Geospatial Technology: Application in Water Resources Management [1st ed. 2020] 978-3-030-24973-1, 978-3-030-24974-8

This book aims to exchange and share the experiences and research results on the geospatial technology applied in water

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Geospatial Technology: Application in Water Resources Management [1st ed. 2020]
 978-3-030-24973-1, 978-3-030-24974-8

Table of contents :
Front Matter ....Pages i-vii
Global Data for Watershed Modeling: The Case of Data Scarcity Areas (Abdelhamid Fadil, Abdelali El Bouchti)....Pages 1-14
Extraction of Water Information Based on SAR Radar and Optical Image Processing: Case of Flood Disaster in Southern Morocco (Sofia Hakdaoui, Anas Emran)....Pages 15-29
Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications (Hibatoullah Laachrate, Abdelhamid Fadil, Abdessamad Ghafiri)....Pages 31-50
Hanaa Aguedai—Intrusion Zones Identification in the Mnasra Aquifer (Morocco) Using the Seawater Intrusion Models and the Geophysics Data (Hanaa Aguedai, Fouad Lahlou, Bouabid El Mansouri)....Pages 51-68
The Effect of Surface Water Pollution on the Incidence of Viral Hepatitis: A Spatial Assessment Using GIS Maps (Nisrine Idrissi, Fatimazahra ElMadani, Meryem Idrissi, Mohammed Ben Abbou, Mustapha Taleb, Zakia Rais)....Pages 69-81
Contribution of the GIS in Terms of Knowledge of the Situation of the Water Resources of the Plain of Saïs Fez and Its Middle Atlasic Borders—Morocco: Aspects, Methods and Quantification of Water Resources (Z. Qadem, Kh. Obda, A. Qadem, M. Lasri, I. Bouizrou)....Pages 83-94
Assessment of Rainfall Soil Loss in Allal El Fassi Watershed (Mean Atlas Morocco) Using RUSLE Method Combining to GIS and Remote Sensing (Younes Jaafari, Mohammed Benabdelhadi)....Pages 95-103
Collaboration Between Water Stakeholders Needs a National Standard for Data Exchange: Exploratory Study (Aniss Moumen, Youssef Fakhri, Hassane Jarar Oulidi, Amel Barich, Bouabid El Mansouri)....Pages 105-111

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Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development

Hassane Jarar Oulidi · Abdelhamid Fadil · Nour Eddine Semane Editors

Geospatial Technology Application in Water Resources Management

Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development Editorial Board Members Anna Laura Pisello, Department of Engineering, University of Perugia, Italy Dean Hawkes, University of Cambridge, Cambridge, UK Hocine Bougdah, University for the Creative Arts, Farnham, UK Federica Rosso, Sapienza University of Rome, Rome, Italy Hassan Abdalla, University of East London, London, UK Sofia-Natalia Boemi, Aristotle University of Thessaloniki, Greece Nabil Mohareb, Faculty of Architecture—Design and Built Environment, Beirut Arab University, Beirut, Lebanon Saleh Mesbah Elkaffas, Arab Academy for Science, Technology, Egypt Emmanuel Bozonnet, University of la Rochelle, La Rochelle, France Gloria Pignatta, University of Perugia, Italy Yasser Mahgoub, Qatar University, Qatar Luciano De Bonis, University of Molise, Italy Stella Kostopoulou, Regional and Tourism Development, University of Thessaloniki, Thessaloniki, Greece Biswajeet Pradhan, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia Md. Abdul Mannan, Universiti Malaysia Sarawak, Malaysia Chaham Alalouch, Sultan Qaboos University, Muscat, Oman Iman O. Gawad, Helwan University, Egypt Series Editor Mourad Amer, International Experts for Research Enrichment and Knowledge Exchange (IEREK), Cairo, Egypt

Advances in Science, Technology & Innovation (ASTI) is a series of peer-reviewed books based on the best studies on emerging research that redefines existing disciplinary boundaries in science, technology and innovation (STI) in order to develop integrated concepts for sustainable development. The series is mainly based on the best research papers from various IEREK and other international conferences, and is intended to promote the creation and development of viable solutions for a sustainable future and a positive societal transformation with the help of integrated and innovative science-based approaches. Offering interdisciplinary coverage, the series presents innovative approaches and highlights how they can best support both the economic and sustainable development for the welfare of all societies. In particular, the series includes conceptual and empirical contributions from different interrelated fields of science, technology and innovation that focus on providing practical solutions to ensure food, water and energy security. It also presents new case studies offering concrete examples of how to resolve sustainable urbanization and environmental issues. The series is addressed to professionals in research and teaching, consultancies and industry, and government and international organizations. Published in collaboration with IEREK, the ASTI series will acquaint readers with essential new studies in STI for sustainable development.

More information about this series at http://www.springer.com/series/15883

Hassane Jarar Oulidi  Abdelhamid Fadil  Nour Eddine Semane Editors

Geospatial Technology Application in Water Resources Management

123

Editors Hassane Jarar Oulidi Mathematics, Informatics and Geomatics Hassania School for Public Works (EHTP) Casablanca, Morocco

Abdelhamid Fadil Mathematics, Informatics and Geomatics Hassania School for Public Works (EHTP) Casablanca, Morocco

Nour Eddine Semane Hydraulics, Environment and Climate Hassania School for Public Works (EHTP) Casablanca, Morocco

ISSN 2522-8714 ISSN 2522-8722 (electronic) Advances in Science, Technology & Innovation IEREK Interdisciplinary Series for Sustainable Development ISBN 978-3-030-24973-1 ISBN 978-3-030-24974-8 (eBook) https://doi.org/10.1007/978-3-030-24974-8 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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

Water, one of the most important resources on Earth, has become the major concern of the century. The demographic and technological transformations related to globalization, trade, and climate change have major impacts on water issues that are key elements of sustainable development and territorial governance. In particular, in the Mediterranean area, where the increasing demand for water is associated with a decrease in its resources, which urges a rational and optimal management. To address this problem, it is necessary to carry on applied research on water management in order to develop innovative practices based on the integration and use of the geospatial technology. The importance of these technologies resides their ability to bring together, in a single tool, variable and geographically located data. They do not only reassemble and communicate information, but also allow to model, analyze, manipulate, and manage it, to simulate various evolution scenarios and present the results. This book illustrates the contribution of geospatial technologies for better water resources management, a preliminary step to establish a real integrated management of water resources. This book is addressed to academicians, scientists, hydrologists, meteorologists, and consultants working in the field of water resources management. It is organized into eight chapters: – Global Data for Watershed Modeling: The Case of Data Scarcity Areas. – Extraction of Water Information Based on SAR Radar and Optical Image Processing: Case of Flood Disaster in Southern Morocco. – Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications. – Hanaa Aguedai—Intrusion Zones Identification in the Mnasra Aquifer (Morocco) Using the Seawater Intrusion Models and the Geophysics Data. – The Effect of Surface Water Pollution on the Incidence of Viral Hepatitis: A Spatial Assessment Using GIS Maps. – Contribution of the GIS in Terms of Knowledge of the Situation of the Water Resources of the Plain of Saïs Fez and Its Middle Atlasic Borders—Morocco: Aspects, Methods and Quantification of Water Resources. – Assessment of Rainfall Soil Loss in Allal El Fassi Watershed (Mean Atlas Morocco) Using RUSLE Method Combined to GIS and Remote Sensing. – Collaboration Between Water Stakeholders Needs a National Standard for Data Exchange: Exploratory Study. Casablanca, Morocco

Prof. Dr. Hassane Jarar Oulidi

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Contents

Global Data for Watershed Modeling: The Case of Data Scarcity Areas . . . . . . . . Abdelhamid Fadil and Abdelali El Bouchti

1

Extraction of Water Information Based on SAR Radar and Optical Image Processing: Case of Flood Disaster in Southern Morocco . . . . . . . . . . . . . . . . . . . Sofia Hakdaoui and Anas Emran

15

Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hibatoullah Laachrate, Abdelhamid Fadil and Abdessamad Ghafiri

31

Hanaa Aguedai—Intrusion Zones Identification in the Mnasra Aquifer (Morocco) Using the Seawater Intrusion Models and the Geophysics Data . . . . . . Hanaa Aguedai, Fouad Lahlou and Bouabid El Mansouri

51

The Effect of Surface Water Pollution on the Incidence of Viral Hepatitis: A Spatial Assessment Using GIS Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nisrine Idrissi, Fatimazahra ElMadani, Meryem Idrissi, Mohammed Ben Abbou, Mustapha Taleb and Zakia Rais Contribution of the GIS in Terms of Knowledge of the Situation of the Water Resources of the Plain of Saïs Fez and Its Middle Atlasic Borders—Morocco: Aspects, Methods and Quantification of Water Resources . . . . . . . . . . . . . . . . . . . Z. Qadem, Kh. Obda, A. Qadem, M. Lasri and I. Bouizrou Assessment of Rainfall Soil Loss in Allal El Fassi Watershed (Mean Atlas Morocco) Using RUSLE Method Combining to GIS and Remote Sensing . . . . . . . Younes Jaafari and Mohammed Benabdelhadi

69

83

95

Collaboration Between Water Stakeholders Needs a National Standard for Data Exchange: Exploratory Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Aniss Moumen, Youssef Fakhri, Hassane Jarar Oulidi, Amel Barich and Bouabid El Mansouri

vii

Global Data for Watershed Modeling: The Case of Data Scarcity Areas Abdelhamid Fadil and Abdelali El Bouchti

  



 

Abstract

Keywords

Data availability is a main element in determining the watershed modeling success. This factor becomes more critical in the case of using spatial models that require space-time distributed data. In developing countries, the implementation of such approaches is often hampered by data scarcity. To deal with this situation, the use of global data captured by earth observation satellites is considered as a major issue. This work aims to show the utility of using this type of information in data scarcity areas, especially for spatial modeling of large watersheds. To estimate the global data contribution, an analysis was performed by comparing them with local measured data. The study focuses on data representing the watershed state (morphological properties) and the input variables of hydrological models (climate data). The quality assessment of these data is calculated through statistical indicators. The comparison of global data grids with local observations shows the utility of using some of these grids for watershed modeling and especially for the state parameters such as topography. For climate parameters, the comparison is very appropriate for the minimum and maximum temperature, and moderate for the humidity and solar radiation but low or very low for rainfall and wind speed data. This work reveals that if global data derived from satellites are an alternative and very promising solution to overcome data scarcity in some areas, they still need to be enhanced to make them more efficient and accurate, especially for climate data.

Global data Data scarcity Spatial data data GIS Remote sensing Modeling Watershed Morocco

A. Fadil (&) Laboratory of Systems Engineering (LaGes), Hassania School of Public Works, Casablanca, Morocco e-mail: [email protected] A. El Bouchti Institute for Forecasting and Futuristics (I2F), Casablanca, Morocco e-mail: [email protected]

1

Climate

Introduction

Water is one of the major challenges of humanity. Its resources become increasingly scarce and particularly in arid and semiarid zones where the pressure on these resources is growing up considerably as a result of the demographic and economic development. Water resource management is therefore one of the fundamental pillars of sustainable development and environmental protection policies. Water resource management is commonly processed at the watershed level, particularly at medium and small scales [1]. The watershed constitutes the main hydrological unit where the complexity and heterogeneity of different forms of processes and interactions of natural, climatic and human factors can be studied and analyzed. Indeed, the relationship between rainfall and stream flows can only be determined through the delineation of the surface contributing to the flow [2]. The watershed, whose size can vary from a few hectares to a few millions sq. km, is then the most suitable space to relate the meteorological and hydrological processes. Water resource management at the watershed scale requires effective and operational tools for understanding and simulating the phenomena associated with these resources. In this context, the modeling use is an effective and valuable tool for decision support [3]. Watershed modeling consists in simulating its behavior and functioning through the study of its interaction and characteristics with the external environment [3]. The aim is to develop a knowledge chain to better understand the system organization, to implement procedures in order to

© Springer Nature Switzerland AG 2020 H. Jarar Oulidi et al. (eds.), Geospatial Technology, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-24974-8_1

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A. Fadil and A. El Bouchti

optimize and monitor the human interventions and to make predictions on the studied system [4]. Successful implementation of a hydrological model depends largely on the data availability, especially for distributed models that require spatially distributed data. Adopting a conceptual or physically based model makes this finding more complicated by requiring data spread over time in order to describe the dynamics and the physical evolution of the watershed and thus allow the model calibration and validation. Data scarcity is the main obstacle to implement such watershed modeling approaches, particularly in developing countries that suffer from huge data access problems. The emergence of remote sensing (RS) and geographic information system (GIS) technologies is a significant achievement in solving both the data availability problem and the implementation of spatial hydrological models. Remote sensing offers new horizons for spatial data collection and model parameter measurement in such a way that some researchers believe that the next big jump in watershed modeling will be made as a result of advances in remote sensing data availability [5]. The target of this investigation is to analyze the utility of using global data derived from earth observation satellites instead of local data measured in situ to conduct watershed modeling, particularly at medium and small scales.

2

Watershed Modeling

A model is a simplified representation of a given physical system at some particular point in time or space [6]. It is often based on a mathematical formalization of laws and concepts reflecting the system behavior [7]. It can also be constructed in the form of a reduced schema (prototype) to better understand the physics of the studied environment. It is therefore a formalized simplification of a real system or phenomenon in order to simulate it through the reproduction of its actors, processes and relationships [4]. At each step of modeling, approximations are used for translating the reality, generally perceived as something complex and difficult to define. Therefore, modeling targets, at the same time, to explain and predict the system evolution. However, it is important to point out that models are a tool and not a goal in itself. The evolution of human knowledge and the development of computer techniques have led to the implementation of series of approaches and models for representing and simulating the watershed systems. This range of tools varies according to the phenomena studied, the processes supported and the considered spatiotemporal variability. The model choice is therefore made according to the targeted objectives, basin properties and available data.

The watershed functioning is described both by its intrinsic properties and by its physical process sets that represent its dynamics and interactions with the external environment. According to [8], the main types of processes guiding the watershed systems are: • Storage/retrieval process: related to the movement of water and the flow of associated components (sediments, nutrients, etc.) • Internal transformation process: related to changes in the state of these flows. • Transfer process to the watershed borders: related to exchanges with the atmosphere and the basin outlet. Face to the multiplicity and diversity of hydrological models, several researchers have proposed a classification of these models according to different categories. Although the implementation of a clear and well-defined typology of these models is generally difficult and ambiguous [3], the literature mentions a classification series proposed by various authors, including that of [9–13]. Each of these typologies was based on a list of criteria including: • Watershed description (physical or mathematical model) • Hydrological process description (empirical, analytical, conceptual or physically based) • Spatial discretization (global or distributed) • Time discretization (continuous or discrete event) • Method of solving equations (deterministic or stochastic). In any case, one of the fundamental elements determining the modeling success is the data availability and the nature of the input data.

3

Input Data for Hydrological Models

Each process is characterized by a set of measurable parameters that can vary in time and space. These elements, called variables, are of three types [2]: • State variables: They represent the data describing the system state. In the watershed case, they are essentially topography, soil and land use. • Input variables: They represent the model’s input data that concerns the external environment of the system. In the watershed case, they are mainly climatic parameters (rainfall, temperature, radiation, etc.) and other factors related to human activity (discharges from agricultural fields, a treatment plant, etc.)

Global Data for Watershed Modeling …

• Output variables: They represent the outputs characterizing the system. For the watershed case, it is mainly the water, sediment or pollutant flow at the outlet. In the present study, we are interested in the analysis of the global data concerning the two main inputs of distributed hydrological models, namely: • The topography from which the morphological properties of watersheds (state variables) are derived • Climatic data describing watershed solicitation (input variables). The utility analysis of these data will be done by comparing the global data with local data measured in situ or extracted from other ground sources. The application will be made in the Sebou watershed located in northwestern Morocco (Fig. 1).

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4

Morphology of Watersheds

4.1 Importance of Topographical Information for Watershed Modeling Topography plays an important role in the watershed system working. It is a fundamental element in determining the basin’s physiographic and geometric characteristics. In addition, it is a main factor in controlling or influencing all the basin processes. Water, which is a key vector of the watershed regime, is directly guided by the topography. The hydrographic network is basically a representation of the change in basin geomorphology. Topography is therefore a main input for hydrological models. This is the basic data used for calculating and generating a set of parameters such as basin area, stream length, slope, flow velocity, time of concentration and so on.

Fig. 1 Sebou watershed location. Source Sebou Hydraulic Basin Agency

4

Nowadays, the topographic data collection can be done through several procedures and techniques such as stereoscopy (by photogrammetry or remote sensing), interferometry (radar), airborne laser (LIDAR), positioning by satellites (GPS), surveying or digitizing topographic maps. The process to be used depends not only on the required quality and precision and the investigation scale but also on the available financial and technological resources. The digital representation of topographical information in computer systems can be done in different ways, namely: • The vector representation through the contours (isolines) • The matrix representation (raster) through a regular grid generally called digital elevation model (DEM) • The triangular representation through the use of a network of irregular triangles called triangulated irregular network (TIN). For modeling the small- and medium-scale watersheds, the most representation used is that of raster type, usually derived from satellite or radar images. Since distributed hydrological models are very sensitive to the input topographic information quality [14], the choice of DEM to be used must be analyzed and argued. In this study, two DEM products derived from earth observation satellites will be analyzed in order to assess their ability to accurately represent the geometry and morphological characteristics of watersheds.

4.2 Presentation of the SRTM and GDEM-ASTER The Shuttle Radar Topography Mission (SRTM) was set up by National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA) in February 2000 to provide three-dimensional cartography of the globe. The technique used is based on the principle of C-band radar interferometry which makes it possible to calculate the ground altitude and its objects [15]. The SRTM data, called SRTM-1, have a spatial resolution of 1 arc-second (approximately 30 m) and offer worldwide coverage between latitudes of 56° south and 60° north. Before 2014, the SRTM-1 covered just the USA, and the rest of the world was covered with SRTM-3 (resolution of 3 arc-second). Absolute vertical accuracy is estimated to 16 m on average [16]. Therefore, SRTM data present some errors due mainly to the acquisition procedure [17]. They are in continuous enhancement by research groups. Indeed, since the publication of the first test version in 2004 and the final version in 2006, several corrected versions of the SRTM have been released [18]. In this work, we used the version 3 of SRTM-1 data that was developed by NASA by eliminating the existing voids in the initial version.

A. Fadil and A. El Bouchti

The GDEM-ASTER is a DEM (Global Digital Elevation Model) derived from the American sensor called Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). This product, developed conjointly by the Japanese Ministry of Economy, Trade and Industry (METI) and NASA, was implemented based on the stereoscopic technique traditionally used in photogrammetry. This DEM, published in June 2009, was developed on the basis of more than 1,260,000 ASTER stereoscopic scenes acquired during the period 2000–2007 [19]. The GDEM-ASTER offers a wider spatial coverage than the SRTM. It covers the entire area between latitudes of 83° south and 83° north. The spatial resolution of this DEM is 1 arc-second, and the vertical accuracy is estimated between 6 and 15 m according to the validation report of this product [20]. However, the ASTER-GDEM data present as SRTM errors and it continues to be improved and corrected [21]. The version used in this study is the second version of the product that was released in October 2011. Both DEMs are provided in the international coordinate system WGS 84 (World Geodetic System 1984) for planimetry and the international system EGM 96 (Earth Gravitational Model 1996) for altimetry. Several qualitative and comparative studies of these two free DEMs have been carried out in many areas and at different scales: [19, 21–25]. These studies, which focus mainly on the comparison of ASTER-GDEM products with SRTM-3 (because SRTM-1 did not cover initially the whole earth), show that ASTER-GDEM represents globally the topographic reality and the geomorphological details more accurate than SRTM-3. They claim that this is mainly due to the finer spatial resolution of GDEM-ASTER (3 times thinner than SRTM-3) [24]. In this study, the analysis of these two DEMs is performed on versions with the same spatial resolution (1 arc-second). The comparison is made by studying the product ability to describe the watershed hydrological characteristics, basically: • Watershed topography presentation • Watershed delineation • Watershed’s stream configuration.

4.3 Watershed Topography Representation In order to analyze the performance of these two DEMs, 500 control points were taken from two topographic maps of the study area at scale of 1: 100,000. The altitude of maps’ points is compared with the altitude estimated by the two sensors (SRTM and ASTER).

Global Data for Watershed Modeling …

5

DEMs is close to zero (Fig. 5a). In areas covered by dense vegetation, the difference is positive in the corridors (Fig. 5b) and negative in the plant space (Fig. 5c). The difference is especially sensitive in tilting zones with steep slopes (Fig. 5d) as it is also reported by [26].

SRTM

600

R² = 0.9886

Altitude (SRTM)

500 400 300 200

4.4 Watershed Delineation

100

The Sebou watershed delineation was carried out from both DEMs using GIS software and by taking a sea as the outlet point of the basin (Fig. 6). The Sebou watershed area generated from the SRTM is 38683.92 km2, while the basin area generated from GDEM-ASTER is 38705.32 km2. This shows that the difference between the two areas does not exceed 0.04% and that the two DEMs are able to delimit the Sebou watershed in a very correct way even by comparing it with the basin official delineation illustrated in Fig. 1. By zooming on the generated contours (Fig. 7), we see that the two DEMs were able to present the basin boundary in almost the same way except at the basin southwestern part extending the Atlantic Ocean (Fig. 7d). This is particularly due to the fact that the altitudes in this area, very close to the ocean, are very low (close to zero). The error slip in the DEMs automatically generates a remarkable change in the flow direction and therefore impacts the watershed boundary accuracy. From these results, it appears that the two DEMs are able to delineate the Sebou watershed area with great precision and with almost the same level of details. However, special attention should be paid to areas bordering the sea where both DEMs deviate from the right boundary.

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Altitude (topographic map) GDEM-ASTER

Altitude (GDEM-ASTER)

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R² = 0.9873

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Altitude (topographic map) Fig. 2 Comparison between global DEM and ground control points

The comparison results illustrated in Fig. 2 show a high correlation (R2 = 0.99) between the altitudes provided by the two DEMs and the real altitudes. This sample shows that the two DEMs globally produce the topographic information in a fairly correct way. The points where the deviation was enormous are located in the slopes where the terrain change degree is very large than the spatial resolution of the two DEMs. The curves also show that both products overestimate altitude in some areas and underestimate them in other areas. Given the similar results of the two DEMs in their comparison with a sample of ground control points, a comparison was conducted between the two DEMs. The difference matrix between these two DEMs (SRTM-GDEM) is illustrated in Fig. 3. The distribution of the difference between the two DEMs expressed in absolute value (Fig. 4) shows that the difference does not exceed 10 m for 90% of the area of the Sebou watershed and 20 m for 99% of the basin. The difference is negative for 49% of the study area, positive for 44% and zero for 7%. This means that the two sensors alternate the altitude estimation. One underestimates it, while the other overestimates it and vice versa. In flat areas with little ground coverage, the gap between the two

4.5 Generation of the Stream Network The performance of the two DEMs is analyzed here in relation to their ability to generate automatically the stream network. For this, the drainage network was extracted from the two DEMs through the GIS by calculating the flow direction and the flow accumulation. The drainage threshold considered is set at 40 km2. The two drainage networks and the real streams are illustrated in Fig. 8. From this figure, it can be seen that the drainage network configuration generated from the two DEMs is generally close to the real network except at the basin downstream part where the two products diverge from the real streams. A deep study of these generated drainage networks reveals that: • They restore the real drainage network with a great accuracy upstream of the watershed (Fig. 9a, b).

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A. Fadil and A. El Bouchti

Fig. 3 Difference matrix between SRTM and GDEM-ASTER

and

• The large gap between the two generated networks and the real streams is at the basin downstream part near to the outlet (Fig. 9e, f). It is a flat area with very strong modern agricultural activity (irrigated agriculture). It is also an area that has experienced several floods following the Sebou River overflow. The divergence of the automatic networks in this place can then be explained by the sensitivity of the flow generation algorithms in the flat zones (very low slope) and the human intervention which impacts and changes the natural terrain (irrigation network installation, flood protection infrastructure, hydro-agricultural development, etc.)

• They diverge from the real network at river confluence points (Fig. 9c). In most cases, it is the SRTM that gives the closest illustration of the actual configuration at these locations. • They diverge in zones where the river direction changes in a rapid and successive manner (Fig. 9d). Here again, it is the SRTM which seems more representative of the network.

This part of the study was devoted to the ability analysis of the two global digital elevation models (SRTM and GDEM-ASTER) to represent the watershed morphology. It turns out from this study that these products make it possible to restore the watershed geometric properties in a very satisfactory way. The watershed delineation and the generated drainage network are adequate to the real ground conditions, although their rendering is less precise in certain watershed areas, particularly near the watershed outlet. These

Cumuliative Area (%)

Distribution of Difference between SRTM & GDEM 100 90 80 70 60 50 40 30 20 10 0

0

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Difference (m)

Fig. 4 Distribution GDEM-ASTER

of

the

difference

between

SRTM

Global Data for Watershed Modeling …

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Fig. 5 Illustrations of difference between SRTM and GDEM-ASTER in some area

anomalies can be well treated and corrected locally when needed [27]. Thus, the global data of elevation constitute a major and reliable alternative for watershed modeling, especially at small and medium scales.

5

Climate Data

The climate defines how the watershed will interact with its external environment. The climatic factors make it possible to determine the quantities received (rainfall) and the lost fractions (evapotranspiration) as well as describing the soil interactions with the water, air and vegetation. They define the impulse received by the watershed and its hydrological response. Climatic data consist basically of two types: • Rainfall which is the main entry of watershed systems and therefore the most important input variable for hydrological models • Other climatic factors such as temperature, solar radiation, humidity and wind speed that define the watershed interaction with their environment. These data must describe both the spatial extent of the study area (topography, proximity to the oceans, etc.) and its temporal evolution (succession of seasons, drought, floods, etc.) The collection of this information must therefore take into account the spatial configuration of the studied basin and timescale of the considered hydrological model.

Given the difficulty to gather such data over time and space, the use of global data grids, provided by satellites, is a major alternative for implementing hydrological models and particularly for spatialized or distributed approach. In this study, we are interested in the most commonly used global data grids for representing climatic parameters and we will analyze their suitability for using in hydrological watershed modeling. The performance analysis of these global data grids generally takes place by comparing them with local data measured in situ in meteorological stations. The comparison is evaluated using statistical indicators to estimate the correlation degree between global and local data. In this study, we will use three statistical indicators: the determination coefficient (R2), the Nash–Sutcliffe efficiency (NSE) coefficient and the percentage of bias (PBIAS). The formulas of these indicators are illustrated in Eqs. (1)–(3). R¼

!2 Pn obs obs sim  Ymean ÞðYisim  Ymean Þ i¼1 ðYi pP ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1Þ Pn n obs  Y obs Þ sim  Y sim Þ mean mean i¼1 ðYi i¼1 ðYi  Pn  ðYiobs  Yisim Þ NSE ¼ 1  Pni¼1 obs obs Þ  Ymean i¼1 ðYi

ð2Þ

 Pn  sim   Yiobs   100 i¼1 Yi Pn PBIAS ¼ obs i¼1 ðYi Þ

ð3Þ

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A. Fadil and A. El Bouchti

Fig. 6 Sebou watershed delineation from SRTM and GDEM-ASTER

Fig. 7 Zoom on some parts of the watershed boundary

(a: North of watershed)

(b: South of watershed)

(c: East of watershed)

(d: West of watershed)

Global Data for Watershed Modeling …

9

Fig. 8 Drainage network configuration derived from SRTM and GDEM-ASTER

where Yiobs Yisim obs Ymean sim Ymean

Observed value of the day i Simulated value (global data provided by the satellite) of the day i Average observation value Average simulated global data value.

5.1 Rainfall Rainfall includes all forms of moisture falling from the atmosphere to the earth surface [8]. It is the main source of man’s supply of water and the almost unique entry of watersheds. The measurement of this parameter is usually done using a rain gauge. The estimation of this parameter from satellite observations has been the subject of many space missions as well as research projects. Several data grids have been developed to provide a spatial and temporal estimate of rainfall [28]. In this work, we will analyze two global rainfall data grids that are commonly referenced in hydrological studies,

particularly in the African context. They are the TRMM 3B42 and ARC2 grid. TRMM 3B42 v7, referenced below by the term TRMM, is the product of precipitation estimation data by TRMM platform “Tropical Rainfall Measuring Mission” set up by NASA’s Earth Sciences Distributed Active Archive Center. This project, launched in 1997, aims to provide estimates of rainfall and latent heat emissions. Basic estimates are made on a three-hour scale and at a spatial resolution of 0.25° over the entire area between 50° south and 50° north. The production of these data is done by combining TRMM images with those of other microwave sensors and geostationary satellites. Adjustment coefficients calculated on a monthly basis in some ground stations are finally applied to the tri-hourly data to generate the final product called TRMM 3B42 version 7 [29]. In 2015, the TRMM platform mission ended and was replaced by Global Precipitation Measurement (GPM). Africa Rainfall Climatology Version 2 (ARC2) is the advanced version of Rainfall Estimate (RFE) daily precipitation data produced from EUMETSAT satellite measurements and GTS rainfall stations [30]. The grid covers the

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Fig. 9 Zoom on the derived drainage networks from SRTM and GDEM-ASTER

area from 40° south to 40° north and 20° west to 55° east with a spatial resolution of 0.1°. ARC2 data are developed by the Climate Prediction Center of National Oceanic and Atmospheric Administration (NOAA). The performance analysis of these global data is carried out by comparing them with the observations at Meknes rain gauge (Fig. 1). The data of this station are compared to those of its closest point in the global grid. The sample of daily rainfall data used is spread over 8 years (2922 points). The results of this study are illustrated in Fig. 10. Performance assessment of these grids by statistical indicators (Table 1) shows that the rainfall global data display a very significant bias compared to the measured data (20% for TRMM and 35% for ARC2). The correlation degree of the data series is quite average with R2 of 0.45 for TRMM and 0.36 for ARC2 and NSE between 0.08 and 0.27. TRMM tends to overestimate rainfall, while ARC2 tends to underestimate it. The graphs in Fig. 10 also show that the

two products fail to represent a lot of rainy episodes, especially for those not exceeding 20 mm. On the other hand, the two products manage to reproduce the extreme events even with a very average precision.

5.2 Temperature, Relative Humidity, Solar Radiation and Wind Speed Temperature is one of the key elements governing a set of hydrological processes such as evaporation, transpiration and snow melting. The integration of this information into hydrological models is therefore very useful, especially for models operating continuously. Climatic parameters for humidity, solar radiation and wind are used primarily in hydrological models for estimating potential evapotranspiration using some physicalbased methods such as Penman–Monteith [31].

Global Data for Watershed Modeling …

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Fig. 10 Comparison of global rainfall data with local data at Meknes rain gauge

Table 1 Indicators for the performance assessment of global rainfall data TRMM

ARC2

R2

0.45

0.36

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0.08

0.27

PBIAS

20.00

35.22

Several satellite missions have been launched to estimate these parameters from space. In this study, we will evaluate the global data provided by one of the most used grids as a data source for a set of hydrological models such as Soil and Water Assessment Tool (SWAT) model [32]. The Climate Forecast System Reanalysis (CFSR) is under the responsibility of the National Center for Environmental Prediction (NCEP). The CFSR data cover the majority of the globe in the form of a grid with a spatial resolution of 0.5°. They were produced as a result of the combination of a model system which interact with the different compartments of the atmosphere, land surface, ocean and ice [33]. This

Similited daily Maximum Temprature CSFR (°C)

Similated daily precipitation ARC2 (mm)

80

system provides data at a basic timescale of 6 h that can be aggregated to a daily scale. Climate variables provided include minimum and maximum temperature, relative humidity, wind speed, solar radiation, etc. [34]. Quality assessment of these data was conducted by comparing Meknes gauge’s data to those of its nearest point in CFSR grid. Regarding the temperature, the comparison of the overall CFSR data, illustrated in Fig. 11, shows a good correlation between the observed temperature and that given by the CFSR grid. The determination coefficient R2 for the maximum and minimum temperature is about 0.95 and 0.89, respectively (Table 2). These results confirm those already found by other researchers [32, 35]; besides, they demonstrate that the minimum and maximum temperature information provided by the CFSR grid can be used with confidence for watershed modeling. For the other climatic parameters, the comparison results are illustrated in Figs. 12, 13 and 14. The graphs show an important bias between the global data and the local data measured at Meknes station. The

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Fig. 11 Comparison of global temperature data with local data at Meknes gauge

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Maximum temperature

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Table 2 Indicators for the performance assessment of global temperature data

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Fig. 12 Comparison of relative humidity data with local data at Meknes gauge

fault the modeling process with huge biases and that can make calibrating and exploiting models very difficult.

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Fig. 13 Comparison of global wind speed data with local data at Meknes gauge

determination coefficient of the relative humidity and solar radiation data are, respectively, of the order of 0.7, but the Nash coefficient is low (Table 3). For the wind speed, the correlation is very poor and the bias is about 52%. This shows that the global data for these parameters are not directly exploitable and must undergo adjustment and correction operations before integrating them into the hydrological models. Direct use of these data can

Conclusions and Recommendations

To handle the increasing pressure on water resources, it is clear that all necessary actions must be put in place to ensure a rational, efficient and integrated management of this vital resource. This management must be based on decision support systems able to explain the watershed organization, assess the existing potential of the water resource, determine its generation and distribution, ensure its valorization and mobilization and protect it against climatic and anthropic hazards. Indeed, understanding and mastering all the processes involved in the water cycle from a quantitative and qualitative point of view become paramount. In this case, the use of watershed modeling becomes one of the preferred solutions. However, the implementation of this approach is generally hampered by data availability problem, particularly in developing countries where there is a serious lack of data. The use of global data from satellites is then a potential alternative to overcome this obstacle. The target of this study was to analyze the global data performance and their suitability use in the watershed modeling process. Two main inputs of hydrological models were studied, namely topography and climate.

Global Data for Watershed Modeling …

Global topography data analysis has shown that these data are able to represent the watershed geometry by offering the possibility of delineating the basin and generating the drainage network with a very satisfactory and widely acceptable precision for watershed studies, particularly at small and medium scales. For the climatic parameters, the study has shown that it is possible to use with confidence the minimum and maximum temperature data provided by the global grids. However, the studied rainfall data (TRMM and ARC2) show significant biases compared to local measurements. The same finding applies to data of humidity, wind speed and solar radiation. These data must then be studied and analyzed in depth before their integration into hydrological models. The availability of a representative sample of local data will make it possible to statistically adjust these data because most of them display a bias that can be modeled and calculated. They can subsequently be used in case of the lack of well-distributed local datasets. These findings support and reinforce the previous results of other researchers who conducted studies of global data grids in other areas [36, 37]. Global data then constitute a major alternative for overcoming data availability problem, especially in data scarcity areas. However, for an efficient and safe use of these global data, the authors recommend to: • Conduct statistical analysis of these data to assess their suitability and performance prior to their integration into hydrological models • Perform a statistical adjustment of global data to eliminate and minimize the bias in these data • Explore the possibility to combine multiple global data sources to represent some parameters such as climatic variables • Investigate the global data sensitivity to the model running in order to estimate the error impact due to data on model outputs • Benefit from the global data grid availability to implement dynamic, scalable and real-time models instead of static models.

References 1. Sivapalan M (2003) Process complexity at hillslope scale, process simplicity at the watershed scale: is there a connection? Hydrol Process 17:1037–1041. https://doi.org/10.1002/hyp.5109 2. Roche P-A, Miquel J, Gaume E (2012) Quantitative hydrology: processes, models and decision support (Hydrologie quantitative: Processus, modèles et aide à la décision). Springer, France 3. Hingray B, Picouet C, Musy A (2014) Hydrology: a science for engineers. CRC Press

13 4. Singh VP, Frevert DK (2006) Watershed models. Taylor and Francis Group, USA 5. Abbaspour KC, Vaghefi SA, Srinivasan R (2017) A guideline for successful calibration and uncertainty analysis for soil and water assessment: a review of papers from the 2016 international SWAT conference. Water (Switzerland) 10. https://doi.org/10.3390/ w10010006 6. Musy A, Higy C (2004) Hydrology, A science of nature (Hydrologie, Une science de la nature). Presses polytechniques et universitaires romandes 7. Singh VP, Frevert DK (2002) Mathematical models of large watershed hydrology. Water Resource Publications, Colorado 8. Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, USA 9. Singh VP (1995) Computer models of watershed hydrology. Water Resources Publications, Colorado, USA 10. Ambroise B (1998) Dynamics of the water cycle in a watershed (La Dynamique du cycle de l’eau dans un bassin versant). Edition HGA, Bucharest, Romania 11. Wheater H (2007) Hydrological modelling in arid and semi-arid areas—an introduction. In: Wheater H, Sorooshian S, Sharma KD (eds) Hydrological modelling in arid and semi-arid areas. Cambridge University Press, p 222 12. Jajarmizadeh M, Harun S, Salarpour M (2012) A review on theoretical consideration and types of models in hydrology. J Environ Sci Technol 5:249–261 13. Gayathri KD, Ganasri BP, Dwarakish GS (2015) A review on hydrological models. Aquat Procedia 4:1001–1007. https://doi.org/ 10.1016/j.aqpro.2015.02.126 14. Lin S, Jing C, Chaplot V et al (2010) Effect of DEM resolution on SWAT outputs of runoff, sediment and nutrients. Hydrol Earth Syst Sci Discuss 7:4411–4435 15. Pillot B, Muselli M, Poggi P et al (2016) Development and validation of a new efficient SRTM DEM-based horizon model combined with optimization and error prediction methods. Sol Energy 129:101– 115. https://doi.org/10.1016/j.solener.2016.01.058 16. Rodríguez E, Morris CS, Belz JE et al (2005) An assessment of the SRTM topographic products, Technical report JPL D-31639. Jet Propulsion Laboratory, Pasadena, California, USA 17. Sharma A, Tiwari KN (2014) A comparative appraisal of hydrological behavior of SRTM DEM at catchment level. J Hydrol 519:1394– 1404. https://doi.org/10.1016/j.jhydrol.2014.08.062 18. Gamache M (2004) Free and low cost data sets for international mountain cartography. Paper presented at the Workshop of the Commission on Mountain Cartography in the International Cartographic Association. Vall de Nuria, Spain 19. Hirt C, Filmer MS, Featherstone WE (2010) Comparison and validation of the recent freely-available ASTER- GDEM ver1, SRTM ver4. 1 and GEODATA DEM-9S ver3 digital elevation models over Australia. Aust J Earth Sci 57:337–347. https://doi. org/10.1080/08120091003677553.Comparison 20. Team AGV (2009) ASTER global DEM validation summary report. METI & NASA, 28p 21. Nikolakopoulos KG, Kamaratakis EK, Chrysoulakis N (2006) SRTM vs ASTER elevation products. Comparison for two regions in Crete, Greece. Int J Remote Sens 27:4819–4838. https://doi.org/ 10.1080/01431160600835853 22. Kervyn M, Ernst GGJ, Goossens R, Jacobs P (2008) Mapping volcano topography with remote sensing: ASTER vs. SRTM. Int J Remote Sens 29:6515–6538. https://doi.org/10.1080/ 01431160802167949 23. Fujita K, Suzuki R, Nuimura T, Sakai A (2008) Performance of ASTER and SRTM DEMs, and their potential for assessing glacial lakes in the Lunana region, Bhutan Himalaya. J Glaciol 54:220– 228. https://doi.org/10.3189/002214308784886162

14 24. Harcourt P (2015) Vertical accuracy assessment of SRTM3 V2. 1 and aster GDEM V2 using GPS control points for surveying & geo-informatics applications—case study of Rivers State, Nigeria 6:81–89 25. Elkhrachy I (2016) Vertical accuracy assessment for SRTM and ASTER digital elevation models: a case study of Najran city, Saudi Arabia. Ain Shams Eng J 2:1–11. https://doi.org/10.1016/j.asej. 2017.01.007 26. Szabó G, Singh SK, Szabó S (2015) Slope angle and aspect as influencing factors on the accuracy of the SRTM and the ASTER GDEM databases. Phys Chem Earth 83–84:137–145. https://doi.org/10.1016/j.pce.2015.06.003 27. Su Y, Guo Q (2014) A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS J Photogramm Remote Sens 87:216–228. https://doi.org/10.1016/j.isprsjprs.2013. 11.009 28. Dembélé M, Zwart SJ (2016) Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa. Int J Remote Sens 37:3995–4014. https://doi.org/10.1080/01431161. 2016.1207258 29. Huffman GJ, Bolvin DT, Nelkin EJ et al (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55. https://doi.org/10.1175/JHM560.1 30. Novella NS, Thiaw WM (2013) African rainfall climatology version 2 for famine early warning systems. J Appl Meteorol Climatol 52:588–606. https://doi.org/10.1175/JAMC-D-11-0238.1

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Extraction of Water Information Based on SAR Radar and Optical Image Processing: Case of Flood Disaster in Southern Morocco Sofia Hakdaoui and Anas Emran

Résumé

Cette étude traite la problématique de détection de l’étendu des inondations causées par des crues subites à la suite de fortes pluies pluviales et le transport des sédiments causant le débordement des eaux de l’oued sur la crête du barrage de Sakia El Hamra, entraînant l’ouverture de deux brèches dans le corps du barrage. La zone d’étude se trouve dans le sud du Maroc dans la région de Laayoune—Sakia el Hamra spécialement la ville de Laayoune. La géomorphologie de la zone et la formation du réseau de drainage provoquent la création d’inondations rapides qui ont eu lieu du 27 au 28 octobre 2016 suite à des précipitations intenses et fortes qui se sont déversées sur la région. Sept images satellitaires Radar et optiques d’avant et d’après cet événement ont été traité pour extraire les informations les plus utiles. Pour atteindre nos objectifs, cette étude a débuté par le prétraitement du radar (calibrage, filtrage du speckle, correction du terrain doppler) et de l’optique (correction atmosphérique, étalonnage de la dérive radiométrique du capteur et correction des distorsions géométriques et topographiques). Quatre indices spectraux ont été extraits, suivis d’une détection de changement sur des images diachroniques multispectrales à partir de trois images sentinel-2 MSI et deux images Landsat-8 OLI acquises avant et après l’évènement. Les indices spectraux Normalized Difference Water Index “NDWI”, Normalized Difference Moisture Index “NDMI”, Normalized Difference Drought Index “NMDI” et l’Albedo “Al” fournissent un espace spectrale bidimensionnel formé par (Albedo, NDMI) qui donne un très bon pouvoir de discrimination permettant de suivre l’évolution spatio-temporel des S. Hakdaoui (&)  A. Emran Geo-Biodiversity and Natural Patrimony Laboratory, Geophysics, Natural Patrimony and Green Chemistry Research Center, Scientific Institute, Mohamed V University, Av. Ibn Batouta, B.P 703, 10106 Rabat, Morocco e-mail: [email protected] A. Emran e-mail: [email protected] © Springer Nature Switzerland AG 2020 H. Jarar Oulidi et al. (eds.), Geospatial Technology, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-24974-8_2

différents niveaux d’humidité du sol dans la zone après les inondations. L’application des méthodes de coregistration et de segmentation sur les images Radar Sentinel-1 avant et après l’évènement complète ce travail. Les résultats obtenus montrent l’importance de la complémentarité de l’imagerie multi capteurs pour la cartographie dynamique des inondations.

Abstract

This study investigates the problem of detecting the extent of inundation caused by flash floods following heavy rainfall and the transport of sediments that cause the overflow of waters of the wadi on the crest of the Sakia El Hamra dam, the opening of two breaches in the body of the dam. The study area is focusing on Laayoune city in Southern Morocco in the region of Laayoune-Sakia el Hamra. The geomorphology of the area and the formation of the drainage network caused the creation of flash floods which took place from October 27 to 28, 2016, following intense and heavy rain in the region. Seven satellite images (radar and optical) taken before and after this event were processed to extract the most useful information. To achieve our objectives, this study began by preprocessing the radar images (calibration, speckle filtering, Doppler ground correction) and optics images (atmospheric correction, calibration of the radiometric and correction of geometric and topographic distortions). In this study, four spectral indices were extracted, then the change of detection approach is used on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI imageries of before and after the disaster event. Normalized Difference Water Index “NDWI,” Normalized Difference Moisture Index “NDMI,” Normalized Multi-band Drought Index “NMDI” and Albedo “Al” provide a two-dimensional spectral feature space resulted which gives a very good power of discrimination to monitor the spatiotemporal evolution of the different levels of soil moisture in the 15

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S. Hakdaoui and A. Emran

area after the floods. The application of the coregistration and segmentation methods on radar Sentinel-1 images before and after the event completes this work. The results obtained show the importance of the complementarity multisensor imagery for the dynamic mapping of floods. Mots-clés



 

Traitement des images SAR Cartographie des inondations Détection automatique des changements Segmentation Inondation Keywords



SAR imaging processing Flood mapping Automatic change detection Segmentation Flash flood

1



Introduction

Flash floods represent a quick and intense flow of high water that occurs in a usually dry area. Alternatively, they can be considered as a rapid water level rise in a stream or creek above a predetermined flood level and can cause serious damage within only six hours (e.g., intense rainfall and dam failure) [1]. The intensity can vary according to the area and increase in effective ways, especially where heavy rainfall results in a rapid rise in floodwaters. This induces a greater runoff volume due to various environmental conditions such as a higher percentage of impermeable surfaces, compacted soils, soil moisture, type and depth of soil, terrain slope, land use and vegetation conduct to short time lags between rainfall and peak discharge. These extreme events tend to occur on a very small spatial and temporal scale (usually in areas of up to a few hundred square kilometers during few minutes to several hours). This type of event develops rapidly, which makes forecasting complex [2, 3]. There are some difficulties during the observation and prediction at the regional scale, especially because of the absence of hydrometric networks in the desert environment. Remote sensing techniques provide safe and cost-effective tools for monitoring, mapping and assessing its evolutionary process and the damages caused by flood events [4–6]. Every year in Morocco, a significant number of heavy rains cause floods, including flash floods, floods of rivers and floods of mud during the rainy season [7]. Indeed, in October 2016, violent storms have caused flooding and impressive river floods in a large part of Southern Morocco, especially in the city of Lâayoune and its regions, all within the Moroccan Sahara. Rainfalls occurred during a span of approximately ten hours (between 28 and 29 October), the volume of water stored at the Sakia El Hamra dam, where its storage capacity is known

to not exceed 110 hm3 and has increased from 7 to 203 hm3. The peak of the flood reached more than 3000 m3/s, which was well above the threshold of the spillway water storage capacity (410 m3/s). The passage of this exceptional flood caused the overflow of the waters of the wadi on the crest of the dam of Sakia El Hamra, leading to the degradation of the “downstream slope” and the opening of two breaches with a size of one hundred meters in the body of the dam, at the level of the minor bed of the Oued. The height of the two breccias then developed as the spill gradually reached almost the level of the wadi (Fig. 1). The impressive mass of water carried by wadi Sakia El Hamra had submerged agricultural farms near Foum El Oued and caused material damage (Fig. 2). According to the Sakia El Hamra and Oued Eddahab Hydraulic Basin Agency, these latest floods have resulted in many victims and homelessness and, above all, serious damages. Knowing that Oued Sakia is at the origin of many tributaries, some of which go back far, sometimes in outer zones, it is known that a great flood of this wadi would endanger the inhabitants of the district’s neighbors. This disaster left hundreds of families of Douar Lamkhaznia homeless (Fig. 3) leaving hundreds of houses to devastation, in addition to the destruction of many agricultural areas (Fig. 2), several road and dam breaks (Figs. 4 and 5). Though it is not the first time that this area had known such catastrophe, flooding events in the region have been observed times over the past 50 years, namely in 1968, 1985, 1995, 2002, 2005 and 2016 [8]. It is the primary reason why preventive measures need to be taken by the Moroccan government to protect the water resource against flooding. This is usually achieved by building, near to Laayoune city, a barrage of over five collinear lakes and an artificial duct for load capacity. It is important to determine quickly the extent of flooding and land use under water, during a flash flood event [9]. Optical remote sensing has been frequently used to detect damages caused by flash flood events due to their synoptic coverage, high resolution, better visual perception and ease of image handling and processing [5]. Optical sensors onboard the Landsat-8 and Sentinel-2 satellites collect data across a variety of spectral bands from the visible spectrum to the SWIR. Different land coverings show specific reflective features for each spectral band, which give many spectral indices that can be used to identify water areas [10]. Optical sensors are passive, with images capturing the solar reflection of the surface or atmosphere of the earth, making it impossible for the sensor to penetrate the cloud cover [6]. This is the main disadvantage of optical satellites, where flood events can occur without taking any images, making these sensors a poor choice for monitoring [11]. However, in desert environments, the scarcity of poor weather conditions makes optical sensors less vulnerable and can monitor in almost real time for mapping this event. It is usually known

Extraction of Water Information Based …

17

Fig. 1 Dam of Sakia El Hamra in left, leading to the opening of two breaches of a hundred meters in the body of the dam, at the level of the minor bed of the Oued represented by circles in the right-hand image

Fig. 2 Wadi Sakia El Hamra had submerged agricultural farms

that microwave remote sensing techniques such as synthetic aperture radar (SAR) have a great potential as a source of relevant and near-real-time information for the early warning of population, mitigation and management of natural disasters. This is mainly due to its observation capability regardless of climate conditions and sun illumination where natural disasters like flash floods are better and most studied using SAR data [12]. SAR data provide robust and gainful tools for monitoring, mapping and assessing the evolution and damages caused by flash flood events [10]. Many

approaches for flash flood forecasting that use multisensory data and neural networks are available [13]. Despite the significance of this phenomenon, little research has been made to characterize, evaluate and monitor flash floods in Southern Morocco with EO technics. The EO data required for this study include both optical and radar data, with preference given to optical data from Sentinel-2 because of its availability date at the peak of the event. The SAR sensor is able to penetrate cloud cover and detect water, making the satellite SAR system a powerful tool for flash flood

18

Fig. 3 Hundreds of damaged houses due to the flash flood

Fig. 4 Flash flood has caused many agricultural fields that were destructed

Fig. 5 Several roads and barrage were damaged

S. Hakdaoui and A. Emran

Extraction of Water Information Based …

monitoring. SAR has a great potential as a source of adequate and near-real-time information [9]. The European Space Agency (ESA) Copernicus Program provides accurate, timely and easily accessible information to improve environmental management, understand the effects of climate change and ensure civil safety [14]. Sentinel-1 SAR data products contribute to this application due to the sensitivity of backscattering to open water, moisture and roughness. The specular reflection of C-band signals over open water means that the signal is significantly lower than the average through radar intensity imaging in the absence of wind. Stagnant water surfaces act as specular reflectors and reflect incoming radiation away from the sensor, which in general results in low backscatter measurements [15]. Different environmental factors like wind-induced waves increase surface roughness, while vegetation and infrastructure or natural high relief emerging from the water surface can produce double-bounce effects [16]. Change of surface states after a flash flooding allows to extract direct information on erosion occurrence [17]. Using the amplitude and phase information transmitted by two radar images with approximately the same geometry and acquired over a certain period, the repeat pass interferometry technique can be applied. This method allows slight (sub-cm) land deformations to be delineated and can be used for generating digital elevation model (DEM). The coherence map is more promising product from SAR imaging. If the characteristics of the soil material are similar in both acquired radar images, the coherence is high. Changes in the topsoil layer due to erosion/deposition caused by a flash flood can lead to significant temporary de-correlation and weak coherence. However, other factors such as differences in satellite pathways for acquired images, vegetation, soil moisture and roughness can cause de-correlation [18]. The topographical effect of de-correlation due to the local terrain slope can be separated from the overall de-correlation between two radar images using the ratio coherence imaging method [19]. This method can expose the de-correlation component effectively due to erosion in consistency maps. However, integration with other spatial data, such as optical images, is necessary for the correct interpretation of coherence images. This work proposes an investigation using remote sensing technology, to carry out and assess temporal and spatial surface changes of inundated areas with multitemporal Sentinel-1 SAR intensity data and multispectral data using MSI Sentinel-2 and OLI Landsat-8 optical sensor. This is the first flash flood study that has been carried out on this area. Many previous studies were based on only a few satellite images and were produced to provide information to decision-makers for specific flood events. In most cases, only two images were obtained prior to and after the flood to

19

prepare the flood maps. The satellite overpass often did not match the flood peak in these cases. In addition, satellite images from the various sensors for the flash flood mapping have been selected, where images from different sensors can cause data integration uncertainty due to different sensor properties such as spatial resolutions and wavelengths. The purpose of this paper is to present a methodology to determine the amount of area directly affected by floodwater and demonstrate how the combination of SAR Sentinel-1 data and optical MSI OLI sensor data can contribute toward a deeper knowledge of the area under consideration. In the present study, Sentinel-1 Level-1 Ground Range Detected (GRD) products have been utilized. They consist of focused SAR data, multilooked and projected to ground range using an Earth ellipsoid model such as WGS84 [20]. The images have been acquired through the Sentinels Scientific Data Hub. The data were processed to reveal and delineate the flooded areas. The Sentinel Application Platform (SNAP), which is an open-source common architecture for ESA Toolboxes, was used for the exploitation of EO data. Both optical imageries have been acquired through the Web at https://glovis.usgs.gov/ and radar data from https://scihub. esa.int/.

2

Study Area

The study area is in the southern region of Morocco in Laayoune city, specially the Oued Sakia El Hamra, and is located at the largest city in Moroccan Sahara. Its area is over 142,865 Km2. Located 27° 09′07.4″N 13° 10′ 49.7″ W in low altitude, the town of Laayoune is threatened by the floods of the wadis surrounding it (Oued Aoudri and Sakia) (Fig. 6). The temperatures are moderate and influenced by the proximity of the Atlantic Ocean. The annual precipitation is low (59 mm in Laayoune station) and irregular (Fig. 7a). The annual average coastal temperature varies from 17 to 25 °C (Fig. 7b). Throughout the year, the winds blow constantly in the region with a monthly average of maximum velocities between 15.4 and 19.2 m/ s, and the annual average is of the order of 17.6 m/ s or 63.4 km/h. By its regularity and intensity, wind is the determining factor in the genesis of the silting phenomenon. It shapes the dune landscapes and conditions the movement of the sand. Two contrasted regimes are observed: that of weak winds, generally going from October to the end of March; that of the strong winds, going from April to the end of September, with a silting volume three times greater than that of the first period. The Laayoune dunes are considered the fastest in the world with an average movement speed of 32 m/year for 9 m high dunes.

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Fig. 6 Study area

Fig. 7 a Average rainfall (mm) from 2000 to 2012 in Laayoune, Morocco (source Worldweather). b Average temperature (C) from 2000 to 2012 in Laayoune, Morocco (source Worldweather)

3

Data and Preprocessing

3.1 Satellite Imageries The data set used in the present research incorporates space-borne images (Table 1) including: (i) Three Sentinel-2 MSI images before and after flash flood event that were used for spectral index extraction

(ii) Two Landsat-8 OLI images (path 205, row 41) before and after flash flood event that were used for spectral index extraction (iii) Two Sentinel-1 (S1) SAR images before and after flash flood event utilized for coregistration and classification to extract flooding area. The satellite reflective bands were combined as four spectral indices to extract urban, vegetation, water and soil wet/dry areas. The original input OLI and MSI bands were

Extraction of Water Information Based …

21

Table 1 Sentinel-1, Sentinel-2 and Landsat-8 satellite data sets Satellite

Instrument

Mode/path–row

Acquisition date

Use

Sentinel-2A

MSI

L1C_TL_SGS

13/10/2016

Two weeks before flash flood event used to calculate reference image

Landsat-8

OLI

LC (205-41)

28/09/2016

One month before flash flood used to calculate reference image

Sentinel-2A

MSI

L1C_TL_SGS

30/10/2016

One day after flash flood event used for flood extent mapping

Landsat-8

OLI

LC (205-41)

13/11/2016

Two weeks after flash flood used for water and moisture/dryness soil information extraction

Sentinel-2A

MSI

L1C_TL_SGS

01/01/2017

Two months after flash flood event used for water and moisture/dryness soil information

Sentinel-1A

C-SAR

IW_GRDH_1SDV (VV, VH)

21/10/2016

One week before flash flood event

Sentinel-1B

C-SAR

IW_GRDH_1SDV (VV, VH)

02/11/2016

Three days after flash flood event

Fig. 8 a Reference image = mean of MSI and OLI before flood. b Sentinel-2 MSI one day after flash flood. c Sentinel-1A three days after flash flood. d Landsat-8 OLI two weeks after flood

then converted to radiance and finally converted to surface reflectance. We applied a georeferencing to all the images (Fig. 8) according to WGS84 datum and Universal Transverse Mercator Zone 28 N coordinate system and also a clipping of every OLI/MSI data set depending on the study area.

3.2 Data Preprocessing The preliminary treatment of the OLI sensor involved the conversion of digital numbers to radiance and reflectance and in accordance with Eqs. 1 and 2, respectively [21–23].

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S. Hakdaoui and A. Emran

 Lk ¼

 LMAX  LMIN ðKcal  Kcalmin Þ þ LMIN Kcalmax  Kcalmin

ð1Þ

  qk ¼ p  Lk  d2 =ðESUNk  cos hÞ

ð2Þ

where k is the band number; L is the spectral radiance at the aperture of the sensor; LMAXk is the spectral radiance scaled to KCALMAX; LMINk is the spectral radiance scaled to KCALMIN; KCALMAX is the maximum calibrated pixel value (corresponding to LMAXk); KCALMIN is the minimum calibrated pixel value (corresponding to LMINk); and KCAL is the minimum calibrated pixel value [24, 25]. In the case of Sentinel-2, we convert the top-ofatmosphere (TOA) reflectance values of L1C Sentinel-2A products to bottom-of-atmosphere (BOA) reflectance values according to Eqs. 2 and 3, respectively, for each band according to image metadata.   DN L1C bandði; jÞ CNkNTDI ði; jÞ ¼ ðfloatÞ QUANTIFICATION VALUE ð3Þ  qk ði; jÞ ¼

p  CNk;NTDI ði; jÞ Ak;NTDI  ES  dðtÞ  cos hS ði; jÞ

 ð4Þ

where ES is the mean exoatmospheric solar irradiance and hs is the solar zenith angle in degrees.

4

Methodology

4.1 Soil Moisture/Dryness Spectral Index Extraction Due to the very distinct reflectance of water, it is relatively easy to separate water in optical images from other land covers such as sand and building area. The reflectance of turbid and muddy floodwater charged with soil particles differs from normal water, but it can be extracted using a specific combination of optical band. A digital elevation model (DEM) is used to extract topographic data on rivers and streams. To map the floodwater extent exactly, knowing that the quality and precision of the flood mapping both depend on the spatial resolution and time of images acquisition, pre-flood images over the same areas are needed to extract normal water conditions and then to be compared to the water extent during the flood time [21, 24]. The spectral variability of soil reflectance has been used to develop a soil moisture content estimation and a mapping approach based on many indexes for characterization of wet/water and dry land change after flash flooding event. Since dry surfaces and wet/water surfaces have distinctly different reflectance characteristics, the optical sensor

can easily identify the surface changes by comparing a pair of images acquired before and after or during the flood. This research is utilizing remote sensing technology to procure indexes to examine their spectral characteristics and define their quantitative relationship with wet/water and dry land change. Albedo, Normalized Difference Water Index (NDWI), Normalized Multi-band Drought Index (NMDI) and Normalized Difference Moisture Index (NDMI) are principal indices used for characterization of moisture/ dryness land change. The Albedo index (Eqs. 5 and 5′) is a physical parameter influenced by land surface conditions [26]. Albedo is increased when the soil is dry and the soil is reduced by the wet/water. Changes in the soil moisture content have an impact on the soil absorption and reflection characteristics. This relationship is used for differentiation of soil color when the Munsell color chart is used. The higher the moisture content of the soil, the darker the color and lower the Albedo [27, 28]. This relationship is only valid for the moisture content of the soil up to the field capacity. In addition to the field capacity, the high concentration of soil moisture no longer darkens the color but begins to accumulate a water layer on the aggregate surface, creating a shiny and better reflective surface that increases the reflection and thus the Albedo [29, 30]. This phenomenon induces different textural classes in soils at the Albedo level. In addition, sandy textured soils drain and dry out much more quickly than other soil types. The main difference in the resulting soil moisture content and the texture classes produces a significant difference in the reflectivity and absorbance characteristics and in the Albedo. The surface roughness is also defined by the type of Lambertian or specular reflection. Smooth surfaces, such as water bodies, plant leaves or wet soil surfaces, can be specular reflectors that have a relatively high Albedo content. Rough surfaces have Lambertian reflection and show lower Albedo values, particularly when the sun’s angle is low, and the shading effect reduces the reflection. Measurable surface roughness differences exist between soil textural classes. Fine-textured, dry soils with small particle sizes produce a high Albedo due to the smooth texture of the surface. Rough soils with coarse texture on the other hand are often wet, and soil moisture absorbs incident radiation and reduces Albedo. In contrast, dry, coarse-textured soils with relatively large particles (sand grains) reflect larger parts of the radiation incident than fine-textured soils. Therefore, Albedo “(5)” and “(5′)” can be used as an important physical parameter reflecting land wet/dry state. Al ¼ ð0:356qb þ 0:130qr þ 0:373qnir þ 0:085qSWIR1 þ 0:072qSWIR2  0:0018Þ

ð5Þ

where qb is blue band (band 2 for OLI or MSI), qnir is near-infrared band (band 5 for OLI or band 8A for MSI),

Extraction of Water Information Based …

23

qr is red band (band 4 for OLI or MSI), qswir-1 is Swir1 band (band 6 for OLI or band 11 for MSI), and qswir-2 is Swir2 band (band 7 for OLI or band 12 for MSI). Data normalization processing: Find the maximum and minimum values of Albedo on study area, and then use them for data normalization processing to obtain the Albedo normalized index. Albedon ¼ ðAl  Almin Þ=ðAlmax  Almin Þ

ð50 Þ

The NDWI “(6)” and “(6′)” was also used for soil water content mapping after flash flood periods [24, 31, 32] NDWI ¼ ðqb  qswir1 Þ=ðqb  qswir1 Þ

ð6Þ

The normalized index is:

NMDI ¼ 700ðqnir  qswir1 þ qswir2 Þ=ðqnir þ qswir1  qswir2 Þ

ð7Þ NMDIn ¼ ðNMDI  NMDImin Þ=ðNMDImax  NMDImin Þ ð70 Þ The NDMI “(8)” and “(8′)” is an index for surface reflectance data developed by [33] using NIR and SWIR1 bands, with coefficients empirically determined as those that transformed a set of training pixels into new dimensions aligned with maximum variability [34, 35]. NDMI ¼ ðqnir  qswir1 Þ=ðqnir þ qswir1 Þ

ð8Þ

The normalized index is

NDWIn ¼ ðNDWI  NDWImin Þ=ðNDWImax  NDWImin Þ ð60 Þ

NDMIn ¼ ðNDMI  NDMImin Þ=ðNDMImax  NDMImin Þ ð80 Þ

In addition, Wang and Qu proposed a new drought index, namely NMDI “(7)” and “(7′)”, for the remote sensing of soil and vegetation water content mapping using three bands in the NIR and SWIR (0.86, 1.64 and 2.13 lm):

After calculating Albedon, NDWIn, NDMIn and NMDIn (Fig. 9), they are stacked in one file image and then we calculate the correlation matrix in order to determine the less correlated indices: NDMI and Albedo with correlation

Fig. 9 a NDMI; b Albedo; c NDWI; and d NMDI

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S. Hakdaoui and A. Emran

Table 2 Correlation matrix

NMDIn

NDWIn

Albedon

NDMIn

NMDIn

1

0.972023

0.719202

0.8808337

NDWIn

0.972023

1

0.751016

0.842221

Albedon

0.719202

0.751016

1

−0.465843

NDMIn

0.8808337

0.842221

−0.465843

1

coefficient of 0.465843 (Table 2). This value is confirmed in the feature space (NDMI and Albedo). Thus, the NDMI and Albedo were chosen as feature space for a better extraction of the area change.

4.2 Characterization with Magnitude and Orientation of Change Vector in the [Δ (Albedo), Δ (NDMI)] Feature Space To further study spatial distribution law of flash flood event in [Δ (Albedo), Δ (NDMI)] feature space, we draw scatterplot chart for a different time after the event. After statistical regression analysis, we obtain Δ (Albedo) and Δ (NDMI) that both have significant negative linear correlation “(9)”. The regression equation is: D ðAlbedoÞ ¼ 234:426  1:041D ðNDMIÞ

ð9Þ

The correlation coefficient calculation resulted in −0.465; this means that with the increase in soil dryness, MI decreases gradually, while Albedo increases. And in the [Δ (Albedo), Δ (NDMI)] feature space, the process of soil wet/dry has been reflected clearly [36]. According to Verstraete and Pinty’s study [37–43], by dividing Δ(Albedo) − Δ(NDMI) feature space in the vertical direction of changing trends we can extract significantly wet and dry soil (Fig. 11). Wet and dry land changes in the study area between the pre- and post-flash flood event are defined by means of a Change Vector Analysis (CVA) applied to NDMI and Albedo feature space. CVA is a multivariate technique [44, 45] that allows pixel-by-pixel evaluation of spectral band or products on two axes on a cartesian plane to evaluate changes between baseline (T1) and later (T2) dates (Fig. 10). The magnitude (M) is derived from the Euclidean distance and indicates “(10)” change intensity. A classified picture is obtained in four categories: low (0–25), medium (25–50), high (50–75) and extreme (75–100), representing the sum of changes between dates. qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi M ¼ ðAlbedo1  Albedo2Þ2 þ ðNDMI1  NDMI2Þ2 ð10Þ

Fig. 10 Change direction and magnitude scheme based on a pixel-by-pixel change rate (Δ) between the baseline date (T1) and a later date (T2)

where subindices 1 and 2 represent images from before and after flash flood. The direction of change represents the type of change (h) occurring in a pixel corresponding to T1 and T2, where each vector is a function of the combination of positive and negative changes occurring in the spectral band or products analyzed “(11)”. The resulting image contains values between 90° and 180° which range from 270° to 360° in drought; it corresponds to bright pixels indicating higher level in wetness, while 0°–90° and 180°-270° represent pixels indicating no change. There is no definitive evidence that drought or wetness is increasing or decreasing. h ¼ tan1

ðNDMI1  NDMI2Þ ðAlbedo1  Albedo2Þ

ð11Þ

4.3 Methodology for Change Mapping 4.3.1 Case of Optical Data Consider two optical images obtained at two different times in the same geographic area and defined in this case as I1 and I2, where I1 is the reference image calculated as the mean of two images acquired before the flood event from

Extraction of Water Information Based …

25

Fig. 11 Methodology applied to optical data represented as a flow diagram

OLI and MSI sensor and I2 corresponds to three multitemporal images acquired at different times after flood from OLI and MSI sensor. Then, as shown in Fig. 11, the proposed method has three main steps: (1) radiometric normalization; (2) spectral index generation, normalization and selection; and (3) magnitude and orientation of change calculation.

4.3.2 Case of SAR Data The pair of SAR images acquired from Sentinel-1C band includes an image of three days after flood, which is called “flood image.” The second image reached before the flood event is called “reference image” [46, 47]. To distinguish the flooded area, a coregistration processing and segmentation have been applied (Fig. 12).

5

Result and Interpretation

Change analysis in the study area provides the basis for identifying trends in change processes. Therefore, we identify from pre- and post-flooding the degree of change that affects inundation and dynamics of water extent.

5.1 For SAR Data The flood map acquired using this method was compared and evaluated using a threshold approach for segmentation. The result is shown in Fig. 13b, where the flooded area appears in

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S. Hakdaoui and A. Emran

5.2 Case of Optic Data 5.2.1 Qualitative Change Analysis by the Color Composite of Δ(Albedo) − Δ(NDMI) Feature Space Figure 14a, b and c shows the color composite RGB in different times after the flood event with red affected to Δ (Albedo), and green and blue affected to Δ(NDMI). In these images, we see areas in red that represent the regions where the Albedo has undergone the maximum change and areas in light cyan where the NDMI has undergone the maximum change between the two dates before and multidate after flooding. It is the river bed and the sabkha areas that have seen their surfaces considerably changed over time after the floods.

Fig. 12 Methodology applied to SAR data represented as a flow diagram

red, representing a higher backscattering area. The flooded areas which remain covered with water three days after the floods appear in blue which represents area with a decreased level of backscattering. The total water extent was calculated to be 36.2 km2 (3.620 ha). The regular water body in the dam (Fig. 13a) or before flood event was 12 km2 (1.200 ha). So, the flooded area for this time is then 24.2 km2 (2.420 ha).

5.2.2 Dynamic Mapping Water Extent and Agriculture Vegetation Loss After Flash Flood For extracting water extent, we regrouped all soils with high level of wetness in one water class (Fig. 15). The extent of regular water body in the dam (Fig. 15a) or before the flood event was 12 km2. During the peak of flood on one day after the event, the extent was 41.44 km2. The flooded area for this time was then 29.44 km2. Two weeks after the event, the flooded area was 8.48 km2. Finally, the flooded area surface was close to zero indicating a return to regular water body in the dam. For agricultural area extraction, the Normalized Difference Vegetation Index (NDVI) “(12)”, being a potential indicator for crop growth and vigor, was used in this study, which is expressed as:

Fig. 13 a Color composite of coregistration of VV polarization band in red: after flooding. In blue: before inundation. b Map of change in red: increase of the backscattering in blue: decrease of the backscattering

Extraction of Water Information Based …

27

Fig. 14 Color composite RGB (red = Δ(Albedo), green and blue = Δ(NDMI)) for a one day after the event, b two weeks after the event and c two months after the event

Fig. 15 Water extent for a before the flood event, b one day after the flood event, c two weeks after the flood event and d- two months after the flood event

    NDVI ¼ qpir  qr = qpir þ qr

ð12Þ

The NDVI image was prepared from images of two dates corresponding to before and one day after the flash flood event and binarized with simple thresholding

segmentation to extract the agricultural area in both images before and after the event (Fig. 16). The surface of the irrigated areas was 3.2 km2 before the floods and 1.66 km2 after the floods representing approximately 48% loss.

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S. Hakdaoui and A. Emran

Fig. 16 Agricultural area extracted from a before the flood event and b one day after the flood event

6

Conclusion

This study investigates the water extent extraction related to flash flood progression on the region after the inundation and uses the spatially distributed water extent for a local-scale flash flood. This research employs a combination of spectral indices derived from multitemporal optical remote sensing imagery. This study therefore integrates spectral indices, such as the Albedo, the NDWI, the NDMI and the NMDI, describing the soil reflectance curve. Furthermore, the correlation analysis has shown that feature space [Δ(Albedo), Δ (NDMI)] is highly discriminating for spatiotemporal changes of soil moisture/dryness after flooding Our results demonstrate that remote sensing and satellite data are effective tools for hazard applications such as flooding disaster that needs a synoptic view. Radar systems are normally more advantageous in data since it is providing a better observation of floodplains than optic systems, probably due to the cloud covering on imagery during flood event. However, in the desert environment optical methods become equivalent to the use of radar sensor and give an optimal result. This is the primary idea shown in this study. This paper shows that the return to normal water level after the floods in arid environment has taken almost two months. Furthermore, the methodology used here has determined the amount of area directly affected by floodwater and has demonstrated the efficiency of the combination of optical and SAR sensor data, which can contribute toward a deeper knowledge of the study area. The distinctiveness of this research resides in its simplicity and efficiency in providing a rapid regional strategy for studying flash floods in the desert to address the real causes of problems and risks in developing countries. The results obtained help to improve the management of water regulatory structures in order to develop a methodology to maximize water storage capacity and reduce the risks of floods in Laayoune city and its regions.

Finally, the results obtained show that the contribution of SAR radar images for flood mapping is complementary to the optical images and that the combination of both methods and data can result in a better precision and continuity to enhance monitoring of flooded areas. Acknowledgements The authors would like to thank the University Mohammed V of Rabat and CRASTE-LF for their logistic support. We would like to thank the NASA-GLOVIS-GATE for the OLI and the MSI data.

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29 29. Chen C, Peihua X, Yihong W, Jianping C, Lianjing Z, Cencen N (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coal mine subsidence areas. Sustainability 2016, 8, 948; https://doi.org/10.3390/su8090948 30. Xiao L (1999) Flash floods in arid and semi-arid zones. IHP-V 1 technical documents in hydrology 1 No. 23, UNESCO, Paris 31. Xiao X, Boles S, Frolking S, Salas W, Moore B, Li C, He L, Zhao R (2002) Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in china using VEGETATION sensor data. Int J Remote Sens 23:3009–3022 32. Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033 33. Crist EP (1985) A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sens Environ 17(3):301–306 34. Chen CF, Valdez MC, Chang NB, Chang LY, Yuan PY (2014) Monitoring spatiotemporal surface soil moisture variations during dry seasons in central america with multisensor cascade data fusion. IEEE J Sel Top Appl Earth Obs Remote Sens 7:4340–4355 35. Fisher A, Flood N, Danaher T (2016) Comparing landsat water index methods for automated water classification in eastern Australia. Remote Sens Environ 36. Wang L, Qu JJ (2007) NMDI: a normalized multi-band drought index for monitoring soil and vegetation moisture with satellite remote sensing. Remote Sens Environ 34:1–5 37. Hostache R, Matgen P, Wagner W (2012) Change detection approaches for flood extent mapping: how to select the most adequate reference image from online archives? Int J Appl Earth Obs Geoinf 19(1):205–213 38. Schott JR, Salvanggio C, Volchok WJ (1988) Radiometric scene normalization using pseudo invariant features. Remote Sens Environ 26:1–16 39. Verstraete MM, Pinty B (1996) Designing optimal spectral indexes for remote sensing applications. Remote Sens Environ 34 (5):1254–1265 40. Zongyi M, Yaowen X, Jizong J, Linlin L, Xiangqian W (2011) The construction and application of an albedo-NDVI based desertification monitoring model. Procedia Environ Sci 10:2029– 2035 41. Mardiana S, Ahmad A, Kadir O (2000) Capability of radarsat data in monsoon flood monitoring. Proc GIS Dev (2000) 42. Martinez J-M, Le Toan T (2007) Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data. Remote Sens Environ 108(3):209–223 43. Wang Y, Hess LL, Filoso S, Melack JM (1994) Canopy penetration studies: modeled radar backscatter from Amazon floodplain forests at C-, L-, and P-band. Proc Int Geosci Remote Sens Sympos II:1060–1062 44. Jüssi M (2015) Synthetic aperture radar based flood mapping in the alam-pedja nature reserve in years 2005–2011. Master thesis in Geoinformatics and Cartography, University of Tartu, Faculty of Science and Technology, Institute of Ecology and Earth Sciences, Department of Geography, pp 1–41 45. Pultz TJ, Crevier Y, Brown RJ, Boisvert J (1997) Monitoring of local environmental conditions with SIR-C/XSAR. Remote Sens Environ 59(4):248–255 46. Martinis S (2010) Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs. Dissertation, LMU München: Faculty of Geosciences 47. Wegmüller U, Strozzi T (1999) Validation of ERS differential sar interferometry for land subsidence mapping: the bologna case study, Proceeding IGARSS’ 99

Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications Hibatoullah Laachrate, Abdelhamid Fadil, and Abdessamad Ghafiri

Abstract

Soil moisture is an important parameter among the fifty “Essential Climate Variables” according to Global Climate Observing System (GCOS). It allows to perform several applications in different fields, especially hydrological, meteorological and agricultural ones. There are several methods for measuring this parameter in two main categories: in-situ methods and remote sensing. In this sense, two well-known satellites are invested, namely Soil Moisture and Ocean Salinity (SMOS) from European Space Agency (ESA) launched in 2009 and Soil Moisture Active Passive (SMAP) from National Aeronautics and Space Administration (NASA) launched in 2015. This work will be dedicated to the state of the art of soil moisture downscaling and applications across various regions of the world, including Canada, USA and Spain to take advantage of these studies for a future effective exploitation of soil moisture mapping in a Moroccan context.

 

Keywords

   

Soil moisture Earth observation Satellite Remote sensing Microwave sensing SMOS SMAP ESA NASA Soil moisture applications Downscaling

H. Laachrate (&)  A. Ghafiri Ben M’sik Faculty of Sciences, Av Driss El Harti, B.P 7955 Sidi Othmane, Casablanca, Morocco e-mail: [email protected] A. Ghafiri e-mail: a.ghafi[email protected] A. Fadil Hassania School of Public Works, km 7 Route D’El Jadida, B.P. 8108 Casablanca, Morocco e-mail: [email protected]

1

Introduction

Soil moisture (SM) can be defined as the water contained in the unsaturated zone [1]. In practice, we use volumetric soil moisture Ө rather than gravimetric one that is defined mathematically as: Ө = Vw/Vwet with Vw: the volume of water and Vwet the volume of the wet material (air, water and solid material as soil particles and vegetation tissues). It can be measured by in-situ methods such as: the gravimetric method, time-domain reflectometer (TDR), frequencydomain reflectometer (FDR), neutron thermalization [2–4] and remote sensing. Remote sensing is a very promising method of SM measurement, especially when the area of study is large, and the budget is limited. To estimate SM from the space, many satellites were invested and the most popular and used ones are: SMOS, SMAP, ASCAT and AMSR-2 [5]. This variable is very important since it can be used for many hydrological, agricultural and environmental applications [4, 6]. In the hydrological domain, this parameter allows us to perform river flow forecasting as well as planning irrigation systems and soil conservation programmes [7], not to mention the efficiency of this parameter for flood forecasting. In agriculture, knowledge of SM allows for optimal management of irrigation, which would reduce water consumption [8]. In addition, a surplus or a lack of water has an influence not only on the growth of the plants but also on the microbiological and soil processes [4]. In the environmental area, SM is a variable key for the understanding of the bio-physical processes related to exchanges of mass and energy between atmosphere, hydrosphere and biosphere, the reducing of soil pollution through phytoremediation and the monitoring of the global energy, hydrological cycle [5] and climate change. Not to mention the fact that SM mapping has an important role in flood and drought monitoring and forecast. This chapter aims to give a brief state of the art of the existing literature of remote sensing satellites for SM retrieval, especially SMOS and SMAP and some of the main

© Springer Nature Switzerland AG 2020 H. Jarar Oulidi et al. (eds.), Geospatial Technology, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-24974-8_3

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applications of it. The paper is organized as follows: first, we will run through some SM measurements in ground and remote sensing. Then, we will move to a historical overview of SM monitoring with remote sensing techniques, some remote sensing satellites and two SM-devoted satellites: SMOS and SMAP. Finally, we will see the main SM applications and downscaling using SMOS and SMAP satellite for many parts of the world including Morocco with some global statistical results of the last three years of the literature.

2

Soil Moisture Measurement Methods

To retrieve SM, two main categories of measurement should be taken into consideration, i.e. in-situ methods and remote sensing.

2.1 In-Situ The literature has detailed these methods, but in this paper we will only have a quick overview about them [2–4, 9–11]. In-situ methods are divided into direct and indirect ones. The only direct method is the gravimetric one, while the others like TDR, FDR and neutron thermalization are indirect [11]. The direct method is based on heating a soil sample and calculating its mass before and after heating. Its main importance is that it serves as a calibration for indirect methods [3]. It is a simple method, but its application is very tedious as it is destructive and we need representative samples of the study area [2, 3]. TDR and FDR are both based on the relation between the dielectric constant of the soil and SM [3] but with different instruments and mathematic equations. Among TDR and FDR advantages are: robustness and stability of instruments and fast response time, but these methods have a small area of influence and need calibration under certain special soil conditions [3]. Neutron thermalization uses the neutron probe to emit neutrons. This method consists of calculating the amount of thermalized neutrons due to their collision with the hydrogen atom [2, 3]. It has a good accuracy after calibration, but it may be dangerous to users’ health [3]. Another method of SM retrieval is based on Global Positioning System (GPS) sensors. Koch et al. [12] explained one of the interesting experiments of using GPS for SM measurement. It is based on L1-band GPS signal highly correlated with changes in the permittivity of the soil which depends on the water content within the soil. This method has been tested for a bare soil at a certain soil depth and with a specific soil texture. So, the next

experiments should test other soil types and depths [12]. All these methods are not suitable for large-scale areas and are expensive in terms of money, staff and time. That is why another category is commonly used: remote sensing.

2.2 Remote Sensing Remote sensing is the process of obtaining information on the Earth’s surface using images acquired from an aerial perspective, using electromagnetic radiation from the ground emitted on one or more electromagnetic bands [13]. The soil radiation depends on its temperature and its emissivity properties which change when SM changes. Then, radiation is a function of SM [14].

2.2.1 Historical Overview To state context, we will explore the history of SM monitoring using remote sensing with a brief overview. Americans were the first to use remote sensing of SM from tower, aircraft and satellite platforms with microwave radiometers, scatterometers, synthetic aperture radar and combined radar– radiometer systems [15]. One of the first experiments of microwave radiometers was tested during February and March 1971 in order to find the dependence of soil emission on SM [16]. Another American project proposed the best range of incidence angle for radar to estimate SM [17]. Later, during March 1977, the L-band was tested for SM estimation using synthetic aperture radar (SAR) [18]. Another study was dedicated to the comparison between sensing SM in the L-band and in the C-band and found that they are complementary and the radar–radiometer combination gives an accurate SM estimation despite the naturally occurring conditions [19]. 2.2.2 Remote Sensing Satellites There are four commonly used SM estimation techniques: the optical/thermal approach [14, 20], the passive microwave sensor approach [14, 21–23], the active microwave sensor approach or radar [14, 23, 24] and combined method approach. Table 1 lists the main advantages and disadvantages of each approach. The use of optical sensors is limited due to their poor temporal resolutions (several days) and their contamination by cloud masks, vegetation, weather and day/night conditions. Thus, microwave remote sensing techniques are the most promising for SM retrieval [25]. A wide range of microwave satellite imagery for measuring SM exists (Table 2). Among them, we find: the Scanning Multichannel Microwave Radiometer (SMMR) operational from 1978 to

Soil Moisture Retrieval Using Microwave … Table 1 Summary of remote sensing techniques for SM retrieval, modified from [5]

Table 2 Non-exhaustive list of active and passive microwave sensors used for the generation of SM data sets since 1978, modified from [26, 27]

33

Spectrum domain

Advantages

Disadvantages

Optical/thermal

Based on mature technology, good spatial resolution and multiple satellites available

Poor temporal resolution, use is limited by vegetation cover, clouds, weather conditions and night-time

Microwave passive

Good temporal resolution, promising results in SM estimation particularly over bare soil surfaces, use not limited by clouds or daytime conditions

SM accuracy influenced by surface roughness and vegetation, coarse spatial resolution

Microwave active

Use not limited by clouds or daytime conditions, fine resolution

Coarse temporal resolution, retrieval influenced by surface roughness and vegetation

Combined methods

Optical and microwave: reducing the spatial resolution with minimizing of vegetation and surface roughness effects. Active and passive: take advantage from the high spatial resolution of active systems and the high temporal resolution of passive systems

Different SM measurement depths, scaling and validation need caution

Satellite/sensor

Period

Type

SEATSAT-SAR

June–October 1978

Active

SMMR

1978–1987

Passive

SSM/I

1987/92/95

Passive

ERS-1

1991–2000

Active

JERS-1

1992–1998

Active

SIR-C/X-SAR

April–October 1994

Active

ERS-2

1995–2003

Active

RADARSAT-1

1995–

Active

TRMM

1997–2001

Passive

MSMR

1999–2001

Passive

Envisat (ASAR)

2001–2010

Active

AMSR-E

2002–2011

Passive

PALSAR

2005–

Active

METOP-ASCAT

2007–

Active

RADARSAT-2

2007–

Active

RISAT

2009–

Active

SMOS

2009–

Passive

SAOCOM (1A-1B)

2011–

Active

Sentinel-1 (GMES)

2011–

Active

SMAP

2015–

Active/Passive

1987 on the edge of the Nimbus 7, the SSM/I satellites since 1987 until now, ERS-1 from 1991 to 2000, AMSR-E since 2002, RADARSAT-2 since 2007, ESA’s SMOS since 2009 and NASA’s SMAP since 2015. In our state of the art, we will focus on SMOS as it is the first satellite dedicated to SM measurement launched by the ESA and on SMAP which is a NASA mission dedicated to SM retrieval too.

3

Dedicated Soil Moisture Satellites

3.1 SMOS Soil Moisture and Ocean Salinity (SMOS) is an ESA mission that is part of the Earth Explorer project. The satellite was launched on 2 November 2009 by ESA. This mission

34

aims to estimate the superficial SM (the first five centimetres) and the salinity of the sea, and it is the first mission of this kind followed then by others like SMAP in 2015. The satellite is equipped of a sensor based on an L-band radiometer (1.4 GHz), its spatial resolution can range from 30 to 50 km depending on the angle of view, and its repeatability is 3 days at the equator (only for ascending orbits) [28]. Its orbit is sun-synchronous with a 6 a.m. ascending orbit and a 6 p.m. descending one. SMOS provides a quasi-complete Earth surface coverage as shown in Fig. 1.1 with SMOS data over the period of 21 to 30 November 2017.

3.1.1 Main SMOS Products There are several levels of SMOS products, and the main ones are: raw data, level 0, 1A, 1B, 1C, 2, 3 and 4. The raw data are data as they are received from the satellite, level 0 data are the raw data sorted chronologically and provided with headers of the Earth Explorer products, and the level 1A data are reformatted and calibrated in engineering units. Level 1B data are georeferenced Fourier components of brightness temperatures. From this, 1B level reprocessed and then sorted geographically results in the 1C level. Level 2 products are georeferenced and calibrated geophysical SM and Ocean Salinity (OS) products. For the level 3 and 4, two processing entities produce the data: the French national entity: CATDS and the Spanish national entity: Barcelona Expert Center (BEC). For CATDS, L3 is based on L1B and among its products: 1-day global map of SM values. Unlike L2, L3 is based on more auxiliary data and improved auxiliary data [30]. CATDS level 4 products are modelled SMOS data or SMOS data combined to data from other sensors [31].

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3.1.2 Soil Moisture Retrieval Algorithm From the level 1B data, an inversion algorithm is applied to the brightness temperatures to yield level 2 SM products with the same resolution of the level 1C data [30]. The algorithm is described in detail in the document provided by ESA: “Algorithm Theoretical Basis Document” (ATBD) for the SMOS level 2 SM Processor Development Continuation Project. Indeed, it is based on an iterative approach that aims to minimize the cost function (sum of weighted squares of the differences between measured and modelled brightness temperatures at different angles of incidence). And since the dielectric constant strongly characterizes the water contained in the soil, two models linking it to SM are used: the semi-empirical model of Dobson and that of Mironov, with Mironov as the default model [32].

3.2 SMAP Soil Moisture Active Passive (SMAP) is a satellite launched on 31 January 2015 based on the L-band (1.41 GHz) within the microwave band which is considered as the most suitable band for SM retrieval. Its orbit is sun-synchronous with a 6 p.m. ascending orbit and a 6 a.m. descending one. Its temporal revisit is about 3 days. Its main objective is to provide frequent revisit and high-resolution and global mappings of SM and landscape freeze/thaw state [33]. It was composed of a radar with 3-kilometre (km) resolution and a radiometer with 36 km to provide a 9-km resolution as a combined product, but on 7 July 2015 the radar stopped transmitting due to a technical anomaly [34]. SMAP provides a complete Earth surface coverage as shown in

Fig. 1.1 A soil moisture and ocean salinity map from centre aval de traitement des données (CATDS) from 21 to 30 November 2017 [29]

Soil Moisture Retrieval Using Microwave …

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Fig. 1.2 A 3-day composite global map of surface SM as retrieved from SMAP’s radiometer instrument between 25 and 27 August 2015 Credits NASA

Fig. 1.2 with SMAP SM data over the period of 25–27 August 2015.

3.2.1 SMAP Products SMAP products are made available publicly through two NASA-designated data centres: National Snow and Ice Data Center (NSIDC) and Alaska Satellite Facility (ASF). ASF is Table 3 SMAP data products, from: [35]

for level 1 radar products and NSIDC for other products [35]. SMAP data products are listed in Table 3. Every data level has its specifications [35]: • Level 0 data are time ordered, reconstructed and unprocessed instrument data at original resolution.

Product

Description

Gridding (resolution)

Latency

Type

L1A_Radiometer

Radiometer data in time order



12 h

Instrument data

L1A_Radar

Radar data in time order



12 h

L1B_TB

Radiometer TB in time order

(36  47 km)

12 h

L1B_S0_LoRes

Low-resolution radar r0 in time order

(5  30 km)

12 h

L1C_S0_HiRes

High-resolution radar r0 in half-orbits

1 km (1–3 km)

12 h

L1C_TB

Radiometer TB in half-orbits

36 km

12 h

L2_SM_A

SM (radar)

3 km

24 h

L2_SM_P

SM (radiometer)

36 km

24 h

L2_SM_AP

SM (radar + radiometer)

9 km

24 h

L3_FT_A

Freeze/thaw state (radar)

3 km

50 h

L3_SM_A

SM (radar)

3 km

50 h

L3_SM_P

SM (radiometer)

36 km

50 h

L4_SM

SM (surface and root zone)

9 km

7 days

L4_C

Carbon net ecosystem exchange (NEE)

9 km

14 days

Science data (Half-Orbit)

Science data (daily composite)

Science value-added

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• Level 1A data are level 0 data referenced and annotated with ancillary information such as: georeferencing parameters and radiometric and geometric calibration coefficients computed and appended, but not applied to the level 0 data. • Level 1B data are level 1A data radiometrically corrected and geolocated after having been processed to sensor units. • Level 1C data are spatially resampled level 1B data. • Level 2 data are geophysical parameters derived from the level 1 data with the same resolution and location. • Level 3 data are geophysical parameters derived from the level 1 or 2 data temporally and/or spatially resampled to a global grid. • Level 4 data are geophysical parameters that have resulted from modelling level 1, 2 or 3 data with a land surface model.

3.2.2 Soil Moisture Retrieval Algorithm As SMAP SM radar data have no longer been available since 7 July 2015, we will only deal with the passive SM retrieval. To obtain the L2_SM_P data, a SM retrieval algorithm is used with input brightness temperature and ancillary data. Then, L3_SM_P data are an aggregation of all the L2_SM_P for the same day. Before the launch of the satellite, four SM algorithms were evaluated: (1) single-channel algorithm at H polarization (baseline) (SCA), (2) single-channel algorithm at V polarization (SCA-V), (3) dual-channel algorithm (DCA) and (4) land parameter retrieval model (LPRM) [36]. The selected algorithm is the first one (baseline) [33]. Yet, recently, NASA has found a replacement for the radar data: the Sentinel-1 and the NSIDC have announced in 2 November 2017 the availability of the beta version of the “SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 km EASE-Grid Soil Moisture” data set [37].

4

Soil Moisture Downscaling and Applications

4.1 Methodology and Research Questions We conducted a statistical study to have an overview of published documents in downscaling and SM applications areas in the last three years, i.e. since 2015 which was the

Fig. 1.3 Document selection process

year of the SMAP satellite launch. Our methodology is based on the systematic mapping study (SMS) methodology [38]. We choose “Scopus” which is a well-known and trustful scientific literature database. In the late December 2017, we did an advanced search using the following search string: TITLE-ABS-KEY(“Soil Moisture” AND (SMOS OR SMAP)) AND PUBYEAR > 2007 AND PUBYEAR < 2018, and we found 1174 documents which is a big number to deal with that is why we restricted our search to 2015–2017. Hence, the number of documents was reduced to 558. Then, by selecting documents based on their content (we only selected documents related to downscaling or SM applications) we reduced the study to 103 documents (Fig. 1.3). In our study, we focused on six research questions (RQ): RQ1: On which period were our researches published in? RQ2: What are the types of our scientific documents? RQ3: What satellites are used in the researches? RQ4: To which continent(s) the study area of the documents belongs to? RQ5: Who are the most active researchers in this scientific field? RQ6: What’s each document subject area and its main purpose?

4.2 RQ1. Year of Publication The distribution of documents per year is: 25 for 2015, 46 for 2016 and 32 for 2017 (Fig. 1.4). It is remarkable that 2016 is the best year within the three ones in terms of the scientific literature production.

4.3 RQ2. Types of Documents We have noticed that our search result documents are divided into four categories (Fig. 1.5): articles, conference papers, chapters and articles in press (accepted articles but not yet assigned to a journal issue [39]). Most of the documents are articles and conference papers (78 articles and 22 conference papers).

Soil Moisture Retrieval Using Microwave …

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Fig. 1.6 “Satellite used” distribution for documents

Fig. 1.4 Document distribution per year

• The use of SMOS has known a decrease from 2016 to 2017 against an increase of the SMAP use and the synergistic use of both.

4.5 RQ4. Continents The distribution of documents all over the world (by the continent of the study area) is mainly shared by North America (USA and Canada): 39% of the documents and Europe (especially Spain: 14 documents out of 16): 15%, whereas other continents constitute: 28% and globe and multi-continental researches take 18% (Fig. 1.8).

4.6 RQ5. Authors

Fig. 1.5 Categories of documents

To have a clear vision about who are the most influential scientists in SM downscaling and applications, we created a word cloud depending on the authors’ name frequency on the papers (Fig. 1.9) and we selected authors with six papers or more (Table 4). Therefore, the top five authors in last three years are: Kerr, Piles, Merlin, Sánchez N. and Walker J.P. This information can help us for a further research in this field using these authors’ works.

4.4 RQ3. Satellites Most of the documents (66%) used SMOS rather than SMAP since SMAP is newer than SMOS (Fig. 1.6) and previous researches (before 2015) are generally SMOS-based because as we said SMOS was launched in 2009 whereas SMAP was launched in 2015. An analysis of the three variables: satellite name, year of publication and number of documents led to some conclusions (Fig. 1.7): • The highest number of documents was in 2016 using SMOS.

4.7 RQ6. Scientific Fields and Main Purpose In our study, the documents are divided into nine research areas (Figs. 1.10 and 1.11): • Hydrology and land surface models (LSM): 32% of papers • Downscaling: 28% • Hazards: 17% • Climate change: 7% • Agriculture: 6%

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Fig. 1.7 A bubble plot and a bar chart mapping the relation between the satellite used, year of publication and the number of publications

Fig. 1.8 Geographical distribution of research documents

• • • •

Land–atmosphere interactions: 5% Global biogeochemical cycles and ecosystems: 4% Numerical weather predictions (NWPs): 1% Military: 1%.

Fig. 1.9 Authors’ word cloud

A deeper analysis can be done for the 103 documents of our study by stating the main purpose of each paper (Table 5) and by giving a global overview of each scientific research field: Hydrology and LSM: SM is an important parameter in hydrological and land surface applications, especially for models’ improvement. Other application areas are also handled using SM: precipitation, groundwater level change, SM drydowns, water balance model estimate improvement, evapotranspiration estimation, SM quality for hydrological applications, soil hydraulic parameters, irrigation and LSM correction, water cycle estimation in a snow-covered area and land surface modelling for the monitoring of evapotranspiration (Table 5). Downscaling: The passive microwave satellite spatial resolution is coarse: from 30 to 50 km for SMOS and 36 km for SMAP, whereas many soil moisture applications like optimal crop growth, irrigation management for agriculture and flood forecasting and monitoring and hydrological modelling need a fine spatial resolution. To reduce the

Soil Moisture Retrieval Using Microwave … Table 4 Most eminent authors

39

Author

Occurrences

Author

Occurrences

Kerr

13

Al Bitar

6

Piles

10

Vall Llossera

6

Merlin

10

Camps

6

Sánchez N

10

Crow

6

Walker J.P.

9

González-Zamora

6

Martínez-Fernández

8

Cosh

6

Fig. 1.10 Number of papers by research field

Optical/Thermal data: Most of the documents [40–56] used the merge of radiometer data (SMOS/SMAP) with optical/thermal data, and the most common algorithm in this category is disaggregation based on physical and theoretical scale change (DISPATCH) [44] which is a deterministic approach based on the use of soil evaporative efficiency (SEE) from optical/thermal satellite’s data like MODIS to reduce the spatial resolution. 2/Radiometer data merged with Radar data: It consists of combining radiometer’s data (SMOS or SMAP radiometer) with radar data like SMAP radar, Sentinel-1 or RADARSAT-2 [56–64]. • Statistical methods: These methods are based on the use of statistical approaches like: bias correction [65–67] and wavelet transform and PCA method [68].

Fig. 1.11 Percentage of papers by research field

multi-kilometric resolution of soil moisture, two main categories of downscaling methods were used: • Satellite-based algorithms: 86.2% of the downscaling documents of our study used this category, and 13.8% used the statistical one. 1/Radiometer data merged with

Hazards: Some natural disasters and hazards can be predicted or monitored using the SM information from remote sensing. Twelve out of 18 documents were devoted to droughts and agricultural risk (Table 5) in addition to another paper in downscaling category [55]. The other papers were linked to floods and shallow landslides (Table 5). Climate change: In a multi-year scale, SM is a useful information for a climate variability observation. Hence, many topics were handled in our study’s documents:

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Table 5 Scientific research fields and main purposes of the documents Field of study

Main purpose

Satellite

Year

Authors

References

Hydrology and land surface models (LSMs)

Hydrologic and/or land surface modelling

SMOS

2015

Yin et al.

[75]

Lievens et al.

[76]

Xu et al.

[77]

Shellito et al.

[78]

Zhuo and Han (a)

[79]

Zhuo and Han (b)

[80]

Lievens et al.

[81]

Leroux et al.

[82]

Thorstensen et al.

[83]

2016

SMAP or both

Precipitation

SMOS

[84]

De Lannoy and Reichle

[85]

Xu et al.

[86]

Zeng et al.

[87]

Patil and Ramsankaran

[88]

2015

Wanders et al.

[89]

2016

Lee

[90]

Brocca et al.

[91]

2017

Román-Cascón et al.

[92]

Both

2016

Koster et al.

[93]

Groundwater level change

SMAP

2017

Kim et al.

[94]

SM drydowns

SMOS

2015

Rondinelli et al.

[95]

SMAP

Downscaling

2017

Blankenship et al.

2016

Shellito et al.

[96]

2017

McColl et al.

[97]

Improving water balance model estimates

SMOS

2017

Tian et al.

[98]

Evapotranspiration estimation

SMOS

2015

Menenti et al.

[99]

2017

Knipper et al.

[100]

SM quality for hydrological applications

SMOS

2015

Zhuo et al.

[101]

2016

Zhuo et al.

[102]

2017

Zhuo and Han

[103]

Soil hydraulic parameters

SMOS

2015

Bandara et al.

[104]

Irrigation and LSM correction

SMOS

2015

Kumar et al.

[105]

Water cycle estimation in a snow-covered area and land surface modelling for the monitoring of evapotranspiration

SMOS

2015

Jarlan et al.

[106]

Downscaling

SMOS

2015

Merlin et al.

[40]

Rüdiger et al.

[41]

Kornelsen et al.

[65]

Verhoest et al.

[66]

Djamai et al.

[42] (continued)

Soil Moisture Retrieval Using Microwave …

41

Table 5 (continued) Field of study

Main purpose

Satellite

Year

Authors

References

2016

Tomer et al.

[57]

Kornelsen et al.

[67]

Piles and Sánchez

[43]

Molero et al.

[44]

Piles et al.

[45]

2017

SMAP

2016

2017

Hazards

Droughts and agricultural risk

[47]

Pablos et al.

[49]

Jiang et al.

[53]

Pablos et al.

[55]

Eweys et al.

[56]

Sánchez et al.

[58]

Montzka et al.

[59]

Das et al.

[60]

Akbar et al.

[61]

Lakshmi and Li

[48]

Senanayake et al.

[50]

Chen et al.

[51]

Wu et al. (a)

[62]

Lievens et al.

[63]

Gabriel and Virginia

[68]

Colliander et al., Wu et al. (b)

[54, 64]

2017

Knipper et al.

[52]

SMOS

2015

Scaini et al.

[107]

SMAP

SMOS

Champagne et al.

[108]

Martinez-Fernandez et al.

[109]

Sánchez et al. (a)

[110]

Sánchez et al. (b)

[111]

Martínez-Fernández et al.

[112]

Thiruvengadam and Rao

[113]

Paredes-Trejo and Barbosa

[114]

Kędzior and Zawadzki

[115]

2016

Velpuri et al.

[116]

2017

Mishra et al.

[117]

Liu et al.

[118]

Alvarez-Garreton et al.

[119]

Romanov and Khvostov

[120]

2017

Shallow landslides

[46]

Jiang and Shen

Both

2016

Floods

Djamai et al.

2015

2017

Seo et al.

[121]

SMAP

2016

Fournier et al.

[122]

2017

Rahman et al.

[123]

SMAP

2016

Avalon Cullen et al.

[124] (continued)

42

H. Laachrate et al.

Table 5 (continued) Field of study

Main purpose

Satellite

Year

Authors

References

Climate change

Wildfires

SMOS

2015

Chaparro et al.

[125]

2016

Chaparro et al. (a)

[126]

Chaparro et al. (b)

[127]

Climatic trends

Agriculture

SMOS

Global biogeochemical cycles and ecosystems

Chaparro et al. (c)

[128]

Kerr et al.

[129]

Forest decline

SMOS

2017

Chaparro et al.

[130]

SM trend and drought analysis

SMOS

2016

Rahmani et al.

[131]

Crop monitoring Agricultural SIG Web service

Land–atmosphere interactions

2016

SMOS

2016

Yang et al.

[132]

SMAP

2016

Bolten et al.

[133]

SMAP

2016

Yang et al.

[134]

2017

Hu et al.

[135]

Irrigation demand

SMAP

2015

McNally et al.

[136]

Crop yield and irrigation demand

SMAP

2015

El Sharif et al.

[137]

Dust emission

SMOS

2015

Gherboudj et al.

[138]

2017

Kim et al.

[139]

Terrestrial evaporation improvement

SMOS

2016

Martens et al.

[140]

SM and land surface temperature (LST) dynamics

SMOS

2016

Pablos et al.

[141]

Land surface and weather models improve

SMOS

2017

Lin et al.

[142]

CO2 exchange

SMOS

2016

Scholze et al.

[69]

SMAP

2016

Jones et al.

[70]

2017

Jones et al.

[71]

Improving gross primary production (GPP) estimation

SMAP

2017

He et al.

[143]

Military

Vehicle speed

SMAP

2015

Frankenstein et al.

[74]

Numerical weather prediction (NWP)

NWP

SMAP

2016

Zhan et al.

[73]

wildfires (57.1%), climatic trends (14.3%), forest decline (14.3%) and SM trend and drought analysis (14.3%) (Table 5). Agriculture: In this field, agricultural application areas are: crop monitoring, agricultural SIG Web service, irrigation demand and crop yield and irrigation demand (Table 5) with a majority use of SMAP (83.3%) that can provide in addition to the 36-km resolution product a 9-km resolution one. Land–atmosphere interactions: Land and atmosphere are interlinked, and the study of the water contained in the top five centimetres of soil (SM) in the land can contribute to explain and model land–atmosphere interactions. Topics reviewed in our study are: dust emission, terrestrial evaporation improvement, SM and land surface temperature (LST) dynamics, land surface and weather models’ improvement (Table 5). Global biogeochemical cycles and ecosystems: The global terrestrial carbon cycle is among the most important global biogeochemical cycles. SM can help to constrain

terrestrial carbon fluxes since terrestrial carbon and water cycles are coupled by biological plant processes through the stomatal gas exchange with the atmosphere [69]. In 2016, Scholze et al. [69] used SM assimilation from SMOS and atmospheric CO2 in-situ observations simultaneously into the carbon cycle data assimilation system (CCDAS) model for the years 2010 and 2011 with global Earth coverage to constrain the global terrestrial carbon cycle. Uncertainties of the model were better reduced for both net (NEP) and gross (NPP) carbon fluxes when the SMOS data were assimilated jointly with CO2 observations than when the assimilation is reduced to the CO2 observations. The study highlighted the importance of considering a SMOS L4 carbon flux product. Then, a L4 carbon flux product—using SMAP—was used later in 2016 and 2017 by Jones et al. [70, 71]. NWP: The numerical weather prediction is a modern method of weather forecasting based on computer and mathematical models for the atmosphere changes [72]. In 2016, Zhan et al. [73] used SMAP to evaluate the impact of SM on two numerical weather forecast models: the NOAA

Soil Moisture Retrieval Using Microwave …

43

Fig. 1.12 Moroccan climate map, translated from the Moroccan National Meteorology Direction website [144] (la Direction de la Météorologie Nationale: DMN)

Global Forecast System (GFS) and NASA Unified Weather Research and Forecast (NUWRF). SM was assimilated into the models. Among improvements of the models, warm biases of GFS forecasts were reduced and for NUWRF rainfall forecasts the reducing of the biases was significantly more for the 2-day forecasts than for the 1-day forecasts. Military: It is well known that military is a very sensitive research area and information is often secret. However, we found an interesting document published in 2015 [74] about the use of SM (SMAP level 3 data) in three sites from: South Korea, Lebanon and Nigeria to calculate soil strength and then vehicle speed for a High Mobility Multipurpose Wheeled Vehicle (HMMWV) M1097. Among results, 64 km/h was found to be the maximum speed for the simulated vehicle for the HMMWV (assuming that the vehicle is off-road and uphill). The study was conducted using one type of military vehicles. Further studies about other types should be done for a better simulation of military vehicles’ speed.

4.8 Soil Moisture Downscaling and Applications for Morocco 4.8.1 The Context Morocco is a country in the subtropical zone of North West Africa. It is characterized by its very diversified climate.

Figure 1.12 shows this diversity through the division of Morocco into four zones: humid zone, subhumid zone, semi-arid zone and arid to extremely arid zone. The littoral zones are of a temperate climate, whereas the climate is desert in the south and the east of the country. The Moroccan climate has many nuances: Mediterranean in the north, oceanic in the west, continental inland and Saharan in the south. The climate also varies depending on seasons. This climate diversity in addition to Moroccan history of floods and droughts and Morocco’s interest in agriculture makes this country a suitable example for hydrological, agricultural and climate change SM applications.

4.8.2 The Projects In Morocco, many projects decided to use L-band microwave SM sensors (SMOS/SMAP) in order to achieve their objectives. Three of them are presented below. The SMELLS project: Soil Moisture for dEsert Locust earLy Survey (SMELLS) is a project financed by ESA, and its main objective is the improvement of the agricultural productivity by a preventive management of desert locust using SM maps in many African countries including Morocco [145]. In this project, two categories of products are available in the SMELLS’s website:

44

H. Laachrate et al.

Fig. 1.13 SMELLS decadal 1-km soil moisture estimate from 21 to 31 December 2017 [145]

• Decadal 1-km Soil Moisture Estimates: Every month is divided into three decades (from day 1 to 10, from day 11 to 20 and remaining days of the month). Every decadal product is estimated from an average from daily SM disaggregated to 1-km SMOS product using DISPATCH [146]. This product is available in two formats: PNG and TIFF, and the time range extends from 2010 to July 2018. Figure 1.13 shows an example of this product for the decade from 21 to 31 December 2017. • 100-m product: It concerns the areas where and when Sentinel-1 acquisition is available for the 2015–2016 range [147] (the access to this dataset is not public). According to the SMELLS project, usual SM values go from 0–0.05 m3/m3 (completely dry soil) to 0.5 m3/m3 (completely wet soil) and relevant range for desert locust monitoring is from 0.10 m3/m3 to 0.20 m3/m3. “We now have the possibility to see the risk of a locust outbreak one to two months in advance, which helps us to better establish preventive control.” said Ahmed Salem Benahi, Chief Information Officer for Mauritania’s National Centre for Locust Control [148]. JECAM’s “Morocco—Tensift” project: JECAM: Joint Experiment for Crop Assessment and Monitoring “Morocco—Tensift” is a project with agricultural purposes in the Haouz Plain of Tensift watershed in Morocco. Among its objectives is: the disaggregation of SM to obtain high-resolution SM data [149]. SM disaggregation may improve or degrade SM information depending on many factors such as: the uncertainty in input data and the

quality of the scale change modelling [40]. A performance metric needs to be defined for the SM disaggregation evaluation since conventional metrics may not be suitable. That is why Merlin et al. defined a new performance metric called GDOWN which shows that the downscaling is effective if GDOWN is positive and that it is not otherwise [40]. In their work, they applied the DISPATCH algorithm described in [146, 150] to Haouz Plain in Central Morocco from 2010 to 2013 and GDOWN is calculated by comparing downscaled data with SM measurements collected by 6 stations in the study area. GDOWN showed that the downscaling was satisfying since it was positive in 74% of the cases and by comparing this metric to conventional ones: RMSD, B, r, R and S, it was found that GDOWN was more suitable and can even be used to evaluate other downscaling methods. The REC project: The REC project is a project for crop irrigation management by multi-sensor approach. It is funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action. This project concerns two sites: the modern irrigated area of Segarra-Garrigues in Lleida (Catalonia, Spain) and an irrigated perimeter of the Haouz Plain (in the Tensift watershed, Morocco). This information and more can be found in the REC project website: [151]. Many REC articles and presentations are available. However, documents using SM from SMOS or SMAP for Morocco focused on downscaling [56, 152, 153]. Ojha and Merlin [152] used the DISPATCH algorithm implemented in the SMOS level-4

Soil Moisture Retrieval Using Microwave …

processor (C4DIS [44]) to downscale the SMOS SM data to 1-km and 100-m resolution using MODIS and LANDSAT optical/thermal data with two validation sites: Sidi Rahhal (31.7035, −7.3535) and Fam El Hisn (29.0160, −8.8417) in Morocco. Eweys et al. [56, 153] also used C4DIS for Sidi Rahhal (area: 5 km2, located 60 km east of Marrakech city, Morocco) to downscale SMOS SM data to same resolutions from 1 January 2016 to 11 October 2016 using MODIS and Sentinel-1. However, results were weakly correlated with in-situ data and the low annual rainfall: 250 mm with bare soil conditions led Eweys et al. to conclude that further efforts should be made with different circumstances to obtain better results. Prospect: We noticed that the three projects have chosen to downscale the SM data from satellite and SMELLS has gone further by using the downscaled data in the locusts’ preventive management for Morocco. REC and JECAM (Morocco—Tensift) restricted the study to the Haouz Plain. So, we need in further studies to take advantage of the downscaled data in other scientific application areas and other geographical study areas of Morocco.

5

Conclusions

To conclude, in this chapter, we explained first why SM is very important in many scientific fields. Then, we captured two major categories of SM measurements: in-situ methods and remote sensing with a brief overview of each category. Remote sensing proved to be more efficient for large-scale SM monitoring, especially with the emergence of downscaling methods that reduces the coarse spatial resolution of microwave satellites from a multi-kilometre scale to a kilometre or a hectometre one. Later, we non-exhaustively listed many SM retrieval satellites including SMOS and SMAP. These two satellites have the uniqueness of being part of two main missions in which the main objective is to emphasize and focus on the SM parameter. We tried to give more information, later, on an entire section about these two SM-devoted missions: an overview about the satellite, main products and the SM retrieval method/algorithm. Then, we conducted a statistical study and review about SM applications and downscaling using SMOS and SMAP in the last three years in six research questions about: year of publication, document type, satellite used, continent, author and scientific area/main purpose. Finally, we have chosen Morocco as a promising area of study for future SM applications and stated the current state of SMOS and SMAP SM applications in this country.

45 Acknowledgements Hibatoullah Laachrate is supported by the Moroccan institute: National Center for Scientific and Technical Research (Centre National pour la Recherche Scientifique et Technique: CNRST) as part of the Research Excellence Scholarship programme.

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49 133. Bolten JD, Mladenova IE, Crow W, Reynolds C (2016) Enhancing the USDA global crop assessment decision support system using SMAP L3 soil moisture data. IEEE, pp 5241–5243 134. Yang Z, Hu L, Yu G et al (2016) Web service-based SMAP soil moisture data visualization, dissemination and analytics based on vegscape framwork. IEEE, pp 3624–3627 135. Hu L, Di L, Yu E et al (2017) Developing geospatial Web service and system for SMAP soil moisture monitoring. IEEE, pp 1–5 136. McNally A, Husak GJ, Brown M et al (2015) Calculating crop water requirement satisfaction in the West Africa Sahel with remotely sensed soil moisture. J Hydrometeorol 16:295–305. https://doi.org/10.1175/JHM-D-14-0049.1 137. El Sharif H, Wang J, Georgakakos AP (2015) Modeling regional crop yield and irrigation demand using SMAP type of soil moisture data. J Hydrometeorol 16:904–916. https://doi.org/10. 1175/JHM-D-14-0034.1 138. Gherboudj I, Beegum SN, Marticorena B, Ghedira H (2015) Dust emission parameterization scheme over the MENA region: Sensitivity analysis to soil moisture and soil texture: dust emission over mena region. J Geophys Res Atmospheres 120:10,915–10,938. https://doi.org/10.1002/2015jd023338 139. Kim H, Zohaib M, Cho E et al (2017) Development and assessment of the sand dust pre-diction model by utilizing microwave-based satellite soil moisture and reanalysis datasets in East Asian desert areas. Adv Meteorol 2017:1–13. https://doi.org/ 10.1155/2017/1917372 140. Martens B, Miralles D, Lievens H et al (2016) Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture. Adv Valid Appl Remote Sensed Soil Moisture—Part 2(48):146–162. https://doi. org/10.1016/j.jag.2015.09.012 141. Pablos M, Martínez-Fernández J, Piles M et al (2016) Multi-temporal evaluation of soil moisture and land surface temperature dynamics using in situ and satellite observations. Remote Sens 8:587. https://doi.org/10.3390/rs8070587 142. Lin L-F, Ebtehaj AM, Wang J, Bras RL (2017) Soil moisture background error covariance and data assimilation in a coupled land-atmosphere model. Water Resour Res 53:1309–1335. https://doi.org/10.1002/2015WR017548 143. He L, Chen JM, Liu J et al (2017) Assessment of SMAP soil moisture for global simulation of gross primary production: SMAP soil moisture for improving GPP. J Geophys Res Biogeosciences 122:1549–1563. https://doi.org/10.1002/2016JG003603 144. DMN (2017) DMN. www.marocmeteo.ma 145. SMELLS (2018) SMELLS—Soil moisture for dEsert Locust earLy survey. http://smells.isardsat.com/?lang=fr 146. Merlin O, Escorihuela MJ, Mayoral MA et al (2013) Self-calibrated evaporation-based disaggregation of SMOS soil moisture: an evaluation study at 3 km and 100 m resolution in Catalunya, Spain. Remote Sens Environ 130:25–38. https://doi. org/10.1016/j.rse.2012.11.008 147. Escorihuela MJ, Merlin O, Piou C, Stefan V (2017) High resolution soil moisture for agricultural applications and low frequency passive microwave user requirement consolidation study 148. Satellites forewarn of locust plagues. http://www.esa.int/Our_ Activities/Observing_the_Earth/Satellites_forewarn_of_locust_ plagues 149. JECAM: Morocco—Tensift. http://www.jecam.org/?/projectoverview/morocco-tensift-watershed-2017 150. Merlin O, Rudiger C, Bitar AA et al (2012) Disaggregation of SMOS soil moisture in Southeastern Australia. IEEE Trans

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Hanaa Aguedai—Intrusion Zones Identification in the Mnasra Aquifer (Morocco) Using the Seawater Intrusion Models and the Geophysics Data Hanaa Aguedai, Fouad Lahlou, and Bouabid El Mansouri

Abstract

Coastal aquifers in Morocco are increasingly impacted by salt pollution. Several factors may account for the anomalies of salinization of a groundwater such as human activities, aerosols, sedimentary paleosalinity or seawater intrusion. The latter may compromise the quality of the water resource for both drinking water supply and its irrigation ability. Using the digital model, the current work aims at duplicating the pattern of evolution of the Mnasra saline intrusion mechanism that boils down to a problem of flow and transport of solutes within the aquifer, in both steady and transient conditions. We simulated the behavior of the aquifer by the numerical code to the finite elements finite element subsurface FLOW (FEFLOW). Finally, to confirm the existence of areas affected by marine intrusion, we combined the results of the FEFLOW marine intrusion model with geophysical data based on electrical tomography (electric tomography profiles). The interpretation of the geophysical data and the establishment of a new map of iso-concentrations have made it possible to identify the most impacted zones by the intrusion. Keywords

 

 

Geology and hydrogeology of mnasra Gharb basin Coastal aquifers Saline intrusion Marine invasion Numerical modeling FEFLOW



H. Aguedai (&)  F. Lahlou Laboratory of Energy Engineering and Materials; Research Team: Mechanical and Energetic, Faculty of Sciences, IbnTofail University, Kenitra, Morocco e-mail: [email protected] B. El Mansouri Lab_Geosciences/Natural Resources (GeNaR) Hydroinformatic Section, Faculty of Sciences, IbnTofail University, Kenitra, Morocco

1

Introduction

Marine intrusion is one of the most common mechanisms of salinization in groundwater. This phenomenon results in both salinity levels that may exceed the water potability standards and also in the compromise of its suitability for use in irrigation and drinking water supply. This problem is aggravated by the concentration of a growing population on the coasts. Human activities, highly based on these areas (industries, agriculture, tourism, etc.), are causing an increase in the exploitation of the resource and promoting an increase in the rate of salinization. The rise in temperature caused by climate change yields an increase in evaporation and thus the drying up of the soil, which limits the infiltration of water causing a reduction in the precipitation recharge of the aquifers. The climate change also causes an elevation of sea levels. Associated with the increase in summer demand for water, the elevation of the sea level weakens the natural balance of coastal aquifers causing the increase of saltwater intrusion. The field study has been selected in a region of high agricultural activity. Due to accelerated pumping in the region, increasing groundwater withdrawals would be observed, which could lead to a saline intrusion of the sea toward the Mnasra coastal aquifer (Fig. 1).

2

Presentation of the Study Area

An extension of the Gharb plain, the area of “Mnasra,” is part of the Gharb groundwater basin constituting and extending over 70 km along the coastal strip between Kenitra south, Oued Sebou, which is extended by the parallel line passing through Sidi Allal Tazi in the east and the Merja Zerga near Moulay Bousselham in the north. The Mnasra area is 600 km2. It tightens in the southern part downstream of the guard barrier with a width of 7 km on average. It widens in the northern part from 12 to 15 km by Sidi Allal Tazi.

© Springer Nature Switzerland AG 2020 H. Jarar Oulidi et al. (eds.), Geospatial Technology, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-24974-8_4

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Fig. 1 Limit and geology of the region Mnasra

2.1 The Region’s General Geological Context Mnasra, the sub-basin enclosing the aquifer, forms the termination of the Gharb sedimentary littoral basin. The latter constitutes the hinge between two structural units which differ in the nature and age of the land, on the one hand, and in the tectonic style and the age of the deformation on the other. • On the northern margin is the Rif which is marked by a great instability hitherto.

• On the southern margin is the Meseta with a rigid and relatively stable Paleozoic base plunging regularly from south to north with a slope of 3°. The Gharb Basin was individualized in the Miocene. Materials from the Rif and the Meseta allowed for the filling of this basin subsiding from the late Miocene. The interaction between tectonic subsidence and changes in the sea level has shaped the overall morphology of sediments. The lithostratigraphic Gharb Basin can be summarized as follows [5]:

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• The Miocene: It is related to the subsidence of the post-tectonic pit. It is characterized by a thick series of gray marls, pyrite, called “blue marl,” whose thickness can reach 2000 m. It constitutes the general substratum of the overlying formations. • The Pliocene: It is represented by deposits of a regressive character, which is not very thick. These are sandy limestone, sands, sandstones and conglomerates. This facies is characterized by high accumulation of mollusk tests and includes pyritization indices. • The Quaternary: – In the coastal zone, it is formed of sediments of marine influence, linked to quaternary transgressions. These are limestone, sandstone, sand and consolidated sands of the dune cords, up to 200 m thick. – In the interior of the continent, they are deposits in which coarse sediments (shingles and pebbles) and fine sediments (silts and clays) alternate in complex ways. Among these deposits, the sprays of shingles and pebbles, assigned to the Villafranchien and ancient Quaternary, could reach 250 m of thickness under the Gharb plain.

2.2 Reservoir Geometry Thanks to studies on the Mnasra region, two aquifer systems have been identified. These are placed on a thick Mio-Pliocene marly series constituting the bedrock aquifer system. In the coastal area, at the right of a two-dune cords, a sand–gravel level flushes with the surface; its thickness varies from area to area 20–30 m in the littoral cord, and 5– 10 m in the inner cord. At this level, there is a superficial water table nourished by the rain infiltrations. Its level of shallow water is located between 2 and 3 m of the ground. Under this superficial water table, and south of Sidi Allal Tazi, there is a thick sandstone level, and to the north we find regular successions between clay and sandstone levels [9]. Below this clayey or clayey–sandy level, some sandstone is encountered that constitutes a captive aquifer under load, its piezometric level being below that of the water table. The entire sedimentary series is characterized by extreme heterogeneity [8] which makes the aquifer system complex. The clay levels, which form individualized lenses, are difficult to correlate with each other. Moreover, the delimitation of the spatial extension of the intermediate screen separating the upper and the lower layers is imprecise because of the discontinuity of the various clayey or clayey–sandy layers (Fig. 2).

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This discontinuity shows that we have a monolayer global system, that is to say a single aquifer called the Mnasra groundwater, having a roof determined by the surface topography and a substratum formed by the series of blue marls of the Miocene (Fig. 3) [6, 15].

2.3 Hydrogeological Context Piezometry: The comparative study of the groundwater piezometric maps drawn up in 1992 by the DRPE (see Fig. 4), the one established in February 2005, and that of 2007 (Fig. 5) shows that the organization of underground flows is divided into two distinct parts: the northern part and the southern part [1]. In the northern part, a convexity is observed that is formed by the piezometric curves. The N-S axis corresponds to a watershed that shows two flow directions: In the west, the flow is in the direction of the ocean, and in the east the flow is made inland. South of Sidi Allal Tazi, the piezometric curves are quasi-parallel to the Sebou River which drains the water table. In the southern part, the piezometric lines curve, just after the dome, toward the interior of the lands forming a depression of the flows and then turn into a curve parallel to the watercourse, reflecting a slight feeding by the Sebou River of the SE.

3

Results and Discussion

3.1 Hydrodynamic Model of the Mnasra Table Steady state Spatial discretization: In order to generate a 3D mesh of the regional hydrodynamic model, the logic of the FEFLOW software running is based on the production of the mesh by the finite element method. In the first step, we have created a mesh of the model covering the area and containing 6077 triangular elements and 6452 nodes (Fig. 6). The second step is the construction of the geological model which consists of converting the area into 3D to represent the geometry of the main structures of the field. In other words, this makes it possible to simplify the presentation of existing geologic formations from the geometrical point of view and from the nature of the stratigraphic units. The discretization of the domain is done taking into account a monolayer table bounded at the top by the topography and at the base by a substratum of blue marl of the Upper Miocene and Middle Pliocene.

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Fig. 2 East–west geological cross section, made through the Plio-Quaternary deposits of the central area [12]

To construct the geometry of the model, we need to create shapefiles composed of X, Y, Z coordinates for the roof and wall of the aquifer. These shapefiles will then be used by the FEFLOW software to construct the 3D geometry of the regional hydrodynamic model thanks to a spatial interpolation method that allows the restitution of the geological formation. Initial and steady-state conditions: In order to establish a hydraulic equilibrium state of the aquifer before the important development of its exploitation, the problem solving is limited to that of a saturated flow which propounds a hydraulic solution of freshwater for the area that is not affected by the transport of marine salts. For a three-dimensional (3D) modeling of the steady-state flow problem in saturated soil, we assume that: • The initially assumed piezometric level is that of the reference state of 1992 [8] (Fig. 4). • The problem being reduced only to that of a flow, we do not take into account the transport of salts at the coastal edge of the aquifer. Hence, the ratio of the densities is zero. • The boundary conditions used in modeling the Mnasra table are as follows [2]:

– North: The boundary is perpendicular to the equipotential lines; therefore, this boundary is considered as a zero-flow leak-tight limit. – South: The boundary is a limit with imposed potential given the presence of Sebou River along this limit. – To the East: This boundary constitutes the direct contact between the Gharb and the Mnasra tables [5]; hence, the lateral inflow is evidenced by the appearance of isopiezes. The average feed rate is estimated at 38 l/s. – West: The direct contact of the aquifer with the ocean is considered as a limit with zero potential. Hydrodynamic parameters of the aquifer system: Recharge: Charging the Mnasra groundwater is mainly done by the infiltration of rain on almost the whole aquifer. It is set up for an average rainfall of 600 mm/year. This recharge takes the form of taxable flows per zone based on a soil survey [13]. The first zone of soils is very permeable with an infiltration coefficient between 20 and 25% and corresponds to sandy soils. The second is moderately permeable with a coefficient of infiltration of 15% and corresponds to

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Fig. 3 Isohypse map of impermeable bedrock ([8], as amended)

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Fig. 4 Piezometric map (m) of the Mnasra aquifer 1992 ([8], as amended)

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Fig. 5 Piezometric map (m) of the Mnasra aquifer 2007

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Fig. 6 Discretization of the Mnasra aquifer

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waterlogged soils. The last zone is slightly permeable where the infiltration coefficient is 3%, which is a case of vertisols [4]. Transmissivity and permeability: The transmissivity values differ from one area to another; there is high transmissivity in the dune sandstones given the important and dominant thickness of sandy–gritty formations in the facies of the aquifer. Transmissivity is less important in the dunes and benches of the alluvial plain. The steady-state model requires the permeability coefficient K as a hydrodynamic parameter. This permeability will be used as input to the steady-state model. The permeability is distributed in six zones of equal permeability values and of unequal importance. The values in these six zones are between 105 m/s and 104 m/s. The system discharge: The losses of the system are divided into three groups: • Natural discharges consisting of the volumes of water disposed of at sea; • Withdrawals, by pumping, for irrigation and drinking water supply; • Underground inflow, but previous studies do not show underground flow outside the field of study. Steady-state simulation: The steady-state simulation that we are undertaking enables us to reproduce the hydraulic balance of the water table, which is not influenced by the contributions of seawater. For this, it is necessary to carry out several phases of calibration up to the achievement of the results of calculations of the piezometric levels which are close to those recorded for 1992. During these many simulations, we manage to carry out local adjustment tests on hydraulic conductivity. The final validated result which represents the computed piezometry comparison map and that of the reference piezometry for the year 1992 is illustrated in Fig. 7, which shows a better restitution of the calculated piezometry coincident with the observed piezometry. After calibration of the model, we obtain two new maps: hydraulic conductivity and recharge (Fig. 8). The water balance of the Mnasra groundwater calculated by the model at the end of the simulation in steady state shows that the difference between incoming and outgoing flows is of the order of −0.12 m3/d; this implies that the model is considered satisfactory (Table 1). Transient state The main objective of this hydrodynamic modeling stage is the setting of the storage coefficient and homogenization of transient hydraulic data and mostly developing an idea about the behavior of the Mnasra table during this period.

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The simulation period of the model in transient mode will spread from the 1992 state to arrive at the 2007 state, with a 30-day time step. The choice of the state is based on the fact that it is the most reliable and available state to date. The model that we started developing in transient mode represents continuity to calibration in steady state. The input data introduced for the model are: • The 2007 piezometry map: It was carried out by the interpolation of the piezometric values that are measured by the Agence du Bassin Hydraulique Sebou (ABHS). • The values of the storage coefficient that was estimated from a hydrodynamic modeling study on the Gharb plain carried out by the Direction de la Recherche et de la Planification de l’Eau (DRPE) between 1992 and 1995 vary between 5 and 12%. • Because the exact flows of water withdrawals were missing, we had to make a distribution of the approximate sampling wells which are based on the total number of wells and the total flow of the samples. Transient flow simulation The simulation is carried out according to the hydraulic balance established in steady state which constitutes an initial condition in the transient mode. The calibration of the transient model starting from the 1992 state with a 30-day step length of time to get to the 2007 state is believed to be satisfactory (Fig. 9), with a difference between the measured values and those restored by the rather weak model. This calibration allowed an improvement in the spatial distribution of the storage coefficients represented in Fig. 10. Comparison of the evolution of calculated and measured piezometric levels The calibration results allowed us to follow the evolution of the calculated and measured piezometric levels. The following charts show that the transient state model renders the pace of the interannual hydrodynamic evolution of the Mnasra groundwater, with acceptable differences between the calculated and measured piezometries (Fig. 11).

3.2 Modeling Seawater Intrusion into the Mnasra Aquifer Steady state Initial conditions for the simulation of saltwater transport: The porous area parameters The hydro-dispersive parameters introduced in the model help solve the transport equations. The main parameters are longitudinal dispersion, transverse dispersion and molecular diffusion which govern the phenomena related to the mechanical dispersion of solutes in a porous place.

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Fig. 7 Comparison between observed and simulated piezometry of the hydrodynamical regional model levels (1992) after calibration in steady state

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Fig. 8 Left: Homogeneous areas distribution map of rainfall infiltration after calibration. Right: Distribution map of permeability equal values in Mnasra aquifer after calibration

Table 1 Water table for the year 1992 in (m3/d)

Inputs

Outputs

Flows across borders

9:533758  104

2:433187  105

Pump sampling



1:425000  105

Recharge of the water table

6:743994  105



Discharge of the water table



3:839184  105

Outgoing–incoming

0:12

Longitudinal, transverse and vertical dispersivity coefficients: In a three-dimensional space and a porous area which is considered as homogeneous and isotropic, the different dispersivity coefficients a can be estimated as follows [10, 11, 14]. • aLongitudinal ¼ 1=10th of the distance traveled by the pollutant, or the length of the plume polluting the water table; • aLateral (on the sides and downwards) = 1/100th of this distance; • aVertical (upwards): very low, in the order of molecular distribution.

However, it is important to mention that mechanical, or hydrodynamic, dispersion depends strongly on the scale of the distance considered. An empirical relationship, based on numerous field observations in the alluvial aquifer context depending on the flow or the flow axis, enables us to estimate the longitudinal dispersivity coefficient (1) [10, 11, 14]: aLongitudinal ¼ ð0:0175  LÞ1:46

ð1Þ

with L = maximum length of the considered stream or of the polluting plume.

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Fig. 9 Comparison between the observed and simulated piezometries of the hydrodynamical model

The parameters introduced in the model are: the porosity value set at 25%. Molecular spread was set at 1  109 m2 s1 for all performed tests. Given the lack of data for the longitudinal and transverse dispersivity values of the area of study, we had to introduce arbitrary values into the model. The average longitudinal and

transverse dispersivity values assigned to the model are respectively 100 and 10 m. Fluid parameters The initial conditions for the transport of salts are determined based on the assumption that the initial aquifer state represents a zero standard concentration throughout the area

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Fig. 10 Spatial distribution of the storage coefficients represented after calibration

and a concentration corresponding to that of the Atlantic Ocean on the western border of the aquifer. The concentration of water in the Atlantic Ocean is equal to 36.5 g/l. This value was introduced along the coastal area to the west of the Mnasra groundwater. Density ratio ¼

qs  q0 1204:5  1000 ¼ 0:0245 ð2Þ ¼ 1000 q0

qs and q0 represent the density of saltwater and freshwater, respectively. Simulation of saltwater transport: After the 1992 steady-state simulation at the level of the study area, we observe a penetration from the coast of the salted bevel to the bottom of the aquifer. Indeed, the result shows that the calculated salinity in the field of study strongly decreases as we move from the coastal side toward

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Fig. 11 Comparison between evolution of the piezometry calculated and measured. 1520/08 (X = 416,990; Y = 448,760; Z = 6) en m. 1502/08 (X = 411,000; Y = 457,590; Z = 25.42) en m

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Fig. 12 Cross section of the iso-concentrations and their positions (steady state)

the inland. To better visualize what is happening within the Mnasra aquifer, we realized in 2D four vertical sections perpendicular to the coastline. The following map shows the positions of the vertical sections (Fig. 12). Following the representation of the iso-concentrations on the vertical sections, we can observe that the marine intrusion is significant. We also note that the salt concentration on the western part which corresponds to the coastline is very high, but they tend to disappear toward the east, that is, inland. Further, we observe that the salted bevel entering the groundwater is positioned in the bottom end of the aquifer, because of the density effect that controls its position. Concentration values that tend to fall toward the surface are justified by the law of conservation of the mass for the salts. At this pace of mass transport, the exploitation wells located on the littoral line will present salinity anomalies of marine origin. The analysis of section (2) enables us to explain and demonstrate the evolution of the shape of the salted bevel which takes a conical form as it heads toward the surface. This is probably attributed to the decrease in the piezometric load caused by the system disturbance due to considerable flow pumping. We can also account for the slight rise of 35 m (for a freshwater/saltwater interface corresponding to 8000 mg/l) and the deep 626 m progression of the salted bevel toward the inland in section (1) by the “upconing” phenomenon. In this case, it is probable that the pumping carried out here has a low enough flow rate, or else the sampling takes place away from the interface that is why it does not seriously disturb the system. Transient state In this part, we will address the salted bevel phenomenon in a transient state starting from 1992 to 2007. The latter represents continuity to steady-state calibration. Although

the model has not been fully calibrated, because of uncertainties in the assigned hydro-dispersive parameters, the result shows reasonable values. The salinity calculated in our aquifer system shows a sharp decrease in inland salinity from the coast. To better understand what is happening inside the aquifer, we have made two 2D perpendicular sections to the salted fringe from the shore. One is located in the extreme north and the other in the extreme south of the region (Fig. 13). The figure shows the two vertical sections perpendicular to the coastline. Following these iso-concentration sections, we find that the marine intrusion is very noticeable. In these figures, we note that the salt concentrations on the western part, which corresponds to the coast, are very high. However, they tend to cancel out toward the east, that is, inland. We also note that the salted bevel entering the groundwater is positioned in the bottom end of the aquifer, given the density effect that controls its position. Concentration values tend to fall toward the surface, which is justified by the law of mass conservation for salts.

4

Qualitative Comparison of the 2D Section Distribution of Iso-Concentration by Electric Tomography Profiles in Steady State

In order to confirm the results we have obtained in the modeling part of the marine intrusion, we will make some 2D sections of the distribution of iso-concentrations that are superimposed on those of the tomography profiles. By combining the two sections, we will be able to validate the realized model [7]. To do this, we have used the ArcGIS software to help us superimpose iso-concentration sections on those of electric tomography profiles.

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Fig. 13 Cross-section of the iso-concentrations and their positions (transient state)

Oulad Mehdi II-4 The Oulad Mehdi II-4 profile perpendicular to the coastline lies in the middle part of the Mnasra region. According to studies carried out in this area, the inversion of Oulad Mehdi II-4 profile shows very high resistivity values (red dominant plaques in the sections) characterizing the sands and perhaps dry sandstone (higher than 300 X m), average values (yellow to green plaques) characterizing the sands and underwater table sandstones (30 to 120 X m) and very low values (blue plaques) for the levels of the saltwater/freshwater contact (below 10 X m). The freshwater/saltwater contact is at altitude −50 m NGM. The Oulad Mehdi II-4 profile presents continuously homogeneous contact between saltwater and freshwater located around −50 m NGM [3]. By superimposing the electric tomography profile and the 2D cross section of iso-concentration distribution that we have previously performed, we will reach a satisfactory interpretation. Assuming that there is a freshwater/saltwater interface corresponding to 8000 mg/l, this fringe will help us wedge the two sections (Fig. 14). The freshwater/saltwater interface in the electric tomography section is located at about 400 m according to the interpretation in the study. As for the cross section of iso-concentration distribution, we observe that the interface is at 310 m, then leaving an error margin of 90 m.

Regarding the depth of the freshwater/saltwater interface proposed by LPEE, it is at −50 m from sea level. This does not perfectly correspond to the depth of the interface in the 2D section of the iso-concentration distribution of the model we have achieved. This is because its depth here is −35 m of sea level. After the comparison of the electric tomography profile and the 2D cross section of iso-concentration distribution, the error margin found at the depth and the extension of the positioning of the freshwater/saltwater interface is tolerable, for, despite the various simulations, our approach had a simplified aspect. Indeed, the FEFLOW tool did not allow us to exploit evaporation data and consider other factors that can influence the extension of the mixing zone in the groundwater as the swell, tide, etc.

5

Conclusion

To conclude, it is important to focus on the contribution of this project which has enabled us to study the problem of groundwater salinization in the Mnasra region by using digital methods. The unique hydrogeological nature of the coastal aquifers is due to the inner encounter of the underground reservoir of continental freshwaters with marine waters. This

Hanaa Aguedai—Intrusion Zones Identification …

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Fig. 14 Qualitative comparison between results of the hydrogeological model and those of the geo-electric inversion model

freshwater/seawater contact responds to a fragile balance that is mainly conditioned by the difference in density between these two waters and expressed by the existence of a generally shallow mixing area and variable geometry. A disruption of this balance leads to an inevitable displacement and a dispersion of this mixing zone, thus causing either the introduction of occasional saline intrusions or a lateral invasion of seawater. Studies have helped us define the general situation of the Mnasra region and particularly define and locate the main coastal aquifer in the region. This study has also allowed us to collect all the necessary data for the development of the flow and transport model. The established model has limited the saline intrusion on the seafront area, which is a very important development, especially in the central part and the extreme south of the area, where the aquifer exploitation is intense. This progress value seems natural in these conditions. If the pumping rate continues to increase, the withdrawal of groundwater will

continually allow the progression of seawater inland in the coming years. Therefore, corrective measures, together with good planning and management of the region’s groundwater resources, must be undertaken in order to avoid reaching an “irreversible” state of large quantities of poor-quality water within our aquifer system, taking into account the human needs of this resource. At the end of this project, we have shown that we have been able to develop fairly realistic digital schemes when calibrating the used FEFLOW model by formulating and approximating the finite elements applied to the marine intrusion problem. The results of the qualitative comparison between the standard profile of electric tomography and the 2D section of iso-concentrations reveal a justified error margin. This raises our awareness to the need for a more reliable database and regular monitoring of this region’s key parameters in order to allow effective management of the problem.

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References 1. Agence du bassin hydraulique Sebou ABHS (2007) Etude de modélisation de la nappe de Mnasra Mission I: Analyse et synthèse bibliographique et acquisition des données et présentation du modèle et de l’approche d’étude 2. Agence du bassin hydraulique Sebou ABHS (2006) Etude de diagnostic de la nappe de Mnasra. ABHS; Coopération Allemande INWENT et MedWet pour les Zones Humides Méditerranéennes 3. Agence du bassin hydraulique Sebou ABHS (2010) Modélisation de la nappe Mission II: sous-Mission II-2: Elaboration du modèle hydrodispersif de l’intrusion marine 4. Boya B, Faouzi1 M, Ben Abbou1 M, Essahlaoui A, Bahir M, Youbi N, Hes-sane MA (2011) L’aquifère côtier des Mnasra (plaine du Gharb, Maroc): hydrogéologie et modélisation hydrodynamique 5. Combe M (1975) Le bassin Gharb-Mamora et les petits bassins septentrionaux des oueds Dradère et Souieire In Ressources en eau du Maroc, tome 2, plaines et bassin du Maroc atlantique. Notes et mémoires du service géologique du Maroc nc231 6. Cirac P (1985) Le bassin sud-rifain occidental au Néogène supérieur. Évolution de la dynamique sédimentaire et de la paléogéographie au cours d’une phase de comblement. Thèsees Sciences, Université de Bordeaux I, France, 283 7. Comte JC (2008) Apport de la tomographie électrique à la modélisation des écoulements densitaires dans les aquifères côtiers

H. Aguedai et al. 8. DRPE (1994) Etude de modélisation de la nappe côtière du Gharb (Région d’EI Mnasra). Ministère des travaux publics de la formation professionnelle et de la formation des cadres, administration de l’hydraulique, direction de la recherche. Rapport inédit 9. El Mansouri B (1999) Développement d’outils et concepts pour la gestion des eaux souterraines. Applications à l’aquifère côtier du Rharb Thèse d’Etat Es-Science Univ. Ibn-Tofail, Faculté des Sciences, Kénitra, p 142 10. Gelhar LW, Welty C, Rehfeldt KR (1991) A critical review of data on field-scale dispersion in aquifers. Water Resour Res 28 (1992):1955–1974 11. Gelhar LW, Mantoglou A, Welty C, Rehteldt KR (1985) A review of field-scale physical solute transport processes in saturated and unsaturated porous media, Final project report, EPRI EA-4190. Electric Power Research Institute, Palo Alto, CA 12. Kili M, El Mansouri B, Chao J, Ait Fora A (2006) De nouveaux éléments structuraux du complexe aquifère profond du bassin du Rharb (Maroc): implications hydrogéologiques C. R Acad Sci Paris 338:1194–1202 13. Office régional de mise en valeur agricole du Gharb ORMVAR (1996) Aménagement hydro agricole de la troisième tranche d’irrigation de la plaine du Gharb, zone Mnasra (zone côtière). Rapport 14. Schulze-Makuch D (2005) Longitudinal dispersivity data and implications for scaling behavior. Ground Water 43:443–456 15. Wernli R (1987) Micropaléontologie du Néogène post-nappes du Maroc septentrional et description systématique des Foraminifères planctoniques. Notes et Mem Serv Géol Maroc 331:265

The Effect of Surface Water Pollution on the Incidence of Viral Hepatitis: A Spatial Assessment Using GIS Maps Nisrine Idrissi, Fatimazahra ElMadani, Meryem Idrissi, Mohammed Ben Abbou, Mustapha Taleb, and Zakia Rais

Abstract

In recent years, the quality of surface water in the city of Fez has deteriorated due to intensive human activities. This can lead to waterborne diseases, such as typhoid fever, viral hepatitis, gastroenteritis, dysentery, to name but a few. The spatiotemporal monitoring of the different parameters that define the quality of the water with regard to 2017 shows that the downstream of Oued Fez and the existing sites in its north-east in the per urban zone Ain Kansara are the most highly polluted. They are characterized by a chronic pollution of organic and phosphoric origin. The mapping of the spatial distribution of the incidence of viral hepatitis infection at the level of the various districts of the city of Fez during the same year illustrates that the district with the highest incidence rate of this disease is Ain Kadouss with more than 80%. This study has revealed the need for constant monitoring in places where environmental degradation is caused by sewage discharges that come from the city of Fez; thus, it is likely to cause waterborne diseases to humans. Keywords

Surface water Viral hepatitis



Fez city GIS



Quality



Health risk



N. Idrissi (&)  F. ElMadani  M. B. Abbou  M. Taleb  Z. Rais Fez Faculty of Sciences, Department of Chemistry, Laboratory of Engineering, Electrochemistry, Modeling and Environment, Fez, Morocco e-mail: [email protected] M. Idrissi Technology School, Fez, Laboratory of Catalysis, Materials and Environment, Fez, Morocco e-mail: [email protected]

1

Introduction

Fez city has been witnessing demographic explosion, industrial and agricultural development, inconsiderate modernization, climate change and so on. In this regard, the control and quality of surface water have a direct impact on human health. The severity of the situation has been increasing at the level of the downstream of Oued Fez and the confluence Fez–Sebou. In this context, the authorities have carried out numerous projects to improve the quality of these rivers, as part of the National Strategy for Environmental Protection [1]. Among the projects realized, we quote: • Dechromatation station, operational since 2003, chromium is one of the toxic metals [2]; • National Environmental Action Plan (PANE). Established in 2004 as part of UNDP’s Capacity 21 programme to strengthen the institutional capacity of developing countries [3]; • Publication of the Decree n° 2-04-553, dated 24 January 2005, relating to spills, run-off, discharges, direct or indirect deposits in surface or underground waters [4]; • Establishment of the surface water quality grid by the Committee Specifications and Standards. This grid is a national tool to standardize and to unify the assessment of the water quality of rivers, lake and reservoirs 2007 [5]; • Global National Environmental Charter as part of the sustainable development process. Excerpt from the Throne Speech of July 2009 [6]; • Increase in the rate of access to sanitation services to 54.84% in 2010 [7]; • Publication of Specific Rejects Limit Values (VLSR), in October 2013 to the official bulletin (BO) [8]; • Publication of New Decree 36-15 n°. 6494-67-94 of August 2016 to the official bulletin (BO) [9].

© Springer Nature Switzerland AG 2020 H. Jarar Oulidi et al. (eds.), Geospatial Technology, Advances in Science, Technology & Innovation, https://doi.org/10.1007/978-3-030-24974-8_5

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Several studies have been conducted on the properties of water in Oued Fez and Sebou River [10]. Most of these studies have detected a surface water contamination in both watercourses downstream from Fez; this problem is due to the demographic rise and the accelerated progress of agriculture and industry in this region as two sectors which are socially and economically of vital importance [11]. Despite these efforts, we have been witnessing the spread of water-related diseases due to poor hygiene practices by the population, in addition to the practice of wastewater reuse in irrigation along with the production and consumption of raw food products. Hence, they are locally marketed [12] as the main vector of pathogens that are associated with excreta. According to the statistics of the public health services, three waterborne diseases are the most frequently prevalent in the city. These are typhoid, viral hepatitis and gastroenteritis, while cholera has disappeared since 1990 [13]. To this end, we are interested in the geographical distribution of viral hepatitis in the districts and communes of the city of Fez and the quality of surface water via GIS tools, with a view to ensuring water security for the purposes of sustainable development and public health.

Fig. 1 Location of the study area

N. Idrissi et al.

2

Materials and Methods

2.1 Study Area The surface waters of the Oued Fez and the Sebou River north-east of the city of Fez were impacted by the discharge of untreated sewage and contaminated water sources, which are the major cause of waterborne diseases of public health importance [14]. When we draw on the main aims of this study and the geographic location, the study area includes: Fez Prefecture is located on the northern part of Morocco (Fig. 1), in Fez-Meknes Region. It is bordered by the provinces of Sefrou, Taounate and Moulay Yacoub; besides, it is characterized by hydrology based on Oued Fez, and it runs from west to east, starting from its springs in Ras El Ma till Oued Sebou [15]. In the north-east of the city of Fez, the activated sludge WWTP is located 10 km from Fez, on the territory of the rural commune of Al Kansara, which is located on the hills to the north-east of Fez, in the region of Fez-Meknes. Oued Fez crosses the city of Fez and its old Medina on a 24-km stretch with a SW-NE direction before joining Sebou River. It takes its source from the big source (Ras El Ma),

The Effect of Surface Water Pollution on the Incidence …

where it is fed by very important sources, namely Ain Ras El Ma, Atrous, Bergama, Sennad, etc. [16]. This watercourse is visible at the location of Ras El Ma domain; the industrial effluents are generated by many industries, including tanneries, oil mills and wastewater from the textile industry, which is rated as the most polluting among all industrial sectors [17], using various pollutants, such as degradable organics, surfactants, metals and dyes [18]; this fact induces serious degradation of the quality of surface water. Oued Sebou originates in the Middle Atlas mountain range in 2030 m of altitude and flows over 600 km into the Atlantic Ocean. Its watershed is located at the north-west of Morocco between parallels 33°–35° north latitude and 4° 15′–6° 35′ west longitude, stretched over nearly 40,000 km2 [19]. It is bordered to the north by the southern front range of

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the Rif Mountains, to the south by the Middle Atlas, to the east by Fez–Taza corridor and the Atlantic Ocean from the west.

2.2 Sampling Sites In the rural area of Al Kansara, field visits enabled us to identify the number of water points and their nearby environment of Oued Fez and Sebou north-east of Fez. The sampling which was done on the seasonal basis of 2017 was taken in stable hydrological conditions at five sampling sites. The geographic locations of these points are shown in Fig. 2. The description of the situation of the five sites studied is presented in Table 1 (Fig. 3).

Fig. 2 Geographic situation of sampling sites

Table 1 Geographic coordinates of the five sampling sites

Sites

Locality

Geographic coordinates Longitude

Latitude

S1

Before the WWTP

4° 55′ 50.0″ W

34° 04′ 49.0″ N

S2

Before the confluence (Oueds Sebou–Fez)

4° 56′ 13.8″ W

34° 04′ 43.1″ N

S3

The confluence (Oueds Sebou–Fez)

4° 55′ 13.9″ W

34° 04′ 34.3″ N

S4

Sebou downstream of the confluence

4° 54′ 53.1″ W

34° 04′ 49.0″ N

S5

Sebou upstream of the confluence

4° 55′ 04.0″ W

34° 04′ 10.2″ N

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Fig. 3 The study area

3

Methodology

3.1 Analytical Procedure 3.1.1 Physicochemical Parameters The samples collected were collected manually at approximately 20 cm below the surface of the water, using 1, 0.5 and 0.25 L high-density polyethylene (HDPE) vials. The flasks were thoroughly cleaned and rinsed with distilled water. At the time of in situ sampling, previously washed flasks were rinsed three times with river water prior to sample collection [20]. Samples were carefully labelled and transported from the sampling site to the laboratory. The tests were carried out in situ, according to the methods described by Rodier [21] for the physical measurement parameters (temperature, pH, dissolved oxygen and CE electrical conductivity), with a multi-probe (Consort C561 portable) calibrated before each campaign and turbidity with a HACH Model 2100P-type turbid metre (Table 2). The chemical tests covered the following elements: total nitrates NO3  , total phosphorus TP, biological oxygen demand BOD5, chemical oxygen demand COD, total nitrogen TN, ammonium, analysed in the Rodier laboratory [21]. The water samples were tested according to the appropriate Moroccan standards [9].

3.1.2 Bacteriological Parameters The FC faecal bacteria count indicator is produced using the MPL method using multi-tube fermentation, using special statistical tables (Mac Crady). The water samples used for the bacteriological tests were taken according to the protocol described below: the sampling was carried out, using borosilicate glass vials thoroughly cleaned with distilled water. The cleaned and rinsed bottles were then sterilized in an autoclave at 120 ° C and pressurized to 120 kg/cm−2 for 30 min. The water samples were tested according to the appropriate Moroccan standards [9]. After storing water samples each in an appropriate 500-ml vial, they were labelled and conserved in a cooler at a maintained temperature between 0 and 4 °C. Then, they were transferred to the laboratory with a sampling sheet indicating all required data, mainly the sampling site and date as well as sanitary conditions in the sampling sites (Table 3).

3.2 GIS The geographic information system (GIS) is the combination of five essential components: data; software: hardware; human resources; procedure; and standards. The first step in developing a GIS project is the collection of databases from hospitals and health centres, with regard to the information

The Effect of Surface Water Pollution on the Incidence …

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Table 2 Grid of physicochemical parameters of the quality of Moroccan surface water in the river [9] Class 5

Class 4

Class 3

Class 2

Class 1

Very poor

Poor

Average

Good

excellent

35


8.5–9.2

6.5–8.5

6.5–8.5

9.2

9.2

Units

°C

Parameters Temperature pH

3>

3–1

5–3

7–5

7
3000

2700–3000

1300–2700

750–1300

100

70–100

35–70

35–70

25

10–25

5–10

3–5

80

40–80

35–40

30–35

50

25–50

10–25

10

mg/L

Nitrates

>8

2–8

0.5–2

0.1–0.5

0.1

mg/L

Ammonium

>3

0.5–3

0.3–0.5

0.1–0.3

0.1

mg/L

Total phosphorus

Table 3 Grid to assess bacteriological parameters of river surface water quality [9] Class 5

Class 4

Class 3

Class 2

Class 1

Very poor

Poor

Average

Good

Excellent



>20,000

2000–20,000 20–2000

system for waterborne disease, mapped using the ‘select by location’ function. Spatial series maps were established using geographic information system (GIS) to introduce the spatial changes in the morbidity rates of viral hepatitis (Fig. 4). Fig. 4 Different steps in the methodology of work

20

Parameters Faecal coliforms/100 ml

This was achieved after grouping geographic coordinates with different analyses into an Excel table, which was then transformed into CSV file readable by the QGIS software 2.8, in order to realize the maps and to have a spatial function of the water quality studied, geo-referenced by the

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GIS tools, in geographical coordinates of Lambert Conic Conformal (Merchich, Morocco). At the same time, microbiological and physicochemical analyses were integrated into the GIS in order to develop quality maps for the studied parameters.

4

Results and Interpretation

4.1 Physicochemical Parameters The results of the mean values as well as the standard deviations of the different physicochemical parameters characterizing the surface waters studied during the whole study period are graphically represented in Fig. 5. According to the quality grid for each sampling site and according to the Decree of Moroccan Standards, which sets the surface water quality standards (MSSW) [9], these results of the physicochemical mean values are represented in Table 4. The temperature has an influence on many physical, chemical and biological processes [22]. In the study area, it was noticed that there were no great temperature variations from one site to another. The values obtained are between 21 °C as minimal value and 25.5 °C as maximum one recorded at the level of the Sebou River, and 25.1 °C as minimal value and 26 °C as maximum one recorded at the level of Oued Fez. This temperature is deemed favourable to the development of bacteria, parasites, mosquito larvae and other microbial germs. The confirmed values (