while in SSA, it is least for Kenya (38.9 min) and highest for Malawi (91.1 min). Membership in farmer association helps improve farmer’s awareness on climate adaptation measures through the exchange of ideas, and thus, it can enhance climate adaptation. In SA, membership in farm association is highest in Nepal (46%), followed by Bangladesh (39%) and India (11%), while in SSA, it is highest in Mozambique (34%) and lowest in Tanzania (6%). Training on agricultural practices can influence the knowledge on new technology and climate adaptation measures. A considerable variation is seen pertaining to farmers’ receiving training on agricultural practices. In SA, 24% of the households in India received training, followed by Bangladesh (7%) and Nepal (4%). In SSA, 44% of the households in Kenya received agricultural training, followed by Malawi (37%), Mozambique (27%), Ethiopia (13%), and Tanzania (11%). Access to credit facilitates in easing liquidity constraints. Our data show that access to credit is higher in SA compared to SSA. It is particularly high in Bangladesh (69%), which may be attributable to the expansion of Gramin bank facilities. In SSA, microfinance or agricultural finance is less common. Still, 59% of the sampled households in Kenya, 39%

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in Malawi, 32% in Tanzania, 25% in Ethiopia, and 15% in Mozambique reported that they have access to formal credit. Livestock and land are the main assets that serve as insurance for farm households during the time of the distress. Average livestock and landholdings in SA are lower than in SSA (Table 1.3). In SA, the average landholding ranges between 0.44 ha for Bangladesh and 0.51 ha for India. The economic status of the household reflects its capability to invest in climate adaptation measures. Across SSA and SA, nearly 33% of the sampled households had higher economic status except for Malawi (15%). Generally, one-fifth of the households in countries under study are engaged in nonfarm livelihood activities; however, it is slightly higher in Mozambique (27%) and Kenya (27%).

Factors Determining Climate Adaptation by Farmers The marginal effects obtained from the probit model are summarized in Table 1.4. The age of the household head is insignificant for all countries except in the case of Nepal and Tanzania—in Nepal, the coefficient of the age of the household head is negative (significant at 10%), while it is positive and significant at 1% in Tanzania, indicating that the household with older heads in Nepal are less likely to adopt the climate adaptation measures whereas it is opposite in Tanzania. The female head dummy is insignificant for all countries in SSA, while it is significant and positive for Nepal, and negative and significant for Bangladesh and India. It means FHHs in Nepal are more likely to adopt climate adaptation measures compared to maleheaded households, which may be explained by the relatively better status of women in Nepalese society. In India and Bangladesh, given the conservative social norms, FHHs are less likely to adopt climate adaptation measures. The marital status of the household is insignificant except in Kenya, where it is negatively associated with climate adaptation (significant at 10% level). Education normally has a positive influence on the adoption of climate adaptation measures through increased awareness and capacity. In SSA, the education of the household head is insignificant while it is significant and positive in all South Asian countries. The household size, a measure of household family labor force availability, is insignificant in all cases. The distance to trading centers is an indicator of accessibility to market for farm inputs and outputs and hence, a predictor of the economic viability of climate adaptation. The results show the distance to the trading center is negatively and significantly associated with the likelihood of adapting to climate risks in Bangladesh (significant at 10%), Nepal (significant at 1%), and Mozambique (significant at 1%), indicating that in these countries, households closer to trading center are more likely to apply climate adaptation measures. Membership in farmer associations usually contributes to farmers’ awareness on climate change adaptation strategies through farmer-to-farmer communications (Aryal et al. 2018, 2020d; Alam et al. 2016). Our results show that it is positive and significant in case of Bangladesh, India, Nepal, Kenya, and Malawi, while it is

Land owned (hectare)

Tropical livestock unit

Access to formal credit

Training

Membership in farm association

Distance to trading center (min)

Household size

Education of head

Marital status: married

0.061***

(0.015)

(0.007)

0.029***

0.043***

0.015**

(0.077)

(0.053)

(0.031) 0.075

(0.052)

0.171***

0.096***

(0.058)

(0.021)

0.115**

0.163***

0.043**

(0.029)

(0.013)

(0.033)

(0.041) −0.026

(0.037)

−0.031**

−0.031

−0.023

−0.017

0.079***

(0.010)

0.019*

(0.093)

0.085

(0.049)

0.171***

(0.039)

0.081**

(0.017)

−0.046***

(0.015)

(0.013)

0.029*

0.038***

(0.023)

(0.083)

0.065

(0.009)

0.033***

0.071***

(0.212)

(0.022)

(0.005) 0.187

(0.015)

0.014

(0.004) −0.011**

(0.008)

−0.037**

(0.008)

Female-headed household

Age of household head

Nepal −0.013*

India −0.002

Bangladesh

−0.004

Variables

Kenya

(0.030)

(0.026)

0.147*

Malawi

(0.015) −0.013

−0.014

0.036**

(0.121)

0.080

−0.000

(0.048)

0.015

(0.126)

0.113

(0.122)

0.077

−0.238 (0.168)

(0.159)

0.511***

(0.001)

0.001

(0.010)

0.001

(0.018)

−0.014

(0.252)

0.046

(0.253)

−0.264

(0.004)

−0.000

(0.226)

0.126

(0.001)

−0.000

(0.024)

−0.036

(0.023)

0.011

(0.035)

0.005

−0.019 (0.017)

(0.126)

(0.135)

(0.126) −0.056

(0.303)

0.115

−0.183

0.804***

(0.150)

0.311**

−0.226* (0.124)

(0.002)

−0.002

(0.001)

0.001

0.017

(0.018)

−0.006

(0.276)

(0.278) 0.300

−0.006 0.015

(0.005) 0.139

(0.168) (0.213)

(0.019)

Tanzania 0.014***

−0.370*

−0.231

(0.005)

−0.002

(0.302)

0.008

(0.289)

−0.215

(0.006)

0.008

Ethiopia

Table 1.4 Factors affecting the decision to apply adaptation measures (marginal effects from probit models) Mozambique

(continued)

0.026*

(0.014)

−0.016

(0.136)

0.028

(0.113)

0.136

(0.105)

0.022

(0.001)

−0.005***

(0.014)

−0.020

(0.016)

−0.021

(0.182)

−0.056

(0.192)

−0.063

(0.004)

−0.003

14 J. P. Aryal et al.

(0.076) 0.245 (0.255)

−0.140

(0.153)

0.103 −432.15

67.83

0.000

0.231

−759.09

Prob > chi2

Pseudo R2

Log likelihood

−631.22

0.094

0.000

63.45

631

(0.144)

0.137

(0.103)

0.224**

(0.023)

Nepal

−300.43

0.1007

0.000

67.25

870

(0.134)

−0.606***

(0.155)

−0.101

(0.127)

0.097

(0.087)

Ethiopia

−276.15

0.037

0.097

21.2

828

(0.138)

0.069

(0.124)

−0.186

(0.143)

0.370***

(0.031)

Kenya

−355.51

0.1021

0.000

80.84

942

(0.121)

0.483***

(0.119)

−0.613***

(0.122)

0.239**

(0.014)

Tanzania

−328.66

0.0768

0.000

54.66

701

(0.131)

−0.080

(0.141)

−0.190

(0.268)

1.110***

(0.030)

Malawi

−521.01

0.0452

0.000

49.37

864

(0.106)

−0.217**

(0.108)

0.009

(0.098)

0.355***

(0.015)

Mozambique

Note *, **, and *** refer to significant at the p < 0.10, p < 0.05, and p < 0.01 level, respectively. Standard errors are in the parentheses. #: degree of freedom is 14 in Nepal, India and Bangladesh and 17 for sub-Saharan countries

0.000

79.91

630

LR chi2 (17)#

641

0.258***

(0.069)

(0.019)

(0.011)

0.183***

India

Bangladesh

Number of observations

Region North, Mozambique

Region Mid Altitude Malawi

Region Eastern Tanzania

Region Western Kenya

Region: Oromia, Ethiopia

Participated in non-farm sector

Economic status: Higher

Variables

Table 1.4 (continued)

1 Climate Risks, Adaptation and Vulnerability … 15

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J. P. Aryal et al.

negative and significant in case of Ethiopia. It confirms that generally, households who participated in farmers’ associations are more likely to adopt climate adaptation measures. Training on agricultural practices largely enhances farmers’ knowledge on climate change adaptation. In our case, the coefficient of the training dummy is positive and highly significant for Bangladesh, India, Nepal, and Ethiopia, implying that in these four countries, the agricultural training increased the likelihood of adopting climate adaptation strategies. Access to formal credit is positive and highly significant only for Bangladesh, which may be linked with the predominance of micro-finance in the country. Livestock ownership is positive and significant for Bangladesh, India, Nepal, and Tanzania, implying that households with more livestock are more likely to adopt climate adaptation measures. Land ownership, which indicates the wealth status of the household, is found to have a positive association with the likelihood of adopting climate adaptation measures by farm households in Bangladesh, India, Nepal, Ethiopia, and Mozambique. Economic status emerges to be an important driver of climate change adaptation. In all countries under study except Ethiopia, the households with better economic status are more likely to adopt climate adaptation measures (at 1% level of significance), indicating the importance of affordability in climate adaptation. Participation in non-farm activities is insignificant except in the case of Tanzania, where it is negative and significant at 1% level of significance. In SSA, we used a regional dummy to control for spatial heterogeneity and found that in Ethiopia, households in the Oromia region are less likely to adopt climate adaptation measures compared to the Southern Nations, Nationalities, and Peoples’ Region (SNNPR). In Tanzania, households in the Eastern region are more likely to adopt climate adaptation measures as compared to households in the northern region. Compared to the households in the Central region of Mozambique, households in other regions are less likely to adopt climate adaptation measures.

Conclusion and Policy Recommendations Farmers in SA and SSA are highly susceptible to climate change because of limited capacity and resources to cope with climate risks. A large proportion of the households in the region experienced climate risks such as drought, flooding, cyclone, salinity, and pest and diseases. Despite the variation in climate risks across the location, drought is more common in all the countries under study except in Bangladesh. Though the farmers in south Asia and sub-Saharan Africa are poor, majority of them adopt climate adaptation strategies. Among the drivers of climate change adaptations, better economic status, asset ownership (livestock and land assets), membership in farm organization, and training on agricultural practices are found to have a positive influence on the adoption of climate adaptation measures. The effects of both accesses to training and education levels attained by the head of the household suggest the importance of farmer-centered knowledge hubs.

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Several policy responses need to be strengthened to prepare rural populations to deal with future climate extremes. Investment in training to improve farmer skills and know-how towards more climate-resilient farming methods is necessary. Second, the finding around the importance of wealth and assets (livestock and land) and higher economic status as key enabling factors to adopt climate adaptation measures is an indication that risk transfer and credit markets are wealth-rationed. To provide a level playing, investments in more equitable financial and insurance markets are needed. Emerging financial technologies enabled by the now-ubiquitous mobile (and smart) phones can provide platforms for mass-market microfinance and micro-insurance solutions to rural families who are currently cut out of these markets. Finally, we note that nearly all households in both regions have experienced climate stress at least twice in the past decade, suggesting a high frequency of adverse climate events. Yet, at the macro level, the adaptive capacity is still very low. This suggests that despite the high incidences of adaptive behavior, the intensity and effectiveness of these activities is still inadequate. This calls for more public and private investments for increasing the adoption intensity of adaptation-enhancing practices. The capacity building and complementary innovations in financial markets can encourage this process along. Acknowledgements This work was carried out by International Maize and Wheat Improvement Center (CIMMYT) as a part of Sustainable Intensification of Maize-Legume Systems for Food Security in Eastern, and Southern Africa (SIMLESA) financed by the Australian Centre for International Agricultural Research (ACIAR) and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), with support from CGIAR Fund Donors and through bilateral funding agreements. For details, please visit https://ccafs.cgiar.org/donors. Further support was also provided by the CGIAR Research Program on Maize Agri-food system (CRP-MAIZE). We thank field teams for collecting data in South Asia and also in Sub-Saharan Africa. The views expressed here are those of the authors and do not necessarily reflect the views of the authors’ institutions.

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Chapter 2

Climate Modeling, Drought Risk Assessment and Adaptation Strategies in the Western Part of Bangladesh Md. Kamruzzaman, Tapash Mandal, A. T. M. Sakiur Rahman, Md. Abdul Khalek, G. M. Monirul Alam, and M. Sayedur Rahman Abstract This study aims to assess the agricultural drought risk for the period of 1960–2011 and to identify the sustainable adaptation measures in the western part of Bangladesh which is most drought-prone areas in the country. The MK test, Sen’s slope estimator, and ARIMA model have been applied to climatic variables for detecting the trends and forecasting future climate scenarios. The Markov chain analysis and Drought Vulnerability Index (DVI) have been used to generate the Drought Index (DI) and Drought Hazard Index (DHI). The pattern of drought risk is mapped by multiplying hazard and susceptibility indices. The annual average temperature in the area is about 25.44 °C, and the annual, as well as seasonal mean temperature is the lowest in north-east and the highest in the south-western part. The predicted annual mean temperatures vary from 24.47 to 26.75 °C and are higher than long term observed mean although the predicted spatial pattern will remain the same in the area. Drought poses a great risk in agriculture in the northern districts and comparatively low in the southern districts (coastal areas) and 21.64%, 26.53% and 29.67% of the area pose a very high, high, and modest risk respectively. Hence, it is urgent to act on adaptation measures in response to future climate change issues such as raising temperature and rainfall variability.

Md. Kamruzzaman (B) Institute of Bangladesh Studies, University of Rajshahi, Rajshahi, Bangladesh e-mail: [email protected] T. Mandal Department of Geography and Applied Geography, University of North Bengal, Siliguri, India A. T. M. S. Rahman Department of Earth and Environmental Science, Kumamoto University, Kumamoto, Japan Md. Abdul Khalek · M. S. Rahman Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh G. M. M. Alam Department of Agribusiness, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_2

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Introduction Drought, the most frequent natural hazards in several parts of the world, has an abundant influence on agriculture and the economy. It is not permanent but the composite characteristics of the climatic environment of a certain region with great significance (Olapido 1985), which is naturally caused by the deficiency of rainfall (Das et al. 2020a), and can arise in both low and high rainfall areas. The main reason for drought is the scarcity of precipitation for a long time. This scarcity of precipitation let down the groundwater table, soil moisture, and stream flow. The results of drought, occurred from the discrepancy of water between supply and demand or deficiency of water, as one of the significant ecological or environmental aspects. Such natural phenomena leave a widespread effect on the socio-economic structure of a province where it occurrences, sometimes needing relief endeavor on a worldwide range (Ahmed 1995). It is a prerequisite to understanding the possible changes in drought in the forthcoming global warming. Despite its ubiquitous nature, our understanding of the commencement, development, and stagnation of drought is in lacking, hampering not only our capability to monitor but also to forecast its happening at periodic time scales, and to assess climate model for estimates of upcoming changes. The key effects of drought (e.g. food security concerns and increasing food imports from overseas) creates a variety of effects for many in the social order (Benson and Clay 1998) and its effects are varying in diverse degrees. For example, food insecurity, is one of the leading allied drought impressions as the food level is generally reduced throughout the drought thereby growing the susceptibility of peoples. Bangladesh, a leading disaster-prone country in south-east Asia as well as in the world. More or less each year, the country faces disasters of one kind or an additional, for instance, tropical cyclones, rainstorm surges, droughts, floods, and coastal erosion causing heavy damage of life and belongings with obstructing the progress events (Alam et al. 2017; Ali 1996). High spatiotemporal variability of climatic conditions, erratic weather events, high frequency of poverty and social inequality, high density of population, inadequate institutional facility, insufficient commercial resources, and scanty set-up have made Bangladesh extremely susceptible to disaster (Alam et al. 2018; Ahmed 2004). Drought is a frequent phenomenon in northwest Bangladesh and has suffered nine severe droughts since its independence in 1971 (Paul 1998). Despite its intermittent and shocking nature, it has paid attention to far-reaching small scientific considerations in Bangladesh than cyclones or floods (Alexander 1995; Brammer 1987). The impact of droughts on agriculture in Bangladesh was studied by Karim et al. 1990; Jabbar et al. 1982; Jabbar 1990; Saleh et al. 2000; Mazid et al. 2005, whereas on production of food by Ahmed and Bernard 1989; Erickson et al. 1993. The hazard risk map of the agricultural drought of Bangladesh was produced first by Karim et al. (1990) bearing in mind the cumulative consequence of dry days, greater temperatures for the period of pre-monsoon season, and the availability of soil moisture. Karim and Iqbal (2001) shaped three diverse drought hazard maps for the seasons of pre-monsoon, monsoon, and winter, classifying drought risk in

2 Climate Modeling, Drought Risk Assessment …

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terms of yield losses for diverse crops, as slight (15–20%), modest (20–35%), high (35–45%), and very high (45–70%). Few studies on drought effects by standard drought modeling of Bangladesh have been carried out by a number of researchers but a significant study has been executed on the impacts and magnitude of drought in the western portion of Bangladesh using Standardized Precipitation Index (SPI) technique in view of physical and socioeconomic indicators of drought vulnerability (Shahid and Behrawan 2008). They produced a drought hazard map for that reason but failed to organize a more precise composite drought vulnerability map because they have taken a linear combination of different physical and socio-economic indicators with different units which is a scientifically illogical concept. Moreover, they gave emphasis to meteorological drought hazard assessment only, but agricultural drought hazard evaluation is a key concern of the agro-based countries like Bangladesh. So far, no extensive studies have been performed on the modeling of climate, risk assessment of drought, and adaptation policies in Bangladesh under changing climate due to global warming. Therefore, there had been an authentic scope to carry out comprehensive research on climate modeling, agricultural drought hazard evaluation, and sustainable adjustment policies. The study will be beneficial for the Government and Non-Government Organizations (NGOs) to take suitable actions for reducing the hostile effect on agriculture and the socio-cultural environment because of drought hazard. It is also assumed that this study will give a better idea into the spatio-temporal features of drought, as well as water scarcity during drought events under changing climate to the researchers, scientists, and policymakers. Therefore this study aims to identify the climatic condition of the western part of Bangladesh by developing a suitable climate model using rainfall and temperature data to estimate future climatic scenarios. Agricultural drought scenario with its spatial variation, rate of recurrence, and harshness based on daily rainfall data using the Markov Chain model. The study also produced drought vulnerability maps based on physical and socio-economic indicators of vulnerability and district level drought risk map depending on drought hazard and vulnerability indexes using Geographical Information Systems (GIS) and finally recommend sustainable management strategies for adaptation of drought risk under climate change.

Data and Methods Selection of Study Area The study area includes 36 districts in western Bangladesh with 12 meteorological stations. The location map with districts and the meteorological stations are shown in Fig. 2.1.

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Fig. 2.1 Map of the study area with meteorological stations

Data Sources The datasets used for the current investigation were gathered from Meteorological Department of Bangladesh (BMD), Dhaka, for the period 1960–2011 except Bhola (from 1966 to 2011) and Khepupara (from 1975 to 2011). In addition, Statistical Year Book of Bangladesh (2011) was used for data of the density of population, literacy rate, male-female ratio, percentage of people rely on agriculture, and land under irrigation to cultivable land and per unit area production of crops.

2 Climate Modeling, Drought Risk Assessment …

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Missing Data Estimation The datasets of every meteorological station contain numerous lost values. Researchers depend on a number of methods to compute the daily missing temperature and rainfall data. However, these techniques can be categorized into three groups: (i) within-station; (ii) between-station; and (iii) regression-based (Allen and DeGaetano 2001). The within station technique is used to calculate the lost daily temperature and rainfall data till three days, where the data are missed exceeds three days then data from adjoining stations is used carrying out regression-based technique. The regression-based technique is also used for monthly data since this technique is circulated for both daily and monthly data (Allen and DeGaetano 2001; Eischeid et al. 1995). Multiple imputation techniques are utilized for both monthly and daily data (Das et al. 2020d; Ingsrisawang and Potawee 2012) to fill missing rainfall data because it properly fixes the standard error for lost datasets (Enders 2010).

Trend Analysis Methods It is very essential to evaluate the trend in the climatic variables especially temperature and rainfall for the research on climate change. In current years, few studies have been performed all over the world for the earliest analysis of the trend in climatic variables like temperature (e.g. Tabari et al. 2012; Del et al. 2011; Croitoru et al. 2012; Shifteh et al. 2013; Kamruzzaman et al. 2016a; Das et al. 2019) and precipitation (e.g. Zhang et al. 2008; Kampata et al. 2008; Das et al. 2020b; Mandal et al. 2020; Kamruzzaman et al. 2015). In Bangladesh, some works have gained good outcomes in detecting trends in climatic data series for instance temperature (Azad et al. 2012) and precipitations (Shahid and Khairulmaini 2009). In this study, Mann-Kendall (MK) (Mann 1945; Kendall 1975) test for trend and Sen’s Slope (Sen 1968) estimates for the magnitude of changes are used to evaluate the temporal variation of the data series (annual and seasonal) of temperature and rainfall data.

Prediction of Climate Variables There are many climatic variables are existing in nature but temperature and rainfall are the most noteworthy factors of the climatic environment. The place to place a discrepancy of temperature and rainfall over the decades is the utmost responsibility for climate change. The forecasting of temperature and rainfall on a time scale has been attempted by several research groups using different climate modeling. The forecasting of these elements on annual and seasonal time scales is not only mathematically challenging but is also vital for planning and developing sustainable agronomic strategies. This study also defines the Box-Jenkins time series for

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forecasting annual average temperature. The ARIMA approach is applied to forecast the temperature of the upcoming five years by examining the last fifty years of data. Earlier data is used to formulate the ARIMA model and the model parameters are identifying using best fitted ARIMA model to the univariate time series, “Package: forecast, Version: 4.8 of R Package”. The performance assessments of the approved models are carried out based on log-likelihood, Akaike Information Criteria (AIC), and Bayesian Information Criteria (BIC). The present study specifies that the ARIMA model arranges for reliable and satisfactory prediction for annual average temperature data. On the other hand, the auto ARIMA models of rainfall data on the annual scale do not fit so nicely and the performances of forecasting are also irrelevant. That is why the Markov Chain model was taken into consideration for reliable and reasonable climate models using daily rainfall data.

Modeling Agricultural Drought Using Markov Chain Markov Chain’s model for agricultural drought assessment has been used by several academicians (e.g. Rahman 1999a, b; Barkotulla 2007; Alam et al. 2013; Banik et al. 2000; Kamruzzaman et al. 2016b) with an appropriate prediction on the basis of daily rainfall data. Markov Chain model consists of two states computation of two conditional possibilities: State-1 (α), the wet week probability follows a dry week, and State-2 (β), the dry week probability follows a wet week. These two states are given below. Present state

Future state Dry

Wet

Dry

1-α

α

Wet

β

1-β

Let X 0 , X 1 , X 2 , …, X n be the accidental variables circulated specifically and compelling only two values, viz. 0 and 1, with entire possibility one, to be precise:  Xn =

0 if the nth week is dry 1 if the nth week is wet

First of all, it may be supposed that, P(X n+1 = x n+1 |X n = x n, X n−1 = x n−1, . . . , X 0 = x 0 ) = P(X n+1 = x n+1 |X n = x n ) where, x 0 , x 1 , . . . , x n+1 ∈ {0, 1} On the other hand, it is expected that possibility of wetness of a week relies only on if the earlier week was dry or wet. Given the experience in the earlier week,

2 Climate Modeling, Drought Risk Assessment …

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the possibility of wetness is supposed autonomous of additional foregoing weeks. Hence, the Markov chain, the stochastic method {X n , n = 0, 1, 2….} is fruitful for the agricultural drought assessment (Medhi 1981). The transition matrix which taken into consideration as; 

P 00 P 01 P 10 P 11



where, P i j = P(X 1 = j |X 0 = i), i, j = 0, 1. Note P00 + P01 = 1 and P10 + P11 = 1 Let p = P(X 0 = 1). At this point, p is the absolute possibility of a week’s existence wet for the duration of the monsoon season. Clearly P(X 0 = 0) = 1 − p. For a static distribution     P 00 P 01   = 1− p p 1− p p P 10 P 11 which gives p=

P 01 1 − ( P 11 − P 01 )

It is further expected that P i j ’s residual constant of the years. The maximum likelihood approximation of P 01 and P 11 are proper relative utilities (Woolhiser and Pegram 1979). A wet spell of extent k is defined as orders of k wet weeks come first and followed by other weeks. The spells of dry are defined respectively. However, “the possibility of the wet spell of extent k” simply describes that the given week is, i.e. k−1 P(W = k) = (1 − P 11 ) P 11

(2.1)

and the possibility of wet arrangements with length more than k is: P(W > k) =

∞ 

k P(W = t) = P 11

t=k+1

Similarly, the possibility of a dry spell of extent m is: m−1 P( D = m) = (1 − P 01 ) P 01

and the possibility of dry orders with length more than m is: P( D > m) = (1 − P 01 )m

(2.2)

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Let Y be the arbitrary variable such that Y-number of wet weeks amid a n-week time i.e., Y = X 0 + X 1 + · · · +X n−1 . For vast n, Y follows the normal circulation with Mean = n× p V ar i ance=n× p×(1 − p) ×

1+ P 11 − P 01 1 − P 11 + P 01

(2.3)

where p is the static possibility of a wet week. This is an asymptote consequence that specifies neither the precise circulation for small n nor the promptly of method to normality (Feller 1957).

Drought Proneness Index P11 denotes the possibility of wet weeks assumed that the preceding week was also wet. When P11 is enormous, the possibility of wet weeks is huge too. But a lesser value of P11 only may not specify high drought proneness. Here, the big value of P01 denotes a great number of small wet durations which can obstruct the incidence of drought. Hence, the drought proneness index may be defined as: D I= P 11 × P 01

(2.4)

This drought proneness index is ranged from zero to one. The high value of DI specifies the lower amount of drought proneness and vice versa (Table 2.1). Annual recurrence, average occurrence interval, and annual percentage possibility have been computed by Raghunath (1995) formulas. The reoccurrence time is the mean period that passes between the happenings of an occasion of a specified proportion (Nzewi 2001). Table 2.1 Drought proneness index with corresponding drought classes

Drought classes

Markov chain model

Extreme/chronic

0.000 < DI < 0.126

Severe

0.126 < DI < 0.181

Moderate

0.181 < DI < 0.236

Mild

0.236 < DI < 0.311

Occasional

0.311 < DI < 1.000

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Drought Hazard Index (DHI) Calculation The index of hazard of drought index (DHI) has been computed by weightiness and rating technique following Shahid and Behrawan (2008), however, weight and ratings are diverse. D H I = (M I Dr × M I Dw) + (M Dr × M Dw) + (S Dr × S Dw) + (E Dr × E Dw)

(2.5)

where, MIDr, ratings allotted for moderate droughts incidence classes; MIDw, weight allotted for moderate drought incidence theme; MDr, ratings allotted for mild droughts incidence classes; MDw, weight allotted for mild drought incidence theme; SDr, ratings allotted for severe droughts incidence classes; SDw, weight allotted for severe drought incidence theme; EDr, ratings allotted for extreme droughts incidence classes and EDw, weight allotted for extreme incidence theme.

Calculation and Classification of Drought Vulnerability Index (DVI) Physical and socio-economic vulnerability indicators have been categorized by equal interval technique where the ranges are well symbolized by their mean values within for each extent are impartially adjacent together (Smith 1986). At first, all the vulnerability indicators values are transformed to the values of Z score using transform )} for comparison as unit less values. For the purpose principles like Z = {X −mean(X S D(X ) of mapping, each group of an indicator is allocated within a scale of Z score values according to their original values. The index of composite drought vulnerability (DVI) of all indicators is calculated as follows: DV I =

P Dr + F M Rr − L Rr + P P R Ar + I LC L r + C Pr N umber o f indicator s

(2.6)

where, PDr, ratings allocated for the classes of population density; FMRr, ratings allocated for female-male ratio; LRr, ratings allocated for literacy rate; PPRAr, ratings allocated for a percentage of people rely on agricultural classes; ILCLr, ratings allocated for land under irrigation to cultivable land classes and CPr, ratings allocated for the production of crops/unit area.

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Index of Drought Risk (DRI) The map of risk pattern drought will be produced using the following formula (Downing and Bakker 2000; Blaikie et al. 1994; Wilhite 2000), D R I = D H I × DV I

(2.7)

where DRI represents the index of drought risk; DHI signifies the index of drought hazard and DVI symbolizes the widest idea of the vulnerability of drought in an area.

Interpolation with GIS Geographic Information System (GIS) with a familiar kriging method has been utilized for spatial climate modeling (Das et al. 2020c). The spatial distribution of the drought hazard index map has been prepared using Eq. 2.5. The resulting drought hazard map is overlapped on the map of the district of the study area to generate the drought hazard map of the district scale (Shahid and Behrawan 2008). Geographical information system is utilized to overlap the drought hazards dataset onto map of the district and then compute the range of each district DHI values and several point values of DHI have been calculated using shown map tips, i.e., predicted value at the cursor on the spatial distribution map of DHI in GIS. The area-weighted mean of DHI values for every district is calculated and used to prepare district level hazard maps of drought (Table 2.2).

Results and Discussions Descriptive Statistics of Rainfall and Temperature The mean annual temperature of western Bangladesh ranges from 1492 to 2766 mm (Fig. 2.2a) with the average rainfall of 1925 mm. The southern portion of the study area particularly on the coastal region of the Bay of Bengal gets the maximum rainfall during the study period. However, the central portion receives lower rainfall than the south and north respectively. Rabi, Pre-Kharif , and Kharif are the three cropping seasons mainly observe in Bangladesh (Banglapedia 2003). Rabi starts at the beginning of November and finishes at the end of February and the season is climatically dry; Pre-Kharif starts at the beginning of March and finishes at the end of June, and Kharif starts at the start of July and finishes in the finishes of October. Figure 2.3 shows the Percentage of annual rainfall receives by each season. 62% of the rainfall occurs in Kharif season, basically known as the wettest season of the year whereas Rabi season receives only 3.24% of the annual rainfall of the western

2 Climate Modeling, Drought Risk Assessment … Table 2.2 Ratings and weights allotted to the velocity of drought with corresponding features of the subjects

31

Drought velocity

Weight

Percentage of occurrences

Rating

Mild

1

5.87–9.27

1

Moderate

Severe

Extreme

2

3

4

9.27–12.66

2

12.66–16.05

3

16.05–19.44

4

7.84–10.29

1

10.29–12.74

2

12.74–15.19

3

15.19–17.65

4

8.00–14.82

1

14.82–21.65

2

21.65–28.47

3

28.47–35.29

4

31.37–36.27

1

36.27–41.17

2

41.17–46.08

3

46.08–50.98

4

Fig. 2.2 Distribution of a annual average rainfall b annual temperature of the study area

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Fig. 2.3 Annual rainfall (%) happens in the cropping seasons

part of Bangladesh. The rest of the rainfall (35.20%) happens during the season of Pre-Kharif . On the other hand, the mean annual temperature of the area varies from 24.18 to 26.17 °C with an average temperature of 25.44 °C. The mean annual temperature of the region rises towards the south from the north due to the closeness of the Bay of Bengal in the south and the Himalayans in the north. (Fig. 2.2b). However, the mean annual temperature is the lowest in the north-east (24.18–24.43 °C) and highest in the extreme south-west particularly on the coastal region of the Bay of Bengal (25.92–26.17 °C).

Analysis of Mean Annual Rainfall Both increasing and decreasing trends of annual rainfall are observed in the study region. But three fourth of them (70.84%) are not statistically significant at 10 and 5% significance levels. MK test results reveal more than half (58.3%) of the stations show increasing trends and only two-fifth (41.68%) of the stations show decreasing trends during the study period. The remarkable significant increasing trends at 5% significance levels are observed at Rangpur, Dinajpur, and Khepupara stations and the magnitude of slopes is 8.56 mm/year and 11.15 mm/year and 13.66 mm/year respectively. The linear trends and the time-series graph of the annual rainfall of these stations are presented in Fig. 2.4. Khulna station also shows a positive trend at a 10% significance level where the degree of change is 5.18 mm/year. In contrast, the Bhola and the Rajshahi stations are found to be significant declining trends at 5% and 10% significance levels, and the rates of change obtained from Sen’s slope are −11.667 and −5.951 mm/year respectively (Fig. 2.4). The distribution of the MK test results (Z statistic) of rainfall (annual) is shown in Fig. 2.5a. The significant inclining trends are found mainly in the northern and southern parts of the study region. The majority portion of the province from north to south shows not significant inclining trend at 10 and a 5% significance level except few pockets of Barisal and Rajshahi division where the trend is decreasing (insignificant).

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Fig. 2.4 Significant linear trends in annual rainfall

Fig. 2.5 Distribution of Z statistics (MK test) of a Annual rainfall b Rabi rainfall c Pre-Kharif rainfall and d Kharif rainfall

Analysis of Mean Annual Rainfall in the Cropping Season Rabi The rainfall in Rabi season shows a positive and negative trend at 58.33% and 33.33% of the stations respectively. The results from the MK test (Z statistics) reveals only two stations (total stations 12) namely Jessore and Satkhira are statistically significant at 5% and 10% significance level respectively. The degrees of changes obtained from the analysis of Sen’s slope of these two stations are 0.18 mm/year and 0.735 mm/year respectively. The spatial distribution of Z statistics of Rabi season rainfall time series

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is shown in Fig. 2.5b. The maximum part of the study area shows statistically insignificant at 10% and 5% significance level except few pockets located in the northwestern portion of the Khulna division. Pre-Kharif Both inclining and declining trends are observed also in the rainfall of the Pre-Kharif season. The results obtained from the MK test indicate 41.68% and 58.34% of the stations show increasing and decreasing trend respectively, however, maximum of the trends are statistically insignificant (83.33%) at 10% and 5% significance levels. The robust significant positive trends are observed at Dinajpur and Rangpur stations in this season and the magnitude of changes are 4.104 mm/year and 3.114 mm/year respectively. The distribution of the z statistics shows a significant increasing trend at 10% and a 5% significance level is mainly observed in the northern part of the study area. South-western portion and few pockets of north show an insignificant increasing trend in this season, while, the insignificant negative trend is observed in the central and south-eastern part of the study area (Fig. 2.5c). Kharif The rainfall in the Kharif season also shows increasing (75% of the stations) and decreasing (25% of the stations) trends during the study period. Among the increasing trend, only 25% are significant at the 5% level of significance. The remarkable significant increasing trend is observed at Khulna, Rangpur, and Khepupara stations with the rate of changes 7.407 mm/year, 6.157 mm/year and 12.73 mm/year respectively. The distribution of the Z statistics (MK) of rainfall series of Kharif season is shown in Fig. 2.5d which shows an increasing pattern of rainfall all over the study area with the exception of Rajshahi station (Fig. 2.5d), however, the trend is insignificant at these particular level of significance. Whereas significant increasing trends are observed in some parts of north and few pockets of Khulna at a 5% significant level, whereas Rajshahi stations and some pocket region of Barisal show an insignificant falling trend.

Analysis of Mean Annual Temperature The percentages of stations in the study area showing inclining and declining trends in annual mean temperatures are 83.33 and 16.67% of stations respectively. The strongest significant inclining trends at 5% significant level in annual average temperature are found at the Rangpur, Bogra, Faridpur, Jessore and Bhola stations and the rate of change are 0.015 °C/year, 0.007 °C/year, 0.013 °C/year, 0.009 °C/year and 0.08 °C/year respectively. The linear trends and the time-series graph of the annual temperature of these stations are presented in Fig. 2.6. Five stations namely Ishurdi, Rajshahi, Khulna, Satkhira, and Barisal also show an inclining trend in average annual temperature, however, these trends are insignificant at this particular level of significance. The results from the MK test (Z statistics) also reveals only two

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Fig. 2.6 Significant linear trends of mean annual temperature

stations (total stations 12) namely Dinajpur and Khepupara show a declining trend in mean annual temperature where the trend of Dinajpur is insignificant at 10% or 5% significance level but the trend of Khepupara is significant at 5% significance level. However, the magnitude of changes (o C/year) obtained from Sen’s slope are −0.006 °C and −0.014 °C/year respectively. The distribution of Z statistics (MK test) of the mean annual temperature is shown in Fig. 2.7a. The distributional map of Z statistics reveals the positive trends are mainly concentrated in the northeast. Whereas the rest of western Bangladesh indicates insignificant increasing trends with these levels of significance except a pocket area in the southern part.

Fig. 2.7 Distributions of Z statistics of mean temperature for a Annual, b Rabi, c Pre-Kharif and d Kharif

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Analysis of Mean Annual Temperature in the Cropping Season Rabi The temperature in the Rabi season shows both positive and negative trends at 76% and 24% of the stations respectively, however, maximum of the trend are statistically insignificant at 0.1 and 0.05 level of significance. The results from the MK test (Z statistics) reveals only two stations (total stations 12) namely Rangpur and Faridpur are significant at 5% significance level with the rates of 0.020 °C and 0.018 °C/year respectively. A significant increasing trend at a 10% level is observed at Bogra station with a magnitude of 0.01 °C/year. In contrast, a significant negative trend is observed at Khepupara station and the rate of decrease is −0.043 °C/year. The spatial distribution of Z statistics of Rabi season rainfall time series is shown in Fig. 2.7b. The distributional map of Z statistics (MK) of the annual mean temperature of Rabi season reveals significant (α = 0.1 and 0.05) positive trend is observed in the north-eastern part of the study area. Whereas, the southern portion of the province shows significant (α = 0.1 and 0.05) negative trend during the study period. However, the area from north-western to south-eastern and the south-western part show insignificant positive and negative trends respectively. Pre-Kharif 58.53% of the stations in the study region show an inclining trend in average annual temperature in the season of Pre-Kharif while 41.67% of the stations show a decreasing trend, but almost all the stations (83.33%) are statistically insignificant at 10% and 5% significance levels. Only two stations (16.67%) show significant (α = 0.05) trends where Bhola shows an increasing trend with the rate of 0.014 °C/year, and Dinajpur shows a decreasing trend with the rate of −0.029 °C/year. On the other hand, Jessore, Rangpur, Ishurdi, Satkhira, Faridpur, and Barisal show an insignificant increasing trend in mean annual temperature with these particular levels of significance. The rate of changes (o C/year) of all the stations obtained from Sen’s slope is varied between -3.667 to 4.104°C/year. However, the stations Bogra, Rajshahi, Khulna, and Khepupara show not significant decreasing trends at 10% and 5% significance levels. Figure 2.7c shows the distribution of Z statistics of the Pre-Kharif season average annual temperature. The spatial map of Z statistics (MK) specifies that both decreasing and increasing trends over the study area are insignificant at 10% and 5% significance levels, where the northern side of the study area shows an insignificant decreasing trend that spreads up to the central part and from the southern part to the central, shows inclining trends which is insignificant, though station-wise assessment specifies significant trends. Kharif In Kharif season all stations show a positive trend of mean annual temperature except Barisal at 10% and 5% significance levels. The results obtained from Sen’s slope reveals the magnitude of changes varies from 0.004 to 0.016°C/year with an average of 0.011°C/year. This season shows the maximum rate of changes among the cropping

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Fig. 2.8 Annual mean forecasting temperatures (°C) from 2012 to 2016

season and most of the trends are significant at 10% and 5% level of significance. The robust inclining trends with significant at 5% significance levels in Kharif season mean annual temperature are found at Jessore, Bogra, Dinajpur, Rangpur, Satkhira, Ishurdi, Bhola, Rajshahi, Faridpur, Khulna, and Khepupara stations and the rate of changes are 0.009, 0.012, 0.016, 0.011, 0.008, 0.014, 0.015, 0.016, 0.011, 0.013 and 0.007 °C/year respectively. The spatial distribution of Z statistics (MK) of Kharif season means the annual temperature is shown in Fig. 2.7d which specifies that the whole study area shows a significant increasing trend at a 5% significance level.

Prediction of Annual Mean Temperature Using the ARIMA Model The ARIMA (Auto Regression Integrated Moving Average) technique of BoxJenkins is applied to forecast the temperature of the next five years by analyzing the last fifty-two years of data. The estimated parameters of the auto ARIMA model with five years forecasting values of annual mean temperatures are shown in Bar chart (Fig. 2.8) of each station. It has been observed that the yearly forecasting temperature is not only fluctuating very little portion over the years but also fluctuating from one station to another. It may be the cause of geographical morphology or the effect of climate change.

Agricultural Drought Modeling Using Markov Chain The rainfall (daily) of Rabi, Pre-Kharif , and Kharif seasons of seventeen standard weeks of the 12 stations of the study region has been taken into consideration for the period of 1960 to 2011 for identifying the parameters of Markov Chain model based on a detail computer simulation programming. First of all, the study has taken into consideration weekly total rainfall equal 5, 10, 15, 20 and 25 mm as threshold

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values for each station as well as for each decade, season and year, for assessing the possibilities of dry and wet weeks and drought proneness index (DI). Lastly, appropriate threshold volume have been taken as a decade, season and year wise by sustaining the static belongings of the model of Markov Chain on the basis of the changing form of the drought indexes. Again, approximately 10-12 wet weeks are essential for harvesting a good crop and an uninterrupted duration of at least 3 successive dry weeks in between the wet weeks would be liable for entire crop failure (Banik et al. 2000). Therefore, the possibility of receiving at least 8, 10, and 12 successive wet weeks and the possibility of receiving at least 3 successive dry weeks have been calculated using Eqs. 2.1 and 2.2 respectively for all stations in view of the higher-order constant values of P01 and P11. Finally, DI value has been computed for each station using Eq. 2.4.

Spatial Distribution of the Seasonal Probability of Dry and Wet Weeks Figure 2.9a-c show that the possibility of receiving at least 8, 10, and 12 successive wet weeks is high for the duration of the Kharif season and low in Rabi season. The figures also specify the possibility of receiving at least 8, 10 and 12 successive wet weeks are high in the southern stations (Jessore, Barisal, Khulna, Satkhira, and Bhola) in comparison to the northern stations. The highest possibility of receiving at least 8, 10, and 12 are in Khepupara and the values are 0.792, 0.741, and 0.693 respectively. The lowest possibility of receiving at least 8, 10, and 12 wet weeks are found in Bogra in Rabi season and Rajshahi in Kharif and Pre-Kharif season. The possibility of receiving three successive dry weeks is lower in Kharif and higher in Rabi. During the Pre-Kharif season, the possibility of receiving at least three successive dry weeks differs lightly. The highest possibility of at least three successive dry weeks for the duration of Pre-Kharif are observed at the Rajshahi (0.851) and the lowest is at Faridpur (0.765). In the case of Kharif , the possibility of receiving at least three successive dry weeks ranges from 0.506 to 0.652, and for Rabi season this probability ranges from 0.973 to 0.990. The possibility of receiving wet weeks reduces with an increasing interval of weeks and this probability also reduces towards the central-western part in the study area. For the duration of the Kharif season, the possibility of receiving at least three successive dry weeks is maximum (0.5 < p ≤ 0.75) over the area and varies from 0.506 to 0.652. Therefore, the study area has high crop prospects in respect of wet weeks, but there is a possibility to suffer from drought. The central-western portion of the area is highly vulnerable by affecting drought.

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Fig. 2.9 a Distribution of possibility of receiving at least 8, 10 and 12 successive wet weeks and at least 3 successive dry weeks in Kharif season, b distribution of possibility of receiving at least 8, 10 and 12 successive wet weeks and at least 3 successive dry weeks in Pre-Kharif Season, c distribution of possibility of receiving at least 8, 10 and 12 successive wet weeks and at least 3 successive dry weeks in Rabi Season

Seasonal Characteristics of DI Indices The high DI value specifies infrequent drought conditions and low value specifies it’s opposite. DI of Kharif season in the study region varies from 0.126 to 0.164. In this season the highest and lowest value (DI) are observed at Bogra (DI = 0.163) and Dinajpur (DI = 0.125) respectively. Therefore, it may be concluded that the Bogra is a low and Dinajpur is high drought-prone area. In this connection, severe droughts in agriculture have occurred in Ishurdi, Rangpur, Rajshahi, Jessore, Bogra, Dinajpur, Khulna, Barisal, Satkhira, and Bhola regions where DI value ranges from 0.126 to 0.164 (Fig. 2.10a) however, the modest drought in agricultural occurred in Khepupara and Faridpur regions with DI values 0.188 to 0.196.

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Fig. 2.10 Distribution of DI in three different seasons a Kharif , b Pre-Kharif and c Rabi

In the Pre-Kharif , the drought in agricultural is severe in all of the stations with DI value varies from 0.047 to 0.078 (Fig. 2.10b). The maximum DI value is observed at Faridpur (DI = 0.078) and the minimum is observed at Rajshahi (DI = 0.047). The other types of drought-prone areas are not observed in any of the meteorological stations of the western part of Bangladesh. In Rabi season, the drought in agricultural is severe also in all of the stations with DI values varies from 0.003 to 0.008 (Fig. 2.10c). The minimum DI value is observed in Dinajpur, Bogra, and Rangpur (DI = 0.003), and the maximum is observed in Barisal, Khepupara, and Bhola (DI = 0.008). The other types of droughtprone areas are not observed in any of the stations of the western part of Bangladesh. This (Fig. 2.10c) also reveals that the whole study region suffers from severe drought in agricultural for the duration of the Rabi season.

The Trend in Drought Index (DI) in Kharif Season The line graphs based on the year of the drought proneness index (DI) of all stations are shown in Fig. 2.11. These figures show, normally the value DI over the area increases except Jessore and Rajshahi areas which means drought proneness of these areas is increasing. The DI values of the Rajshahi area 1962 to 1980 are comparatively higher than those for the period 1981–2010. During the later period, the lower DI values are frequently found and the similar results are also found for the Jessore, Dinajpur and Rangpur areas.

The Trend in Drought Index (DI) in Pre-Kharif Season The line graphs based on the year of the drought proneness index (DI) of all stations with proper threshold values for detecting the trend of drought index are shown in Fig. 2.12. This figure shows a general increase in the DI values over western

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Fig. 2.11 Time series of the graph of DI with linear trends in Kharif season

Bangladesh. The highest DI values near to zero lines are observed in Rangpur, Dinajpur, Ishurdi, and Jessore areas for the duration of the Pre-Kharif season. For this reason, these areas have become more droughts prone compared to some additional areas in very recent decades.

Annual Occurrences of Agricultural Drought The percentages of annual drought occurrence in agriculture classes are also estimated in this study (Fig. 2.13a-d). Mild occurrences of drought (%) are very high in the south and low in the north (Rangpur division) of the area (Fig. 2.13a). The percentage of annual mild drought occurrence is about 8.19 to 11.94 in the Rajshahi division and its surrounding areas and about 11.95 to 15.68% in the northern portion of the Khulna division (central part the study area). The moderate drought percentage of occurrences is very high in the northern and eastern parts whereas it is comparatively low in the south-western parts of the Rajshahi division and few pockets of Barisal and Khulna division within the study area (Fig. 2.13b). About 7.48% to 9.53% is observed in the middle of Khulna division and about 9.54% to 11.68% is observed in the southern part of the Khulna division. 21.65% to 28.47% annual occurrence of

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Fig. 2.12 Time series graph of DI with linear trends in Pre-Kharif Season

Fig. 2.13 Distributions of annual occurrences in Kharif season

extreme drought is observed in the northern portion (Rangpur division) and 14.83 to 21.66% is observed in the rest of the region (Fig. 2.13c). The percentage of annual extreme drought occurrence is high in three pockets like the southeastern portion, middle of the study area, and northwestern part of the Rajshahi division (Fig. 2.13d). 33.33 to 37.7425% of extreme drought events are seen in the northeastern portion of Rajshahi and Rangpur divisions, some pockets of Khulna division, and southern portion of western Bangladesh. 37.7425 to 42.155% extreme drought events are seen in the central portion of the area extended from northern to southern direction.

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Drought Hazard, Risk and Vulnerability Analysis The food security and economy of a region are closely related to the climatic variation and climatic catastrophes have intense effects on civilization. Under these conditions, the climate can be assumed as a menace to a resource. Cyclones, droughts, and floods are significant examples of extreme climatic hazards. Such risky events may be identified by its features such as velocity, frequency, magnitude, speed of onset, area of impact, and length of time span. These physical characteristics affect the nature of human reactions (Burton et al. 1978). Risk is the outcome of well-defined hazards interrelating with uncovered systems- considering the belongings of the system, such as their feeling or social susceptibility. Risk also can be thought the mixture of an incident, its probability, and its significances. Risk equivalents the possibilities of climate hazard reproduced by a specified system’s susceptibility. Hazard is an incident that has the capability to cause mortalities, health damage, loss of agricultural, property damage, and harm to the environment, disturbance of business. Susceptibility is the extent to which an organization is susceptible too, or not capable to survive with, hostile effects of climate change, comprising the variability of climate and its extremes. Susceptibility is an operation of the signature, degree, and rate of the variation of climate to which an organization is disclosed in relation to its thoughtfulness and its adaptive capability (Ramamasy and Baas 2007).

Drought Hazard Index and Hazard Map Index of drought hazard (DHI) on the basis of the percentage of annual drought events incidences in Kharif for each and every stations have been computed using Eq. 2.5 and are given in Table 2.3. The drought hazard map has been prepared by the ordinary kriging method using geo-statistical tools is shown in Fig. 2.14. Table 2.3 reveals that the highest (50.98%) percentage of annual severe drought incidence and lowest (5.89%) percentage of annual random drought incidence with the maximum index of drought hazard (DHI = 32) are found in Dinajpur station compare to other stations in the area. District-wise distribution of DHI in the study region is shown in Fig. 2.14a. The DHI values in the study area vary from 15 to 32. Within the district boundaries, the high DHI values cover Thakurgaon and Rajshahi districts and the values are 29.43 and 29.27 respectively. The minimum DHI values cover in Borguna and Bagerhat districts and the values are 17.24 and 17.62 respectively. Figure 2.14b reveals that Thakurgaon, Panchagarh, Dinajpur, Nawabganj, Nilphamari, Rajshahi, Naogaon, and Natore districts are fall in severe hazardous zone linked up to other districts of the study area.

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Table 2.3 Percentage of annual drought incidences classes and DHI for Kharif season Stations name

Percentage of annual occurrences Occasional

Mild

DHI

Moderate

Severe

Extreme

Dinajpur

5.89

9.81

15.68

17.66

50.97

32

Rangpur

7.85

5.87

17.66

35.28

33.34

25

Bogra

19.62

7.85

15.68

25.48

31.38

22

Ishurdi

16.01

14.01

14.01

8.01

48.01

28

Rajshahi

7.85

7.85

15.68

21.58

47.07

31

Faridpur

19.62

11.77

13.74

19.62

35.28

18

Jessore

19.62

15.68

7.85

11.77

43.15

20

Khulna

19.62

11.77

7.85

23.54

35.28

17

Satkhira

25.48

7.85

9.81

19.62

37.26

17

Barisal

19.62

13.74

15.68

11.77

39.23

22

Bhola

20.01

13.34

15.57

15.57

33.34

21

Khepupara

25.01

19.45

11.12

11.12

33.34

15

Fig. 2.14 a District wise distribution of DHI and, b classified district wise drought hazard map

Drought Susceptibility of Socio-economic and Physical Indicators Bangladesh positioned low on just about entire measures of economic development. This low growth, accompanied with some other features such as its climate and

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geography, makes the region quite susceptible to climate change. With a population of above 149.8 million in the country and density of population more than 1,115 persons/km2 and 73% of the population lives in rural areas of Bangladesh (Bangladesh Demographic & Health Survey Report-2011 published in 2012). The impacts assessment of hazards from drought and susceptibility to climate change and successively exercise adaptation needs an excellence information. This information consists of climate data, for instance, temperature, rainfall, and the occurrence of extreme events for instance, the present condition on the ground for diverse sectors comprising agriculture, water resources, human health, biodiversity, and terrestrial ecosystems, and littoral zones. To do this, climate change data containing future influences and susceptibilities requires to be assimilated with socio-economic data and investigates through a range of segments, and the results must be personalized for stakeholders and policymakers (UNFCCC 2007). However, all the indicators are not enthusiastically accessible or measurable for Bangladesh. After cautious though of accessible or measurable physical and socioeconomic indicators, two physical/structural and four socio-economic indicators are considered to denote the susceptibility of numerous geographic elements to the influence of droughts (Shahid and Behrawan 2008; Rahman 2001). In the present study, to represent drought vulnerability these socio-economic factors have been taken into consideration because agricultural drought harmfully affects these elements. The socio-economic indicators are male-female ratio, the density of population, percentage of populaces dependent on agriculture, and literacy rate. Two structural/physical indicators are percentages of land under irrigation to cultivable land and production of crops/unit area. Four classes were chosen for each of the above six indicators varying from the lesser to the higher values to produce district-level susceptibility maps of the area. The equivalent interval technique (Slocum 1999) is utilized to develop the groups or classes.

Population Density Population density defines the number of persons per square kilometer. The degree of catastrophe allied disaster is related to the density of the population of any region. The severity of the disaster is higher in a densely populated area in comparison to the less populated area. In Fig. 2.15a, the high Z score values symbolize the high influence on drought susceptibility and vice versa. The very high Z score values range from 0.85-1.85 which covers 16.55% of the study area and very high Z score values are found mainly in the north-eastern districts. The high Z score values (0.15 to 0.85) are found in 36.19% of the total area and high values are found in main districts situated in the central region. The modest Z score values cover 31.53% of the total study area. The values of low Z scores are mostly observed in southern districts like Khulna, Patuakhali, Jhalokati, Borguna and Bhola that covers 10.94% small portion of the study region.

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Fig. 2.15 Map of Z score of a density of population per km2 , b male-female ratio, c literacy rate, d people rely on agriculture (%), e land under irrigation to total cultivable land and f production of crops

Female to Male Ratio The number of thousand women to the number of thousand men in an area represents female to male ratio. It has been exposed in broadly different populations that women are much at risk when disaster develops (Vaughan 1987). The death rate for women was almost five times larger than for men when a cyclone and floods take on Bangladesh in 1991, (Huq and Ayers 2007). The map of the Z score of the sex ratio of drought susceptibility is shown in Fig. 2.15b. Very high Z score values range from 1.60 to 2.66 which covers Jhalokati district/5.09% of the study area. The high Z score value is found in about 24.02% of the total area. Moderate Z score values cover 40.39% of the total study area. Low Z score values are observed in mainly northern districts that cover 24.44% of the study area.

Literacy Rate Literacy is a key element in creating the attentiveness of the population in the social order. Well-educated people can be aware easily as they understand easily with less effort and money (Balaji et al. 2002). Literate people can make attentiveness amidst the people who are suffering from hazards from drought through mass media too.

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Literate people can also ensure awareness through valuable writings regarding the drought hazard consequences in different types of social media like newspapers, magazines, pamphlets, and also they can organize the valued seminars and symposiums about the impact of drought vulnerability. Besides these, they can also play a vital role in exposing the devastating effect of drought vulnerability in participating in different electronic media like radio, television, documentary film and street plays, etc. Thus, the very high Z score values indicate very low susceptible and low values indicate the opposite. The Z score map of the literacy rate element of drought susceptibility is shown in Fig. 2.15c. The very high Z score values range from 1.50 to 2.46 which cover Pirojpur, Jhalokati and Lalmonirhat, districts/5.97% of the study, and these regions less susceptible in terms of literacy rate than other areas. The Z score values of literacy rate range from −1.35 to −0.38 which covers 25.81% of the region presenting high susceptibility in comparison to the other district and these Kurigram, Naogaon, Rangpur, Bogra, Kushtia, Magura, Meherpur, Rajbari, Pabna, Rajshahi Natore, Sirajganj, and Nawabganj districts.

People Reliant on Agriculture The percentage of people dependent on agriculture includes both farmers and agrarian workers. Above 75% of the populations of Bangladesh depends on agriculture (Shahid and Behrawan 2008). Agriculture is the most influenced sector by drought and flood in Bangladesh. The district-level Z score map of the study region considering the socio-economic indicator i.e., percentage of people rely on agriculture is presented in Fig. 2.15d. Very high Z score values range from 0.37 to 1.38 which covers 21.19% of the study region presenting a very high amount of people depends on agriculture, if drought affects livelihood activities of these people will be in danger. Very low Z score values of literacy rate are observed in the southern districts of Kushtia, Bagerhat, Jhalokati and Khulna that cover only 4.57% of western Bangladesh.

Irrigated Land to Cultivable Land It signifies the percentage of land under irrigation to cultivable land. Nearly, 45.77% of agricultural lands of Bangladesh are under irrigation (BBS 2011). Approximately 75% of irrigation water supplied by underground water and the rest of 25% comes from surface water (Bari and Anwar 2000). Surface and underground water accessibility is directly associated with meteorological drought. Decreasing the quantity of rainfall is steady with decline trends of groundwater levels in northwestern districts in Bangladesh (Rahman et al. 2016). The district-level Z score map of this aspect is displayed in Fig. 2.15e. The very high Z score values range from 0.71 to 1.53 which is mainly concentrated in northeastern districts and covers 17.59% of the study area

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presenting very high influence on the agricultural sector in these areas. And high values are observed in other northern districts which cover 22.13% of the study area. On the other hand, low values are found in southern districts that cover only13.78% of the area.

Crop Production Droughts have a higher negative impact on the economy in higher crop production areas than that of lower crop production areas. The district-level map of Z score of this susceptibility indicator is presented in Fig. 2.15f. The very high Z score values range from 1.23 to 2.13 which covers 9.28% of the study area covering Dinajpur, Pabna, Jessore and Bogra districts. If a drought occurs, crop failure in these districts will be higher than in other districts. The Z score values in other northern districts are high (0.34 to 1.24) which covers 25.77% of the study region. However, the Z score values are low in southern districts which contain 21.41% of the study region.

Drought Susceptibility A composite susceptibility of drought map is drawn by using all socio-economic and physical susceptibility indicators. In addition, the district wise combined layer is grouped rendering to DVI values in Eq. 2.6 into four groups with the same interval method to prepare the compound drought susceptibility map of the study region which is displayed in Fig. 2.16a. In Fig. 2.16a, the pattern of drought susceptibility in the study area can be divided into four susceptible zones: low vulnerability observed in Rangpur, Bhola, Satkhira, and Rajbari districts, moderate susceptibility found in Sirajganj, Borguna, Chuadanga, Gopalganj, Bagerhat, and Patuakhali district, high susceptibility found in Jessore, Khulna, Jhalokati, Kushtia, Jhenaidah, Magura, Lalmonirhat, Madaripur, Panchagarh, Meherpur, Narail, Naogaon, Pirojpur, Rajshahi and Thakurgaon district and very high susceptibility found in Bogra, Gaibandha, Dinajpur, Faridpur, Joypurhat, Nawabganj, Kurigram, Pabna Nilphamari, and Natore districts. The highest susceptibilities are observed in the north and north-eastern portion of the study region where all the susceptibility indicators are relatively high excluding literacy rate.

Drought Risk Drought hazard and drought susceptibility maps are assimilated using GIS to prepare drought risk maps of the study region. The DRI of every district of the combined

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Fig. 2.16 a Drought susceptibility map and, b map of drought risk of the study area

layer is computed using Eq. 2.7. The districts of the combined layers are then grouped according to values of DRI into four groups considering equal interval technique to prepare the hazards of agricultural drought map measured by the Markov chain model. Figure 2.16b specifies the agricultural droughts pose a high risk to few northern districts of the study area. Some districts in the southern portion of the study region are recognized as modest risk. The three districts in the coastal area and one district in the northern part and two districts in the eastern part of the study region appear as less risk to droughts of this group. Table 2.4 shows that 21.63% of the area is displayed to very high risk, 26.55% of the area to higher risk, 29.67% of the area to modest risk, and 22.16% of the area to lesser risk. Hence, the high production of the crop, dependent on agriculture and irrigation along with high variability of annual precipitation has prepared the northern part higher risk to droughts in relation to the other parts of the province. Poverty mitigation and conservation of water are prerequisites for decreasing the drought influence in the region. Table 2.4 Area (%) under various drought risk classes

Drought risk

Area (%)

Very high

21.64

High

26.55

Moderate

29.67

Low

22.14

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Conclusions and Policy Recommendations This study aims to identify the climatic condition of the western part of Bangladesh by developing a suitable climate model using rainfall and temperature data to estimate future climatic scenarios. Rainfall is considerably higher in the south and the northern portion than the central portion of the study region. The annual average rainfall of the study region is 1925 mm. However, the annual mean temperature is around 25.44 °C. The lowest annual mean temperature is observed in the north-east and increases towards the south and reaching the extreme in the south-west along the Bay of Bengal coast. Kharif season is the wettest season, while Rabi season is the dried out and coolest season. Increasing and decreasing both trends are observed in rainfall (seasonal and annual) time-series data. The significant increasing trends in annual and Kharif rainfall are found at Rangpur, Dinajpur, and Khepupara at a 5% significance level. Conversely, two significant (α = 0.05) falling trends in rainfall are observed at Bhola (-11.68 mm/year) and Rajshahi (-5.96 mm/year) correspondingly. However, most of the study area shows no significant trends in precipitation in the northwest part for the duration of Rabi and pre-Kharif at 5% significance level. About 83.32% and 16.68% of the stations show increasing and decreasing trends in mean annual temperature. Significant (α = 0.05) increasing trends in mean annual temperature are observed in the north-eastern part and while, insignificant trends are observed in the north-western part of the study area. Rising and falling trends of mean annual temperature both in Rabi and pre-Kharif are observed but most of them are insignificant. In the Kharif season, all the stations show significant increasing trends in annual temperature except Barisal station. Sen’s slope analysis reveals that the amount of changes varies from 0.004 to 0.016°C/year with an average 0.011°C/year during this particular season. The results of the ARIMA model indicate predicted temperatures for all of the years are higher than the long term observed mean temperature. Though the temperature is rising, the spatial pattern will remain the same in the future. The north-western region like Rangpur, Dinajpur, Ishurdi, and Rajshahi are much drought prone as the possibility of receiving at least 8, 10 and 12 wet weeks are relatively low and the possibility of at least 3 dry weeks are relatively high and estimated number of wet weeks remain between 7 to 9. Conversely, the area under Bogra, Bhola, Khepupara and Faridpur stations are less drought-prone in terms of receiving wet and dry weeks, and the estimated number of wet week’s remain between 10 and 13, but Khepupara and Bhola are low crops prospect region due to the problem of salinity. Annual percentage, frequency, and occurrences of annual probability indicate that the possibility of severe drought incidence increases in current decades in northwestern areas like Dinajpur, Ishurdi, and Rajshahi areas in relation to the other parts of the study region. The station wise drought hazard indexes (DHI) is maximum in the Dinajpur (northern) and less in the Khepupara (southern). But within the district boundaries, the greater DHI values (29.44 and 29.26) cover northern districts

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such as Thakurgaon and Rajshahi. The lesser DHI values are observed in southern districts like Bagerhat (17.62) and Borguna (17.24). Furthermore, the distribution of indexes of drought hazard specifies that the central and northern districts are much drought-prone than the southern districts. The study of drought susceptibility specifies that the northern portion of the study area represents high to very high drought susceptible and the southern portion is modest to low. Drought risk investigation shows that agricultural droughts pose a maximum risk in northern districts. Some districts in the southern portion of the study region are signifying to moderate risk. The study also indicates that 29.68%, 26.53%, and 21.63% of the region are considered as moderate, high, and very high drought risk. Intensive production of crops, and irrigation alone with high variability of annual rainfall have made the northern portion a high drought risk area compared to other portions of the study region. Policy recommendations To minimize the adverse effect due to droughts the following steps should be undertaken to face the climate change impact: • As Panchagarh, Dinajpur, Thakurgaon, Nilphamari, Naogaon, Nawabganj, Rajshahi and Natore districts are becoming areas of high hazard-prone due to drought, hence an effective hydrological adaptation measure must be taken along with traditional adopting measures. • Accordingly, priority should be given to ranking risks like timing, the importance of the sector, and climate associated events based on the frequency and severity of impact. • Effective drought forecasting and warring and analysis of probability of seasonal rainfall is needed. • Detail research works on droughts for risk management considering all possible causes of droughts such as natural, structural, geomorphological features, etc. • Public awareness about the severity of droughts and proper education of nature and frequency of droughts. • Review the droughts management activities to improve the process and plan for the future. • Improved existing water management systems and introduce new irrigation methods like drip irrigation. • There should be a master plan for each drought-prone areas and integrated water resources management (IWRM) need to take preparation for minimizing adverse effect due to drought and managing the changing environment.

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Chapter 3

Contextualizing Resilience Amidst Rapid Urbanization in Kenya Through Rural-Urban Linkages Risper Nyairo, Ruth Onkangi, and Merceline Ojwala

Abstract The extending urban fabric in developing countries is gradually but steadily blurring the rural-urban divide. Decentralization of Government in Kenya through devolution is envisioned to hasten this transformation. The resulting linkages between urban and rural areas reinforce each other on a macro and micro level at the ecological, economic, social, and spatial scales. With greater urbanization, the everincreasing inequalities of the populace become obvious and threaten the stability of socio-technical regimes, connectivity nodes and ecological niches. Uncertainty of the stability of rural-urban linkages is further exacerbated by climate change and other visible and invisible environmental pressures. Climate action and future proofing of assets seem to lean heavily on urban infrastructure with lesser attention to rural-based sectors. This chapter evaluates this bias through empirical and desktop reviews and discusses the density and direction of rural and urban linkages in Kenya as far as resilience is concerned. It follows the logic of “network” linkages as opposed to the logic of linear linkages. The findings indicate an archipelago of climate action whose conglomeration is insignificant to realise robust national resilience. Fragmented action in future proofing of sectors imbalances forward and backward linkages of the rural-urban areas continuum. Overall, the “chain” is weakened and resilience impaired. The chapter proposes tangible solutions to support retention of functional linkages and equitable climate-resilient development in Kenya, which may be extended to other countries in the global South.

R. Nyairo (B) Ritsumeikan University, Kusatsu, Japan R. Onkangi National Construction Authority (NCA), Nairobi, Kenya M. Ojwala Directorate of Resource Surveys and Remote Sensing (DRSRS), Nairobi, Kenya © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_3

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Introduction Economic growth and development are on an upward trajectory in developing countries as per the non-decelerating (at least not so significantly) annual single digit economic growth rates. Urban areas continue to be the epicentres of economic booms while rural areas are the supportive players progressively being muted in the background. Rural areas are increasingly noticed to be the invisible foundations responsible for the robust economies and the force behind the march to economic prowess and middle or high-income country status. Industrialisation is mainly domiciled in urban centres while rural areas are seemingly a preserve for raw material and labour production. Rural areas are receiving the breadcrumbs of attention in terms of economic, infrastructure, technological (Cowie et al. 2020) and climate action while urban areas take the lion’s share of financial allocations, socio-economic and environmental programs, policy implementation and plans. The chasm between urban bourgeoise and the rural proletariat widens as developing countries climb the world economic rankings. This economic imbalance has a ripple effect socially, economically, environmentally and spatially, that reverberates from the Central Business Districts (CBDs) of capital cities to the grassroots of every village within the geographical boundaries of developing countries. It reinforces social inequalities and exacerbates socio-economic vulnerabilities while entrenching two-way dependencies. This two-way dependency pans out into a network of linkages between the rural and urban spheres (Tacoli 2003). For sustainable socio-economic development to be achieved in the global South, rural-urban linkages need to be recognized and deliberately nurtured. The United Nations through the Sustainable Development Goals (SDGs) underscores the importance of rural-urban linkages. SDGs 2 (Zero hunger), 9 (Industry, Innovation and Infrastructure), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on land) directly advocate for sustainability and climate action in the rural-urban interactions. In the face of climate change and pandemics such as the most recent COVID-19, the interdependency between urban and rural areas has become even more obvious. The resilience of economies has been weighed and found wanting. As simple as the rural-urban linkages may appear to be, anything severing them (be it a public health scare such as COVID-19 or environmental pressures such as climate change) greatly jeopardises economic stability. Therefore, the stability and sustainability of economies greatly hinges on the resilience of rural-urban linkages. For many, the idea and goal of economic growth and development is seemingly, blotting out rural areas and replacing them with urban centres. This is very utopian and thriving on the “othering” perspective more so of rural folk. While the urban fabric is extending without responsible resource consumption and production, the need to maintain nature’s contribution to people (NCP; Diaz et al. 2015) is becoming increasingly apparent, including organic food production as seen in the wave of urban agriculture campaigns and activities. The challenge of water insecurity (Liu et al. 2019) also arises and is associated with land use changes,

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as rural areas are baptised into urban areas. Natural capital cannot be completely delinked from country economic performance and growth. In choosing between keeping up with fast paced economies and achieving development within decades where first world countries have taken centuries, emerging economies are openly skewed towards the “develop now and clean later” (UNIDO 2004) option. This short termism attracts retrogression and will not keep progress in its path. It is therefore crucial for developing countries to stop procrastinating decoupling of environmental degradation from economic growth. The absence of rural areas or small-scale presence of rural areas should not be the yardstick of development. The Authors’ opinion is future proofed rural-urban linkages should be a yardstick for robust economies. COVID-19 has exposed the unpreparedness of most economies to handle a global pandemic and nowhere has this been more apparent than in Kenya, where the government had to borrow or receive in kind billions of shillings for management and recovery. Although not badly hit at the onset of the pandemic in 2020, the country was for many months unable to even meet the demand for test kits or protective equipment for frontline workers. But there is an even greater pandemic awaiting the country unless deliberate planning is undertaken to adapt to anticipated negative impacts of climate and social change. Already droughts and floods are serious threats to food production and infrastructure in the country. Since stringent climate mitigation may further threaten food security (Hasegawa et al. 2018), prioritizing adaptation is inevitable. The country is still largely agricultural but with little actual investment in agricultural development. Majority of Kenyans are smallholders who have not yet adopted a market-oriented farming model. Most just grow food for local consumption (if they can store it long enough). And when the crop fails (especially in the drier parts of the country) there is a food crisis. At the same time the population is growing and urbanizing, with a significantly large proportion of urban poor. Resilience mechanisms, which focus on the system or its governance, would help reduce physical, social and economic challenges along the rural-urban continuum in the country. This chapter explores some of the rural-urban linkages that can be strengthened if not exploited to reduce unnecessary vulnerability and promote synergies. The study reveals the need to flatten the system to enable rapid response to the needs of the people and enhanced capacity to develop quick, relevant mechanisms that improve resilience. Moreover, omnivorous options such as diversification of energy access can significantly improve resilience. The chapter is organized as follows: Sect. 2 discusses Linear and Non-Linear Linkages in the Rural-Urban context and the role of climate change; Methods are presented in Sect. 3; Results are given and discussed in Sect. 4 while Sect. 5 concludes with Recommendations and Policy implications.

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Rural-Urban Linkages and Climate Change Linear and Non-linear Linkages in the Rural-Urban Context Rural-urban linkages are the economic, social, cultural, and political relationships which create connections between rural and urban areas. The concept of rural-urban linkages therefore requires complimentary functioning and flows between rural and urban areas, forming interdependencies and synergies (Steinberg 2014). Rural-urban linkages may include: (a)

(b)

(c)

(d)

(e)

(f)

Population and human capital—more people tend to move to urban areas from rural ones in search of better opportunities. As a result, urban areas experience increased human capital, while rural areas experience a decrease (Githiri and Foster 2020). Investments and economic transactions—investments by government mostly depend on the level of urbanization in an area. Urban areas thus tend to receive larger allocations as compared to rural ones. Products and services—rural areas provide access to raw materials for industries in urban areas. They also provide labour for manufacturing processes. Urban areas on the other hand provide a direct market for products and enable the linkage of the suppliers with the buyers (Ros-Tonen et al. 2015; United Nations Human Settlements Programme 2017). Environment—land use is highly dependent on the movement of people that lead to changes or redesignation. In terms of quality, industries in urban areas may pollute the environment while people in rural areas may pollute agricultural produce through use of harmful pesticides; in turn affecting human health. Governance—inadequate governance structures can lead to higher inequalities and damages to the environment in both rural and urban areas. Furthermore, lack of transparency in governance may lead to stunted economic growth in both rural and urban areas based on the movements of people and management of resources (Ros-Tonen et al. 2015). Information and data—data and information continually increase regarding various aspects of the rural-urban nexus. Data on migration from rural areas to urban areas can provide context on reasons for movement. Such data is critical in making decisions that improve living conditions in the respective areas and curb increased migration (United Nations Human Settlements Programme 2017).

Infrastructure and the built environment in its entirety underpin these linkages. It is therefore worth every future proofing investment, for socio-economic resilience and sustainability.

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Climate Change as a Threat-Multiplier Climate change threatens the stability of rural-urban environments. Impacts such as water shortage threaten food production in rural areas causing shortage in urban areas that depend on rural areas for food supply (Broekhuis et al. 2004). Urban areas require water for proper functioning as a utility in industries and for domestic use. Repeated water crises have been reported in countries such as South Africa. The lack of clean, continuously accessible water may challenge sanitation and lead to mass migration (Hassan and Tularam 2018). Urban populations significantly contribute to climate change through emission of greenhouse gases. With increased migration from rural to urban areas, these effects compound, increasing management challenges (Sietchiping et al. 2014).

Methods The Study Area Kenya is one of the East African countries and lies on latitude 0.0236° S and longitude 37.9062° E. Since the passing of the Devolution Act, the country has been divided into 47 counties which are both geographical and administrative units. Counties are headed by governors and enjoy allocation of funds that can be spent according to their unique needs. The presence of county headquarters has led to many areas becoming urbanized or semi-urbanized; but Nairobi, the capital, remains to be the largest urban area. This study focuses on Nairobi and its environs (Fig. 3.1) while drawing examples from many other rural and urban areas in the country.

Data Collection and Analysis The study involved desktop research and estimation of land use changes deduced from data collected in the period 1990–2018. For land cover changes, the lower section of Kiambu County-specifically Kiambu and Thika Towns neighbouring the capital city—Nairobi—was studied. Figure 3.2 shows the status of land cover/use in a section of Thika town from the year 2003 through to 2019 obtained from Google Earth images. Data for mapping changes was obtained from the Directorate of Resource Surveys and Remote Sensing (DRSRS). This data had been generated from Landsat satellite images of 30 m by 30 m resolution with the classification being done using pixel-based supervised classification in Random Forest algorithm. The Inter-governmental Panel on Climate Change (IPCC) broad land categories of Forestland, Grassland, Cropland, Wetland and Otherland were mapped. However, the class Cropland was sub-categorised into Perennial and Annual croplands to clearly depict

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Fig. 3.1 Location map of the study area

the trend of change over the period under consideration. Settlements, one of the IPCC categories, and also a class of interest in this study, could not be mapped separately because settlement areas have very mixed land covers and huge variation in spectral signatures, making it quite challenging to map them from Landsat without causing large errors in the classification. In this study Settlements were therefore mapped under the annual croplands and Otherland. Tools such as the Kenya Climate Act of 2016, the Kenya Climate Change strategy (2018–2022), Kenya Climate Smart Agriculture Strategy, Kenya National Biodiversity Strategy and Action Plan, the Food and Agriculture Organization (FAO), IPCC and the United Nations Framework Convention on Climate Change (UNFCCC)

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Fig. 3.2 A section of Thika in the years 2003, 2013 and 2019. Source Google Earth Images

reports and other relevant literature were reviewed. Government policy and project funding in key rural-urban ecosystems were also reviewed. Google scholar and PubMed Central (PMC) were the primary online information sources.

Results and Discussion Anchorage of Rural-Urban Linkages in Kenya 1.

Infrastructure development—with increased projects related to the development of roads, electricity and other essential services, government is increasing

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connectivity (Mulongo et al. 2010). These initiatives show commitment to foster the movement of goods, services and people. Devolution—devolution refers to the decentralization of services into smaller administrative regions by the government. This provides the opportunity for regions to develop independently with support from the national government and in turn creates employment opportunities over time (Ros-Tonen et al. 2015). Budget allocations—with agriculture and tourism being major sources of funds for African countries, budgetary allocations are increasing in these sectors (United Nations Human Settlements Programme 2017). Therefore, with these primarily located in rural areas, such allocation provides opportunities for growth and development of these areas.

Status of the Road to Resilience in Kenya According to the latest population census report (GoK 2019a), there are about 48 million Kenyans, and assumptions in the Shared Socio-economic Pathways (SSPs) indicate that the population will continue to increase (KC and Lutz 2017). High population growth is currently being experienced in middle- and low-income countries; most of which are in the global South. The 2.2% growth rate in Kenya represents a decrease of approximately 0.7% relative to a decade ago, pointing to progress in managing population growth in the last decade (assuming low birth rate vs death rate). This trend is favourable for adapting to climate change as population is among the key determinants of future land-use, along with agricultural productivity and land-use regulation (Stehfest et al. 2019). Apart from the population size dynamics, the density pattern is also changing. Mass migration of people from rural to urban areas in search of jobs has been on the rise. But still unemployment remains high because Kenya has failed to revive most of its industries which collapsed partly due to poor governance. With the coming of the COVID 19 pandemic, thousands more jobs have been lost. Recently, climate change has also been shown to be a major driver of rural-urban migration (Kumari et al. 2018) and sometimes cross-country migration (Feng et al. 2010). Towns in Kenya continue to grow partly due to such migration and partly due to devolution. The Fig. 3.3 shows projected urban trends until the end of the century drawn from recent data (Gao and O’Neill 2020). Reports (Seto et al. 2012) have shown that places like the shores of L. Victoria are undergoing rapid urbanization due to population increase coupled with factors such as navigation systems and proximity to fishing that make it an in-migration hotspot (Kumari et al. 2018). Kisumu city is not unique; Mombasa, the second largest city, boarders the Indian Ocean. According to UN-Habitat, a large percentage of world cities are very near water bodies. But expansion of urban areas especially in developing countries is often towards agricultural areas because of the absence of strategic land use plans (Satterthwaite et al. 2010). Most landowners within the case study area have been clearing their cash crops (specifically tea and coffee) to give room for real estate expansion. This can

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FracƟon of total world urban area (%)

Increasing share of Kenyan urban area to global urban area 0.16

0.12

0.08

0.04 2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Year SSP1

SSP2

SSP3

SSP4

SSP5

Fig. 3.3 Proportion of Kenyan urban land to total world area

be seen in the decrease in grasslands and Perennial croplands and steady increase in Annual croplands (Figs. 3.4 and 3.5). Drastic decrease of coffee production was also observed in the Kilimanjaro farming system in the neighbouring country of Tanzania (Charlery de la Masseliere et al. 2020). The resulting peri-urban landscape has an unbalanced character with negative ecological implications (Meeus and Gulinck 2008). The creation of impervious surfaces for example may lead to run-off, and in extreme cases, flooding. Flooding is already a chronic problem in most sections of

Fig. 3.4 Landcover change between 1990 and 2018 in the lower Kiambu area. Source DRSRS

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60000.00 Area in Ha

50000.00 40000.00 30000.00

Grassland

20000.00

Perrenial Cropland

10000.00

Annual Cropland

0.00 1990

2000

2010

2018

Year

Fig. 3.5 Graph showing trends of grasslands and croplands in the study area between 1990 and 2018

Table 3.1 Magnitude of change in various land cover categories in the study area

Landcover type Forestland Grassland Perennial Cropland Annual Cropland Wetland Otherland TOTAL

1990 1498.77 55210 9176.04 5591.70 386.73 3007.80 74871.45

Area in Hectares 2000 2010 3656.79 1709.19 52908 46913 8245.53 9174.15 614.70 272.07 74871.45

5108.85 19880.37 410.49 849.60 74871.45

2018 1476.99 37806 4840.65 28904.67 377.01 1466.19 74871.45

Nairobi city and elsewhere in the global South (Fatemi et al. 2020). This is partly due to reduced infiltration capacity in surrounding rural areas and inadequate waste disposal in the city, that sees drainages blocked. The magnitudes in changes of various land covers are presented in Table 3.1. Observed trend of decrease in grasslands agrees with other studies (Stehfest et al. 2019) although it was reported under the SSP1 (sustainability) scenario. Pasture area is impacted by among other factors, land-use regulation. There is a general lack of proper land-use plans and zoning in Kenya and a lax in enforcing any existing regulations. The land tenure system allows an individual owner to determine use of their piece of land at any given time. The study area, being very close to the capital city, becomes the next available expansion place for putting up housing to accommodate the increased population and for agricultural activities to meet growing food demand. Observed increase in cropland is also consistent with global expected trends for SSP2 (Business As Usual). Cropland is most sensitive to population, consumption preferences and agricultural productivity (Stehfest et al. 2019). Ramankutty et al. (2002) observed similar trends where high population areas also had larger cropland areas. A lack of fast adoption of innovative productivity-enhancing technologies due in part to inadequate extension (lack of information) and high costs (financial

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challenges) would be said to contribute to the trends in Fig. 3.5. Even though some evidence suggests that with the right incentives future increase in food demand can be met with existing technologies, persistent accessibility challenges in rural areas in developing countries reduce effective demand for land. Kenya has also been identified as one of the rapidly motorizing countries and transport emissions are increasing faster than other sectors. Projects such as Advancing Transport Climate Strategies (TraCS) that is run by the German Development Agency (GIZ) have come into ensure proper monitoring and reduction of greenhouse gases in the transport sector. Entry of electric vehicles is a huge win for Nairobi City (considering heavy traffic jams) but it remains to be seen if such a venture can be scaled. Inefficiency in electric supply is one big impediment. With increasing population, electric cooking could be another way to reduce pollution at household level. Currently, more than 80% of energy consumption still comes from wood (EED 2020), pointing to policy gaps. The rural areas largely lag. Apart from emissions, the transport sector in the city is notorious for car wash activities next to rivers raising serious concerns. River pollution by industries impacts rural communities downstream as well. Poor planning and sanitation are the other environmental challenges that characterize most towns. The fraction of population with access to sanitation increased only marginally from 25 to 30% between 2000 and 2015. Kisumu for example suffers from a major ecological challenge: the invasive water hyacinth (WH) which is a result of eutrophication (Wilson et al. 2007) and is threatening livelihoods and the ecosystem balance. The government has warmed up to the idea of exploiting the weed as a biofuel, an alternative which may be sound economically but remains a threat to biodiversity. Also, evidence (Omondi et al. 2019) suggests that WH contains a large amount of non-biodegradable content; thereby requiring other biomass for optimal digestion. With increased population and pollution, the fish resource will continue to face pressure from the WH and possibly over-extraction. Moreover, increased globalization and tourism around the lake region due to attractions such as the Impala Park will likely pose further challenges for control of introduction pathways of alien species. As regions continue to face these challenges, inequality is rising. Kenyan incomes have generally improved, with a steady rise in total Gross Domestic Product (GDP) since 2015 and the country now ranks as a lower middle-income country. Increased incomes almost always change consumer behaviour and dietary needs. There is tendency towards processed foods that are easily accessible. But a large proportion of the population is still classified as poor, with the driest (rural) parts which have little to no agricultural output having the highest poverty rates. This case points to the need to re-evaluate budgetary allocations to benefit not only urban and productive areas but also those areas that are resource-poor. Policies such as lowering pass marks for students from hardship regions when joining high school attempt to enable fair and equitable access to education, although these are a treatment of symptoms and not preventive measures. But with initiatives such as the introduction of an equalization fund, such historically neglected areas have a chance to turn things around. Devolution, if well managed, can help to further spur economic growth

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in these regions. Environment departments within counties should prioritize green development to reduce regional and household inequality. In highly unequal societies, lack of cohesion retards action on important issues such as climate change. Garissa, Makueni and Wajir counties have taken the lead in establishing climate change funds. Such marginalized counties have rich resources outside of agriculture that can be exploited for economic gain. The discovery of oil in Turkana County, which also hosts L.Turkana-an oasis in an otherwise predominantly dry area, is one such example. Monetizing the rural economy is not always beneficial though. In Turkana, there are fears that communities are being excluded from shared gains of oil exploration ventures; while others end up losing land to forced acquisitions or prospective buying. The alternative camel market economy is individualized; exposing people to risks as opposed to previous non-monetary forms. Exclusion risks rural communities becoming poorer than before natural resource exploitation begun. While it is assumed that poverty is rife in rural areas, a good number of Kenya’s poor live within or near towns and cities. Recent reports estimate about 745, 321 poor people in Nairobi County alone (EED 2020). As cities expand, peri-urban areas evolve, and rural areas retreat. Pastoralists have been pushed to town life and consequently are forced to adopt other kinds of animals (Watson et al. 2016). This increases their vulnerability since they both lose their livestock and become landless. The slums of Kibera, Mathare, Majengo, Ngomongo, among others in Nairobi and its environs, despite having poor sanitation conditions, are home to masses of people since they offer first affordable housing to rural-urban migrants. The distributional inequality in cities is most manifest in these low-income communities. It has been argued that large informality is an indication of widening gap between government and the governed. The mushrooming of informal settlements has led the well-to-do to desire gated communities and secluded spaces where they feel safe. Consequently, the masses have been excluded from access to green infrastructure (GI), which is often privatized. In this regard, the recent upgrade of Michuki Park is a step in the right direction. Nonetheless, in Nairobi there are cases of issues of privatization of public roads along wealthy estates, raising serious concerns about the concepts of good city versus right to the city. Same case applies to garbage collection which works well in the suburbs but suddenly becomes non-existent elsewhere. Sections of Nairobi River are seriously polluted because they are the dumping sites for both solid and liquid wastes by the locals (Fig. 3.6). The only designated dumping site (Dandora) in Nairobi was declared full decades ago but is yet to be decommissioned. These issues point to a case of urbanization without ecological civilization. These poverty trends are likely to continue unless radical changes in agriculture, the backbone of Kenya’s economy, are undertaken. With almost 75% of the population directly engaged in some form of agriculture, this sector is the largest employer; 75% of it being small-scale. Small-scale farming is mostly subsistence (Fig. 3.7) and insufficient for providing real incomes for households. Subsistence farming, being annual and semi-annual, is also highly vulnerable to climatic shocks. For those who undertake export farming, such challenges as exploitative middlemen, vulnerability

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Fig. 3.6 Solid and liquid waste dumping in Nairobi River at the slums. Source DRSRS

Fig. 3.7 Sacks of food sent to families in Nairobi from upcountry. Source Google

to shocks in prices and stringent regulations, threaten incomes. For government, exports lead to loss of revenue and failure to create jobs; benefits that would accrue from processing raw materials into finished products. Of course, processing requires access to sufficient, stable supply of electric power, a pipe dream for Kenya (for now at least). One of the strategic objectives of the Climate Change strategy is to support smallscale farmers to transition to market-oriented output. But some basic road infrastructure will be required to reduce transaction time and costs. Smooth transportation encourages buyers to go to rural areas (Gebre and Gebremedhin 2019). The emergence of camel milk in supermarkets, which is touted as being nutritious and good for diabetics, is a good example of rural-urban dependencies. Objectives to reduce failures along the value chain such as marketing and storage challenges, that inevitably lead to food loss, should also be considered. In Kenya, high food loss (FAO 2014) is a contributor to unnecessary emissions, but instead of reducing food loss and improving productivity, more land continues to be converted to cropland (Fig. 3.5). A classic example of storage failure is the recent revelation by Kenya

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National Bureau of Standards (KEBs) on the levels of aflatoxin in maize meal, a Kenyan staple, which led to some brands being banned from the market. Lack of proper storage by producers led to possible exposure and negative impacts on the urban population that consumes the final processed product. Agriculture also needs proper management as it is a leading driver of biodiversity loss, along with climate change, urbanization (Nyairo and Machimura 2020), invasive alien species, pollution and overexploitation, among others (NBSAP 2019). Kenya is yet to ban majority of the persistent organic pollutants (POPs) that have already been declared harmful and banned in other countries, raising concern for not just the environment but also human health. According to Kenya Plant Health Inspectorate Services (KEPHIS), the country is also struggling to control the spread of more than 30 invasive alien species including WH (Eichhornia crassipes), prosopis and lantana camara. Some of these are spread by trade routes and threaten to extirpate native species on which communities rely for food, feed and income. There are cases where some chemicals have been banned but still can be consumed through food imported from neighbouring countries. Agriculture also indirectly affects biodiversity by contributing about 28% of human greenhouse emissions (IPCC 2019), leading to climate change. After agriculture, the mitumba business is a source of livelihood for thousands of urban traders, with estimated 8.3 million informal operators. This business may be beneficial, but it has environmental and economic cost: there is increased ecological footprint from transportation, and the trade encourages consumerism at source. The business is highly vulnerable to exogenous shocks as well (as was demonstrated during the COVID-19 global pandemic). Moreover, the wellbeing of traders is often ignored, with people working in poor and unsafe conditions for long hours while others take the risk of breaking rules to hawk their goods in city centres. People engaged in this sector are already underprivileged, and so failure by government to support them (Chalhoub 2012) only exacerbates their vulnerability. Mitumba business is more pronounced in cities and towns than rural areas but often it is the rural-urban migrants that conduct the business. With increased urbanization and unemployment, this sector will likely continue booming. Overall, there is inadequate governance when it comes to matters sustainability. Better governance would result in both increased quantity and quality of infrastructure, for it is the quality that separates sustainable from unsustainable development. Climate change units in most state departments have not yet been setup or become operational, thus hindering progress in capacity building. The Built environment is guided by the Building Code of 1968 which was heavily borrowed from the United Kingdom (Der-Petrossian 1995). This code is yet to be updated and a locally relevant one adopted. The policies governing the built environment are not specific and deliberate on climate resilience and future proofing of assets (Onkangi and Getugi 2020). Mainstreaming climate action is thus stagnating due to lack of anchorage in the policy framework. Management of funds taken for development projects also remains questionable.

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Conclusions and Policy Implications Pathways to Improved Resilience The findings above reveal the need for: Land-use planning and zoning in the country and ensuring strict enforcement of existing regulations supporting the same. In undertaking such planning, buffers (green areas such as marshland capillaries beyond which settlements should be more sparsely populated and probably put to other uses such as recreation or sustainable agriculture) should be considered. This would curb the constant dynamism in landcover use/change and at the same time make it easier to attribute any changes occurring over time, thus aiding the estimation of greenhouse gases. Although regulations are needed, they should be carefully thought out to avoid introducing the risk of food insecurity. Green spaces in every major town; Green management coupled with grey engineering can cost-effectively resolve some of the issues such as flooding. Green infrastructure (GI) enhances resilience (Staddon et al. 2018). There have been attempts to monetize GI which were fought by environmentalists such as the Nobel peace prize laureate Wangari Maathai, pointing to the need for strong governance and political goodwill when it comes to resilience. Perhaps there should be building regulations that compel developers to include GI in the apartment complexes or nearby. Another possibility would be the government developing GI in peri-urban locations. Reduction of environmental impacts of urbanization—greenhouse emissions and spatial inequalities (Nilsson et al. 2014) may be mitigated through the circular economy and sensitization. People need to be sensitized on the importance of proper nutrition and access to food (Githiri and Foster 2020). If farmers for example are properly incentivized, they can take up practices such as organic and vertical farming, both of which are beneficial for biodiversity, reduced non-point pollution of waters and reduced vulnerability to social and climate shocks. Participatory planning and implementation strategies that include project-affected persons in instances of resource exploitation and in cases where communities are faced with natural disasters such as flooding. Public engagement ensures transparency, thus increasing ownership and acceptability and overall project success. The proposed gentrification of Kibera partly failed because the socio-economic context was not adequately considered. Apparently, residents refused to move into upgraded housing because in the new units they were not allowed to use low-cost fuels. This case highlights two important government failures: the lack of national programs that promote access to clean cooking solutions for the poor, and lack of participatory planning in defining invited versus invented spaces. In agriculture, the traditional topdown paradigms of technology transfer that ignore the role of producers as sources of innovation must also undergo shifts to realize the so-called flat system. Environmental education and awareness: Capacity building on the interlinkages between agriculture and environment and rural-urban linkages is needed. County governments can adopt the recently released guide on solid waste management (EPA

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2020) whose aim is to capacity-build local decision-makers in developing countries. The Climate Change Act, which provides for integration of climate change into curricula and charges the Kenya Institute of Curriculum Development with the responsibility to ensure this, should be quickly implemented. The government should also endeavour to raise awareness through events such as on the International Day of Biodiversity (a strategic target of the NBSAP 2019) and giving incentives. The National Transport and Safety Authority (NTSA) whose role should include climate change adaptation and mitigation has for many years only been limited to vehicle inspection. This body should come out strongly to promote purchase of environmental-friendly vehicles (and government should provide the incentives for use) and the design of environmental-friendly roads for overall road transport sustainability. Capacity-building funds should be allocated to authorities. Improved data and knowledge management—The use of systems such as GIS and GPS that can map the spatial flows of people to understand their movements and possible causes needs to be widely adopted. Data layers of social and economic behaviours of the populace need to be incorporated in infrastructure planning and design to strengthen and sustain critical rural-urban linkages. Monitoring should also be utilized in ensuring more stringent phytosanitary standards for goods entering the country. KEBs and KEPHIS should be strengthened to handle such responsibilities. In the water sector, there is need for river health monitoring stations. In agriculture, innovative communication platforms (e.g. Shamba shape-up) have sometimes been adaptive. Improved governance and legislation—policies need to deliberately loop in ruralurban linkages while agencies need to collaborate in mainstreaming climate action. The current structure of the climate change coordination team seems to suggest that NTSA is at the same level as the state departments (GoK, 2019b; p. 3). We propose that the ideal case would be strengthening NTSA and all departments falling under the authority. Further, the working of Kenya Rural Roads Authority (KeRRA), Kenya Urban Roads Authority (KURA) and Kenya National Highways Authority (KeNHA) as separate entities does not support integrated road development that strengthens rural-urban linkages. These institutions should be afforded opportunities to as much as possible work together to meet the needs of people on both sides of the spectrum as well as those at the interface. Additionally, all Government offices should not be headquartered in one city; instead, various cities and towns can headquarter various Government Agencies. Pockets of good governance have been observed in counties like Makueni that has established processing industries for its agricultural produce and established a Climate Change Fund. Inclusive financing—rural and urban areas must be afforded equitable investments. This will reduce migration and decongest the cities. Mechanisms need to be put in place for outcome-based lending where governments are compelled to demonstrate how a funded project would help enhance resilience, including deliberate efforts to integrate climate-smart development. Since the most common financing for GI comes from CSR and public investment, attempts should be made to keep these streams alive. Funding for research is also needed, to increase scenario-based studies. Resilience mechanisms should focus on anticipation by scenario planning

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(Wardekker 2018). Current focus on recovery has not born much fruit because recovery is better able to be effective in rural areas where social ties are strong, a weakening aspect in the face of urbanization.

Policy Implications Since national population growth is slowing down, it would be advisable for the country to follow the sustainability scenario (SSP1) which follows rapid urbanization in the transition phase but low rates in the stabilization phase. This would allow the country to achieve the basic infrastructure necessary for promotion of rural-urban linkages while limiting unnecessary growth (SSP5) which would potentially lead to higher emission and pollution levels. One way to achieve sustainability is to encourage vertical, compact growth as opposed to the traditional expansive growth. Such a strategy would also incorporate a balance between home and workplace and help reduce transport emissions. More specifically, climate action needs to be balanced by mainstreaming it in every sector of the economy without leaving the built environment behind as this severs critical rural-urban linkages. Investment in rural areas can no longer be ignored. To increase the production of healthy and indigenous foods to urban areas and at the same time reduce migration, financing, capacity building and technology transfer to rural areas must be undertaken.

References Broekhuis A, De Bruijn M, De Jong A (2004) Urban-rural linkages and climatic variability. In: Dietz AJ, Ruben R, Verhagen A (eds) The impact of climate change on drylands, environment and policy. Springer, Dordrecht, pp 301–321 Chalhoub H (2012) From recyclers to risk-takers: the social, economic and political challenges of selling second-hand clothes in Kenya. Independent Study Project (ISP) Collection 1387 Charlery de la Masseliere B, Bart F, Thibaud B, Benos R (2020) Revisiting the rural-urban linkages in East Africa: continuity or breakdown in the spatial model of rural development? The case of the Kilimanjaro region in Tanzania. Belgeo 1 Cowie P, Townsend L, Salemink K (2020) Smart rural futures: will rural areas be left behind in the 4th industrial revolution? J Rural Stud 79:169–176 Der-Petrossian B (1995) Importance of appropriate building codes and regulations in improving low-income settlements conditions in African region. J Netw Afr Countries Local Build Mater Technol 3(3):1–15 Diaz S, Demissew S, Carabias J et al (2015) The IPBES conceptual framework—connecting nature and people. Curr Opin Environ Sustain 14:1–16 EED Advisory (2020) Energy safety nets: Kenya case study. Nairobi, Kenya EPA (2020) Best practices for solid waste management: a guide for decision-makers in developing countries. Unite States Environmental Protection Agency, EPA 530-R-20-002

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Ros-Tonen M, Pouw N, Bavinck M (2015) Governing beyond cities: the urban-rural interface. In: Gupta J, Pfeffer K, Verrest H, Ros-Tonen M (eds) Geographies of urban governance. Springer, Cham, pp 85–105 Satterthwaite D, McGranahan G, Tacoli C (2010) Urbanization and its implications for food and farming. Phil Trans Biol Sci 365(1554):2809–2820 Seto KC, Güneralp B, Hutyra LR (2012) Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. PNAS USA 109:16083–16088 Sietchiping R, Kago J, Zhang XQ et al (2014) Role of urban–rural linkages in promoting sustainable urbanization. Environ Urbanization ASIA 219–234 Staddon C, Ward S, De Vito L et al (2018) Contributions of green infrastructure to enhancing urban resilience. Environ Syst Decisions 38:330–338 Stehfest E, van Zeist W, Valin H et al (2019) Key determinants of global land-use projections. Nat Commun 10:2166 Steinberg F (2014) Rural–urban linkages: an urban perspective, Working Paper Series N° 128. Working Group: Development with Territorial Cohesion. Territorial Cohesion for Development Program, Rimisp, Santiago, Chile Tacoli C (2003) The links between urban and rural development. Environ Urbanization 15(1):3–12 UNIDO (2004) Industrial development organization, industrialization, environment and the millenium development goals in Sub-Saharan Africa. UN Publication, Vienna United Nations Human Settlements Programme (2017) Implementing the new urban agenda by stregnthening urban-rural linkages—leave no one and no space behind. UN-Habitat, Nairobi Wardekker A (2018) Resilience principles as a tool for exploring options for urban resilience. Solutions 9:1 Watson EE, Kochore HH, Dabasso BH (2016) Camels and climate resilience: adaptation in northern Kenya. Human Ecol 44:701–713 Wilson JRU, Ajuonu O, Center TD et al (2007) The decline of water hyacinth on Lake Victoria was due to biological control by Neochetina spp. Aquat Bot 87(1):90–93

Chapter 4

Modeling and Forecasting Climate Change Impact on Groundwater Fluctuations in Northwest Bangladesh Md. Abdul Khalek, Md. Mostafizur Rahman, Md. Kamruzzaman, Zubair Ahmed Shimon, M. Sayedur Rahman, and Md. Ayub Ali Abstract Water is essential for life but its availability at a sustainable quality and quantity is threatened by many factors, in which climate plays a leading role. Northwest Bangladesh is a severely drought prone area in the country. For forecasting the impact of climate change on groundwater table (GwT) in the drought-prone areas two data sets: (i) weekly groundwater table time series from Bangladesh Water Development Board (BWDB), and (ii) yearly temperature and rainfall (mm) from Bangladesh Meteorological Department (BMD) from January 1991 to December 2018 were collected. The findings showed the superiority of the nonlinear autoregressive modeling with exogenous inputs (NARX) based approach for groundwater tables in arid groundwater aquifer system revealed that the coefficient of determination (R2 ) lies between 0.547 and 0.854 in the validation period. However, compare to other models applied on the same data sets, the proposed model reduced 50% of the mean absolute error (MAE). Increasing trend in maximum temperature is a cause of high evaporation as well as uncertainty of trans-boundary water movement was found to be strongly influencing the depletion of groundwater level. The results show that Bayesian Regularization (BR) is the most accurate method for forecasting groundwater table with an error of ±0.43 m.

Introduction Groundwater is a vital reserve for agricultural, domestic, and industrial segments in the world. Thus, forecasting groundwater table variation is important for groundwater resource management. Generally, physical groundwater models are engaged Md. Abdul Khalek (B) · Md. Mostafizur Rahman · M. Sayedur Rahman · Md. Ayub Ali Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh e-mail: [email protected] Md. Kamruzzaman Institute of Bangladesh Studies, University of Rajshahi, Rajshahi 6205, Bangladesh Z. Ahmed Shimon Department of Finance, University of Rajshahi, Rajshahi 6205, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_4

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to portray groundwater table variation regarding time. Physical models involve huge specific contributions to allocate the corporal possessions of the existent method to provide consistent outcomes (Anderson et al. 2015; Bear and Cheng 2010). World’s people are growing and the abstraction of groundwater is increasing expressively too. Groundwater withdrawal is one of the primary reasons of swelling groundwater level in the world (Shamsudduha et al. 2009; Akther et al. 2009; Bui et al. 2012). Groundwater decrease has documented by way of a universal problematic and its forecasted groundwater withdraw for the period 1900–2008 is around 4500 km3 through the supreme costs happening from 2000 to 2008 (Konikow 2011). In recent, climate change problem is a documented element and the Intergovernmental Panel on Climate Change (IPCC) has also recognized it and blames it as a risk to sustainable progress (Bindoff et al. 2013; Lee and Resdi 2016; Ahmed and Neelormi 2008). Climate variation can be well-defined as a trend of climate that comprises among others possessions for example precipitation, moisture, temperature and wind speed (Haji 2011; Kamruzzaman et al. 2016a; Rahman et al. 2016b; Bates et al. 2008). Most of the current studies displayed an increasing inclination in groundwater table depth trend that suggests an inappropriate situation of groundwater table in Bangladesh. Sarkar and Ali (2009) deliberated the dynamics of the groundwater table and presented a sharply growing trend in the groundwater table. Ali et al. (2012) also incorporated the similar techniques to keep sustainability of water resources. The investigation presented a analogous result; growing trend in groundwater level depth or decreasing trend in groundwater table in the northeast Bangladesh. Parametric regression method has also been used which presented decreasing trend of groundwater table in Barind region (Khalek et al. 2019; Jahan et al. 2010). Numerous statistical techniques were used for trend analysis that differs from linear regression to more progressive non-parametric methods (Chen et al. 2007). The greatest popular non-parametric technique for investigating the trend in the time series is the MK test (Kendall 1955; Mann 1945). MK test is a common practice intended for investigating the movements in evaporation, stream movements and groundwater fluctuation (Kamruzzaman et al. 2015; Ramazanipour and Roshani 2011; Patle et al. 2013; Bandyopadhyay et al. 2009). The NARX neural system is good forecaster of data analysis (Mohanty et al. 2015). Actually, NARX perception is a non-linear simplification of the Autoregressive Exogenous (ARX) model, which is a ordinary apparatus in black-box method documentation (Chen et al. 1990). NARX could be used to build a wide-ranging variability of non-linear dynamic systems and useful in several applications incorporating data modeling (Cadenas et al. 2016). NARX neural system has been commonly applied in preceding years, because it can forecasted the seasonal time-series that has a periodic factor (Chan et al. 2015; Lin et al. 1996; Chen et al. 1990). It has been used to forecast time series (Siegelmann et al. 1997) due to its influential forecasting ability that can estimate almost entirely non-linear functions (Guzman et al. 2019, 2017a; Nayak et al. 2006; Daliakopoulos et al. 2005). It has demonstrated that NARX neural networks are also accomplished of forecasting performances of the non-linear dynamics system, so, mainly applicable for modeling data (Cadenas et al. 2016; Chen et al. 1990). To determine the greatest

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effective training construction for NARX, investigators are useful to estimate the competence of the Bayesian Regularization and Levenberg-Marquardt procedures (Guzman et al. 2017a). The LM and BR procedures are the utmost extensively used amongst the functions for forecasting and exercise time series networks (Khaki et al. 2015; Kisi 2007; Adeloye and De Munari 2006; Daliakopoulos et al. 2005). As far as this, NARX neural system model takes is not used to prediction groundwater level. Therefore, an innovative NARX neural network model is proposed to predict groundwater level with exogenous variables (max temperature, total rainfall, humidity and wind speed) in this study.

Research Problem Floods, cyclone and drought were regular phenomena, amongst them scarcity is twisted through the trouble of groundwater sequence. Each year, our country experiences a drought retro for six months (Nov–Apr) while rainfall is usually low. Bangladesh practiced an improved incidence of drought in current year (Rahman et al. 2016a; Adhikary et al. 2013). It’s a regular wonder in particular portions of the nation, but northwest part of Bangladesh is sternly drought prone for the inconsistency popular precipitation (Rahman et al. 2017; Kamruzzaman et al. 2016b; Shahid and Behrawan 2008). All the streams and canals in the zone dry up through the dry period, and brand the individuals entirely reliant on groundwater, specifically for irrigation. The tendency of the annual mean max. temperature, the annual mean min. temperature, the yearly mean total rainfall, the yearly mean relative humidity, the yearly mean wind speed and their influence on the groundwater table were also examined.

Objective of the Study The main objectives are to measure the effect of climatic variability arranged groundwater table reduction of northwest Bangladesh. The specific purposes are: 1.

2. 3. 4.

to measure the spatial and temporal changeability of annual average max. temperature, average temperature, mean precipitation, wind speed and relative humidity; to explore the variability of groundwater table; to discover longitudinal and historical variation of groundwater level depth; and to explore the renewal effect of climatic variability on groundwater table.

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Methods and Materials Trend analysis of the climatic variables and groundwater table data was investigated using Microsoft Excel 2016 and R 4.0.4. Monthly data (max. temperature, average max. temperature, average total rainfall, wind speed and relative humidity) calculated from the time series. To evaluate the decadal discrepancy data also organized in decade wise. The outburst of geo-statistical exploration was used to investigate the spatial disparity of climatic variability and fluctuations of northwest Bangladesh groundwater table.

Study Area and Data Collection Considering northwest Bangladesh as study area, which consisting eight districts namely, Chapai Nawabgonj, Rajshahi, Naogaon, Natore, Pabna, Jaypurhat, Bogura and Sirajgonj and the coordinates is 23.76° N and 88.32° E (Fig. 4.1). For attaining the objectives, used monthly climatic variables (average max. temperature, average total rainfall, wind speed and mean relative humidity) for the period January, 1991– December, 2018 from Bangladesh Meteorological Department (BMD). We also used monthly groundwater table time series data for the same time period from Bangladesh Water Development Board (BWDB).

Fig. 4.1 Study area map with well locations

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NARX Model Architecture Artificial Neural Network (ANN) process is an influential instrument for modeling and predicting groundwater resource. ANN is constructed on the assembly of associated components, named artificial neurons. Artificial neurons may have one constraint. Various layers may make various types of changes to their data sources. After negotiating each layer many times, the data is transferred from the key layer (information layer) to the last layer. It is very effective to apply the calculation of nonlinear functions, because it can achieve good results. The ANN was recognized with a NARX neural network purpose. The equation of NARX model is defined as follows:   z(t) = f (z(t − 1)), z(t − 2), . . . , z(t−n z ), y(t − 1), y(t − 2), . . . , y t−n y (4.1) where, z(t) denotes a certain time series, y(t) denotes exogenous time series data, nz and ny are the break of the aim and exogenous time sequence data, and ƒ is nonlinear purpose which is usually unknown. The black-box function is degenerated to the mark time series by adjustment the number of unseen layers. This black-box function permits the contribution and exogenous data through a definite quantity of hidden coatings, and the NARX system is accomplished by confident algorithm to yield best competition between the efforts and the objective variable. The function practices a dynamic recurring neural network, which accepts contribution time series and feeds it hindmost from the yield to the purpose up to extents convergence. NARX systems are considerable quicker than other ANN purposes, and their performance is improved when the forecasting procedure includes longstanding dependencies (Piotrowski and Napiorkowski 2013). Moreover, to the relatively active learning method, NARX network touches earlier and generalizes improved than other ANNs outstanding to the practice of comeback yield as the training involvement in the barred circle (Izady et al. 2013). In particular, NARX showed to be greater to further kinds of artificial neural networks in forecasting groundwater table of ANNs (Humphrey et al. 2005; Kingston et al. 2005). The model was assessed to define best calculations and parameter for an ideal implementation. The LM and BR system was applied and associated. At that time, different interruptions and unseen layers were varied until the perfect execution result was attained. Through the BR intention, the perfect construction with 15-time interruptions and 2 unseen layers was specified. The BR has the benefit of overwhelming the common problematic of over decent as it could randomize the prejudices and weights which removed the propensity to decrease in local minima (Piotrowski and Napiorkowski 2013; Izady et al. 2013).

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Normalization In the study, the contribution records were nominated to characterize the climatic variability and groundwater table situations of the province. Collected records were distributed into two sets: The first 22 years (1991–2012) considering as training and the remaining 6 years (2013–2018) considering as test and validation. According to convention considering 70% as training and remaining (30%) was accepted as test and validation (Lohani and Krishan 2015; Coulibaly et al. 2001). The training steps were preprocessed, and the input and feedback data sequences are cleaned and managed.

Training Algorithms Levenberg-Marquardt (LM) The Levenberg-Marquardt procedure is used to train the NARX network. The LM system is a widely used training process for ANN-based groundwater modeling (Wunsch et al. 2018; Khaki et al. 2015). The LM system combines the benefits of the Gaussian-Newton and sharpest ancestry approaches (Samarasinghe 2016). Basically, the LM process examines for the purpose minima and improves the result. The LM method estimates the Hessian matrix as monitors (Sahoo and Jha 2013; Bishop 1995): −1 T  J (ω)e(ω) ω = J T (ω)J (ω) + λI

(4.2)

where, ω is the load vector, J T J is the Hessian matrix and, J mean Jacobian matrix, J T is the transposition of J and λ is a learning parameter. I is the unit matrix and e is the trajectory of the network inaccuracy. The learning continual μ is appropriate to determine the minima in each repetition constructed on the error detection. Used Jacobian matrix (J) with unit matrix, λI certifies that the Hessian matrix is continually optimistic. This suggestively decreases the computational price of the process Hence, the LM process is measured more effective as a training procedure in judgment to other another order approaches (Yu and Wilamowski 2011; Wilamowski and Yu 2010).

Bayesian Regularization (BR) The study used different types of Bayesian training technology which is moderately basic and entirely available. By using the regular training technique, the study discovers a single optimum weight direction (w) that is maximum likely to reproduced the set of pragmatic mark data, y = (y1 , y2 , …, yN ), given the inputs χ = (x 1 , x 2 , …, x N ).

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The determination of Bayesian teaching is to forecast the posterior possibility delivery of the heaviness given by the pragmatic data P(w |y, χ). It is complete by using Bayes formula and consuming the material delimited in the statistics to bring up-to-date any information approximately the weight cost before gaining the data (Kingston et al. 2005): P(ω |y , χ ) =

p(y |ω , χ ) p(ω)  p(y |χ ) = p(y |ω , χ ) p(ω)dω

(4.3)

where, P(ω) refers to the prior load distribution and P(y |ω, χ) the likelihood purpose which defines any material about ω in the data. The likelihood function is articulated as L(ω).

Data Preprocessing The leading computational phase of the suggested modeling method is to worsen the groundwater table. The de-trending technique is proposed to remove the effects of outside effects on the numbers. The process will efficiently recover the NARX modeling process. Alternative significant benefit to achieve when groundwater table is that it confirms stipulating the important amount of autocorrelation lags for the groundwater time series as highly trended time series will possible have robust autocorrelation amounts (Granger 2010; Lee et al. 2005; Granger et al. 2001; Aldrich 1995; Granger et al. 1974). One of the broadly conventional detrending measures in groundwater modeling is the first order polynomial detrending procedure (Cao and Zheng 2016; Ghanbari and Bravo 2011). A simple linear regression model is built-into the groundwater time series based on a minimum squares benchmark. The de-trended groundwater indicator is then used as an involvement for the NARX model, forecasting groundwater variability autonomously of the properties of outside changes. The fitted movement is then auxiliary posterior to the forecast groundwater table by NARX to comprise the result of the outside changes.

Evaluation of Performance The established model was constructed on the conventional exercise of chronologically distributing the pragmatic data into training, validation and test sub-sets. However, in order to confirm the robustness of the model presentation, a continuing validation process with amendment was accepted (Bishop 1995). In this practice, the NARX model is assembled using 60% of the existing data with 80% of the subgroup used for system training and 20% used for system justification. Next, the system is built using 80% of the existing data with the similar data dividing ratios for training

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and justification purposes. Finally, 100% of the data is used to build the NARX at the third-round validation process. This developing justification technique can efficiently guarantee that the NARX model is best fitted. MSE: The forecast performance of the BR function was assimilated based on equations of the courtesy of fit. The mean squared error (MSE) estimates the change between experimental and forecast values (Guzman et al. 2017b): MSE =

N N 2 1  2 1  yt − yˆt et = N t=1 N t=1

(4.4)

where, yt denotes the actual values, yˆ t is the forecast values and N is the number of instant. It calculates the mean value of the sum of squares errors, that is, the mean squared difference between the actual and forecast values. MSE tends to be rigorously positive (and not zero) because of the investigator does not receipts into reflection that material might estimate more precisely (Lehmann and Casella 2011). The lowermost MSE delivers the greatest forecast performance. NSE: The Nash-Sutcliffe quantity of efficacy (NSE) (Nash and Sutcliffe 1970) for measuring hydrological forecasts is specified in the resulting equation: N  N SE = 1 −

t=1 yt N t=1 (yt

− yˆt

2 (4.5)

− y¯ )2

where, yt is the actual values at time t, yˆt is the predicted and y¯ is mean actual value. Nash-Sutcliffe Efficiency (NSE) ranges from −∞ to 1. As said by Eq. (4.5), NSE evaluates the efficiency of the model inside the justification period qualified to the complete dataset difference from the average. NSE = 1 is connected to the ideal contest between actual and forecast data. NSE = 0 establishes that the forecasted accuracy as the mean value of the actual data. Together, the average actual value is a better forecaster, and the competence is lower than 0 (NSE < 0). R2 : The coefficient of determination (R2 ) was used to investigate the finest linear fitting between the actual and forecast values. Taking into account the total variation ratio of the results illustrated by the model, it gives the ratio of the observations replicated by the model. R2 is given by:

R2 =

⎧ ⎪ ⎪ ⎨

n



⎫ ⎪ ⎪ ⎬

− y¯ ) yˆt − yˆ¯  

2 ⎪ ⎪ ⎪ ⎪ ⎭ ⎩ n ((yt − y¯ ))2 n ˆ y ˆ − y ¯ t t=1 t=1 t=1 (yt

(4.6)

where, yt is the actual groundwater length at time t, yˆt is the forecasted groundwater length at time t, y¯ is the average of the actual groundwater length, yˆ¯ is the average of the forecasted groundwater length and n is the length of time periods in validation stage. The R2 indicates the strength of relation between the actual and forecast values,

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and ranges between 0 and 1, here 0 defines no association and 1 indicates a perfect association between the actual and forecast values.

Data Analysis and Discussion Results were presented into two parts, in the first part presents spatial and temporal investigation of weather variables and second part comprises spatial and temporal investigation of groundwater table.

Trend of Annual Average Maximum Temperature Figure 4.2 displays the increasing tendency of annual average max. temperature at 95% confidence interval. Max temperature has improved 0.01 °C for the decade first and second and 0.32 °C for the decade second and third decade. There are seasonal fluctuations in the max. temperature. In winter, the max. temperature demonstrations a constant movement, falling 0.59 °C from the first to the third decade, and the trend continues until before the monsoon season but is a little flatter than the Winter. Monsoon season has a suggestively rising trend in max. temperature. In first and third decade has been increased 0.49 °C, which is the uppermost value and display a decreasing trend in winter season and has dropped 0.59 °C considering 28 years trend Post-monsoon also demonstrations a rising trend in temperature (Figs. 4.3, 4.4, 4.5 and 4.6).

Fig. 4.2 Trend of annual average max. temperature

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Fig. 4.3 Trend of annual average max. temperature in winter (Dec–Feb)

Fig. 4.4 Trend of annual average max. temperature in pre-monsoon (Mar–May)

Fig. 4.5 Trend of annual average max. temperature in monsoon (Jun–Sep)

Fig. 4.6 Trend of annual average max. temperature in post-monsoon (Oct–Nov)

Yearly Average Min Temperature Trend Similar the annual mean min. temperature, the trend of annual mean min. temperature also displays a declining trend (Fig. 4.7) and which is 0.81 °C within first

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Fig. 4.7 Trend of annual average min. temperature

to third decade. The outcome of the study is verified the consequences of IPCC et al. (2007) report and discoveries of Ahmed (2006). In pre-monsoon, monsoon and post monsoon average min temperature has declined more than other season. In the winter, average min temperature has improved 0.06 °C inside first to third decade and 0.75 °C within second to third decade, so within the last decade means min temperature has been reduced. In pre-monsoon the average min temperature has improved 0.60 °C till third decade and in the monsoon the average min temperature has enlarged 1.01 °C within the similar period. In the post-monsoon the mean min. temperature has improved 0.03 °C from first to third decade (Figs. 4.8, 4.9, 4.10 and 4.11). Fig. 4.8 Trend of annual average min. temperature in winter (Dec–Feb)

Fig. 4.9 Yearly average min temperature trend in pre-monsoon (Mar-May)

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Fig. 4.10 Yearly average min temperature trend in monsoon (Jun–Sep)

Fig. 4.11 Yearly average min temperature trend in post-monsoon (Oct–Nov)

Fig. 4.12 Yearly average temperature trend (1991–2018)

Yearly Mean Temperature Trend This series displays a growing trend of yearly mean temperature (Fig. 4.12). Considering the decadal investigation, average annual temperature has declined 0.13 °C after first and second decade and rising 0.26 °C for second and third decade.

Yearly Mean Rainfall Trend The yearly average rainfall displays a fairly decreasing trend (Fig. 4.13), here the significant decreasing initiates from the last three decades. Forth assessment IPCC report, also revealed the decreasing trend of precipitation in Bangladesh (Denton et al. 2014).

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Fig. 4.13 Yearly average rainfall trend (1991–2018)

Trend of Yearly Average Relative Humidity (%) In the monsoon and winter season the precipitation presents a strongly declining trend (Fig. 4.14), in the period before the monsoon the trend in nature increases strongly (Fig. 4.15), but in the period after the monsoon the precipitation shows only a very strong one low decreasing trend. Regardless of the 28-year precipitation trend, precipitation has decreased significantly in all seasons over the past ten years (Figs. 4.16 and 4.17). Considering the annual percentage change in the precipitation pattern, it is found that since 1991 both the average and the decreasing average rainfall have increased, but over the past decade the percentage rainfall has decreased and then increased (Fig. 4.18). 28 years of relative humidity revealed a clearly rising trend. Until 2001 the relative humidity was below 78.56% and until 1992 the growing rate of relative humidity was 1.65%. Afterwards, the growing rate was 1.47% within 2011, and from 2011 to 2018 the incremental rate was 1.57% (Fig. 4.19). Means that there was a static trend from the second to the third decade, after which it shows a slightly decreasing trend in the last decade, which is 2.70%. Fig. 4.14 Yearly average rainfall trend in winter (Dec–Feb)

Fig. 4.15 Yearly average rainfall trend in pre-monsoon (1991–2018)

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Fig. 4.16 Yearly average rainfall trend in monsoon (1991–2018)

Fig. 4.17 Yearly average rainfall trend in post-monsoon (1991–2018)

Fig. 4.18 Pattern of yearly average rainfall change in percent (1991–2018)

Fig. 4.19 Yearly average relative humidity (%) trend from 1991-2018

Interaction of Climatic Indicators in Northwest Bangladesh Spatial distribution of fluctuating patterns of climatic pointers of northwest Bangladesh and their collaboration appearances precisely the hypothetical interface of climatic pointers (Figs. 4.20, 4.21, 4.22 and 4.23). South part of northwest Bangladesh demonstrations rising trend of yearly mean maximum and yearly mean min temperature.

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Fig. 4.20 Spatial discrepancy of average max temperature

As an effect the atmosphere of the study zone can hold fewer water gurgle, which meaning little proportion of rainfall and relative humidity. Whereas the north part of this study zone has less rising trend of max and min temperature. Thus, the atmosphere of the region has relatively more water holding capacity, so the relative humidity is greatly developed here. So, we may conclude that north region of the study area is much higher annual average rainfall.

Groundwater Table Depth: Temporal Analysis For investigating groundwater table depth of northwest Bangladesh consists eight districts has valid 211 wells out of 243 has been selected and remaining 32 wells don’t have sufficient data. The geographical situation is practically similar in the complete study area, so precipitation is the single source of regeneration. Figure 4.24

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Fig. 4.21 Spatial discrepancy of average min temperature

clarifies a significant growing tendency of groundwater table length over the time but annual maximum rainfall shows decreasing pattern (Fig. 4.25). In the monsoon, max rainfall is declining and means groundwater table infiltration is raising which means in monsoon revitalization is receiving lower (Figs. 4.26, 4.27, 4.28, 4.29, 4.30, 4.31, 4.32 and 4.33). After 1996, rainfall has increased but groundwater level depth has also improved instead of declining and the same phenomenon has occurred in 2006. In the monsoon, since 1991–1994 groundwater table length was relatively same but bearing in mind the interaction of precipitation and groundwater table length, in 1996–1998 precipitation was improved to 260 mm but groundwater table was declined up to 8.90 m which is the maximum in terms of length (Fig. 4.29) in Rajshahi region is located beside Padma river that revitalizing groundwater aquifer. Figures 4.24, 4.26, 4.28, 4.30 and 4.32 show an increasing trend of groundwater depth. From 1991 to 2001 groundwater level length was around 10.5 m and can grasp less water bubble, which indicate low percentage of rainfall and relative humidity.

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Fig. 4.22 Spatial discrepancy of mean rainfall

In 2001, in monsoon season groundwater table length has improved rapidly. In 2011 precipitation was decreased associated to 2001–2016 but groundwater table has increased. This setting means that the impact of precipitation for revitalization is attain lower in current decades (Fig. 4.29). Groundwater depth is showing a continuous growing trend of groundwater table length since 1991 (Fig. 4.28). Within the period of 1991 maximum precipitation was at maximum rate, 622.00 mm, in 1998 average precipitation rate was lesser rate, 86.08 mm. However, there was increased precipitation in 2001 and in 2011 but groundwater length was improved more than 6.56 m and 3.67 m respectively (Figs. 4.31 and 4.32).

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Fig. 4.23 Spatial discrepancy of average relative humidity Fig. 4.24 GW table depth trend of NW Bangladesh: all season

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4 Modeling and Forecasting Climate Change Impact … Fig. 4.25 Comparison of yearly max rainfall and GW table: all season

Fig. 4.26 GW table depth trend in pre-monsoon (Mar–May)

Fig. 4.27 Comparison of annual max rainfall and GW table in pre-monsoon (Mar–May)

Fig. 4.28 GW table depth trend in monsoon (Jun–Sep)

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94 Fig. 4.29 Comparison of annual max rainfall and GW table in monsoon (Jun–Sep)

Fig. 4.30 GW table depth trend in post-monsoon (Oct–Nov)

Fig. 4.31 Comparison of annual max rainfall and GW table in post-monsoon (Oct–Nov)

Fig. 4.32 GW table depth trend in winter (Dec–Feb)

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Fig. 4.33 Comparison of annual max rainfall and GW table in winter (Dec–Feb)

Modeling Results As discussed earlier, the study uses the NARX neural network algorithm. Multivariate time series model was applied. All the obtained results were based on programming in R 4.0.3. Due to the random characteristics on ANNs, different neural network models are established by changing the neuron amount and time delays. The accuracy of this model was tested by the standard error evaluation procedure and the results were listed for comparison. The error evaluation techniques used to measure prediction precision were namely, Mean Square Coefficient of determination (R2 ), Error (MSE) and Nash-Sutcliffe Coefficient of Efficiency (NSE) (Figs. 4.34, 4.35, 4.36 and 4.37). LM algorithm is compared with BR algorithm. The original data were divided into different subseries, and the normalized time series data was chosen as the target of the NARX. The first 264 data point (January 1991–December 2012) was used to regulate the model. In addition, the last 72 data points (January 2013–December 2018) were used for testing the model. After the training for the network was finished, the neural network performance curve (Fig. 4.34) was used to verify the performance. For both figures mentioned in Fig. 4.34 show that the error curve of the verification set reaches the minimum value under different iterations, which shows that the data division is good. After training and testing, it was found that 2-time delays and 15 hidden neurons are the most influential for algorithms by trial and error. The mileage

Fig. 4.34 Neural network performance curve (LM and BR)

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Fig. 4.35 Observed and modeled groundwater table at different justification rounds Fig. 4.36 Actual and forecasted groundwater depths for LM algorithm

Fig. 4.37 Observed and predicted groundwater depths for BR algorithm

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for training was set to 500. The training of NARX ceased on the occasion where the error achieved 10–5 or the number of epochs reached 500. The NARX was then applied to forecast the test data for the variables, respectively. The R-values are greater than 0.9 (Fig. 4.38) for both algorithms, which indicates that the training data were well fit. From error histograms (Fig. 4.38), it is observed that analysis performance found in BR were better than LM. In the BR model, error is smaller in comparison to that in LM model, because more data can be observed to be near zero error line. Using the developed networks, the groundwater table of the proposed well was forecasted. Both models were based on the data from the past 22 years (1991–2012) values. Therefore, every prediction depends on the previous value. Based on these figures, the forecast for the next six years (2013–2018) has been completed. The comparisons between the actual and predicted values 6 years ago based on both LM and BR algorithms are shown in Figs. 4.36 and 4.37. From the graph drawn, the deviation of BR algorithm (Fig. 4.37) is smaller than that of LM algorithm (Fig. 4.36). Therefore, it can be said that for this study, the BR algorithm is more suitable than LM algorithm. MSE, R2 and NSE of predicted and actual groundwater table for LM and BR algorithms are available in Table 4.1. As mentioned above, the lower the MSE gives better the model performance and an efficiency of 1 agrees to a perfect match between

Fig. 4.38 Regression value of NARX network during training (LM and BR)

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MSE

NSE

R2

LM

0.04752

0.8062

0.964

BR

0.00376

0.8528

0.968

NSE and R2 . Therefore, it can be decided that the BR procedure is more suitable than LM procedure for this study. In general, NARX can be recommended as an effective groundwater table prediction tool from the comparison of results, although data are limited. However, a comprehensive study is still needed to assess the long-term tendency of groundwater tables based on the data for a long period.

Major Findings Max and Min temperature both are presenting increasing tendencies. But Max temperature is growing more quickly than of the Min temperature. In winter, Max temperature is growing but Min temperature is practically fixed but it is noted that from 2005 Min temperature is declining more rapidly. Annual average precipitation presents very little declining tendency in northwest Bangladesh. It shows a declining trend of precipitation from 2005. The 28 years tendency of relative moisture shows a significant growing trend, but relative humidity is nearly fixed from 2000. This is mainly increased due to both the Max and Min temperature increasing. Relative humidity declines significantly during monsoon season. Whole selected wells of the study region are presenting a growing tendency of groundwater level in northwest Bangladesh. Amongst them the groundwater reduction rate is much higher in study region than other parts of Bangladesh. Groundwater revitalization rate is insufficient during monsoon. Evapotranspiration may increases due to increased temperature. It causes decrease of groundwater availability in drought prone zone. However, precipitation is not only adequate to revive groundwater level. The spatiotemporal distribution, intensity of precipitation, and change in water level significantly impacted on the dynamics of groundwater.

Conclusions This article determines the capacity of NARX neural network to forecast monthly groundwater level in different times. Researcher tried to establish the most accurate and effective algorithm, through using precipitation, temperature, wind speed and humidity data as input factors, and forecasted groundwater level over the seven years ahead. In order to improvement the training algorithms changed the number and times of hidden nodes. Depends on statistical performance principles and training results, the most accurate architecture (MSE = 0.00376) of the study was BR with

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2 delays and 9 hidden nodes. In terms of model convergence, the LM algorithm essential the least number of iterations, and the time reduction was faster than BR algorithm. However, the overall performance of BR was forecast groundwater table to achieve in a stable and inclusive manner. The findings attained in this research also established that the NARX-BR and NARX-LR models are effective for evaluating monthly groundwater table, although the input variables have a strong seasonal trend. As a result, such networks are strongly dependent on the availability of data to a large extent, while forecasting such network depends on the quality of input data and training algorithm.

Recommended Policy Future research will create a groundwater surveillance network across the area and estimate the yield of NARX-BR constructed on updated data. From this study it could be said that the water level forecast can provide more references for several decisions regarding groundwater extraction. Acknowledgements We would like to acknowledge and sincere appreciation to Bangladesh Water Development Board (BWDB) for given essential data to complete this study. We would also like to express our sincere acknowledgments to the Ministry of Education for monetary support to complete this research project (Project No. PS142260). Finally, we are grateful to the reviewers for their valuable observations to improve this manuscript.

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Chapter 5

Modeling Household Socio-Economic Vulnerability to Natural Disaster in Teesta Basin, Bangladesh Sosimohan Pal, Abu Reza Md. Towfiqul Islam, Masum Ahmed Patwary, and G. M. Monirul Alam Abstract The Teesta basin of Bangladesh experiences natural disasters every year that bring huge agricultural production losses. This study has developed the socioeconomic vulnerability index (SeVI) to quantify the vulnerability of people in the drought-prone areas in northern Bangladesh. A total of 134 respondents were interviewed from Gangachara and Kaunia Upazilas of Rangpur district. The study employed 36 indicators and IPCC dimensions for developing SeVI. Results show that Gangachara upazila is more vulnerable than Kaunia due to a limited number of resources with a lower capacity to counter climate stress. Ganachara upazila has higher exposure and sensitivity with lower adaptive capacity to climate change compared to Kaunia. Household dependent on agriculture is more vulnerable in both the Upazilas. Food crisis and health related problems are common issues after the occurrence of disasters. The disaster warning system is very poor in both the Upazilas which need to be developed. The study result indicates a realistic approach of SeVI needs to be adopted, which may be an effective tool for community development in the study area and also a guideline for further study.

Introduction Geographically, Bangladesh is a disasters-prone country due to extreme weather events, high spatial and temporal climatic variability, high population density, poor infrastructure, and poor institutional capacity (Alam et al. 2018; Rahman and Islam 2019). The issue of vulnerability and adaptive capacity has been widely applied for illustrating the societal aspect of climate change (Ahsan and Warner 2014; Alam et al. S. Pal · A. R. Md. T. Islam (B) Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh e-mail: [email protected] M. A. Patwary School of Science and Engineering, Teesside University UK, Middlesbrough TS1 3BA, UK G. M. M. Alam Bangabandhu Sheikh Mujibur Rahman Agricultural University, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_5

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2017; Islam et al. 2020a). According to IPCC (2007), vulnerability is the figuration of three fundamental factors- exposure, sensitivity, and adaptive capacity. Exposure defines as the expansion and duration of the climate-related extreme events like drought, flood, riverbank erosion, cyclone and storm surge, and anomalies of climatic parameters as anomalies of temperature and patterns of precipitation (Urothody and Larsen 2010; Alam 2017; Islam et al. 2019; Rahman and Islam 2019). Sensitivity is a degree of the system that is influenced by the factors of exposure and adaptive capacity is the system of the ability to resist and resurrect from exposure (Ebi et al. 2006; Alam et al. 2017). The exposure and adaptive capacity of people depend on geographical locations. The factors of exposure are also different from place to place in the aspect of vulnerability to climatic disasters (Alam 2017). Bangladesh has witnessed many drought events such as 1973, 1978, 1979, 1981, 1982, 1992, 1994, 1995, 2000, and 2006 (Habiba et al. 2012; Islam et al. 2017). Bangladesh experienced another 24 formidable droughts, including the 1984, 1982, 1981, 1975, 1972, 1961, 1958, 1957, and 1951 drought, which affected 47% of total areas and 53% of total population (Uddin et al. 2020). The witness of high magnitude drought events in the country was in 1995, 1994, 1992, 1989, 1982, 1981, 1979, 1978, and 1973 (Islam et al. 2017). In contrast, the most formidable flood occurred in 1987, 1988, and 1998 (Mirza 2003). Another severe hydrological disaster is riverbank erosion. In the aspect of climate events, it was estimated that drought directly affected 42% of the land cultivated and 44% of the population by reducing 2 million tons of rice production (Brouwer et al. 2007; Finch et al. 2010). One of the tethering factors of the agriculture sector in Bangladesh is the drought that brings about extensive damage to crop production (Uddin et al. 2020). Unfortunately, the frequency and intensity of climatic and natural disasters are increasing day by day. Climate change influences the frequency of natural disasters as especially floods and droughts (Islam et al. 2020b). The degree of exposure is increased by the high intensity and frequency of disasters. High-frequency disasters can bring huge losses to society. People are unable to struggle with economic crises. People also do suffer from physical and mental disorders. Suffering condition creates different vulnerability in society. People cannot resist the long-lasting suffering condition. Then, the adaptive capacity to disaster will reduce the sufferings in the disaster-prone areas. The scale of adaptation relies on the vulnerability of people (Alam et al. 2017; Salam et al. 2020). The adaptive capacity also depends on the level of economic growth. Most scholars have indicated that adaptive capacity is a most serious issue that increases the resistance power to vulnerability (Habiba et al. 2012; Alam 2017; Islam et al. 2020a; Salam et al. 2020). Most scholars reported that the northern part of Bangladesh is more vulnerable to natural disasters as flood, drought, and riverbank erosion. It was also mentioned that the frequency of disasters has increased in the last decades because of the effects of climate change. Every year, the northern part of Bangladesh experiences natural disasters that bring huge agricultural production losses. Floods in monsoon season, loss of agricultural land after flooded and droughts during or after the monsoon are a yearly routine of the people of this region. Because of agricultural losses, poverty runs after these people after the occurrence of disasters. As a result, different vulnerabilities increase in the region. The vulnerability index is one of the effective

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methods for assessing socio-economic vulnerability. Therefore, the socio-economic vulnerability index (SeVI) study is essential for the northern part of Bangladesh. Only a few studies have been done based on the LVI approach in the disaster-prone region. This study highlights the vulnerability index through two approaches such as vulnerability index (VI) and IPCC vulnerability index (IPCC-VI) for calculating the socioeconomic vulnerability index (SeVI) through 36 factors, 8 indicators, and 3 IPCC dimensions. This study has three major objectives: (i) to compute HHs socio-economic vulnerability index for the two vulnerable natural disaster regions of northern Bangladesh, and (ii) to investigate the spatial pattern of socioeconomic vulnerability in the study upazilas. The results of this study may be helpful for decision-makers for policy formulation in the drought-prone areas in the country.

Literature Review Vulnerability is an operation of exposure to a stressor, sensitivity and adaptive capacity (Adger 2006; De Lange et al. 2010; Salam et al. 2020). It is specific stress and highly varies from region to region (Khailani and Perera 2013). Vulnerability is a controversial topic among scholars and does not contain a straightforward concept (Lee 2014). In addition, vulnerability is susceptible to exposure, sensitivity, and adaptive capacity (Khan 2012; Alam 2017; Dintwa et al. 2019). In relation to society, vulnerability defines the condition that precedes disasters and includes elements of society, economy, and institution (Mirza 2003; Rakib et al. 2014; Rabby et al. 2019). However, scholars have indicated that vulnerability is a function of susceptibility to generate losses and of recovery capacity that is referred to as resilience (Rabby et al. 2019). Vulnerability is two types namely social and physical vulnerability (Birkmann 2006). Social vulnerability defines as the region that disasters include social, economic, political, and institutional elements (Habiba et al. 2012). Physical vulnerability emphasizes the probability of exposure to risks associated with natural disasters (Cutter 1996). The aspect of climate change, it associates with the consequences of climate-related disasters such as floods, storms, sea level rises, hot weather, anomalies of rainfall that create effects on crop productivity, changing ecosystems and disruption to economic growth (Mavhura et al. 2017). The impacts of climate change also vary from region to region or state to state. Urban areas have experienced significant climate change effects owing to high-density population, economic activities, infrastructure, and surrounding assets (Khailani and Perera 2013). Not only urban areas gain experiences of the impacts of climate change, but also those of rural areas owing to geographical location, low socio-economic conditions, and unconsciousness. Bangladesh as a developing country, rural people are more vulnerable to natural disasters than urban areas. Different classes of people survive in a society in different occupations. So, Social vulnerability indicators are essential for SVI. Methods of SVI are two main classes as the first class of approaches in general and the second is objective versus subjective (Urothody and Larsen 2010). The first type denotes

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the effects of all disasters including its factors as poor infrastructure and health, low socio-economic growth (Lee 2014). The second focuses on public infrastructure and population density and disaster awareness (Khan 2012; Rabbi et al. 2013). A mounting body of studies had been performed by different scholars from different countries about vulnerability index calculation (Birkmann 2006; Brouwer et al. 2007; Can et al. 2013; Cutter 1996; Ebi et al. 2006; Ahsan and Warner 2014; Tasnuva et al. 2020). The vulnerability index method has been widely used over the world to assess the vulnerability of different climatic disasters. A large number of studies (Apotsos 2019; Tasnuva et al. 2020; Salam et al. 2020; Islam et al. 2020a; Azam et al. 2019) have investigated the social vulnerability to certain disasters, and their responses to those disaster-prone regions. When considering differences in socioeconomic condition, region, and scale, most prior studies confirm that natural disasters have a pivotal and adverse impact on human socio-economic and community development. However, little work has been carried out on the integrated vulnerability index (VI) and IPCC vulnerability index (IPCC-VI) at a household level for assessing socio-economic vulnerability to disaster-prone areas. The method has been performed on the eastern and western parts of Bangladesh by some researchers (Ahsan and Warner 2014; Bhuiyan et al. 2017). This study is the first to perform on the northern part of Bangladesh to assess SeVI and LVI in two ways (overall index and HH’s index).

Methodology Study Area Description SeVI focuses on Gangachara and Kaunia Upazilla that are the most vulnerable Upazillas among eight Upazillas of Rangpur Zila. The upazila occupies an area of 269.67 km2 . It is located between 25°48’ and 25°57’ north latitudes and between 89°05’ and 89°21’ east longitudes. The Upazilla was under the bed of the river which is locally termed as Gang. Gradually a char developed in the side of the river bed. Gang River and established char were locally combined name Gangachara (Islam et al. 2014). The Kaunia upazila occupies an area of 147.64 km2 . It is located between 25°42 and 25°50 north latitudes and between 89°18’ and 89°30’ east longitudes (Fig. 5.1). The upazila is bounded on the north by Gangachara upazila and Lalmonirhat Sadar upazila of Lalmonirhat zila, on the east by Lalmonirhat sadar upazila and Rajarhat upazila of Kurigram zila, on the south by Pirgachha upazila and the west by Rangpur sadar upazila (BBS 2018). Gangachara and Kaunia are most vulnerable to natural disasters mainly for floods, drought and riverbank erosion. The Teesta River is one of the major rivers in Bangladesh and enters into the north part of Bangladesh. The river also flows through Gangachara and Kaunia. So, Floods, droughts, and riverbank erosions are common disasters of these upazillas. Most of the people lost their agricultural land to riverbank

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Fig. 5.1 The map showing study profile with HHs locations

erosion. Flood and drought also bring huge damage each year. It seems that natural calamities are the major cause of socioeconomic vulnerability.

Techniques of Data Collection To analyze the SeVI, primary and secondary data are used in the study. Quantitative and qualitative data are collected by Focus Group Discussion (FDG), and Household Survey (HHs). FGD is an important and common method of data collection. It was used to capture and gain a better concept of socioeconomic indicators including factors and overall condition of the study area. Also, this is a widely used method of data collection in the aspect of social research. It reflects the actual conditions of the socioeconomic of the respondent. The researcher can conceive these conditions during data collection by the general observation of the household. To performing HHs, a set of questionnaires prepared following different factors of the socioeconomic condition. The indicators of vulnerability were chosen from secondary data in the period of performing the literature review. The literature review indicates in the study what types of indicators are perfect in the aspect of the northern part of Bangladesh. HHs were conducted randomly in 134 households in Gangachara (67 HHs) and Kaunia (67 HHs) for SeVI. HHs performed in the study areas for collecting

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data of socioeconomic factors as mainly demographic profile, the status of climate led disasters, and the status of physical and infrastructure. Respondents were selected randomly. Household head was the respondent. Finally, data are decorated with the application’s different software.

Indicators and Factors of the Study for SeVI Total 8 indicators were selected for the performance of SeVI. These indicators were also divided into IPCC’s three dimensions as exposure, sensitivity and adaptive capacity. Further, these indicators were also divided into different probable factors as sub-indicators. Total 36 factors including units and potentialities were being selected (Appendix Table 5.5). The indicator of exposure is only one, which is a natural disaster and climatic stresses (Appendix Table 5.6). The study discusses the natural disaster from climactic point of view. Sensitivity consists of three indicators as food, water, and health. These factors are related to each other. The rate of vulnerability will be enhanced in society if these factors are affected by climatic stresses. Besides, adaptive capacity consists mainly of 4 indicators as demographic profile, economy, awareness, and physical infrastructure, which contribute to reducing vulnerability.

Calculation Procedure of SeVI Model SeVI is the composite component framework method. SeVI was developed by calculating IPCC-three dimensions as exposure, sensitivity and adaptive capacity (Azam et al. 2019; Tasnuva et al. 2020). All components are both qualitative and quantitative in nature. In this way, 36 factors developed SeVI. These factors were measured in different units and scales which are predetermined. The study used Eq. (5.1) that accepted from a life expectancy index by UNDP (Bhuiyan et al. 2017) to form the index score of factors for standardization. Index scorei =

X I − X min X max − X min

(5.1)

where, X I = the original value of factor in the household. X min = the lowest value of factor in the household. X min = the highest value of factor in the household. In addition, some factors need to inverse index score. The factors are education, income crops yield, livelihood diversification, the life expectancy of birth, and also which types of factors are not represented socioeconomic vulnerability. Factors which promote the socioeconomic growth and capacity need to calculate inverse index score following Eq. (5.2).

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1 1 + observe index scorei

Inverse index scorei =

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(5.2)

Each factor has calculated by the above equations. The original value collected from households during the questionnaire survey and also FGD. Maximum and minimum values were delimited by following the scale and unit of factors. The numerical values of factors range from 0 to 1. The maximum and minimum values adjusted and avoided more 1 and less than 0 these of values (Ahsan and Warner 2014). Values of all factors were determined by the above formula. After standardization of all factors, the values of factors were averaged forming indicators in the following Eq. (5.3) (Alam et al. 2018; Salam et al. 2020). n Sc =

k=1

Index score ik n

(5.3)

where, Sc = One of the indicators. Index scoreik = values of factors. And, n = number of factors. Each indicator needs to average to calculate SeVI of the study area. To identify the SeVI of each factor, it needs to aggregate in a value which indicates each vulnerability of these indicators. Average of indicatorsN S Li =

k=1

N Sc

N

(5.4)

where, S L i = IPCC dimensions and capitals Socioeconomic and livelihood vulnerability respectively. N = Number of indicators. Sc = Values of indicators. Socioeconomic and livelihood vulnerability are identified by the description of indicators and S L i values. In this study, the scale of SeVI defined as a range from 0 (least vulnerable) to 1 (most vulnerable).

Calculation Procedure of IPCC-SeVI As an alternative method, the study also examined the IPCC indicator-based framework approach. All indicators were divided into three IPCC dimensions to perform the IPCC–SeVI. Exposure includes natural disasters and climatic stresses which are a threat to society or a geographical location. In addition, exposure indicators produce the probability of different vulnerabilities. Sensitivity has been described in following the condition of health, food, and water because these indicators are more sensible during disasters and critical stage of vulnerability (Alam et al. 2019). In last, adaptive capacity is generalized by the recent status of economic growth, demographic

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profile, physical infrastructure and awareness. The inverse index score equation has been used in some cases of indication for the calculation of IPCC-SeVI. The inverse index score is obligatory to the social development factors such as farmland. The study aggregated the value of defined indicators by following Eq. (5.5) IPCC−SeVI = (Exposure value− Adaptive capacity value) × sensitivity value (5.5) Here, exposure value is the aggregation of exposure indicators values. In this way, the study generated the adaptive capacity and sensitivity values. The study was also scaled the IPCC–SeVI from -1(least vulnerable) to 1(most vulnerable).

Results Adaptive Capacity Index of the Study Area The factors considered for developing adaptive capacity of the household are presented in Table 5.1. Adaptive capacity includes four indicators and sixteen factors for both Upazila. The lowest index value is 0.23 in Gangachara for all indicators of adaptive capacity (demographic profile, awareness, economy, and physical infrastructure). The lowest value index is 0.12 for the economy factor in Kaunia. The demographic profile includes 5 indicators. The dependency ratio of Gangachara upazila is 0.38 and of Kaunia is 0.47. Female-headed household (HH) and children drop out from the school of Gangachara (0.22 and 0.25) are more vulnerable than Kaunia (0.18 and 0.20). In the aspect of an economic indicator, agricultural dependent occupation is more vulnerable in both Upazila. The migration rate is higher in Gangachara (0.30) than Kaunia (0.27). The people of Gangachara have gained the experience of different natural disasters. The percentage of disaster-experienced persons is higher Gangachara than Kaunia. Also, Gangachara represents less demographic vulnerability (O.30) than kaunia (0.35). However, in the aspect of an economic indicator, Kaunia slightly represents less vulnerability (0.34) than Gangacgara (0.36). Kaunia (0.44) is less vulnerable than Gangachara (0.49) in the aspect of awareness factor. The index values of physical infrastructure for both Gangachara and Kaunia are 0.49 and 0.47 respectively. The percentage of brickfield HH in Kaunia Upazila is 62% and in Gangacgara 58% (Table 5.1). There are no latrines in each HH in both the Upazilas. Also, Gangachara (0.72) represents short entertainment sources as TV/radio in HH. Most of scholar stated that adaptive capacity is a powerful system for resisting disaster vulnerability. The consequences of disasters depend on the adaptive capacity system of any vulnerable region. Disasters bring easily huge economic loses in low adaptive capacity regions. Gangachara shows more experiences in the occurrence

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Table 5.1 Overall Index of indicators and factors (adaptive capacity) for both Upazilas IPCC Dimension Adaptive capacity

Indicators

Demographic Profile

Economy

Awareness

Physical infrastructure

Factors of indicator

Index score values

Indicator wise index value

Gangachara

Kaunia

Gangachara

Kaunia

Dependency ratio

0.38

0.47

0.30

0.35

Female headed HH

0.22

0.18

Average HH members

0.22

0.53

Number of HH earner

0.43

0.38

Number of children drop out from school

0.25

0.20

Dependent on agriculture

0.98

0.96

0.36

0.34

Dependent on 0.06 natural resources as water reservoir for fishing

0.03

Number of migration for income

0.30

0.27

Children as a family earner

0.12

0.10

Percentage of participating in volunteerism

0.33

0.26

0.49

0.44

Major disasters experience person in last 30 years

0.79

0.75

Participation 0.36 of disaster knowledge related programs from different NGOs

0.30

Percentage of 0.58 brick-built HH

0.62

0.49

0.47 (continued)

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Table 5.1 (continued) IPCC Dimension

Indicators

Factors of indicator

Index score values

Indicator wise index value

Gangachara

Kaunia

Gangachara

Having no electricity or solar system

0.13

0.10

Absence of latrine

0.52

0.49

Having no TV/radio

0.72

0.66

Kaunia

of disaster than Kaunia. The result of the study also shows more vulnerability in Gangachara than Kaunia (Table 5.1) (Fig. 5.2).

Exposure Index of the Study Area Exposure represents the cause of vulnerability. It is related to different climatic stresses and parameters. Exposure to the natural disaster in a geographical region is created by the consequences of climate change. The study also discusses the major parameters of climate change (average, maximum and minimum temperature and precipitation). The study performs on the mean standard deviation of daily temperature and precipitation (1965–2017). The index values of the mean standard deviation of climatic parameters for both Upazila are the same because there is a common weather office. The index values of the same factors for Gangachara and Kaunia are mean standard deviation of daily average maximum temperature (0.32), minimum temperature (0.41), average temperature (0.58) and precipitation (0.42) (Table 5.2). In the aspect of HHs vulnerability index, the highest values of natural disasters and climatic stresses are 0.76 and 0.75 for Gangachara and Kaunia respectively (Fig. 5.3). The lowest values of this factor are 0.23 and 0.21 for Gangachara and Kaunia respectively. The index values of the two upazila are approximately similar. Comparatively, Gangachara is more vulnerable than Kaunia. The frequency of flood and drought is higher in Gangachara (maximum 3 times for flood and maximum 2 times for drought) in the year than Kaunia (Table 5.2). The index values of affected HH and death and injuries during disasters in Gangachara are 0.88 and 0.66 respectively. The disaster warning system is very poor in both Upazila. The poor warning system brings huge agricultural losses and casualties. Erosion is a devastating event in both regions. The values of land losses are Gangachara (0.35) and Kaunia (0.25). The indicators wise vulnerability index values are Gangachara (0.47) and Kaunia (0.42). The SeVI value of exposure is similar to indicator values. The results of the study indicate that the study regions are more vulnerable in exposure to natural disasters and

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Fig. 5.2 Spatial distribution maps of indicators index calculating per HHs

climatic stresses. Natural disasters and climatic stresses create threats to agricultural production, especially boro rice production. The Major boro rice production field of Bangladesh is the northwestern part. As a result, the impacts on rice production in the northwestern part may create threats to the national economy in Bangladesh.

Sensitivity Index of the Study Area The study discusses the sensitivity indicators that are divided into food, water and health. It is very important to study because it is influenced by exposure and adaptive

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Table 5.2 Index of indicators and factors (exposure) for both Upazilas IPCC Dimension Exposure

Indicators

Natural disasters and climatic stresses

Factors of indicator

Index score values

Indicator wise index value

Gangachara

Kaunia

Gangachara

Kaunia

Number of 0.58 floods per year in last 30 years

0.55

0.47

0.42

Number of droughts per year in last 30 years

0.25

0.10

Affected HHs in last 30 year by natural disasters

0.88

0.79

Death or 0.66 injuries for climatic events

0.52

Percent of 0.35 HHs reporting land loss by climate related extremes and disaster during past 30 years

0.25

Percent of HHs reporting received disaster warning

0.21

0.25

Mean standard 0.32 deviation of daily average maximum temperature by month (1965–2017)

0.32

Mean standard 0.41 deviation of daily average minimum temperature by month (1965–2017)

0.41

Mean standard 0.58 deviation of daily average temperature by month (1965–2017)

0.58

(continued)

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Table 5.2 (continued) IPCC Dimension

Indicators

Factors of indicator

Index score values

Indicator wise index value

Gangachara

Gangachara

Mean standard 0.42 deviation of daily precipitation by month (1965–2017)

Kaunia 0.42

Fig. 5.3 Spatial distribution maps of indicators index calculating per HHs

Kaunia

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capacity factors. Food, water and health are the most sensitive indicators in any region. These sensitive indicators are adversely affected by natural disasters and climatic stresses. In the aspect of spatial distribution map analysis, health is the most vulnerable than other sensitivity indicators (food and water). The highest/lowest value of health is 0.94/0.44 and 0.99/0.42 for Gangachara and Kaunia, respectively (Fig. 5.3). The highest and lowest values of food are 0.86 and 0.88 for Gangachara and Kaunia respectively. Vulnerability will increase in a region if natural disasters and climatic stress affect the most sensitive indicators as food, water and health. About 98% of HH in both Upazilas is directly involved in agriculture. The inverse index values of the land farm are Gangacgara (0.28) and Kaunia (0.35) (Table 5.3). Food crisis (3–4 month) is a severe problem in Gangachara Upazila (0.66). Food indicator values are 0.67 and 0.62 for Gangachara and Kaunia respectively. Health is a severe problem in both regions than food and water. The values of Health SeVI are 0.73 and 0.71 for Gangachara and Kaunia respectively. Flooded water is more problematic than freshwater. Expect for the dry season, the people of the north part of Bangladesh can easily extract fresh water from the ground. But the water crisis creates a problem in the dry season or the period of drought. Gangachara represents an average of 3.5 months for water crisis and Kaunia 2.5 months (Table 5.3). Food and health are severe affected indicators in the study upazilas. Food crisis is to be severe after the occurrence of disasters as floods or droughts. Health problems and food crisis are common issues after the occurrence of disasters.

IPCC-SeVI of the Study Area The calculation of the Socioeconomic Vulnerability Index (SeVI) is divided into two-phase indicator wise analysis and IPCC dimension analysis. Table 5.4 indicates the dimension values for two Upazilas. Gangachara and Kaunia represent more vulnerability in the factors of sensitivity than the other two dimensions. Sensitivity index values are 0.52 for Gangachara and 0.48 for Kaunia. Gangachara is greater vulnerable to exposure than Kaunia. Exposure index values are 0.47 for Gangachara and 0.42 for Kaunia. Overall based on three IPCC-dimension, Gangachara is higher vulnerable than Kaunia. In the aspect of spatial distribution maps of IPCC dimension, the highest index of exposure is 0.77 for Gangachara and 0.76 for Kaunia (Fig. 5.4). The lowest value of exposure is 0.21 for Kaunia. The sensitivity dimension is more vulnerable than the adaptive capacity dimension for both Upazilas. The highest/lowest values of sensitivity are 0.68/0.33 for Gangachara and 0.76/0.29 for Kaunia. The index of Adaptive capacity ranges 0.26 (lowest) to 0.61 (highest) for both Upazilas (Fig. 5.4). IPCC-SeVI for per HHs ranges -0.186 to 0.251 for two Upazila. The highest and lowest values contain Gangachara (-0.186 and 0.251). Also, the highest and lowest values of IPCC-SeVI are 0.193 and -0.161.

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Table 5.3 Index of indicators and factors (Sensitivity) for both Upazilas IPCC dimension Sensitivity

Indicators

Food

Water

Health

Factors of indicator

Index score values

Indicator wise index value

Gangachara

Kaunia

Gangachara

Kaunia

Number of HHs 0.99 that depend on own farm for food

0.99

0.67

0.62

Inverse index of 0.28 land for farm

0.35

Number of HH 0.66 for food crisis in month (3–4) last 10 years

0.48

Difficulty in managing diet food for HH members

0.75

0.67

Percent of HHs reporting they have heard any conflict for water in the society

0.20

0.15

0.22

0.16

Average time to 0.08 fetch water

0.05

Month of water crisis for all activities in last 30 years

0.29

0.21

Percent of HHs 0.28 that collect water directly from water reservoirs (river, streams, pond) for domestic purposes

0.22

Percent of HHs where a family member had to miss work or school in the last 10 weeks due to illness

0.95

0.89

0.73

0.71

Average time to 0.52 go health community on foot (minutes)

0.56

(continued)

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Table 5.3 (continued) IPCC dimension

Indicators

Factors of indicator

Index score values

Indicator wise index value

Gangachara

Gangachara

Percent of 0.71 households with family member with chronic illness Table 5.4 Overall IPCC-SeVI dimension calculation for Gangachara and Kaunia

Kaunia

Kaunia

0.69

IPCC dimension

Gangachara Upazila

Kaunia Upazila

Adaptive capacity

0.4

0.38

Exposure

0.47

0.42

Sensitivity

0.52

0.48

IPCC-SeVI

0.037

0.016

Fig. 5.4 Spatial distribution maps of dimension index and IPCC-SeVI calculating per HHs

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IPCC SeVI of Gangacgara and Kaunia are 0.037 and 0.016 respectively (Fig. 5.6). IPCC SeVI indicated that − 1 value is least vulnerable and 1 is most vulnerable for IPCC vulnerability index calculation (Table 5.4). The results of the study reported that Gangachara upazila is higher vulnerable than others. In Gangachara Upazila, the majority of HH depend on agriculture for their daily needs. But agricultural productions have been affected by the occurrence of different natural disasters and climatic stresses. The warning system and disaster management programs are very poor in the aspect of vulnerability. They cannot save their assets before upcoming disasters hit the region. Economic loss and poverty increase the intensity of vulnerability to disasters. Except for agriculture, there are no options for income sources to survive in the region. For income sources, people have seasonally migrated to different cities. People have migrated to cities, especially in a period when there have no income opportunities in their regions. Some families have permanently migrated to different cities as Dhaka due to income sources. In contrast, Kaunia Upazila represents less vulnerability than Gangachara. Also, people of the upazila have migrated to different cities.

Overall and HHs SeVI Score Finally, based on 8 indicator scores, SeVI has prepared for the two Upazilas. The range of SeVI for indicators is 0 (least vulnerable) to 1 (most vulnerable). A vulnerability scale has been produced by the study for the precise analysis of the vulnerability. The highest weighted scores of the two Upazilas are 0.73 (Ganghachara) and 0.71 (Kaunia) for health (Figs. 5.5 and 5.6.). After health, the highest and lowest weighted scores are for food and water. The highest SeVI of awareness and physical infrastructure is the same (0.49) for Gangachara Upazila (Fig. 5.6). The most important indicator of the study is natural disasters and climatic stresses. The index values of natural disasters and climatic stresses weighted 0.47 (Gangachara) and 0.42 (Kaunia). It is the cause of enhancing vulnerability. Climatic stresses will bring natural hazards and disasters if the parameters are the anomaly. In the aspect of study areas, the factor has geographically influenced by the lower Teesta basin. Devastating floods in the seasonal period and droughty conditions after the flood due to river siltation have created severe vulnerability region in Bangladesh. Land and household loss and crop damage are severe and common phenomena in the study region. These phenomena bring huge economic losses. SeVI of the economy is 0.36 for Gangachara and 0.34 for Kaunia (Fig. 5.6). This finding is quite similar in the two upazilas. Demographic profile is low vulnerable compared to other indicators except for water. Comparatively, Kaunia (0.35) is higher vulnerable than that of Gangachara (0.30) for demographic profile. Overall, both two upazilas are vulnerable but Gangachara is more vulnerable than Kaunia. Maps of spatial distribution represents that Gangachara upazila occupies the highest and lowest index values (-0.186 and 0.251). Kaunia upazila is also vulnerable and shows analogous values (-0.161 and 0.193) to Gangachara upazila (Fig. 5.4).

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Fig. 5.5 Spider diagram of indicator wise SeVI

Benefits of SeVI and IPCC-SeVI Approaches The effectiveness and impact of an event or a program can be analyzed by the SeVI methods. It is a way to perform on both the positive and negative consequences of the event or the programs. For example, if a person involves in disaster-related workshops and programs, he or she will gain enough knowledge on disaster vulnerability reduction. On the contrary, natural disasters bring huge economic losses. These disasters may create severe vulnerability in society. But, participation in disaster-related programs and natural disasters can be both used in the SeVI method as an indicator to find out each SeVI new score. The study has examined the SeVI by indicator wise and IPCC dimension. Primary household data can be used in the SeVI and least affected by self-reported data error and measurement-source error (Adger 2006, Hossain and Miah 2011). The study had chosen the indicators and factors based on the study area and recent publications. Local knowledge experts are used for selecting indicators and factors. It also collected data from households based on recent effective methods and approaches. Vulnerability studies related methods and approaches successfully applied in the study for the primary household data collection. It is a method to help collect high-quality primary household data with the frequencies of low missing responses. The assessment of SeVI for individual indicators can be possible by the methods. The indicators of SeVI can also be applied by the IPCC dimension method. Different development organizations can work on more vulnerable indicators for developing communities. So, it could be effective for the study area.

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Fig. 5.6 Overall proposed socio-economic vulnerability scale of two Upazilas (including indicators and dimensions vulnerability index in the right side kaunia Upazila and the left side Gangachara Upazila)

Discussion A significant number of studies (Habiba et al. 2011; Islam et al. 2014; Salam et al. 2020) have been performed in Bangladesh to examine the socio-economic relationship, but research on socioeconomic vulnerability has not been studied at a household level. The assessment of SeVI is complicated for maintaining capital indicators, factors and dimension index calculation. SeVI is complex due to the presence of many related approaches based on the IPCC-SeVI. The study assessed the SeVI through primary HHs and secondary data used in the literature review to justify the study rationale. The study can bring about effective information for policy implications. Overall, it may be helpful to develop a strategy for the society or the community if

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NGOs were performed on vulnerability factors and indicators. The study can bring about effective information for policy implications. The study provides some policy implications for the effectiveness of the study. The SeVI could be used to analyze climate-related to any disaster vulnerability as flood, drought, riverbank erosion and other natural calamities. It may be an effective tool for specific factors or indicator development. Our findings of socio-economic vulnerability showed that each indicator of the integrated IPCC-SeVI score in the two rural areas at the local government level exhibits an irregular spatial distribution pattern. The key outcomes of the study analysis imply that social vulnerability to natural disasters is unevenly distributed in the two vulnerable regions. The composite score that associates 8 indicators and 36 factors show that most high to very highly vulnerable upazilas are positioned near the lower Teesta basin. This finding depicts a spatial inhomogeneous pattern that social vulnerability is affected by geo-ecological attributes. Though, the outcomes for other indicators of social vulnerability exhibit a somewhat homogenous pattern, indicating that social vulnerability definite to an indicator is affected by social characteristics. For example, vulnerable Gangachora upazila is located near the river of Teesta according to marginal and ethnic people and when these people come to enter demography, rural area is more susceptible than other areas. Consistent with the conceptualize theory and prior SeVI study, this outcome reveals that the spatial distribution of each vulnerability indicator and factor from two vulnerable regions based on a composite score is diverse, suggesting that exposure of two upazilas to natural disasters is reliant on local as well as socio-economic condition. The findings of this study, thus, confirm the idea that vulnerability is not only naturally occurring but also social factors influenced them. Social vulnerability, therefore, should be combined with the basic understanding of the natural interaction that makes people exposed to hazards related to natural disasters. Upazila is the small administrative unit of assessment for socioeconomic vulnerability in Bangladesh and it is the most effective local government administrative scale where people representatives are mainly elected via whom the government gives emergency goods to make for and improve from natural disasters. Like other studies that explored drought risks, social and climatic vulnerabilities have applied the upazila as their scale of assessment (Ferdous and Mallick 2019). However, outcomes from this multi-level assessment show that within an upazila may not represent a similar social vulnerability. Prior studies have addressed the vulnerability related to hazards whereas concentrating on only a few variables of social vulnerability including population density. This study includes the evaluation of inclusive socio-economic vulnerability (Shahid and Behrawan 2008; Habiba et al. 2012). Nevertheless, reliability with an earlier study in terms of analogous recognition of highly susceptible regions gives an indicative validation of the outcomes in this analysis. Outcomes of this study are imperative for the decision-makers to effectively devise social science-based policy for the regions in the northern areas that lack social capital for resilience. This study will provide insights which regions are more susceptible and which regions need more prioritized support for disaster preparedness. Similar outcomes

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are found in other countries such as Zimbabwe (Mavhura et al. 2017), Botswana (Dintwa et al. 2019), Indonesia (Siagian et al. 2014), Nepal (Aksha et al. 2019), Norway (Holand and Lujala 2013), Romania (Arma¸s and Gavri¸s 2013), and China (Yoon 2012). It is hoped that other researchers can gain instructions for further research work on SeVI analysis.

Conclusion The main objective of the study is to model the socio-economic vulnerability of household in the disaster-prone region in the lower Teesta basin of Bangladesh. Survey data were collected from Gangachara and Kaunia Upazilla Upazilas of Rangpur district. The study employed 36 indicators and IPCC dimensions for developing SeVI. The results indicate that household in Gangachara upazila is more vulnerable than Kaunia upazila due to higher illiteracy rate and poverty. The lowest adaptive index value was 0.23 for Gangachara upazila. The highest values of natural disasters and climatic stresses are 0.76 and 0.75 for Gangachara and Kaunia respectively. Sensitivity index values are 0.52 for Gangachara and 0.48 for Kaunia. The frequency of flood and drought is higher in Gangachara (maximum 3 times for flood and maximum 2 times for drought) in the year than Kaunia. It can be said that low socio-economic condition is related to high vulnerability. Agriculture dependent household is more vulnerable in both the upazilas. Health problems and food crisis are common issues after the occurrence of disasters. The disaster warning system is very poor in both the Upazilas. The poor warning system brings huge agricultural losses and casualties. Water crisis creates a problem in the dry season or the period of drought. Gangachara represents an average of 3.5 months for water crisis and Kaunia 2.5 months. For sustainable socio-economic livelihood development in the study region, the government should take effective policy and safety net programs targeting to increase the resilience and adaptive capacity of the households.

Appendix See Tables 5.5, 5.6 and 5.7.

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Table 5.5 Indicators and factors consisting of adaptive capacity for developing SeVI IPCC dimension

Indicators

Factors of indicator Units

Potentiality of factors

Adaptive capacity

Demographic Profile

Dependency ratio

Identify dependency ratio

Economy

Awareness

Physical infrastructure

Ratio

Female headed HH Percentage

Female contribution in family

Average HH members

Count

Length of HH members

Number of HH earner

Person per family

Earning condition of HH

Number of children Person per family drop out from school

Literacy condition of HH

Dependent on agriculture

Percentage

As a major occupation

Dependent on Percentage natural resources as water reservoir for fishing

Alternative occupation

Number of migration for income

Percentage

Population dimension

Children as a family earner

Percentage

Child labor

Percentage of participating in volunteerism

Percentage

Volunteering activities

Major disasters experience person in last 30 years

Percentage

Awareness of disaster knowledge

Participation of Percentage disaster knowledge related programs from different NGOs

Contribution of disaster management works

Percentage of brick-built HH

Percentage

HH condition natural stresses resistance house

Having no electricity or solar system

Percentage

Noticeable energy source

Absence of latrine

Percentage

Healthy sanitary condition (continued)

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Table 5.5 (continued) IPCC dimension

Indicators

Factors of indicator Units

Potentiality of factors

Having no TV/radio

Source of entertainment

Percentage

Table 5.6 Indicators and factors consisting of exposure for developing SeVI IPCC dimension Indicators Exposure

Factors of indicator Units

Natural disasters Number of floods and climatic stresses in last 30 years

Potentiality of factors

Per year

Frequency of flood

Number of droughts in last 30 years

Per year

Frequency of flood

Affected HHs in last 30 year by natural disasters

Percentage Impacts of natural disaster on HH

Death or injuries for climatic events

Percentage Impacts on health

Percent of HHs Percentage Effects of River reporting land loss bank erosion by climate related extremes and disaster during past 30 years Percent of HHs reporting received disaster warning

Percentage Presence or absence of warning system

Mean standard deviation of daily average maximum temperature by month

°C

Average maximum temperature

Mean standard deviation of daily average minimum temperature by month

°C

Average minimum temperature

Mean standard deviation of daily average temperature by month

°C

Average temperature

Mean standard deviation of daily precipitation by Month

In mm

Rate of daily rainfall

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Table 5.7 Indicators and factors consisting of sensitivity for developing SeVI IPCC dimension Indicators Factors of indicator

Units

Potentiality of factors

Sensitivity

Number of HHs that depend on own farm for food

Percentage

Agricultural dependent HH

Inverse index of land for farm

Acre per HH

Ability of managing food crisis

Number of HH for food crisis in month (3–4) last 10 years

Count

Identification of food crisis

Difficulty in managing diet food for HH members

Count

Inability managing diet food for good health

Percent of HHs reporting they have heard any conflict for water in the society

Percentage

Limitation of water sources

Average time to fetch water

Minutes

Distance of water sources

Food

Water

Month of water crisis Month per year Duration of dryness for all activities in last condition 30 years

Health

Percent of HHs that Percentage collect water directly from water reservoirs (river, streams, pond) for domestic purposes

Dependency on different natural water reservoirs for daily activities

Percent of HHs where Percentage a family member had to miss work or school in the last 10 weeks due to illness

Work breaking or school missing for illness

Average time to go health community on foot

Distance of health community

Munities

Percent of households Percentage with family member with chronic illness

Cost of Medication of HHs

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Chapter 6

Post-cyclone Occupational Vulnerabilities of Farmers in South-West Coastal Region of Bangladesh Lubaba Khan, Tuhin Roy, and G. M. Monirul Alam

Abstract The coastal areas of Bangladesh are often hit by climate induced Cyclones which have devastating effects on livelihood of people. The aim of the study was to understand the long-term occupational effect of climate change induced cyclone on farmers in the coastal area. To address the objective of the study various qualitative methods such as in-depth interviews, focus group discussion and key informant interview were employed for data collection in 2020. The study was conducted in Kamarkhola union under Dacope upazila of Khulna district in Bangladesh. The study reveals that due to cyclone farmers faced various degrees of difficulties to maintain their livelihood in the post-disaster period. Cyclone Aila caused damage to the embankment in the area as a result many agricultural land experienced waterlogging for more than three years. Consequently farmers could not produce crops and created long-term unemployment of the agriculture depended households. Regular flooding of agricultural land decreased fertility of land because of increased salinity and siltation on top of the land. Therefore, the crops production reduced significantly when the farmers resumed cultivating. To cope with this situation, many marginal farmers and agricultural labourers have changed their occupation and are working as day labourers. Some of them also have migrated to neighbouring cities in search of work. To reduce the vulnerabilities, the farmers have suggested for implementation of a long-term disaster recovery plan by the government.

Introduction Bangladesh is one of the most climate vulnerable countries in the world. Climate change has resulted the increase of frequency and intensity of natural disasters such as cyclone, storm surge, flood, and sea level rise etc. (Sarker et al. 2019; Alam et al. 2018). The entire coastal areas of Bangladesh particularly the southwest coast L. Khan (B) · T. Roy School of Social Science, Sociology Discipline, Khulna University, Khulna, Bangladesh G. M. M. Alam Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_6

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experiences frequent exposure to climate change induced disasters specially cyclones as this zone is found only around one meter over the mean ocean level (Barange et al. 2018; Mohammad 2015). Occupation of most of the people in coastal territories depends to a great extent on natural assets which make them reliant on nature and climatic conditions (Torikul et al. 2015). In south-western coastal territory, principal occupations are farming, fishing, and collecting natural resources from sea, rivers and jungles (Shamsuddoha and Chowdhury 2007). Cyclone is constantly repeated by storm floods and heavy rainfalls by which coastal farmers of Bangladesh needs to confront three sorts of disaster all the while that make their occupation uncertain (Benson and Clay 2003; Jahan 2012). Back to back attack of climate change induced cyclone i.e. Sidr, Aila, Mahasen, fani, bulbul in this coastal region has created occupational-related vulnerabilities and essentially influenced the farming sector (Khan and Nahar 2014). Among the recent cyclones that heat the coastal region of Bangladesh, cyclone Aila was the greatest dangerous affected the life and livelihood of most number of people in this region (Hossain 2009; Saha 2015). Agriculture in this region was affected terribly: crops of around 77,486 acres of land completely destroyed and 245,968 acres of land partly (Hossain et al. 2012). Within this southern western costal region, Koyra, Dacope, and Shymnagar Upazila were hit the hardest by the cyclone Aila. Tempest flood washed away every one of the houses, crops, and farming land (Ashraf and Shaha 2016; Rahman 2014). It had a long-lasting effect on the occupational groups depended on agriculture. Agricultural lands went under saline water. As cyclone Aila broke most of the all embankment in Koyra, Dacope, and Shymnagar Upazila, farm lands in those upazillas were flooded daily (Paul 2013; Ullah and Rahman 2014). As a result, farmer could not cultivate those land and had to look for other option for living (Paul 2013). Additionally, a portion of the agricultural land is still submerged or have become fallow land due to increase salinity (Jahan 2012; Saha 2015). During the postcyclone period many agriculture depended people became unemployed and faced occupational vulnerabilities as the farming land were under water all the year round (Azad and Khan 2015; Hossen 2016). Additionally, farmers have been confronting many issues to bring all the farming land under harvest development such as waterlogging, salinity intrusion, and siltation. Apart from the above mentioned cyclone affected issues, during winter season agricultural lands remain uncultivated because of salty water in the river, deficiency of fresh irrigation water, and low premium credit (Talukder et al. 2018). Considering the above mentioned issues, cyclone Aila is influencing every aspect of farmers’ livelihood and daily life. When they are confronting vulnerabilities in their occupation, they are compelled to take up secondary occupation for living. As a result their occupational patterns changing and vulnerabilities are increasing, which leading to displace more people from coastal areas of Bangladesh. There is a need to assess their occupational difficulties due to climate change induced frequent cyclone. So for this reason, this research aims to provide a better understanding of the seriousness of farmers’ occupational vulnerabilities in the coastal areas of Bangladesh.

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Methods Design The study was conducted applying qualitative research approach. Qualitative research approach ensures an in-depth understanding of the research problem. The main objective of this study is to understand the long-term effect of climate change induced cyclone Aila on farmers’ occupational vulnerabilities. So to fulfil this objective and understand of the vulnerabilities qualitative research approach had been chosen for this study. Qualitative techniques such as in-depth interviews (IDI), focus group discussion (FGD) and key informant interview (KII) were selected to get in-depth information about farmers’ individual, community and institutional level experiences regarding the occupational vulnerabilities of the post-disaster period. In-depth interviews involves conducting intensive individual interviews with a small number of respondents to explore their perspectives on a particular idea and it provide much more detailed information from respondents (Schoonenboom and Johnson 2017). Focus group discussion ensures detailed information from group perspectives and key informant interview ensures detailed information from institutional level. Data were collected from the small scale farmers who have land less than 100 decimals.

Study Area The study was conducted in Kamarkhola union of Dacope upazila of Khulna district of southwestern coastal zone of Bangladesh. The study area is the direct coastal part of Bangladesh and one of the most disaster-prone areas. The study area was purposively chosen as this union was one of the severely affected union by Cyclone Aila and farming is also one of the main occupations of this area and has been affected tremendously by cyclone. In the Kamarkhola union, the study was conducted in the ward number 1 and this was purposively chosen by researchers (Fig. 6.1).

Sampling and Participants Participants for In-depth interview and focus group discussion were chosen by following criteria (1) resident of Kamarkhola union, (2) The participants must be engaged in farming, (3) must be experienced cyclone Aila. For key informant interviews chairman of the Kamarkhola union and headmaster of Kamarkhola School were chosen. To choose participants for this study purposive sampling had been used. In this study, total 15 participants were selected, 5 in-depth interviews, 1 focus group discussion with 8 participants, 2 key informant interviews.

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Fig. 6.1 Study area (LGED, 2019)

Additionally, in this study most of the participants do not have any farming land of their own, they usually take a lease from landowners to cultivate. Except Kabir Ali (31) and Sabbir Sheikh (65) have a small piece of land of their own and most of the farmers also do not have any homestead land, they live in the Khas (government owned) land (Table 6.1).

Data Collection Tools and Data Collection For primary data collection semi-structured interview schedule were prepared for indepth interviews and key informant interviews, guidelines for focus group discussion. In those guidelines and semi-structured interview schedule, the first section was based on farmers’ pre-cyclone occupational situation and impact of cyclone on occupation. The second section was based on occupational vulnerabilities of the farmers of postcyclone period. All questions were open-ended to gather detailed information about farmers’ vulnerabilities. A pre-test was conducted in December 2019 in the study area with an initial semi-structured interview schedule. The guideline and semi-structured interview schedule were finalized incorporating the pre-test observation and flow of discussion. Firstly, data collection tools were prepared in English and then translated in Bengali. Data were collected through face-to-face conversations with the respondents from 10 to 20 January 2020. Focus group discussion were conducted by two researchers where the principle research facilitated the discussion. In-depth interviews and KIIs

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Table 6.1 Profile of participants S. No Pseudonym

Age Sex

1

Sikhdar Uddin

42

Male 4

Education Occupation Location Farming

Kamarkhola In-depth interview

Method

2

Kabir Ali

31

Male 7

Farming

Kamarkhola In-depth interview

3

Sabbir Sheikh

65

Male 0

Farming

Kamarkhola In-depth interview

4

Asad Morol

40

Male 3

Farming

Kamarkhola In-depth interview

5

Mohibul Gazi

46

Male 4

Farming

Kamarkhola In-depth interview

6

Rahim Sheikh

35

Male 8

Farming

Kamarkhola Focus group discussion

7

Habib Gazi

45

Male 5

Farming

Kamarkhola Focus group discussion

8

Kutub Mia

38

Male 7

Farming

Kamarkhola Focus group discussion

9

Shajedur Gazi

40

Male 3

Farming

Kamarkhola Focus group discussion

10

Habib Ullah

51

Male 0

Farming

Kamarkhola Focus group discussion

11

Karim Uddin

43

Male 3

Farming

Kamarkhola Focus group discussion

12

Selim Ullah

45

Male 2

Farming

Kamarkhola Focus group discussion

13

Mejbah Sheikh 40

Male 3

Farming

Kamarkhola Focus group discussion

14

Siddikur Gazi

50

Male 15

Teaching

Kamarkhola Key informant interview

15

Abdul Karim

45

Male 12

Politician

Kamarkhola Key informant interview

Note Owing to the confidential issue, informant’s pseudo names were used

were conducted by the principle researcher. All interviews and discussion were both audiotaped and noted in written from.

Data Analysis Qualitative techniques were used to analyse and interpret the findings and drew a conclusion in light of the aim. After completing data collection, the recordings were transcribed. Then after a group meeting of two researchers by reading through the

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transcription based on respondents’ discussion, important topics were identified. Then researchers discussed the topics and produced themes following the aim and questions of the research and farmers’ discussion. The impact of climate change induced cyclone on occupation, occupational vulnerabilities of the post-cyclone period, cyclone impact on life, and solution to reduce vulnerabilities were emphasized during the analysis. Thematic analysis with narratives was used as data analysis methods and for an in-depth understanding, respondents’ direct quotes have been given.

Ethical Consideration Before data collection, the purpose of this study had been told to the respondents and consent was taken from the respondents. Moreover, to keep the anonymity of the participants of the study, participants’ pseudo name had been used.

Results and Discussion Theme 1: Pre-Cyclone Occupational Situation and Impact of Cyclone All participants described that during the pre-cyclone period they were earning enough through agriculture to maintain their family expenses. However, like all the other occupation, farmers also identified some pre-cyclone occupational problems. In the study area, farmers could only cultivate rice once a year from June to August. Rest of the time they could not cultivate rice due to lack of fresh water for irrigation. In the study areas, a large portion of the agricultural land were used for shrimp cultivation. As a result of saline water shrimp cultivation in the agricultural land has increased salinity which reduced the amount of crops production. Shrimp farming also affected the marginal farmers, as their lands were leased by the big shrimp farm owners. Due to increase of shrimp production in the region agricultural labourers found difficulties to manage work since shrimp farming requires less labour than rice production. As a result, many marginal farmers and agricultural labourers had to change their occupation to make their ends meet. To describe the situation one of the respondents explained as: Kabir Ali (31) said that “When the situation was quite normal, I could earn enough money for my family. I could cultivate the land only once in a year that was a problem except this I had not any other problem” (Quote from In-depth interview).

All of the participants of IDIs and FGDs unanimously expressed that cyclone Aila was the most devastating disaster they have witnessed in their life time which changed

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their life tremendously. Cyclone Aila broke major portion of the river embankment and washed away their villages. Homestead of these farmers and other community members was under water for several months. Many of them could not even went back to their homes for months. Except the roads, both homestead and farming land had gone underwater. So farmers lost the crop of that year. All of them had lived on the street in a small house with their families in a miserable condition around three years with thousands of other marginal people from the village. Rahim Sheikh (35) one of the participants stated his suffering through following words “We did not get any early warning about the cyclone, we were in our houses with the family and suddenly saw water flooding our homestead and ponds. We quickly shifted to a nearby safe place with few cloths and belongings. Our whole village had gone under the water, we had to take shelter on the road for years” (quote from FGD).

Theme 2: Post-Cyclone Occupational Vulnerabilities by Waterlogging and Salinity Intrusion Homestead and the agricultural land of the marginal farmers were submerged underwater for years. During high tide, the agricultural land used to go 10 feet underwater. As the agricultural lands were waterlogged for three years, the farmers could not cultivate their land for several years. The famers become unemployed. Initially after the cyclone, they got relief support from both government and NGOs. Though, they received support from various sources but those were not good enough to make a sustainable living. To adapt with this climate change situations, a portion of the farmers started catching fish from the river and majority of them worked as day labourers. Even though they were ready to shift their occupation but manging work in their locality was very challenging as the main economic sector (agriculture) was hampered severely. A few of the respondents said that they have to move to another place in search of work. Some of the farmers around them permanently migrated to other nearby cities like Khulna, Jashore and Barishal in search of work. One of the farmers Siddikur Uddin (42) stated that “At that time I used to work as a day labourer in various cities to maintain my family and use to receive 20 kg of rice from GO and 10 kg of rice from various NGOs in a month” (Quote from In-depth interview).

After three years, the embankments were re-build and finally agricultural land and the homestead came out of water. Farmers started to move back to their houses and started rebuilding their houses. Though they were successful to rebuild their houses but they faced new challenges to resume their agricultural work. As their agricultural lands were inundated regularly for more than three years with saline water so the salinity of the soil increased which reduced the fertility of the land. All the farmers stated that when they started to cultivate rice again they did not get as much crop as they used to get before the cyclone. To explain the reasons behind low production of crops, they have expressed that the land become salty and the variety of crops they were producing was not saline resilient which resulted low production of rice.

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With time salinity of the soil was reducing. As a result the amount of rice production was increasing gradually. However, the farmers told that it might take several years to get back to their earlier situation and get maximum production of rice. They are also in fear that the production will not go back to the pre cyclone status because of increased frequency of cyclone in this region. One of the farmers Sabbir Sheikh (65) stated that “After three years of the cyclone the embankments were rebuild, I had begun to cultivate again but never got as much produced as I used to get before Aila.” (Quote from in-depth interview). Another farmer Kutub Mia (38) said about his adaptation strategies to climate change that “To cultivate in this salty land, the cost of production has increased significantly as I am using different type of fertilizers and new seeds. However, I am getting the amount of crop equal to pre-disaster period” (quote from FGD).

Theme 3: Post-Cyclone Occupational Vulnerabilities: Sand Falling, Crisis of Irrigation Water and Loan Debt After Aila, the agricultural lands were inundated daily during the high tide. As a result the agricultural lands were covered by high layer of sand. Thus, the agricultural become barren land with a layer of sand. Farmers were unable to remove the layer of sand from top of soil. Therefore, many acres of lands remain unused for more than decade. So total available cultivable land decreased and farmers lost the opportunity of getting huge amount of crop. Apart from there were some other agricultural lands which were covered by a very tinny layer of sand, farmers started cultivating those land once the embankments were rebuild removing the sands from the top of the soil. However, in the first few year’s farmers got less amount of crop as they could not remove the sand completely from the soil. Till now the rice production have not reach the optimum level as it was before the cyclone. One of the farmers Karim Uddin (43) stated that “There are some agricultural lands in our community which are completely unusable because of a thick layer of sand on top of the soil. Though I have started cultivating on my land removing the sands from the top of the soil, the rice production reduced significantly compared to pre-cyclone period as it is not possible for me to bring the land in pre-cyclone condition” (quote from FGD).

As mentioned above, due to lack of irrigation water throughout the year, farmers in this area cultivate only once crop in a year which in in the rainy season. Farmers use canal water for irrigation. Only in the rainy season, canals contain fresh water which the farmers in this region use of irrigation. As a result of cyclone Aila all the embankments were broken, so during high tide river water flows through both lands and cannels. Due less of water flow in the canals for a long time the majority of canals in the study area lost is depth due to siltation. As a result, when the farmers resumed cultivation after cyclone they did not get enough water for irrigation from canals. Asad Morol (40) said that “The problem of water in his area is severe, in terms of drinking water and irrigation water. All the farmers in other places can cultivate two to three times in

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a year but we cultivate only once. After Aila all small canals are filled by soil and sand and we are not getting enough water for irrigation from the nearby canal. Therefore, irrigation of crops has become most costly and time consuming for the farmers.” (Quote from in-depth interview).

As the farmers lost not only their home but also their income due to Aila. Most of the farmers started to engage in new occupation but never could maintain their families’ expenses in this new situation. As an adaptation mechanism to climate change situation, some of them first sold their assets and finally took loan from micro-finance organization. When they were asked about the repayment of loan most of them told that they have been not been able to repay the loan even after ten years. On an average, every farmer is engaged with 3 micro-finance organizations. Every year farmers take a loan from NGOs to cultivate the land because of lack of capital. Some the farmers take to repay loans. So are in the vicious cycle of loan. Mohibul Gazi (46) stated that “Every year I take loan from NGOs to cultivate the land but due to the low price of rice, I cannot pay back it within the time. So loan becomes a burden for me” (quote from in-depth interview).

Theme 4: Impact of Occupational Vulnerabilities on Life and Adaptation to Climate Change In their present situation, the farmers cannot earn sufficient amount of money from farming to run their families. So, they are facing many adverse effects of occupational vulnerabilities on their life. Like all of the respondents including the people of this union have no source for drinking water, they have to depend on the rainwater but when rainwater get finished only one remains that is buying drinking water. But for getting less money from farming they cannot buy drinking water. Also, most of the respondents of the focus group discussion told that their children’s education is hampered due to lack of income. As the farmers do not get enough earning from agriculture to maintain daily expenses, to adapt with climate change situation, their children are dropped from schools and are dragged into child labour to support their parents. Siddikur Gazi (50) also agreed with farmers and stated that “Farming is not very much profitable in this days, therefore, we face difficulties to maintain family expenditure and compelled to stop sending children’s to school rather engage them to work at an early age to support family incomme” (quote from KII).

As earning from agriculture is not good enough to make living, as an adaptation strategy many farmers work as day labourer in the nearby city centre such as Khulna, Dhaka, and Barisal, etc. in salt companies, brick industries, and various construction projects. The key informant interviewee mentioned that most of the farmers of this area are involved in the secondary occupation. As stated by Siddikur Gazi (50): They get only one crop once a year if somehow there is any damage to the crops, they have to go out for other work. Few of them do little businesses in their area. Before Aila,

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they were more dependent on farming but now they are more dependent on these secondary occupations (quote from KII).

Most of the respondents told that many farmers around them gave up farming and work permanently as day labourers. Also, many families have left this area and have not returned, they usually go the Dhaka or Khulna city and work as a day labourer. So migration has been found as an adaptation strategy to climate change among marginal farmers.

Theme 5: Solution to Reduce Vulnerabilities To move out of the occupational difficulties and adapt to climate changes, farmers have suggested several ideas. To solve irrigation water scarcity problem, they have suggested to revive the canals which are almost dying. Build switch gates on the canals to prevent saline water to enter in the paddy field and hold fresh water from the river during the rainy season for using in the winter season for crop cultivation. Farmers also suggested that if government could remove the sand from land those are remaining unusable from many years by filled with tons of sand by cyclone. Then farmers could cultivate on those agricultural land and got much more crops. According to the participant of focus group discussion, Habib Ullah (51) stated that “The problems can be solved by removing sand from land with the help of the government. Also by cutting all the canals again so we will get the irrigation water for land” (quote from FGD).

In addition, farmers also said that introduction of saline resilient seeds verity will help them to cultivate crops two or three times per year. To ensure fair price of paddy farmers also asked for government interventions. They have told that the Govt. should fix a minimum price considering the production cost of paddy in per acre which will good price of crops for the farmers. Majority of the marginal farmers face capital shortage during cultivation period. The farmers recommended to introduce special low interest loan packages for marginal farmers so that farmers can cultivate their land properly.

Discussion From the findings of research it can be clearly understood farmer are in more vulnerable situation as result of climate induced cyclone. Immediate impact of cyclone Aila was so devastating. They lost their home, home staffs and livelihood. After cyclone Aila farmers could not cultivate their land for at least for 3 years because of agricultural lands were waterlogged and flooded daily during high tide. As a result, they farmers were forced to take up another occupation for living. When farmers started to cultivate land after three years, they did not expected amount of crops from field

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in the first few years because of salinity intrusion. As the lands were under water for a long period of time which resulted salinity intrusion and turned the soil infertile. Even ten years, farmers are getting less crops as the salinity has not completely gone. Toufique and Yunus (2013) have found the same result that farmers are getting less crop due to salinity intrusion by climate induced cyclones. Problem of sand falling, crisis of irrigation water and loan debt were found in the post-cyclone period, with water sand has come and filled the agricultural lands. For that, those huge numbers of land remaining uncultivated and ineffective. The situation has not changed event after ten years of the cyclone. Agricultural lands which had a thin layer of sand on top the soil were cultivate by the farmers after few years but the farmers did get the amount of crops as they used to get before cyclone. Though the farmers were able to remove the layer of sands but the land did not get back to the pre-disaster situation. Unavailability of fresh water for irrigation throughout the year has been a long standing problem for the farmers in this region. Their only irrigation water source is the river water but that river contains saline water most the time in a year. The river water only gets fresh in the rainy season that time they cultivate the land. Islam and Paul (2018) also described in their research that farmers are sufferings for scarcity of fresh irrigation water and the limitation of coastal area farmers that they can only cultivate once a year. However, the farmers of other regions can cultivate two–three times a year with fresh irrigation water from different sources. But after cyclone Aila, canals in the study area lost it depth due to siltation and made the situation worse for the farmers. After Aila, farmers took loan from micro-finance organization with high-interest rate to cope with the situation. As they have lost their main source of income some of the farmers still could not repay the loan. Some farmers even took loan to repay another loan. Eventually, they got trapped in the vicious loan cycle. As the small farmers do not have sufficient capital for paddy cultivation, they usually take loan from the micro-finance institutions because of its easy accessibility. As they are cultivating paddy with loan money and higher irrigation cost, the cost of production increases every year. But the farmers are not getting fair price of paddy in the market. Some farmers have told that in the last two years they are not making a profit by selling paddy even sometimes they are in loss. Situation is even worse for the farmer lease land for cultivating paddy as they have to pay landowners. The complexities are at the highest level and affecting severely farming activities. When a problem affects severely it made the occupation in problematic condition but here are interactions of complexities. Khan and Nahar (2014) found out that when for a long time, so many complexities interacting together and affect an occupation so the occupation already reach to a vulnerable condition. The farmers are already surrounded by so many problems which pushed them in a vulnerable situation. In any new problems arise such as if there any natural disaster occurs then the situation would go to an unsolvable condition. Findings suggest that living with this occupational vulnerability farmers cannot maintain their family properly, according to the results almost every farmer is engaged in secondary occupation to maintain a sustainable livelihood. Saroar et al. (2015) in a research also

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found that the pattern of seasonal migration of coastal people in search of livelihood, in a certain time of year they migrate to city areas. In our study the same result also has been found like for around six months they go to other places like Dhaka, Khulna, Barisal, and many other districts of the country and work as a day labourer in the brick industries, construction projects, and salt companies. Farmers also work in the garments industries in Dhaka, they also work in other places of Dacope upazila as agricultural day labourers in the time of harvesting. They are now mainly dependent on their secondary occupation rather than primary occupation farming. But many farmers in Kamarkhola union already gave up farming and work permanently as day labourers. Also, many farmers have permanently migrated to nearby city centres with their family. They usually go the Dhaka or Khulna city and work as a day labourer. In a study by Islam et al. (2014) found that a vast migration is happening in the coastal area supporting the findings of our study. To solve sand fall on the land problem, government and non-government organizations have to take initiative to remove the sand from the land. After that, it will be possible to use thousands of fertile land. To reduce the crisis of irrigation water, enough big canals have to make by government. Those farmers will be able to reserve freshwater and can use it another time of the year. Then they will be able to cultivate more than one crop in a year. To tackle the salinity intrusion and use the salty water of river, farmers have to use saline resistant seeds and NGOs need to take initiatives for that. These will reduce occupational vulnerabilities of farmers.

Conclusion and Policy Recommendation This study aims to understand the long-term occupational effect of climate change induced cyclone on farmers in the coastal area of Bangladesh. Qualitative data were collected through in-depth interviews, focus group discussion and key informant interview in 2020. The study reveals that major occupational vulnerabilities faced by that farmers are salinity intrusion, waterlogging, unemployment, siltation, unavailability of agricultural land, less amount of crop production, irrigation water crisis, and the burden of loan. Farming in the area is in unfavourable condition in the post-cyclone period. As a result they are facing so many occupational vulnerabilities which worsen their livelihood and food security. They did not receive adequate help to cope with these vulnerabilities. Some of the farmers permanently migrated to other nearby cities like Khulna, Jashore and Barishal in search of work. They need external interventions to improve their livelihood. Providing saline resistant seeds, dig big canals for irrigation water, loan at low interest rate and longterm disaster management activities need to be strengthened to reduce occupational vulnerabilities and improve their livelihood.

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References Alam GMM, Alam K, Mushtaq S, Khatun MN, Leal Filho W (2018) Strategies and barriers to adaptation of hazard-prone rural households in Bangladesh. In: Filho WL, Nalau J (eds) Limits to climate change adaptation, climate change management. Springer International Publishing AG, Cham, pp 11–24. https://doi.org/10.1007/978-3-319-64599-5_2 Ashraf MA, Shaha SB (2016) Achieving community resilience: case study of Cyclone Aila affected coastal Bangladesh. Int J Soc Work Human Serv Prac Horizon Res Publ 4(2):33–41 Azad MAK, Khan MM (2015) Post disasters social pathology in Bangladesh: a case study on AILA affected areas. Sociol Anthropol 3(2):85–94 Barange M, Bahri T, Beveridge MC, Cochrane KL, Funge-Smith S, Poulain F (2018) Impacts of climate change on fisheries and aquaculture. In: Synthesis of current knowledge, adaptation and mitigation options. Food and Agriculture Organization of the United Nations, Rome Benson C, Clay E (2003) Economic and financial impacts of natural disasters: an assessment of their effects and options for mitigation: synthesis report. Overseas Development Institute, London Hossain MA, Reza MI, Rahman S, Kayes I (2012) Climate change and its impacts on the livelihoods of the vulnerable people in the southwestern coastal zone in Bangladesh. In: Climate change and the sustainable use of water resources. Springer, pp 237–259 Hossain S (2009) Assessment of damage due to Aila of post SIDR agricultural rehabilitation: insights from Sharankhola. BRAC University, Bagerhat Hossen M (2016) Livelihood vulnerability assessment and local adaptations against climate change in South West Coastal Belt of Bangladesh. Khulna University of Engineering and Technology (KUET), Khulna, Bangladesh Islam M, Paul S (2018) People’s perception on agricultural vulnerabilities to climate change and SLR in Bangladesh: adaptation strategies and explanatory variables. Int J Agric Res Innov Technol 8(1):70–78 Islam MM, Sallu S, Hubacek K, Paavola J (2014) Migrating to tackle climate variability and change? Insights from coastal fishing communities in Bangladesh. Clim Change 124(4):733–746 Jahan I (2012) Cyclone Aila and the Southwestern Coastal zone of Bangladesh: in the context of vulnerability Khan MMH, Nahar N (2014) Natural disasters: socio-economic impacts in Bangladesh. Banglavision 13(1):58–67 LGED (2019) Dacope Upazila. Upazila Picture Mohammad N (2015) Climate change and displacement in Bangladesh: issues and challenges. In: Handbook of climate change adaptation, pp 177–194 Nishat A, Mukherjee N (2013) Climate change impacts, scenario and vulnerability of Bangladesh. In: Climate change adaptation actions in Bangladesh. Springer, pp 15–41 Paul SK (2013) Post-cyclone livelihood status and strategies in coastal Bangladesh. Rajshahi Univ J Life Earth and Agric Sci 41:1–20 Rahman SU (2014) Impacts of flood on the lives and livelyhoods of people in Bangladesh: a case study of a village in Manikganj district. BRAC University Rakib MR, Islam MN, Parvin H, van Amstel A (2018) Climate change impacts from the global scale to the regional scale: Bangladesh. In: Bangladesh I: climate change impacts, mitigation and adaptation in developing countries. Springer, pp 1–25 Saha CK (2015) Dynamics of disaster-induced risk in southwestern coastal Bangladesh: an analysis on tropical Cyclone Aila 2009. Nat Hazards 75(1):727–754 Sarker MNI, Wu M, Alam G, Shouse RC (2019) Livelihood vulnerability of riverine-island dwellers in the face of natural disasters in Bangladesh. Sustainability 11(6):1623 Saroar MM, Routray JK, Leal Filho W (2015) Livelihood vulnerability and displacement in coastal Bangladesh: understanding the nexus. In: Climate change in the Asia-pacific region. Springer, pp 9–31

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Schoonenboom J, Johnson RB (2017) How to construct a mixed methods research design. Kolner Zeitschrift Fur Soziologie Und Sozialpsychologie 69(Suppl 2):107–131. https://doi.org/10.1007/ s11577-017-0454-1 Shamsuddoha M, Chowdhury RK (2007) Climate change impact and disaster vulnerabilities in the coastal areas of Bangladesh. COAST Trust, Dhaka Talukder MF, Shamsuzzoha M, Hasan I (2018) Damage and agricultural rehabilitation scenario of post cyclone mahasen in coastal zone of Bangladesh. J Sociol 2(1):36–43 Torikul M, Farjana S, Mujtaba S (2015) Climate change, natural disaster and vulnerability to occupational changes in coastal region of Bangladesh. J Geogr Nat Disast 5(134):2167–0587.10001 Toufique KA, Yunus M (2013) Vulnerability of livelihoods in the coastal districts of Bangladesh. In: The Bangladesh development studies, pp 95–120 Ullah M, Rahman M (2014) Assessing vulnerability and adaptation to climate change by farming communities in southwestern coastal Bangladesh. J Environ Sci Nat Resour 7(2):31–35

Chapter 7

Modeling of Greenhouse Gas Emission and Its Impact on Economic Growth of SAARC Countries Rocky Rahman, M. Sayedur Rahman, and Md. Sabiruzzaman

Abstract The investigation of economic sides of Greenhouse Gas (GHG) emissions and its penalties is very important, particularly in terms of its volume at the present increasing trend. Carbon dioxide (CO2 ) emissions account for the largest proportion of total greenhouse gas emissions produced mainly by human activities. So, the prediction of air pollution due to emissions of CO2 can give the right way to policies accepted. In the economics literature of the last decades, the relationship between emissions of CO2 and financial progress is of great interest. This study aimed at modeling and forecasting some environmental and economic variables and investigating the existence of long-run equilibrium relationship between major GHG emissions and economic growth in eight SAARC countries—Bangladesh, India, Pakistan, Sri Lanka, Nepal, Bhutan, Maldives, and Afghanistan. Time series data from 1990 to 2018 was collected from World Bank data archive. Autoregressive Integrated Moving Average (ARIMA) Models and cointegration theory was applied to analyze the data. While forest areas in most of the countries showed decreasing trend, GHG and CO2 emission showed increasing trend in majority of the countries. Industrial and GDP growth in the region was slowly growing over time. ARIMA models fitted well to the data except the cases where data did not show any variability. Mix results were obtained regarding the existence of coingration between CO2 emission and industrial growth and that between CO2 emission and GDP growth. These findings would enable the environmental authorities to understand the environmental impacts of economic development on degradation and to use time series approaches to handle environmental problems.

Introduction South Asian Association for Regional Cooperation (SAARC) consists of eight countries which are categorized by comparatively high densities of population, low per capita income and literacy rate, and unintentional use of knowledge in many areas R. Rahman · M. S. Rahman (B) · Md. Sabiruzzaman Environment and Data Science Research Group, Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 G. M. M. Alam et al. (eds.), Climate Vulnerability and Resilience in the Global South, Climate Change Management, https://doi.org/10.1007/978-3-030-77259-8_7

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that causes environmental deprivation. Conventional wisdom is that higher economic growth requires huge energy consumption which causes emission of higher levels of CO2 and this in turn deteriorates environmental pollution and threatens the sustainability of the environment. Now a day’s climate change and global warming have attracted considerable attention worldwide. Atmospheric pollution, land degradation, soil pollution, water pollution, and noise pollution are the major forms of pollution. While, atmospheric pollution sources contain of (a) burning of fuels to make energy for warming and power manufacture in both the household and industrial areas; (b) deplete emissions from the vehicles that consume petrol, diesel oil, etc.; and (c) waste gases, dirt, and heat from several industrial sites comprising chemical manufacturers and stations of electric power generating. Similarly, sulfur dioxide (SO2 ), nitrogen dioxide (NO2 ), and particulate matter (PM) are major pollutants of ambient air quality. Generally, carbon dioxide (CO2 ) and methane (CH4 are the main contributor in the greenhouse gas emission inventory (Srivastava et al. 2010). CO2 emissions account for the largest proportion of total greenhouse gas emissions produced mainly by human activities (World Bank 2013). The rapid increase in CO2 emissions is mainly due to the growth and industrialization of human activities in the last decades. The energy consumption that is predictable for financial progress is highly dependent on it. In the economics literature in the last decades, the relationship between emissions of carbon dioxide (CO2 ) and financial progress is of great interest. Time series modeling become quite popular following the publication of the Text time series analysis forecasting and control by George Box and Gwilym Jenkins in 1976. They provide examples of using the Autoregressive Integrated Moving Average (ARIMA) specification to interpret a large class of models which could describe the behavior of observed time series. Pao and Tsai (2011) used GM and ARIMA models for modelling and prediction of CO2 emissions, energy usage and financial progress in Brazil. They verified prediction accuracy using the mean absolute error percentage (MAEP). The results reflect both the projected accuracy of the GM model and that of ARIMA (Acaravci and Ozturk 2010; Bhattacharya et al. 2016). Apergis and Payne (2010) found that there is a causal relationship between energy consumption and CO2 emissions, and between energy consumption and economic growth and further reported that the CO2 emissions Granger caused by energy consumption and financial development. Shabbir et al. (2014) applied the Structural Vector Auto-regression (VAR) approach to investigate the relationship between renewable and non-renewable energy use, real GDP and CO2 emissions in Pakistan. Their findings indicate that, in the short term, growing energy demand is fulfilled with the help of non-renewable and renewable energy usage. Shyamal and Rabindra (2004) observed the factor’s subjective variations in the level of electricity-related CO2 emissions using a decomposition technique. They show that in the industrial sector, CO2 emissions have shown a decreasing trend due to improved energy efficiency and fuel transfer. Sheinbaum-Pardo et al. (2012) examined CO2 emissions intensity and its components considering 16 industrial sectors of Mexican industrial sector over the period 1996–2009. They found that energy intensity of economic

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sectors had the most influential impact in the determination of the CO2 emissions intensity. Several researchers conducted hypothetical and experimental research on the relationship between carbon dioxide emissions and financial progress from the viewpoint of the EKC hypothesis and decoupling theory (Bhattacharyya and Ghoshal 2009; Bartleet and Gounde 2010; Bayar 2014; Chen et al. 2007; Coondoo and Dinda 2002; Fodha and Zaghdoud 2010a, b; Hussain et al. 2008; Jiang et al. 2004; Kayacan et al. 2010; Li 2003; Lean and Smith 2009; Lotfalipour et al. 2010; Menyah and Rufael 2010; Shyamal et al. 2012). Niu et al. (2011) revealed that the eight Asia–Pacific countries have long-term equilibrium relationships between energy consumption, GDP growth and CO2 emissions. In comparison to developing countries where the relationship is not present, causality varies from energy consumption to CO2 emissions, GDP is responsible for increasing energy consumption, and there is clear causality between GDP and CO2 emissions over the long term in developed countries. This study investigated the potential relationship between emissions of CO2 emissions and economic growth in SAARC countries during the period of 1990–2018. Forest area, Greenhouse Gas (GHG) and CO2 emissions, GDP and industrial growth were modeled with ARIMA model and forecasted according. The long-run relationships between CO2 emissions and economic growth are verified using cointegration theory.

Objectives of the Research The objectives of the study are as follows • Modeling and forecasting the economic and environmental indicators of SAARC countries. • Verifying long-run relationship between the economic and environmental indicators of SAARC countries.

Data and Method Data Source The study area of this research work is the eight SAARC countries—Bangladesh, India, Pakistan, Sri Lanka, Bhutan, Maldives, Nepal, and Afghanistan. For this study, yearly time series data on proportion forest area, CO2 emission, total GHG emission, GDP growth rates and industrialization rates of these countries spanned from 1990 to 2018 had been collected from World Bank data archive.

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Time Series Models and Methods Time series data are realizations of a stochastic process. Historical data over progressive periods of time form a time series. Environmental and financial data are most of recorded over time and, therefore, time series tools and techniques are used to analyze such types of processes. Time series methods provide specific procedures or techniques used to identify select, process and analyze information about a topic. Most of the time series methods are based the very basic properties of the data series what is known as stationarity. A particular time series is said to be stationary if the distribution does not depend on time. A week form of stationarity is that the mean, variance and autocovariance of the series are time independent. If a time series is not stationary it is called nonstationary. Among other methods, Augmented Dickeyfuller (ADF) test is most popular for testing the stationarity/nonstationarity of a time series. A nonstationary time series can be transformed to stationary by differencing the series. A nonstationary series is said to be integrated of order d, I(d), if d-times differencing is needed to make it stationary.

Autoregressive Integrated Moving Average (ARIMA) Model The Box-Jenkins model or Autoregressive Integrated Moving Average (ARIMA) models, is a class of linear models capable of representing both stationary and nonstationary time series. An ARIMA process of order p, d and q is denoted by ARMA (p, d, q) and is written in the form ∇ d Yt = c + φ1 ∇ d Yt−1 + · · · + φ p ∇ d Yt− p + εt + θ1 εt−1 + · · · + θq εt−q ,

(7.1)

where ∇ = difference operator (∇ 1 et = et − et−1 ); Yt = observed time series; εt = white noise error term; c, φ1 , φ2 , · · · , φ p , θ1 , θ2 , · · · , θq are parameters; p = number of autoregressive terms; q = number of moving average terms; d = number of differencing. In cases, where Yt is white noise (no autocorrelation), no ARIMA modeling is necessary. The Box and Jenkins (1976) developed a general framework for ARIMA modeling. They suggested a model-building strategy where three steps—model identification, estimation and diagnostic checking are done iteratively to select the best possible model for a given series. Once an adequate model for historical data is selected, forecasting for subsequent period can be performed. The computational detail is available in Box and Jenkins (1976) and in more recent text Brockwell and Davis (2002). However, now a day, computation can be done easily in R and other software as well (Ripley 2002).

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Cointegration Granger (1981) and Engle and Granger (1987) were the first to formalize the idea of nonstationary variables sharing an equilibrium relation. They termed this property by cointegration. Suppose Yt represents a vector of variables, which are individually integrated of order d, I(d). If there exists a nonzero vector β such that the linear combination of these I (d) variables, denoted by β  Yt , is integrated of order d ∗ , where 0 ≤ d ∗ ≤ d, then the variables in the vector Yt are said to be cointegrated of order (d − d ∗ ) with cointegrating vector β. The two most popular methods for testing cointegration between time series variables are: the Engle-Granger method and the Johansen method. For example, if y and x are two I (1) variables but their linear combination is I(0), then y and x are cointegrated. This implies, there exist a co-movement or long-run equilibrium relation between y and x. Technically, cointegration between y and x can verified by running the regression: yt = a + bxt + εt and then test the stationarity εt ; i.e., if εt is found to be I(0), y and x are cointegrated. It should be noted that the primary condition for existence of cointegration is that both variable must be nonstationary or integrated of order other than zero.

Results and Discussions Estimated ARIMA models of forest area in SAARC countries are given in Table 7.1. Forest areas of Maldives and Afganistan were found to be constant over the study period. In other countries models were well fitted with significant autoregressive and moving average parameters. Observed series along with forecasted values are displayed in Fig. 7.1. In Bangladesh, Pakisthan and Sri Lanka, proportion of forest area showed decreasing trend, while it had been increased a little in only two countries—India and Bhutan. Up until 2004, forest area of Nepal was decreasing and then remained constant to the end of the study period. The highest proportion of forest area in 2018 was 72.48%, which belongs to Bhutan and the lowest was 2.06%, which belongs to Afghanistan. Five out of eight countries in this region had forest areas less than 25% of their total land. Only Bhutan, Sri Lanka and Nepal have forests more than 25% of their total lands. Estimated ARIMA models of total GHG emissions in SAARC countries are given in Table 7.2. No recorded data was available for GHG emissions of Maldives. In other countries models were well fitted with significant autoregressive and moving average parameters. Observed series along with forecasted values are displayed in Fig. 7.2. It is evident that over the last three decades, emission of GHG in seven countries (Bangladesh, India, Pakistan, Sri Lanka, Nepal, Maldives and Afghanistan) has been

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Table 7.1 ARIMA models of forest area data in SAARC countries Country

Model

AIC

BIC

Coefficients Estimate

ARIMA (1, 0, 0)

−123.63 −119.75 AR1

India

ARIMA (1, 1, 0)

−111.01 −108.5

Pakistan Sri Lanka

Bangladesh

Nepal

Bhutan Maldives

SE

p-value