Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda: A Case Study of Floods and Landslides 9819917506, 9789819917501

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Lanhai Li Richard Mind’je

Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda A Case Study of Floods and Landslides

Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda

Lanhai Li • Richard Mind’je

Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda A Case Study of Floods and Landslides

Lanhai Li Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi, Xinjiang, China

Richard Mind’je Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi, Xinjiang, China

ISBN 978-981-99-1750-1 ISBN 978-981-99-1751-8 https://doi.org/10.1007/978-981-99-1751-8

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

In recent time, it has become trendy to discourse hydrogeological hazards and risk in a diversity of contexts and diligently undertake their assessments. This book entitled Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda—A Case Study of Floods and Landslides is related to the application of geospatial tools and systems to implement probabilistic and statistical models to spot the spatial distribution of flood and landslide susceptibility and assess the possible risk in Rwanda. Hydrogeological hazards especially flood and landslide are the most deleterious hazards instigating widespread (large-scale) vandalism to human lives and properties in the East African regions. Several evidences exist on the devastating flood and landslide phenomenon in Rwanda which caused colossal destructions. Different scientists attempted to study floods/landslides and attempted to develop quite a lot of methods and approaches since long ago to scrutinize the damaging behavior of floods and landslides. Currently, the quantitative assessment of influencing parameters and the locations of the historical hazards has become a more substantial aspect in these sorts of studies. The role of climatic, geomorphological, hydrologic, environmental, and anthropogenetic factors in the incidence of flood and landslide is broadly recognized. This current research dealt with numerous floods and landslides influencing factors, including the altitude, slope, aspect, curvature, proximity to rivers, proximity to roads, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), normalized difference vegetation index (NDVI), Land Use Land Cover (LULC), soil texture, rainfall, and lithology. The geoinformatics system was extensively exploited to develop all these data layers and to build the models with regard to flood and landslide susceptibility. Moreover, the perceptions of disaster risk are accepted as an ultimate element that influences the behavior or character of the local communities and accordingly has a decisive impact on their resilience. Regrettably, community perception on flood and landslide risk has actually not received enough attention as an influencing parameter of an area’s susceptibility. Hence, the book has also considered this aspect having great potential to instigate these hazards and their related damages. v

vi

Preface

The book covers three parts, each subdivided into different chapters. Part one consists of hydrogeological hazard susceptibility subdivided into five chapters: Chap. 1 (introduction), Chap. 2 (basic information on hydrogeological hazards), Chap. 3 (description of Rwanda), Chap. 4 (data preparation for hazards modeling and mapping), Chap. 5 (susceptibility modeling and mapping). Part two details the local community perception on flood and landslide risk subdivided into three chapters: Chap. 6 (introduction), Chap. 7 (local community sampling and questionnaire), and Chap. 8 (local community perception). Part three delves into the conclusion and recommendations subdivided into two chapters: Chap. 9 (conclusion) and Chap. 10 (recommendations). This book will be used as a strong foundation for planners, future researchers, disaster risk managers, and will be used as a supplementary decision-making tool in the country. It also informs the improvement of existing community knowledge of hazards to examine the extent of information that is available to the public and the level of trust existing relatively to the organization in charge of hydrogeological risk management all targeting the flood and landslide risk reduction and management, inform the government on relocation processes by highlighting safer and exposed zones. The study also attempts to come up with concrete prevention and mitigation measures for better landslide and flood risk management in Rwanda in a scientific perspective. It is significant to disclose that this is not a book about flooding or landslide but in contrast, this is a book about the spatial distribution of flood and landslide susceptibility and the representation of how they can affect the community. Though a comprehension of flood and landslide is central in the simulation of a flood and landslide probability, it is only the first step in the process. The other contributors to this book are listed below: In part 1: Patient Mindje Kayumba (Chaps. 4, 5); Dr. Jean Baptiste Nsengiyumva (Chaps. 2, 4, and 5); Amobichukwu Chukwudi Amanambu (Chaps. 4, 5); Dr. Lamek Nahayo (Chap. 2); Dr. Omar Althuwaynee (Chap. 5). In part 2: Mapendo Mindje (Chaps. 6, 7); Albert Poponi Maniraho (Chaps. 7 and 8); Amobichukwu Chukwudi Amanambu (Chap. 8). In part 3: Dr. Jean Baptiste Nsengiyumva. Urumqi, Xinjiang, China

Lanhai Li Richard Mind’je

Acknowledgments

The completion of the book has brought sharply into focus the debt of gratitude owed to many. First and foremost, we extend our cordial gratitude to the key program for international cooperation of the Bureau of International Cooperation, Chinese Academy of Sciences (Grant Number:151542KYSB20200018), the projects of the Third Xinjiang Scientific Expedition Program (2022xjkk0602) and the Sino-Africa Joint Research Center of Chinese Academy of Sciences (Grant Number: SAJC202107) for funding and supporting this project. Authors would also like to acknowledge the Alliance of International Science Organization (ANSO) under the Chinese Academy of Sciences (CAS) for the scholarship award to conduct the doctoral studies (Ph.D.) of which this project is a part. We sincerely acknowledge the Ministry of Emergency Management (MINEMA) in Rwanda for making the National Risk Atlas available for which this research was based upon and the United States Geological Survey (USGS) Earth Explorer for freely giving adequate data for this work project without any difficulties. The support from the Xinjiang Institute of Ecology and Geography (XIEG), CAS in collaboration with the University of Lay Adventists of Kigali (UNILAK) for always providing a good and peaceful laboratory environment for this project is also not left behind. Finally, we would like to express our sincere gratitude and appreciations to all of our well-wishers who always supported us to be involved with such academic works over the years, provided with stimulation when working on this project, most especially Prof. Xi Chen, Prof. Wenjiang Liu, Dr. Alishir Kurban, Prof. Tie Liu, Dr. Christophe Mupenzi, Prof. Jean Ngamije, Dr. Emmanuel Hakizimana, Dr. Moses Kayongo, Dr. Canisius Mugunga, Mr. Christian Bahati Mindje, Victor Mind’je Bin Youlou, and Charlotte Mukamazera.

vii

Contents

Part I

Hydrogeological Hazard Susceptibility

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

Basic Information on Hydrogeological Hazards (Flood and Landslide) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Key Concepts Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Flood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.4 Landslide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5 Susceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Types of Floods and Landslides . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Major Types of Floods . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Major Types of Landslides . . . . . . . . . . . . . . . . . . . . . 2.3 Causes of Floods and Landslides . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Atmospheric Precipitation . . . . . . . . . . . . . . . . . . . . . . 2.3.3 The Topography of the Area . . . . . . . . . . . . . . . . . . . . 2.3.4 Urbanization and Land Use Change . . . . . . . . . . . . . . . 2.4 Impacts of Floods and Landslides . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Agriculture and Food Security . . . . . . . . . . . . . . . . . . . 2.4.2 Human Settlement and Infrastructure . . . . . . . . . . . . . . 2.4.3 Water and Sanitation . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 Health and Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.5 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.6 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Hydrogeological Hazards Profile and Susceptibility Context in Rwanda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 5 7 7 7 7 8 8 8 8 8 10 12 12 13 13 14 14 14 15 15 16 16 17 17

ix

x

Contents

2.6 Historical Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

19 20

3

Description of Rwanda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Location and Administrative Division . . . . . . . . . . . . . . . . . . . 3.2 Demography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Relief and Topography . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Hydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

23 23 23 24 26 27 28 29

4

Data Preparation for Hazards’ Modeling and Mapping . . . . . . . . . 4.1 Hazards Inventory System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Influencing Factors for Floods and Landslides Occurrence . . . . . 4.2.1 Altitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Slope Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Aspect (Slope Orientation) . . . . . . . . . . . . . . . . . . . . . 4.2.4 Curvature (Slope Curve) . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Proximity to Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.6 Proximity to Roads . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.7 Topographic Wetness Index (TWI) . . . . . . . . . . . . . . . 4.2.8 Stream Power Index (SPI) . . . . . . . . . . . . . . . . . . . . . . 4.2.9 Sediment Transport Index (STI) . . . . . . . . . . . . . . . . . 4.2.10 Normalized Difference Vegetation Index (NDVI) . . . . . 4.2.11 Land Use Land Cover (LULC) . . . . . . . . . . . . . . . . . . 4.2.12 Soil Texture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.13 Rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.14 Lithology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Multicollinearity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

31 31 32 34 35 36 37 38 38 39 41 42 43 43 45 47 48 49 51

5

Susceptibility Modeling and Mapping . . . . . . . . . . . . . . . . . . . . . . . 5.1 Flood Susceptibility Modeling and Mapping . . . . . . . . . . . . . . . 5.1.1 Methods: The Multivariate Logistic Regression (LR) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Spatial Correlation Between Influencing Factors and Flood Occurrence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Flood Susceptibility Mapping (FSM) . . . . . . . . . . . . . . 5.2 Landslide Susceptibility Modeling and Mapping . . . . . . . . . . . . 5.2.1 Methods: The Bivariate Frequency Ratio (BFR) Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Spatial Correlation Between Influencing Factors and Landslide Occurrence per Class . . . . . . . . . . . . . . . . . . 5.2.3 Prognostic Weights and Ranking of the Factors Based on their Influence Level . . . . . . . . . . . . . . . . . . . . . . . 5.2.4 Susceptibility Mapping for Landslide . . . . . . . . . . . . . .

55 55 55 57 57 62 62 64 72 74

Contents

xi

5.3 Models’ Accuracy and Validation . . . . . . . . . . . . . . . . . . . . . . 5.4 Limitations and Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part II

77 79 80

Community Perception on Flood and Landslide Risk

6

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 88

7

Local Community Sampling and Questionnaire . . . . . . . . . . . . . . .

91

8

Local Community Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Experience and Belief Toward the Risk and Mitigation . . . . . . 8.2 Capacity, Concern and Responsibility . . . . . . . . . . . . . . . . . . 8.3 Mitigation Initiatives and Mechanisms . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

93 93 94 96 99

Part III

. . . . .

General Conclusion and Recommendations

9

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

10

Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 The Government (Ministry in Charge) . . . . . . . . . . . . . . . . . . 10.2 The Local Community . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

105 105 106 106

Abbreviations

AHP ANSO AUC BFR CAS CHIRPS CI CR DEM FEMA FLAASH FR FSI FSM GDP GIS GPS INP LR LSI LSM LSVM LULC MIDIMAR MINAGRI MINALOC MINEMA MLC MOEA NASA

Analytical Hierarchy Process Alliance of International Science Organizations Area Under Curve Bivariate Frequency Ratio Chinese Academy of Sciences Climate Hazards Group InfraRed Precipitation with Station Consistency index Consistency Ratio Digital Elevation Model Federal Emergency Management Agency Fast Line-of-sight Atmospheric Analysis of Hypercubes Frequency Ratio Flood Susceptibility Index Flood Susceptibility Map Gross Domestic Product Geographical Information System Global Positioning System Investigative Network Process Logistic Regression Landslide Susceptibility Index Landslide Susceptibility Map Linear Support Vector Machine Land Use Land Cover Ministry of Disaster Management and Refugee Ministry of Agriculture and Animal Resources Ministry of Local Government Ministry of Emergency Management Maximum Likelihood Classification Minnesota Office of Environmental Assistance National Aeronautics and Space Administration xiii

xiv

NBI NCEA NDVI NIR NSFC O.A PFRV PW REMA ROC RS SPI SRTM STI TWI UN UNDP UNDRR UNILAK UNISDR USAID USGS VIF WFPA WMO XIEG

Abbreviations

Nile Basin Initiative National Center for Environmental Assessment Normalized Difference Vegetation Index Near Infrared National Natural Science Foundation of China Overall Accuracy Prioritized Factors Rating Value Prognostic Weight Rwanda Environmental Management Authority Receiver Operating Curve Remote Sensing Stream Power Index Shuttle Radar Topography Mission Sediment Transport Index Topographic Wetness Index United Nation United Program Development Program United Nations Office for Disaster Risk Reduction University of Lay Adventists of Kigali United Nation International Strategy for Disaster Risk United State Agency for International Development United State Geological Survey Variance Inflation Factor Washington Forest Protection Association World Meteorological Organization Xinjiang Institute of Ecology and Geography

Part I

Hydrogeological Hazard Susceptibility

Chapter 1

Introduction

Almost no part of the earth’s surface is free from the impacts of natural hazards. Although they occur in all parts of the world, some regions are more vulnerable to certain hazards than others (WMO 2021). Though, it may not be feasible to control nature and stop the progress of natural phenomena, but the efforts could be made to avoid disasters and alleviate their effects on human lives, infrastructure, and property by adopting suitable disaster mitigation strategies (UN 2010; Gayathri 2019). Hydrogeological hazards (i.e., flood and landslide etc.) cause significant societal and economic damage and large number of fatalities worldwide (Salvati et al. 2018). Hence, the incidences of hydrogeological hazards, such as floods and landslides, have been on the increase in the recent times due to a rapid pace of developments in developing countries (Anbalagan and Singh 1996). Floods and landslides are frequent and destructive hydrogeological hazards that cause various deaths and millions of dollars in property damage every year (WMO 2021). Meanwhile damages to settlements and infrastructure as well as human casualties caused by these hazards are increasing worldwide (Singhroy et al. 2004; Yang and Adler 2008). Additionally, casualties due to slope failures and inundations are larger in developing countries and economic losses are more severe in the industrialized world (Reichenbach et al. 1998). These disasters claimed over 90,000 lives, affected over 1.4 billion people and cost about $210 billion in the past and their impacts are often felt most acutely in less developed regions (Hong et al. 2006). Factors such as climate change, heavy rains, melting glaciers and human activities help shape the collapse of the landslide, increase inundations, and consequently, human, their properties and environment suffer its negative effects (Allen et al. 2010; Field and Barros 2014). Regionally, in East Africa, flood and landslide disasters have serious and diverse impacts and, existing figures have confirmed their rise in damages and losses (Nsengiyumva et al. 2018). The figures are much more serious in the last decade, with significant fatality recorded. Monitoring, mapping and forecasting these hazards are less than adequate as required within different countries in the world (Petley 2012). The geographical features of any given area can make such an environment © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_1

3

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1 Introduction

prone to flooding and landslide. This can be said to be true in Rwanda as Douglas et al. (2008) and Bizimana and Sönmez (2015) pointed out that the geographical features and climatic profile of Rwanda have made it prone to various hazards especially localized floods and landslides. Recently, the rate of flood and landslide occurrence has been on the increase for each rainy season of the year (AsumaduSarkodie et al. 2017). Moreover, Twagiramungu (2006) pointed out that different areas in Rwanda are prone to flood and landslide due to various aspects such as geo-aspects, land use types and others. The hilly topography and high annual precipitation rates with exploitation of the natural environment such as deforestation, inappropriate farming and poor housing techniques have accelerated disaster risks and hence results into loss of lives and damages to property from the community exposed to these disaster risks (MINEMA 2018). Therefore, the priority of identifying landslide and flood risk zones by producing susceptibility maps is paramount. It is reported that different areas are either vulnerable to or currently facing serious floods and landslides which was not the case in the past (Nsengiyumva 2012; Habiyaremye 2016). Thus, flood and landslide constitute normal hydrogeological phenomena with which society must live (Moeyersons et al. 2008). They have been amongst the major disasters and have had great impacts on human lives and development, properties, infrastructure as well as environment (Nsengiyumva 2012). It is also reported that rural citizens do not have incentives to undertake prevention and mitigation measures especially in mountainous areas of Rwanda (Nsengiyumva 2012; Habiyaremye 2016). Therefore, due to these limitations, these disasters have had significant impacts, while limited measures exist to minimize them. Flood and Landslide susceptibility assessment has become a major subject for authorities responsible for regional land use planning and environmental protection. Thus, a growing research effort deals with the creation of susceptibility or hazard maps describing the actual or future threat from unstable slopes and land cover (Neuhäuser and Terhorst 2007). Despite the growing number of studies focusing on one or a few aspects of a flood and landslide risk assessment, only very few studies produce risk and susceptibility maps (Vranken et al. 2015). There have been recent studies about disaster management in Rwanda with significant emphasis on the description of hazards (Bizimana and Sönmez 2015), awareness and capacity building (Habiyaremye 2016; Nahayo et al. 2018), early alert and vulnerability (Nahayo et al. 2017). Many of the studies were not conducted countrywide, but rather limited to the district and province levels using qualitative, social, descriptive approaches and secondary data sources. Furthermore, authors conducted these researches with a limited focus on influencing factors (Piller 2016; Nahayo et al. 2017). The above pointed researches outline the limitation in these studies for risk analysis while Van Westen et al. (1997) explained that studies involving hazard mapping related to disaster risk have to be highlighted by a susceptibility analysis involving important influencing variables. Furthermore, according to Cao et al. (2016), in order to attain precise results, it is so vital that all input factors grasp a spatial correlation with the hazards. One of the reasons why flood and landslide susceptibility assessment and mapping remain difficult relates to the lack of temporal data on the occurrence of

References

5

these hazards (Pellicani et al. 2014) and mostly this lack of data is particularly an issue in developing countries where inventories are rare (Vranken et al. 2015). The identification of high-risk zones for floods and landslides can lead to a better understanding of hydrogeological disaster risks and put in place measures for risk reduction hence contribute a lot to the process of sustainable management of hydrogeological disaster risks (Brandolini et al. 2012, MIDIMAR 2012). Consequently, as Rwanda is prone to a wide range of natural hazards with limited adequate information that is essential for effective hydrogeological disaster risk management due to limited scientific researches on susceptibility analysis (Nsengiyumva et al. 2018).

References Allen CD et al (2010) A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259(4):660–684 Anbalagan R, Singh B (1996) Landslide hazard and risk assessment mapping of mountainous terrains—a case study from Kumaun Himalaya, India. Eng Geol 43(4):237–246 Asumadu-Sarkodie, S., et al. (2017). Situational analysis of flood and drought in Rwanda Bizimana H, Sönmez O (2015) Landslide occurrences in the hilly areas of Rwanda, their causes and protection measures. Disaster Science and Engineering 1(1):1–7 Brandolini P et al (2012) Geo-hydrological risk management for civil protection purposes in the urban area of Genoa (Liguria, NW Italy). Nat Hazards Earth Syst Sci 12(4):943–959 Cao C et al (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):948 Douglas I et al (2008) Unjust waters: climate change, flooding and the urban poor in Africa. Environ Urban 20(1):187–205 Field CB, Barros VR (2014) Climate change 2014–impacts, adaptation and vulnerability: regional aspects. Cambridge University Press Gayathri A (2019) Coping with natural disasters (objectives and plans of disaster management). In: Strategies for disaster management: a multidisciplinary approach.” ISBN: 978-81-933447-9-8, p 15 Habiyaremye G (2016) Disaster risk and capacities assessment in the north-west parts of Rwanda. Proceedings of the International conference “InterCarto. InterGIS” 20:262–263. (In Russ.). https://doi.org/10.24057/2414-9179-2014-1-20-262-263 Hong Y et al (2006) Evaluation of the potential of NASA multi-satellite precipitation analysis in global landslide hazard assessment. Geophys Res Lett 33(22). https://doi.org/10.1029/ 2006gl028010 MIDIMAR (2012) Impacts of floods and landslides on socio-economic development profile. r. a. p. awareness. kigali, ministry of disaster management and refugee affairs. http://midimar.gov.rw/ uploads/tx_download/Floods_and_landslides_in_Musanze.pdf MINEMA (2018) National contingency plan for floods and landslides. kigali, ministry in charge of emergency management. https://www.minema.gov.rw/fileadmin/user_upload/Minema/Publica tions/Contingency_Plans/Contingency_Plan_for_Floods_nd_Landslides.pdf Moeyersons J et al (2008) Mass movement mapping for geomorphological understanding and sustainable development: Tigray, Ethiopia. Catena 75(1):45–54 Nahayo L et al (2017) Early alert and community involvement: approach for disaster risk reduction in Rwanda. Nat Hazards 86(2):505–517 Nahayo L et al (2018) Extent of disaster courses delivery for the risk reduction in Rwanda. International journal of disaster risk reduction 27:127–132

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1

Introduction

Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86(1–2): 12–24 Nsengiyumva J (2012) Disaster high risk zones on floods and landslides. MIDIMAR, Kigali Nsengiyumva JB et al (2018) Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Int J Environ Res Public Health 15(2):243 Pellicani R et al (2014) Assessing landslide exposure in areas with limited landslide information. Landslides 11(3):463–480 Petley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930 Piller AN (2016) Precipitation intensity required for landslide initiation in Rwanda Reichenbach P et al (1998) Regional hydrological thresholds for landslides and floods in the Tiber River basin (Central Italy). Environ Geol 35(2):146–159 Salvati P et al (2018) Gender, age and circumstances analysis of flood and landslide fatalities in Italy. Sci Total Environ 610:867–879 Singhroy V et al (2004) Landslide hazard team report of the CEOS disaster management support group. In: CEOS disaster information server, vol 4. National Academy Press, Washington, DC, pp 130–132 Twagiramungu F (2006) Environmental profile of Rwanda. Consultancy Report, vol 78. European Commission UN (2010) Don’t wait for disaster. Retrieved April 22, 2022, from https://www.un.org/sg/en/ content/sg/articles/2010-03-19/dont-wait-disaster Van Westen CJ et al (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86(2):404–414 Vranken L et al (2015) Landslide risk assessment in a densely populated hilly area. Landslides 12(4):787–798 WMO (2021) Weather-related disasters increase over past 50 years, causing more damage but fewer deaths. Retrieved April 22, 2022, from https://public.wmo.int/en/media/press-release/weatherrelated-disasters-increase-over-past-50-years-causing-more-damage-fewer Yang H, Adler RF (2008) Predicting global landslide spatiotemporal distribution: integrating landslide susceptibility zoning techniques and real-time satellite rainfall estimates. Int J Sediment Res 23(3):249–257

Chapter 2

Basic Information on Hydrogeological Hazards (Flood and Landslide)

2.1 2.1.1

Key Concepts Definitions Hazard

A hazard can be defined as potential risk of havoc to the functioning of the society including their lives, assets and their environment posed by “the intersection of human systems, natural processes, and technological systems” (Cutter 2003). Hazards are sources of danger that may or may not lead to an emergency or disaster and are named after the emergency/disaster that could be so precipitated (Haddow et al. 2008). Such sources of danger may arise as the consequence of geological, meteorological, oceanographic, hydrological, or biological earth processes, or may be technological in origin.

2.1.2

Risk

A risk is the potential disaster losses (in terms of lives, health status, livelihoods, assets and services) which could occur to a particular community or a society over some specified future time period (UNISDR 2009). Hazard, vulnerability and exposure are influenced by a number of risk drivers, including poverty and inequality, badly planned and managed urban and regional development, climate change and environmental degradation (UNDRR 2019). Besides the consideration of hazards, disaster risks also necessitate the resilience of the community and systems to withstand and recover from disasters. Risk is a combination of three components: hazard, exposure, and vulnerability. Data from each of these categories can be used to paint a picture of risk in a certain location and over time.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_2

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Flood

This is the most frequent and pervasive hydrological natural event caused by an overflow of water that submerges a usual dry land (Alabi et al. 2017). Generally, flooding can occur as a result of continuous periods of intense rainfall, the fast glacier/snow melting as a result of temperature rise, ocean waves, and the failure of constructed dams or levees.

2.1.4

Landslide

Landslides are defined as a broad range of geological phenomena blamed for the downward and outward movement of slope-forming material made of rock, soil, artificial fills, or a mixture of all down a slope (Nahayo et al. 2019). Landslide refers to five different types of slope movements namely slides, falls, flows, topples, and spreads. These last can also be categorized according to the nature of geologic material such as bedrock, debris, or earth (https://www.usgs.gov/faq/naturalhazards).

2.1.5

Susceptibility

Hazard susceptibility is the likelihood of a hazard occurring in an area based on local terrain conditions (Brabb 1985; Reichenbach et al. 2018). Regarding flood and landslide, susceptibility refers to the extent to which an area can be influenced by the movements of slope or water. This last takes neither the temporal probability (time and frequency of hazard incidence), nor the hazard’s magnitude into account.

2.2 2.2.1

Types of Floods and Landslides Major Types of Floods

Floods can be classified into different types.

(a) Flash Flood The National Weather Service defines a flash flood as a rapid and extreme flow of high water into a normally dry area, or a rapid rise in a stream or creek above a predetermined flood level, beginning within 6 h of the causative event (e.g., intense

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Types of Floods and Landslides

9

rainfall, dam failure, ice jam) (FEMA 2006). The fluctuation of the real time threshold depends on the area’s location. Flood incidence becomes flash flooding whenever rainfall with high intensity drive to a quick outpouring of rising waters and downpours. This can arise in highlands with steep topography and fragile soil depths. Moreover, the upsurge, high speed of water running with huge amounts of debris characterize flash floods. Major factors in flash flooding are the intensity and duration of rainfall, the steepness of watershed, and stream gradients (FEMA 2006).

(b) Riverine Flooding As opposed to flash floods, river floods, generally occur over a period of days up to months. This is because they occur in large basins involving ‘main stem’ rivers (Doswell III 2003). Some rivers are more likely than others to experience seasonal or annual flooding. In line with the United States Geological Survey (USGS), this type of flooding is associated with larger rivers (catchments), and typically strikes when there is extreme runoff from durable downpours, and occasionally combining with upstream flood and/or with melting snow in areas with a wet season. The incidence of riverine flood exhibits variations based on the topographic nature of the terrain. For instance, plane land locations may face slow-moving water for a long period whereas floods in elevated areas occur in a short period (minutes) after intense rainfall. The short notice, large depths, and high velocities of flash floods make these types of floods particularly dangerous (FEMA 2006).

(c) Coastal Flood Coastal flooding occurs suddenly in coastal environment as a result of coastal processes in a short-term. These processes include the upsurge in water level due to storm surge or heavy rainfall, extreme tides, waves among others (https://www. coastalhazardwheel.org/coastal-flooding/). The extension in the magnitude of this type of flood rests on the storm surge conditions, the topographic settings of the coast, and the bathymetric width of the coastal environment. Most of the time, the incidences of coastal flooding are more frequent in lower altitude of the coastal regions. The range of a coastal flooding is determined by the elevation of floodwater infiltrating the inland. The latter is influenced by the topographic nature of the coastal area prone to flooding (https://dbpedia.org/page/Coastal_flooding).

(d) Urban Floods In many cases, floods are not caused by rivers overflowing only, but are also caused by inadequate drainage facilities (Senarathne 2006). Globally, urban planners are facing significant challenges related to the rising trend of urban floods ranging from relatively localized incidents to major incidents. Urban flooding typically occurs

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owing to insufficient percolation and retention capacity as a result of poor land use planning. The latter instigates the community’s encroachment around the drainage and culvert systems causing their disruption by lowering their capacity to convey rainwater. Moreover, urban rivers rise more quickly during heavy rain which increases flood peak discharge compared to rural rivers due to the reduction in the capacity of water storage in urban basins (https://ndma.gov.in/Natural-Hazards/ Urban-Floods). With urbanization process, the increase of impervious surfaces affects the infiltration capacity which intensifies the speed of rainwater flow and, therefore, aggravate the incidence of flood impacts.

2.2.2

Major Types of Landslides

Generally, landslides are pronounced using two concepts, i.e., the materials and the type of movement. Although many types of mass movement are included in the general term “landslide”, the more restrictive use of the term refers only to mass movements, where there is a distinct zone of weakness that separates the slide material from more stable underlying material (Hungr 1995). The main types of slides as follow:

(a) Rotational Slides Following Varnes (1958)’ classification of landslides, rotational slides converges with the ground surface of rupture which is concavely curved upward and move roughly in rotational way along an axis analogous to the surface and transverse to the slide (Fig.2.1a). In this context, “a slump” is the term used to define a rotational slide having one or more curved slip surfaces in which the material moves partially and leaving single slumped blocks. However, the saturation of the soil and the vibrations created by the movement leads to viscous fluid which influences rotational slides turning into mudflows after moving a few meters.

(b) Translational Slides With the transitional slides, the movement of landslide mass lay on a rugged plane surface having little rotational movements or in a reverse orientation (Fig.2.1b). In this, “a block slide” is a concept defining a translational slide when the mass sliding downslope involves a single or more closely connected units moving as a relatively cohesive mass. Moreover, in the strictness sense, a slide is basically characterized by failure of material at depth and then movement by sliding along a rupture of slip surface (Hungr 1995). The block slide, therefore, occurs when the slide movement mainly happens on a flat slip surface (Fig.2.1d). On the other hand, the material moving

2.2

Types of Floods and Landslides

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Fig. 2.1 Major types of landslides: (a) Rotational slide (b) Translational slides, (c) Topple, (d) Block slides, (e) Lateral slides (adopted from https://pubs.usgs.gov/fs/2004/3072/fs-2004-3072. html)

sideways, driven by a compelling influencing factor like an earthquake and influencing the rapid movement of the ground surface is termed “lateral slide” (Fig.2.1e). The latter is particular as it is more prevalent on very gentle slopes of plane areas.

(c) Topple Toppling failures (Fig.2.1c) are distinguished by the forward rotation of a unit or units about some pivotal, below or low in the unit, under the actions of gravity and forces exerted by adjacent units or by fluids in cracks (Xia et al. 2018). Thus, a toppling movement occurs as a result of overturning of blocks rather than sliding or falling (Regmi et al. 2015). Out of the slope, topple induces some forward rotational movements of a certain soil or rock’s amount under the pressure of the displaced mass. Moreover, storms that produce intense rainfall for periods as short as several hours has triggered abundant landslides in many parts of the word (Van Den Eeckhaut et al. 2005). Thus, water-induced landslides are grouped as deep-seated landslide and shallow landslide depending on the depth and mode of failure (Petley and Allison 1997; Schilirò et al. 2016). The type of landslide is generally influenced by a set of variables including the area’s topographic settings, land-cover and land use, geomorphology and lithology among others in addition to the intensity and frequency of a specific temporal distributed rainfall events.

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(d) Deep-Seated Landslide Deep-seated sliding refers to landslides with a relatively deeper sliding surface, lower velocity, less disturbance upon the movement and, in general, an arc sliding surface (MOEA 2014). Although deep-seated sliding tends to occur in gentle slopes with a gradient between 5° and 30°, there are also examples of deep-seated sliding that occurred in steep slopes. Ten to hundred feet can be counted at this depth. These types of landslides typically occur as a result of differing changes in hydrogeological processes in the area such as higher groundwater levels and earthquakes. Once formed, deep-seated landslides can persist for a few years, even centuries (WFPA 2017).

(e) Shallow Landslide This is the type of landslide in which the sliding surface is located within the soil mantle or weathered bedrock typically to a depth from a few decimeters to several meters (Gomes et al. 2013; Pellicani et al. 2017). The surface of the slope in steep hilly and mountainous regions is quite often underlain by a plane of weakness lying parallel to it and hence, shallow landslides are predominant (Trigila et al. 2015). Although the earth and debris involved in landslide events are often small in size, they have significant power and velocity which may result in a great influence on environment and society.

2.3

Causes of Floods and Landslides

Climate change in combination with the characteristics of an area, high atmospheric precipitation and urbanization (human activities) are responsible for most widespread flood and landslide occurrences (Alcántara-Ayala 2002).

2.3.1

Climate Change

Climate change is defined as the change in the statistical distribution of weather patterns over an extended period of time (Edenhofer et al. 2011). It is responsible for the regulations of snowmelt and precipitation in terms of frequency, magnitude and intensity, cyclonality, seasonality, and the corresponding variations, as well as the key factors driving to floods and landslides. Various unexpected flood and landslide incidences have raised questions regarding their potential relation with climate change due to the overall projections of increased peak of precipitation over different areas. Nevertheless, aside from the fact that there is usually growing uncertainties,

2.3

Causes of Floods and Landslides

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there is no general response regarding the magnitude or even the direction of the change. Location-specific flood and landslide risk analyses should take account of all risk-related trends, including geographic changes in the catchment area, changes in exposure of assets and population, as well as climate change (Van Aalst 2006).

2.3.2

Atmospheric Precipitation

Heavy precipitation events can lead to disasters through interaction between exposed and vulnerable people and the natural systems (Kumpulainen 2006; Vörösmarty et al. 2013). The average global precipitation has increased since 1900, but some areas have had increases greater than the average, while others have had decreases (Wang et al. 2017; Weiler et al. 2018), which resulted in more frequent extremes in terms of rainstorms. These changes have generated numerous risks and the most cited resulting on the changing earth’s surface temperature and precipitation are for instance flooding, landslide, tornadoes, Tsunamis, droughts, etc. Heavy rain events reduce slope stability by rapidly raising the water table (or groundwater elevation) and by enhancing water drainage through the soil to lower layers (Borgatti and Soldati 2010). In addition, intense rainfall can erode surface sediments, and higher streamflow during these events can transport more sediment downstream (Curran et al. 2016). Different patterns of rainfall affect which slopes to be destabilized, and where erosion and sediment transport are most important (Delcambre et al. 2013). Prolonged heavy rainfall is the main cause of flood incidences. A rapidly ensuing heavy rain will prevent rainwater from infiltrating properly, and it will instead enter the river by surface runoff.

2.3.3

The Topography of the Area

The topography of low land catchments is very important while the patterns of precipitation change extensively all over the country due to different factors including the local characteristics, elevation, and patterns of atmospheric pressure. The variables affecting both floods and landslides are particularly sensitive to topographic variations. By increasing the land surface, the sensitivity of flood parameters to topography decreases and finally in very steep catchment, it has no sensitivity (Masoudian and Theobald 2011). The incidences of landslide are always associated to slope instability. With the occurrence of landslide, a single or various landslide controlling factors with only one trigger can usually be identified. The distinction between these two ideas is delicate but significant. The causes of landslide relate with different predisposing factors that increase the slope’s susceptibility to failure with a single trigger that finally instigates landslide events. Thus, the causes combine to make a slope vulnerable to failure, and the trigger finally initiates the movement (Jian et al. 2014).

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Basic Information on Hydrogeological Hazards (Flood and Landslide)

Urbanization and Land Use Change

Recurring natural disaster incidences and the lack of employment prospects are the main factors that frequently drive people from rural to urban areas. However, at the same time many mega-cities are built in areas where there is a heightened risk for earthquakes, floods, landslides and other natural disasters (Douglas et al. 2008). Different places are undergoing a rapid urbanization as a form of land use change and result into developmental constructions onto riverbanks and slopes. Land use dynamic has a potential and strong impact on floods and landslides occurrence as the society have seriously altered the natural landscapes. For instance, the conversion of grassland and forest cover to agricultural or urban areas, is a significant cause of floods and landslide. Decreases in vegetation/forest cover due to human activities can increase landslide activity, soil loss and the abandonment of the lands in the terraced slopes (Rogger et al. 2017). As a result of forest cover clearance on big areas, there is either the declining or increasing antecedent soil moisture which can finally trigger soil erosion. Moreover, for the purpose of farming and agricultural production, highlands are altered, resulting in altered flow routes, flow speed, and water retention, as well as altered flow connections and times of concentration. Additionally, platy thick soil horizons with special lateral flow are formed due to intensified agricultural activities. The latter slows the vertical rain water penetration in the soils but intensify the lateral mass flow besides the declined filter and buffer procedures in deeper soil horizons. It is likely that hydrologically significant changes will continue in the next decades due to loss of agricultural land and forests (Barrère et al. 2012).

2.4

Impacts of Floods and Landslides

The impacts from any flooding or landslide event will vary based upon several factors such as source of water, location of water flow, duration/intensity of rainfall or source release, topography, presence and/or effectiveness of flood control systems, changes in land use, vegetation, etc. (Guzzetti et al. 2005). In any event of floods or landslides (MINEMA 2018), the following are the sectors which are likely to be affected: agriculture and food security, human settlement and infrastructure, water and sanitation, health and nutrition, education, infrastructure, and crosscutting issues.

2.4.1

Agriculture and Food Security

Agriculture and the related food security continues to endure the damaging impacts of disasters. Flood and landslide events jeopardize agricultural growth and

2.4

Impacts of Floods and Landslides

15

productivity, and frequently have negative impacts on national economies (FAO 2015). However, these disasters have varying impacts on agricultural productivity in different parts of the world based on the type of crops, the disaster’s nature, its severity, and, most significantly, a region’s capacity to prepare for and recover from disasters. During flood or landslide events, croplands may be damaged or carried away, resulting in the total loss or reduced yield. These disastrous events of flood and landslide lead to a decline in soil productive capacity owed to micronutrients loss that would have been carried away, as well as shrink yield as a result of germplasm’s loss of traditional diversities that have been adapted. Flood and landslide’s impacts on agriculture has a direct impact on food security as they decrease the availability of food items in local markets, hence food inflation. Indeed, these disasters have a significant negative influence on food security as they seriously impact infrastructure, current crop yields, and arable land, all of which have an impact on the production, processing, and transportation of food.

2.4.2

Human Settlement and Infrastructure

Floods and landslides phenomena produce deleterious impacts to human settlements and infrastructure. Currently, some population choose to reside in settlements along the slopes to keep themselves away from windy interfluves or also live along flood plains for different interests such as water resources (Vaculisteanu et al. 2019). In this context, owing to high-water levels from intense rainfall, people residing in a certain distance from the river or drainage system become vulnerable to the loss of their housing and ill-suited settlements (the entire collapse of structure) and infrastructure. If these localities overlap on highly exposed communities, they can obviously impact the design and functionality of a whole settlement. The affected settlements or infrastructure are either located in susceptible areas to flood (downstream) or landslide (slope area).

2.4.3

Water and Sanitation

Water supply and sanitation are important factors in determining health, particularly in times of disasters such as floods and landslides (Cobbing et al. 2013). However, if these services are degraded or ineffective, they may represent a risk and be a source of contamination that has an impact beyond local and national scales. Generally, the quality of water become the main endpoint issue during flood or landslide events to the system of water supply and sanitation and, the beginning of an amplified waterbased disorders. During soil and water pollution resulting from either a flood or landslide discharges, the supplied effluents usually exhibit elevated concentrations of contaminants which might end up into streams and rivers. Additionally, during flooding and landslide, the natural ecological systems’ capability to absorb wastes is

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impacted by inadequate water for sanitation. From these disasters, drinking water become exposed to high risk of pollution due to discharges from sanitation facilities including septic tanks and latrines. Consequently, affected communities become more vulnerable to numerous water-borne diseases such as dysentery, cholera, and hygiene-associated diseases. Furthermore, during these events, the high risk of lacking fresh water for domestic activities become highly increasing.

2.4.4

Health and Nutrition

Floods and landslides disrupt the stability, availability, accessibility, efficiency and security of food for the population, affecting the nutritional health particularly for children. They both increase the exposure and susceptibility to infections such as diarrhea and acute respiratory diseases, potentially jeopardizing young children’s nutritional status. Following Du et al. (2010), flood and landslide-related health impacts can be generally be classified into direct and indirect impacts. Health impacts happen directly from the exposure to rainwaters or indirectly through to the effects of water or runoff on the built and natural environment such as the social support systems, disruption to properties, ecological systems, water and food supplies. Direct impacts include for instance damage to sanitation systems, injuries from debris, drowning, soil mass movement, hypothermia, and chemical pollution among others. Indirect impacts are for instance vector infestation, waterborne diseases, infectious diseases, reduction in food production and malnutrition (lack of supplementary feeding), and illnesses linked to displaced populations. On the other hand, owing to the destruction of transport and healthcare infrastructure, the population may have limited access to medical facilities. To the above, mental health condition may also appear from physical health issues or from individual losses, economic deprivation, and social disruption (Ahern et al. 2005).

2.4.5

Education

Education is among the main crucial element for attaining sustainable development, and also considered as a key tool for changing behavior and attitudes (Badea et al. 2020). Unfortunately, increasing frequency and intensity of floods and landslides in the educational sector leave a series of devastation including the destruction of school infrastructure that may consequently cause the education system to attain a point where it cannot be rescued. Schools (colleges, institutions or universities) can be closed while the community might stop attending schools or increasing absenteeism. Additionally, schools may also occasionally serve as evacuation hubs during flood or landslide events. In some cases, people are displaced, and, therefore, may inhabit in school buildings as emergency shelter imposing provisional schools’

2.5

Hydrogeological Hazards Profile and Susceptibility Context in Rwanda

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closure that may otherwise be located on highland and not directly impacted by the disasters.

2.4.6

Infrastructure

Floods and landslides impact essential infrastructure and services, where some areas become not accessible as basic sustenance infrastructure including transportation structures, bridges, culverts, sections of roads, boreholes among others, are carried away. Moreover, flood and landslide events put communication and electricity supply infrastructure at risk of being disrupted.

2.5

Hydrogeological Hazards Profile and Susceptibility Context in Rwanda

The country’s vulnerability to climate change is projected to increase, resulting in increased temperatures, intensified rainfall, and prolonged dry seasons (Tompkins and Caporaso 2016). This presents different challenges for different regions: the mountainous west of the country will be subject to landslide and erosion, parts of the central north and south will experience severe floods, and the east and southeast will suffer from droughts and desertification (NCEA 2015). This coupled with man-made degradation of natural resources due to charcoal production, small-scale mining and farming practices accompanied with the scarcity of land, has increased Rwanda ‘vulnerability to hydrogeological hazards especially floods and landslides. Over the last decade, the frequency and severity of floods and landslides have significantly increased, with increasing toll of human casualties as well as economic and environmental losses, which constitute its hydrogeological hazard profile (MIDIMAR 2015). Many cases of floods and landslides are particularly linked to the geographical, historical and social-cultural aspects of the country. In Rwanda, like any area in the world, the probability for loss, damage, or destruction of properties due to a hazard exposing a vulnerability associated to the event can be used to characterize the risks associated with hydrogeological hazard (flood and landslide). Both the probability and the consequences of hydrogeological hazards such as floods and landslides are expected to increase in Rwanda, as a result of climate change and increased vulnerabilities, especially in urban areas (MIDIMAR 2012). The susceptibility to hydrogeological hazards (floods and landslides) is largely due to its topographic and demographic characteristics (Nsengiyumva et al. 2018). The above is then aggravated by climate change’s impacts namely the increasing change in the frequencies and intensity of rainfall; which consequently, induce climatic-related hazards like floods, landslides, droughts, extreme temperatures, and prolonged dry spells among others. The report of the baseline information and

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indicators for Rwanda commissioned by REMA (2015) have shown that a trend of declining overall rainfall, interspersed with years of excessive rainfall as well as steadily increasing average temperatures from 32.7 to 35.4 °C. In Rwanda, the frequency and intensity of extreme weather-related disasters particularly flood, landslide and drought are increasing as a result of unprecedent climate variations. Moreover, the risk of disasters is also exacerbated by the socio-economic, cultural, and physical vulnerabilities. Over the previous 10 years, there is high poverty rate notwithstanding with the recorded-high growth (https://climateknowledgeportal. worldbank.org/country/rwanda/vulnerability). The Third Integrated Household Living Conditions Survey report prepared by the Ministry of Finance and Economic Planning (MINECOFIN) and the National Institute of Statistics in Rwanda (NISR) revealed that still about 45% of the country remain under the poverty line. This poverty rate especially in rural areas embodies the country’s socio-economic susceptibility, which contributes to disaster risks when challenged by the occurrence of natural hazards at an increasing frequency and intensity (Jones 2016). It relegates the poorest of the poor to subsistence livelihoods, poor housing conditions, settlements built in hazard-prone areas such as steep slopes or along riverbanks and valleys, and oftentimes causes people’s lack of access to social services and inadequate financial capacity to meet day-to-day living needs, and not to mention the lack of capacity to cope with when disaster strikes (UNDP 2013). In Rwanda, the physical vulnerability has become another important challenge. The Economic Development and Program poverty Reduction Strategies (EDPRS) II and numerous other documentations have highlighted the scarcity of land resource. For this, people tend to settle in steep slopes, saturated hillsides, flood plains or low-lying valleys which are often catch basins of water flowing from upstream (Twagiramungu 2006; Nsengiyumva et al. 2018). This type of vulnerability is highly caused by the inappropriate and uncontrolled land use practices in a rugged area of different parts of the country. The Rwanda’s land use planning has also been overtaken by a rapid urban sprawl. For example, in Kigali (the capital city of Rwanda), urban settlements are growing very fast, which is therefore posing great challenges on its built-environment and the city’s physical planning. According to Nahayo et al. (2018), In the absence of a disaster risk assessments and risk profiles, infrastructures are likely to be erected without disaster risk reduction considerations and let alone comply to disaster risk reduction standards (i.e., residential houses are built in areas highly vulnerable to natural hazards). Strategies can be developed to reduce either the probability of an event or the consequences, or both (Brooks 2003; Sarewitz et al. 2003). To reduce risks, both physical and non-physical initiatives can be implemented. The decisions and implementation of these initiatives can be done at several levels, from the individual to the national, international political and administrative levels.

2.6

2.6

Historical Records

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Historical Records

Over the last decade, the frequency and intensity of natural hazard-induced disasters, particularly floods and landslides, have significantly increased (MIDIMAR 2015). They have resulted in a number of fatalities, population displacement, differing infrastructure’s destruction such as buildings (houses, schools, hospitals, etc), bridges, roads (Fig. 2.2), in addition to agricultural product’s devastation and severe environmental degradation. Therefore, all these events are recorded and mapped as inventories to ease the prediction of future occurrence. Upon the availability and accessibility of previously recorded data on floods or landslides, it is possible to assimilate these data into formal frequency by typical graphical representation or even the quality assessment

Fig. 2.2 Photographs displaying flood (left side) and landslide (right side) events in different part of Rwanda. Source: different online sources and field visits

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Table 2.1 Main disasters’ impacts caused by floods normalized by province Hazards Flooding

Landslide

Provinces Kigali North South East West Kigali North South East West

Timeframe 1988–2020

People affected 7032 66,440 508 361 84,850 75 17,620 219 120 24,362

devastated cropland (ha) 310 52,864 1433 65 63,489 231 31,060 2481 1110 33,320

Ruined buildings 20 23,649 62 361 51,369 39 1185 5232 1215 1753

Livestock loss 12 1150 21 58 3569 12 985 706 NA 1769

Source: Compiled from the national risk atlas of Rwanda and different reports

of fitted distribution. The historical information is derived from either published documents, official governmental records, tangible proofs, verbal history or news from press. The recorded floods or landslides’ incidences are the primary source of information to comprehend the conditioning factors to their occurrence. In Rwanda, through their database known as disaster loss inventory (Desinventar), events such as floods and landslides are recorded (Table 2.1). Consequently, many losses have been observed in different districts of the North-western provinces such as Nyabihu, Rubavu, Musanze, Burera and Gakenke (Nsengiyumva 2012). Table 2.1 presents the main losses incurred from flood and landslide records (1988–2018) in Rwanda.

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Curran CA et al (2016) Sediment load and distribution in the lower Skagit River. US Geological Survey, Skagit County, Washington Cutter SL (2003) GI science, disasters, and emergency management. Trans GIS 7(4):439–446 Delcambre SC et al (2013) Diagnosing northern hemisphere jet portrayal in 17 CMIP3 global climate models: twenty-first-century projections. J Clim 26(14):4930–4946 Doswell III CA (2003) FLOODING. Norman, Oklahoma 73069, USA Douglas I et al (2008) Unjust waters: climate change, flooding and the urban poor in Africa. Environ Urban 20(1):187–205 Du W et al (2010) Health impacts of floods. Prehosp Disaster Med 25(3):265–272 Edenhofer O et al (2011) IPCC special report on renewable energy sources and climate change mitigation. In: Prepared by working group III of the intergovernmental panel on climate change. Cambridge University Press, Cambridge FAO (2015) The impact of disasters on agriculture and food security. FAO Rome, Italy FEMA (2006). Types of floods and floodplains Gomes RAT et al (2013) Combining spatial models for shallow landslides and debris-flows prediction. Remote Sens 5(5):2219–2237 Guzzetti F et al (2005) Evaluation of flood and landslide risk to the population of Italy. Environ Manag 36(1):15–36 Haddow GD et al (2008) Introduction to emergency management (Butterworth-Heinemann homeland security series). Elsevier Science Limited Hungr O (1995) A model for the runout analysis of rapid flow slides, debris flows, and avalanches. Can Geotech J 32(4):610–623 Jian W et al (2014) Mechanism and failure process of Qianjiangping landslide in the three gorges reservoir, China. Environ Earth Sci 72:2999–3013 Jones R (2016) Poverty assessment in Rwanda through participatory rural appraisal. In: Participatory research methodologies. Routledge, pp 67–84 Kumpulainen S (2006) Vulnerability concepts in hazard and risk assessment. Special Paper-Geol Survey Finland 42:65 Masoudian M, Theobald S (2011) Influence of land surface topography on flood hydrograph. J Am Sci 7:354–361 MIDIMAR (2012) Impacts of floods and landslides on socio-economic development profile. r. a. p. awareness. kigali, ministry of disaster management and refugee affairs MIDIMAR (2015) The National Risk Atlas of Rwanda. Nairobi, Ministry of Disaster Management and Refugee Affairs MINEMA (2018) National contingency plan for floods and landslides. kigali, ministry in charge of emergency management MOEA (2014). What is deep-seated sliding?. from https://www.moeacgs.gov.tw/eng/faqs/faqs_ more?id=4169004303e149ce821f51839e60d545 Nahayo L et al (2018) Extent of disaster courses delivery for the risk reduction in Rwanda. Int J Disaster Risk Reduct 27:127–132 Nahayo L et al (2019) Estimating landslides vulnerability in Rwanda using analytic hierarchy process and geographic information system. Integr Environ Assess Manag 15(3):364–373 NCEA (2015) Climate Change Profile: Rwanda. from https://ees.kuleuven.be/klimos/toolkit/ documents/687_CC_rwanda.pdf Nsengiyumva J (2012) Disaster high risk zones on floods and landslides. MIDIMAR, Kigali Nsengiyumva JB et al (2018) Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Int J Environ Res Public Health 15(2):243 Pellicani R et al (2017) Susceptibility mapping of instability related to shallow mining cavities in a built-up environment. Eng Geol 217:81–88 Petley DN, Allison RJ (1997) The mechanics of deep-seated landslides. Earth Surface Processes Landforms J Br Geomorphol Group 22(8):747–758 Regmi NR et al (2015) A review of mass movement processes and risk in the critical zone of earth. Develop Earth Surface Processes 19:319–362

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Basic Information on Hydrogeological Hazards (Flood and Landslide)

Reichenbach P et al (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91 REMA (2015) State of the environment and outlook report in Rwanda. Greening agriculture with resource efficient, low carbon and climate resilient practices. Government of Rwanda, Kigali Rogger M et al (2017) Land use change impacts on floods at the catchment scale: challenges and opportunities for future research. Water Resour Res 53(7):5209–5219 Sarewitz D et al (2003) Vulnerability and risk: some thoughts from a political and policy perspective. Risk Anal Int J 23(4):805–810 Schilirò L et al (2016) Prediction of shallow landslide occurrence: validation of a physically-based approach through a real case study. Sci Total Environ 569:134–144 Senarathne PC (2006) Learning to live with FLOODS: natural hazards and disasters, Colombo Tompkins AM, Caporaso L (2016) Assessment of malaria transmission changes in Africa, due to the climate impact of land use change using coupled model intercomparison project phase 5 earth system models. Geospat Health 11(s1) Trigila A et al (2015) Comparison of logistic regression and random forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy). Geomorphology 249:119– 136 Twagiramungu F (2006) Environmental profile of Rwanda. Consultancy Report, p 78 UNDP (2013) Building national and local capacities for disaster risk Management in Rwanda. United Nations Development Programme, Kigali UNDRR (2019) Understanding disaster risk. from https://www.preventionweb.net/understandingdisaster-risk/component-risk/disaster-risk UNISDR (2009) Terminology on disaster risk reduction. Retrieved April 26, 2022, from https:// www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf Vaculisteanu G et al (2019) Natural hazards and their impact on rural settlements in NE Romania–a cartographical approach. Open Geosci 11(1):765–782 Van Aalst MK (2006) The impacts of climate change on the risk of natural disasters. Disasters 30(1):5–18 Van Den Eeckhaut M et al (2005) The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides. Geomorphology 67(3-4):351–363 Varnes DJ (1958) Landslide types and processes. Landslides Eng Pract 24:20–47 Vörösmarty CJ et al (2013) Extreme rainfall, vulnerability and risk: a continental-scale assessment for South America. Philos Trans R Soc A Math Phys Eng Sci 371(2002):20120408 Wang X et al (2017) Future extreme climate changes linked to global warming intensity. Sci Bull 62(24):1673–1680 Weiler F et al (2018) Vulnerability, good governance, or donor interests? The allocation of aid for climate change adaptation. World Dev 104:65–77 WFPA (2017) Deep-seated and shallow-rapid landslides: know the difference. from https://www. wfpa.org/news-resources/blog/deep-seated-landslides-shallow-landslides-washington/#:~: text=Once%20formed%2C%20deep%2Dseated%20landslides.rainfall%20and%2For%20 rapid%20snowmelt Xia M et al (2018) Geologic structure, mechanism, and conditions for rock topples on cataclinal slope, Jinchuan, China. Geomat Nat Haz Risk 9(1):1006–1018

Chapter 3

Description of Rwanda

3.1

Location and Administrative Division

Rwanda is geographically located in Central-East Africa between 1°04′ and 2°51′ south latitude, and between 28°45′ and 31°15′ East longitude. It is a land-locked country occupying a total surface area of 26,338 km2, bordered by Burundi in the South; Tanzania in the East; Uganda in the North, and the Democratic Republic of Congo in the West. The country is divided into four provinces and the city of Kigali which are also further divided into 30 districts (Fig. 3.1). Moreover, the districts are further divided into 416 Sectors and sectors further divided into 2148 cells and lastly, these cells are divided into 14,837 villages. The village is the smallest politicoadministrative entity of the country (MINALOC 2014). All these subdivisions are headed by different people at every level and they all have different roles though directing towards the same cause.

3.2

Demography

Rwanda is a geographically small country with one of the highest population densities in sub-Saharan Africa. Despite a high population density (Fig. 3.2), the dominant pattern is one of extreme dispersal. Almost three-fourths of the population is rural and lives in nuclear family. The Rwanda’s rate of population growth is greater than that of the global average but similar to that of neighboring countries. The total population is approximately 13,246,394 according to the fifth population and housing census published by the National Institute of Statistics (NISR) in Rwanda and is projected to reach 23,048,005 by the vision 2050 (Fig. 3.3) (https:// www.worldometers.info/world-population/rwanda-population/). Over a decade (from the last population census carried out in 2012 to date), the population has increased by approximately three million. The population of Rwanda is still largely © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_3

23

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3

Description of Rwanda

Fig. 3.1 Administrative map of Rwanda

rural, with 83% living in rural areas. There are some clear differences among the provinces. This high population of Rwanda is the main driver to land pressure which consequently results in the environmental disasters and encroachment on the fragile ecosystem (Piller 2016, Nsengiyumva et al. 2018).

3.2.1

Relief and Topography

The country is characterized by hilly and mountainous relief with an altitude of about 1000 m above sea level and its high altitude found in the northwest region (Asumadu-Sarkodie et al. 2017). Its dominant feature is a chain of mountains of rugged beauty that runs on a north-south axis and forms part of the Congo-Nile divide. This relief feature contributes to the change in weather patterns and disaster incidence in different areas of the country. The country is characterized by hilly and mountainous relief with a minimum altitude of about 921 m and its high altitude found in the northwest region (approximately 4510 m) above sea level (Nduwayezu et al. 2015) in the swampy Kagera (Akagera) River valley in the east. The highest point is on Mount Karisimbi at 4507 m above sea level. Rwanda has volcanic mountains at the northern fringe and undulating hills in most of the central plateau (https://www.fao.org/in-action/kagera/rwanda/rwanda/es/). However, the eastern

3.2

Demography

25

2050 2045 2040 2035 2030 2025 2020 2015 2010 2005 2000 1995 1990 1985 1980 1975 1970 1965 1960 1955

2,25,00,000

1,75,00,000

1,25,00,000

Population

Period

Fig. 3.2 Population density map of Rwanda normalized by sector. Source: https://www. citypopulation.de/en/rwanda/sector/admin/

75,00,000

25,00,000

Period

Population

Fig. 3.3 Population growth and its projection in Rwanda (1955–2050)

part of the country is relatively flat with altitudes well below 1500 m. Moreover, it is separated by valleys that fall into depths between 15 and 50 m. The relief pattern (Fig. 3.4) gives Rwanda a mild and cool climate that is predominantly influenced by

26

3

Description of Rwanda

Fig. 3.4 Major forms of relief in Rwanda (adopted from https://elearning.reb.rw/course/view.php? id=267§ion=6 prepared by the Rwandan Education Board)

altitude. The interior highlands consist of rolling hills and valleys, yielding to a low-lying depression west of the Congo-Nile divide along the shores of Lake Kivu (https://www.britannica.com/place/Rwanda).

3.2.2

Climate

The climate of the country is made up of four seasons (Fig. 3.5); long dry (June— September), short dry (mid- December—mid-February), Long rainy (late February – late May) and short rainy (late September—early December) (Muhire et al. 2015). Rwanda enjoys a moderate tropical-temperate climate because of its high altitude, and average temperature standard ranging from 16–20 °C even though it is entirely situated near the equatorial belt (Muhire et al. 2015). The average annual rainfall in the latter is about 45 inches (1140 mm), which is concentrated in two rainy seasons (roughly February to May and October to December). There are significant variations, however, between the region of the volcanoes in the northwest, where heavy

Demography

27

32°C 89°F

30 days

28°C 82°F

25 days

24°C 75°F

20 days

20°C 68°F

15 days

16°C 60°F

10 days

12°C 53°F

5 days

8°C 46°F

PRECIPITATION

TEMPERATURE

3.2

0 days Jan

Feb

Mar

Apr

May

DAY

Jun NIGHT

Jul RAIN

Aug

Sep

Oct

Nov

Dec

SNOW

Fig. 3.5 The national average monthly temperature and precipitation (adopted from http:// hikersbay.com/climate/rwanda?lang=en)

rainfalls are accompanied by lower average temperatures, and the warmer and drier interior highlands. Under ordinary conditions, much of the rainfall is expected to occur during the long rainy season. The northwest and a part of south regions receive much rainfall and resulting in more landslides and floods while the eastern part of the country is the most vulnerable region to droughts due to low rainfall intensities (Twagiramungu 2006).

3.2.3

Hydrology

Rwanda is divided into two major drainage basins: the Nile to the east covering 67% and delivering 90% of the national waters and the Congo to the west which covers 33%and handles all national waters (MINITERE 2005, NBI 2005). In Rwanda the abundance of water resources is reflected by the existence of a network of wetlands in various parts of the country. Wetlands and aquatic lands are generally represented by lakes, rivers and marshes associated with these lakes and rivers (MINITERE 2005). The country’s hydrological network (Fig. 3.6) includes numerous lakes and rivers and its associated wetlands. A recent inventory of marshlands in Rwanda conducted in 2008 identified shows 2860 marshlands, covering a total surface of 278,536 ha, which corresponds to 10.6% of the country surface, 101 lakes covering 149,487 ha, and 861 rivers totaling 6462 km in length (REMA 2008). The major lakes include Kivu, Bulera, Ruhondo, Muhazi, Cyohoha, Sake, Kilimbi, Mirayi, Rumira, Kidogo, Mugesera, Nasho, Mpanga, Ihema, Mihindi, Rwampanga and Bisoke. The major rivers include the Akagera, Akanyaru, Base, Kagitumba, Mukungwa, Muvumba, Nyabarongo, and Ruvubu in the Nile Basin and Koko,

28

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Description of Rwanda

Fig. 3.6 Hydrological networks map of Rwanda

Rubyiro, Ruhwa, Rusizi, Sebeya in the Congo Basin (NBI 2005, Kabalisa 2006). Data on ground water and aquifers in Rwanda is incomplete. However, information available estimates that the discharge for the available resource is 66 m3/s and there are about 22,000 recognized sources which have a discharge of 9.0 m3/s (NBI 2005, Kabalisa 2006).

3.2.4

Economy

Rwanda has recently enjoyed strong economic growth rates, created new business prospects and lifted people out of poverty. The country’s economy is overwhelmingly agricultural, with the majority of the workforce engaged in agricultural pursuits. Broadly diversified cultivation is practiced throughout the country (USAID 2022). Dry beans, sorghum, bananas, corn (maize), potatoes, sweet potatoes, and cassava are the primary crops grown in Rwanda. While beans, sorghum and corn are harvested seasonally at the onset of the two dry seasons, bananas, sweet potatoes, and cassava can be grown and harvested throughout the year (https://www. britannica.com/place/Rwanda/Demographic-trends). According to the government of Rwanda, the economy has tremendously recovered over the last two decades. The country registered an average Gross Domestic Product (GDP) growth of around 8%

References

29

Fig. 3.7 National gross domestic product of Rwanda (2012–2022) as adopted from the world bank’s official data (https://tradingeconomics.com/rwanda/gdp)

per year, with a double-digit growth recorded in the last two quarters of 2019. Following the world bank’s official data (Fig. 3.7), Rwanda’s GDP has risen from $752 million in 1994 to $7.65 billion in 2012 and then to $11.07 billion in 2021 (https://tradingeconomics.com/rwanda/gdp). The sustained economic growth has led to one million people being lifted out of poverty (between 2000 and 2017) while the life expectancy has risen from 29 years in 1994 to 67 years in 2016 (https://www. gov.rw/highlights/economy-and-business).

References Asumadu-Sarkodie, S., et al. (2017). Situational analysis of flood and drought in Rwanda Kabalisa VP (2006) Analyse contextuelle en matière de Gestion Intégrée des Ressources en Eau au Rwanda, Kigali MINALOC (2014) Administrative structure of Rwanda, Republic of Rwanda MINITERE (2005) Rapport du Projet de Gestion Nationale des Ressources en Eau. Composantes D : Etudes Techniques. Kigali, Ministère des Terres, de l’Environnement, des Forêts, de l’Eau et des Mines (MINITERE) Muhire I et al (2015) Spatio-temporal variations of rainfall erosivity in Rwanda. Journal of Soil Science and Environmental Management 6(4):72–83 NBI (2005) National Nile basin water quality monitoring report for Rwanda. Nile Transboundary Environmental Action Project, Kigali, Nile Basin Initiative (NBI) Nduwayezu E et al. (2015) Meteorological Hazard assessment and risk mitigation in Rwanda. EGU General Assembly Conference Abstracts

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Description of Rwanda

Nsengiyumva J et al (2018) Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Int J Environ Res Public Health 15(2):243. https://doi.org/10.3390/ ijerph15020243 Piller AN (2016). Precipitation intensity required for landslide initiation in Rwanda REMA (2008) Etablissement d’un inventaire national rapide des marais et élaboration de cinq avant projets d’arrêts ministériels relatifs aux marais (4 modules). O. R. d. P. d. l’Environnement, Kigali Twagiramungu F (2006) Environmental profile of Rwanda. Consultancy Report 78 USAID (2022) ECONOMIC GROWTH AND TRADE. Retrieved April 27, 2022, from https:// www.usaid.gov/rwanda/economic-growth-and-trade

Chapter 4

Data Preparation for Hazards’ Modeling and Mapping

The adequate recording of historical disaster events that happened over a period has a huge impact on the precise reliability of susceptibility analysis (Merz et al. 2007). The success of this analysis was achieved using the precise information and detection of the historical floods and landslides data that occurred in past years countrywide (inventory) and a set of different influencing factors for both hazards.

4.1

Hazards Inventory System

For any kind of susceptibility study, a correct database is the pre-requisite (Van Westen et al. 2008, Pourghasemi et al. 2012). Besides, the hazard inventory mapping is the most fundamental step in any susceptibility modeling and mapping (Pradhan 2010). It allows the development of knowledge about the past hazard types, failure mechanisms and conceptual knowledge about the relations between existing hazard and triggering factors. Flood and landslide inventories were created using several location points depending on the hazard. These points represent sites where floods and landslides had occurred in the past. The coordinate location of each site was collected from the historical records and disaster reports information from the former Ministry of Disaster Management and Refugee (MIDIMAR) which currently became the Ministry of Emergency Management (MINEMA), district offices and upon field survey that collected the geographical coordinates of the areas using the global positioning system (GPS). The main objective of the field survey and interviews was to validate flood and landslide areas based from the collected historical records. The overall collected points were divided into a 3/4–1/4 proportions for training and testing (validation) datasets, respectively using the geostatistical analyst extension through its subset feature tool which divides the original dataset into two proportions in ArcMap. Thus, training points were used for model building while testing points were used for validation purpose (Chung and Fabbri 2003). For flood (Fig. 4.1), a total of 281 points (211 training and 70 testing © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_4

31

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Data Preparation for Hazards’ Modeling and Mapping

Fig. 4.1 Considered flood inventory map of Rwanda (2012–2021)

points) were used while for landslide (Fig. 4.2), a total of 806 points (with 284 points collected using GPS and 522 points collected from the Desinventar) were used. Following the repartition, 605 points were applied to build the model (training points) and 201 points used to validate the model (testing points).

4.2

Influencing Factors for Floods and Landslides Occurrence

In susceptibility modeling, analysis and mapping, it is essential to assume that future hazards will occur under the same condition that caused the past events (Nsengiyumva et al. 2018). The physical attributes of the study locations can determine the characteristics of influencing factors to be considered in any research. While only one variable may, to a large extent, contribute to flood or landslide in a specific area, it may have no impact in another region (Kia et al. 2012). Despite a number of studies conducted in relation to flood and landslide impact factors, no specific rules have been set for the knowledge of how many factors should be selected for the susceptibility analysis of a given study area as the occurrences of the hazards is connected to natural and man-made factors (Pawluszek and Borkowski 2017). Rather, these factors can be deduced from information acquired

4.2

Influencing Factors for Floods and Landslides Occurrence

33

Fig. 4.2 Considered landslide inventory map of Rwanda (2012–2021)

from a field survey, literature review of different past researches and, therefore, analyze each factor’s predictive capability before modeling (Chen et al. 2017, Chen et al. 2018). For this, a total of 14 influencing factors were considered to capture appropriate information and avoid any unnecessary level of complexity in spatial modeling at the national scale. These include: (1) Altitude, (2) Slope gradient, (3) Aspect, (4) Curvature, (5) Proximity to rivers, (6) Proximity to roads, (7) Normalized Difference Vegetation Index (NDVI), (8) Rainfall, (9) Topographic Wetness Index (TWI), (10) Stream Power Index (SPI), (11) Sediment Transport Index (STI), (12) Land Use Land Cover (LULC), (13) Soil texture and (14) Lithology. All these factors were taken out from the constructed spatial database (Table 4.1) that integrated appropriate remote sensing (RS) datasets in GIS 10.8 software. The Shuttle Radar Topography Mission (SRTM), 30 m resolution (1 arc-second), Digital Elevation Model (DEM) was acquired from the National Aeronautics and Space Administration (NASA) (www.dwtkns.com/srtm30m/) and projected to WGS_1984_UTM_Zone_36S projection system. The pre-processed DEM was then used to extract geomorphometric output and the topographic characteristics of the study area such as the altitude, slope angle, slope length and steepness, aspect, curvature, proximity to rivers, proximity to roads, the topographic wetness index (TWI), the Stream Power Index (SPI) and Sediment Transport Index (STI) using ArcGIS 10.8.

34

4

Data Preparation for Hazards’ Modeling and Mapping

Table 4.1 The spatial characteristics of the considered datasets and their source No 1

Data structure Vector

Data pixels National scale 30 m 30 m 30 m

USGS, earth explorer through DEM

30 m

USGS, earth explorer through DEM

6

Slope gradient (degree) Aspect (slope orientation) Curvature (slope curve) Proximity to rivers

Data source Extensive field work, Desinventar and GPS USGS, earth explorer, http: www.dwtkns. com/srtm30m/ USGS, earth explorer through DEM

30 m

7 8 9 10 11

Proximity to roads TWI SPI STI NDVI

30 m 30 m 30 m 30 m 30 m

12

LULC

30 m

13

Soil texture

250 m

14

Rainfall

0.05°

15

Lithology

30 m

River networks from DIVA-GIS (www. diva-gis/Data) RTDA (www.rtda.gov.rw) USGS, earth explorer using DEM USGS, earth explorer using DEM USGS, earth explorer using DEM Landsat 8–OLI (USGS), https:// earthexplorer.usgs.gov/ Landsat 8–OLI (USGS), https:// earthexplorer.usgs.gov/ MINAGRI, http://www.minagri.gov.rw/ index.php? Id = 16 CHIRPS (chg.Geog.Ucsb.Edu/data/ chirps) http://www.rnra.rw/index/php?id=15

2 3 4 5

Considered datasets Flood and landslide inventory Altitude (meter)

Raster

It should be noted that all these datasets (influencing factors) were resampled to the same spatial resolution (pixels) and transformed into a grid spatial database using GIS before running the applied models.

4.2.1

Altitude

The Rwandan terrain is rugged with steep hills and deep valleys, rising in the north to the highest peak, which lies in a range of volcanoes. Hence, the altitude is one of the most effective parameters that play an important role in flood and landslide in different regions of the country (Tehrany et al. 2014, Choubin 2019). It has been frequently used as a relief indicator at large scale (Rahmati et al. 2018). Generally, there is an inverse relationship between the altitude and flooding while the relationship is direct for landslides in region. It is believed that higher altitude is more prone to the landslides in comparison to lower altitude which faces flooding (Devkota et al. 2013, Ali et al. 2021). For this reason, the altitude (Fig. 4.3) has been chosen as the

4.2

Influencing Factors for Floods and Landslides Occurrence

35

Fig. 4.3 Altitude map of Rwanda

important factor to be considered based on the study area’s topographic nature which influences the occurrence of flood and landslide. This topography plays an essential role in the behavior of both hazards through a primary interplay involving the altitude of the landscape across various spatial and temporal scales (Dodangeh et al. 2020).

4.2.2

Slope Gradient

Rwanda is generally characterized by steep slope in different regions often affected by landslides except the eastern part which is relatively flat (REMA 2015, Nsengiyumva et al. 2018). This is the reason why the slope angle (Fig. 4.4) was considerably employed in the Rwanda’s flood and landslide susceptibility study, since in the study area, the sliding of the loose materials is directly linked to the slope gradient. Moreover, many factors affect catchment hydrologic characteristics, which ultimately influence the production of surface runoff. One of the important factors controlling runoff is surface slope (Tehrany et al. 2013). On the steeper slopes, infiltration will be less and runoff will be more. This excessive runoff will cause flooding of the down slope flat areas. Thus, flat areas near and adjacent to high gradient slope generally have high probability of occurrence of floods. Therefore, the

36

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Data Preparation for Hazards’ Modeling and Mapping

Fig. 4.4 Slope gradient map of Rwanda

slope gradient was also mapped as a factor with high influence on the occurrence of flood and landslide.

4.2.3

Aspect (Slope Orientation)

According to some researchers, the aspect at a point on the land surface is the direction that the tangent plane passing through that point faces and is expressed in degrees (the angle defined in the clockwise direction from the north) (Cellek 2021). In its simplest form, the aspect is a data type that expresses the geographical direction in which the slopes develop (Cellek 2021). The aspect of an area represents the orientation of the surface slope (Pham et al. 2020). It has been considered as an important factor in flood and landslide susceptibility assessment because of the role it plays in microclimate and hydrology due to differences in exposure to sunlight, winds, rainfall (degree of saturation) and discontinuities (Yalcin et al. 2011). The aspect also displays no slope area (flat) where no surface slope is present; this is generally at the base of the hills or near lakes. Areas with low slope or high slope are more vulnerable to flood where water accumulates and rises or landslide, respectively (Hoang et al. 2018, Bui et al. 2019). Therefore, with this parameter, the high,

4.2

Influencing Factors for Floods and Landslides Occurrence

37

Fig. 4.5 Aspect (slope orientation) map of Rwanda

flat or low regions can easily be identified. For this, the aspect was generated and classified into nine classes (Fig. 4.5).

4.2.4

Curvature (Slope Curve)

The curvature is the slope’s curve derivative that indicates the slope’s steepness at a certain point by the line tangent, indicating the slope change rate at that particular point. It indicates the degree of deformation of the terrain. The general curvature indicates the shape of the ground surface which has a great impact on both hazards while the plan curvature indicates the steepness of slope which is formed due to the intersection of the horizontal plane and surface landforms (Chen et al. 2018) and consequently, the profile curvature denotes the vertical plane parallel to slope direction (Yilmaz et al. 2012, Ding et al. 2017). These three curvature factors interlink with each other to control the acceleration and deceleration of surface flow and thus affecting the process of the flooding and land sliding (Ali et al. 2021). Therefore, the curvature (Fig. 4.6) was also selected as an essential topographic factor to consider in the Rwanda’s flood and landslide susceptibility study whereby the positive curvature is termed convex, zero curvature defines flat areas, and negative curvature termed concave.

38

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Data Preparation for Hazards’ Modeling and Mapping

Fig. 4.6 Curvature map of Rwanda (slope curve)

4.2.5

Proximity to Rivers

Rwanda is a country that was blessed with various river networks (Mind’je et al. 2021). The relationship of the area’s characteristics to river network’s structure plays an important role in susceptibility assessment (Raja et al. 2017). Proximity to rivers is an important influencing factor since rivers and their adjacent lands of a certain slope gradient are the main pathways for flooding and trigger of landslide. Hence, the proximity to rivers (Fig. 4.7) in the study area was measured to reflect how distant are different rivers to a slope gradient or elevation in terms of proximity analysis to finally divulge the spatial likelihood of flood or landslide. Thus, proximity to rivers was calculated using the Euclidean distance tool in ArcGIS software 10.8 based on the vector layer of a river network at a scale of 1:500,000 downloaded from DIVA-GIS, a free computer database for mapping and geographic data analysis (http://www.diva-gis.org/). The classes for the raster of the proximity to rivers were adjusted according to the natural breaks (Jenks) grading method.

4.2.6

Proximity to Roads

Rwanda experiences different disturbances such as excavation activities as well as the construction of new roads, wastes dumping which may cause flood and landslide

4.2

Influencing Factors for Floods and Landslides Occurrence

39

Fig. 4.7 Proximity to rivers map of Rwanda

hazards due to the cut off of the natural slope and blockage of waterways (Nsengiyumva et al. 2018). Therefore, authors judged reasonable to consider the proximity to roads (Fig. 4.8) as one of the factors that may influence flood or landslide occurrence, while analyzing susceptibility in the study area. Thus, proximity to roads was also generated using the Euclidean distance tool in ArcGIS software 10.8 based on the vector layer of a road networks at a scale of 1:500,000 downloaded from DIVA-GIS. The classes for the raster of the proximity to roads were also adjusted according to the natural breaks (Jenks) grading method.

4.2.7

Topographic Wetness Index (TWI)

The topography is a first-order control on spatial variation of hydrological conditions influencing the spatial distribution of soil humidity and groundwater movement. Therefore, TWI is usually used to measure the topographic control on hydrological processes (Wang et al. 2015). In the Rwanda’s flood and landslide susceptibility, TWI (Fig. 4.9) was computed using the flow accumulation obtained from the flow direction extracted from DEM (Santos et al. 2019). All processes were done in the hydrological toolset from the spatial analyst Tool of ArcGIS 10.8. TWI was calculated using the below Eq. (4.1):

40

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Data Preparation for Hazards’ Modeling and Mapping

Fig. 4.8 Proximity to roads map of Rwanda

Fig. 4.9 Topographic Wetness Index map of Rwanda

4.2

Influencing Factors for Floods and Landslides Occurrence

TWI = log e

Ω tan θ

41

ð4:1Þ

where, Ω is the total upslope catchment area draining downward from a point with a slope angle of θ.

4.2.8

Stream Power Index (SPI)

Rwanda is endowed with different specific catchments namely the Nyabarongo, Mukungwa, Sebeya, Akanyaru, Rusizi, among others, and most of them are located in the western part (MINIRENA 2015). Thus, as the specific catchment area and gradient increase by precipitation for instance, the amount of water infiltration in the soil and upslope areas also increases. This, consequently, results into an accelerated soil erosion, slope instability and floods. Owing to the above processes, SPI was considered as one of important components of flood and landslide occurrence. As one of topographical indices, SPI (Fig. 4.10) was computed for the study area to measure the erosive force of the streams or catchments (Danielson 2013, Gayen et al. 2020). It was measured using the flow accumulation and the slope angle obtained from DEM using Eq. (4.2).

Fig. 4.10 Stream power index map of Rwanda

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Data Preparation for Hazards’ Modeling and Mapping

SPI = Ωtanθ

ð4:2Þ

SPI is computed and produced after defining the tangent of the slope (tan θ) and the flow directions to allow the calculation of the flow accumulation (Ω) defined in the specific catchment area (m2/m).

4.2.9

Sediment Transport Index (STI)

The Sediment Transport Index (STI) by surface flow determines the amount of the transported sediments. STI was selected in the Rwanda’s flood and landslide susceptibility study as it epitomizes the influence of terrain on erosion (Moore and Wilson 1992) and reflects the intensity of sediment movement due to hydrological movement (Billi 2011). The active volume of transported sediments with the sensitivity of land permeability can intensify the frequency of floods and landslides. Its physical meaning is the ability of transported sediments controlled by a specific catchment area and slope gradient (Pourghasemi et al. 2012). This different irregularity can be considered as an essential indicator of both flood and landslide probability of occurrence (Nefeslioglu et al. 2008). Following Moore and Burch (1986), STI (Fig. 4.11) was expressed as:

Fig. 4.11 Sediment transport index map of Rwanda

4.2

Influencing Factors for Floods and Landslides Occurrence

STI =

Ω 22:13

0:6

sin θ 0:0896

43

1:3

ð4:3Þ

where Ω is the specific catchment area (m2/m) and θ is the slope gradient (radian).

4.2.10

Normalized Difference Vegetation Index (NDVI)

According to Restrepo and Alvarez (2006), flood and landslide are composite process that can be powerfully influenced by different attributes of vegetation. Attributes such as the mosaic grassland, forest and shrubland of the study area were seriously degraded, followed by scarce vegetation, grassland and has regularly flooded (Ndayisaba et al. 2016). This situation is said to be the result of an accelerated deforestation caused by agricultural activities and infrastructure developmental facilities such as roads, recreational and grouped settlement. So, this trend of vegetation triggers the occurrence of flood and slope instability (landslide). Based on the above, NDVI, the most frequently used remote sensing dataset, was considered as a significant factor that cannot be ignored when studying the Rwandan flood and landslide susceptibility. This factor was particularly used to measure the vegetation trend which can affect hillslope mechanical properties and hydrological processes linked to land and slope instability (Schwarz et al. 2010, Sambasivarao 2015). NDVI (Fig. 4.12) was estimated in ArcGIS using the bands from a Landsat at 30 m spatial resolution assembled by the United States Geological Survey Earth Explorer (USGS) to highlight the difference of the spectral responses of vegetation at the red (R) and near infrared (NIR) bands. At given pixel, the result always ranges from -1 to +1 such that areas of barren rock, sand, or snow usually show very low NDVI values (for example, 0.1 or less). Sparse vegetation such as shrubs and grasslands or senescing crops may result in moderate NDVI values (approximately 0.2 to 0.5). High NDVI values (approximately 0.6 to 0.9) correspond to dense vegetation such as that found in temperate and tropical forests or crops at their peak growth stage. NDVI is mathematically computed using Eq. (4.4): NDVI =

4.2.11

NIR - R NIR þ R

ð4:4Þ

Land Use Land Cover (LULC)

Different LULC types may affect the hydrological system/process and the stability of slopes because LULC can change the hydrological functioning of hillslopes, rainfall partitioning, infiltration characteristics, runoff production, and furthermore,

44

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Data Preparation for Hazards’ Modeling and Mapping

Fig. 4.12 Normalized Difference Vegetation Index map of Rwanda

the shear strength of the soil (García-Ruiz 2010); and consequently, result into flooding and landslides. To assess the spatial distribution in LULC of the study area (Fig. 4.13), Landsat imageries of the study area (path-row: 172–61, 172–72, 173–61, 173–62) at 30 m spatial resolution acquired from the United States Geological Survey (USGS), Earth Explorer was processed and classified using the Maximum Likelihood Classification (MLC) algorithm in ENVI software version 5.3 (Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation, Boulder, CO, USA) (Basnet and Vodacek 2015, Muhire et al. 2015). The atmospheric and radiometric corrections were executed with the Fast Line-of-Sight Atmospheric Analysis of Hypercube (FLAASH) tool to lessen atmospheric effects and radiometric errors in order to increase the interpretability and quality of the image before the classification procedure (Umwali et al. 2021). Due to the accelerated probabilities of errors existing in digital imageries, accuracy assessment has become a vital process (Rwanga and Ndambuki 2017, Umwali et al. 2021, Umugwaneza et al. 2022). Therefore, the accuracy of the classified LULC map was also assessed. 358 ground truth points of data from all land-use types were sampled with 317 points as true tested values from an original source (online GIS-based higher-quality world imagery, version 2 tiling schemes). These points were all used to refine, verify, and validate the classified LULC using the overall accuracy and Kappa statistical index given by Eqs. (4.5) and (4.6), respectively.

4.2

Influencing Factors for Floods and Landslides Occurrence

45

Fig. 4.13 Land Use Land Cover map (2018) of Rwanda

X × 100 X0

ð4:5Þ

r r i = 1 xii i = 1 ðxi þ xþ1 Þ r 2 N - i = 1 ðxi þ xþ1 Þ

ð4:6Þ

O:A = K=

N

where: O.A stands for overall accuracy, X is the total number of correct samples, X’ is the total number of samples, K is the Kappa index, r = the number of rows in the matrix, xii = the number of observations in row and column i while xi + and x + 1 are the marginal totals of row i and column i, respectively, and N = the total number of observations. The classified LULC have been proved to be accurate and satisfactory to be used as an influencing factor for flood and landslide susceptibility mapping as it exhibited 88.5% and 85.4% for the overall accuracy and Kappa coefficient, respectively (Table 4.2).

4.2.12

Soil Texture

Soil is one of the important factors affecting infiltration and runoff and, thus, has a great impact on flooding and landslide. It is confirmed that the Rwandan soils

Forestland Grassland Cropland Built-up Wetland Water bodies Total PA (%) OA (%) K (%)

Forestland 42 5 2 0 0 1 50 84 88.5 85.4

Grassland 4 36 1 0 3 0 44 81.8

Reference test information 2018 Cropland Built-up Wetland Water bodies 2 0 1 0 3 0 2 0 113 0 4 0 1 72 1 0 4 0 23 1 2 0 4 31 125 72 35 32 90.4 100 65.7 96.8

Total 49 46 120 74 31 38 358

UA (%) 85.7 78.3 94.1 97.3 74.2 81.6

4

PA stands for producer accuracy, OA is the overall accuracy, UA is the user accuracy, and K represents the kappa statistical coefficient.

LULC Categories Online GIS-based world imagery classification

Table 4.2 The confusion error matrix for LULC classification

46 Data Preparation for Hazards’ Modeling and Mapping

4.2

Influencing Factors for Floods and Landslides Occurrence

47

Fig. 4.14 Soil texture map of Rwanda

are mainly fragile. According to previous soil assessment and investigations (Nzeyimana et al. 2013), Rwanda’s soil properties do not vary much in time and space. In the study area, these properties influence the occurrence of flood and landslide hazards and affects the spatial distribution of their risk and susceptibility. Hence, due to its significance in influencing the susceptibility level, the soil textural dataset (Fig. 4.14) was acquired from the geological, mining and soil information database of Rwanda found in the Ministry of Agriculture (MINAGRI) after an extensive soil survey and mapping conducted by Hengl et al. (2015).

4.2.13

Rainfall

Rainfall variability is a common phenomenon in Rwanda. In particular, an unprecedented fluctuation in precipitation patterns in the last decades has been witnessed. This led to differences in rainfall distribution across the country. The annual high intense rainfalls mainly have negatively affected the soils by giving high saturation of soil profiles that result into frequent occurrences of floods and landslides. Previous studies (Muhire et al. 2015, Ndayisaba et al. 2016) in Rwanda have echoed the lack of complete field datasets mainly because most of the meteorological infrastructures were devastated during the 1994 war and genocide. Therefore, given the

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Data Preparation for Hazards’ Modeling and Mapping

Fig. 4.15 Mean annual rainfall map of Rwanda

incompleteness of the gauged meteorological data in Rwanda, as confirmed by the diagram of station data against time delivered by the Rwandan Meteorological Agency (www.meteorwanda.gov.rw), we have been constrained to use the satellite-derived mean annual rainfall (Fig. 4.15) data provided by the Climate Hazards Group Infrared Precipitation (CHIRPS) at 0.05 degrees spatial resolution (chg.geog.ucsb.edu/data/chirps) (Funk et al. 2015, Karamage et al. 2017).

4.2.14

Lithology

The lithology of a terrain is viewed as a major factor influencing the type, intensity and the conditions of the morphodynamical processes (Magliulo et al. 2008, Kutlug Sahin and Colkesen 2021). Each lithology unit separately hastens the runoff and determines the ability of water infiltration through soil permeability (Li et al. 2022). This increases the risks and susceptibility of different hazards among which erosion, landslides and floods are the most prevalent in Rwanda. Thus, the classification of lithological units is essential as each class may present its own influence on the occurrence of flood or landslide (Ibrahim et al. 2017). For this, the data on lithology at 1:100,000 scale was acquired from the Rwandan database on mining, geological, and soil maps (Rushemuka et al. 2014). The classification of the resulting

4.3

Multicollinearity Analysis

49

Fig. 4.16 Lithological units map of Rwanda

lithological map (Fig. 4.16) revealed ten classes namely the colluvial, fluvial, basic igneous rock, schist, granite, basalt, organic, quartzite, volcanic ash, and water bodies.

4.3

Multicollinearity Analysis

Modeling approaches fused with the eminence of the applied input data can disclose the quality of the assessed hazards susceptibility (Chen et al. 2017). To model and assess the susceptibility of any hydrogeological hazard, it is of great significance to cautiously optimize and excellently choose a set of influencing factors among the possible nominated one using a multicollinearity test (Hong et al. 2020, Chen and Chen 2021) that helps in the assortment of suitable factors to be introduced in the model and eliminate unnecessary factors. The test should be performed as the applied models might be sensitive to collinearities. Factors presenting multicollinearity may create perturbation during the modeling process and, therefore, reduce the eminence of the models to precisely predict the susceptibility of the hazards being assessed (Chen et al. 2018). Different methods have been previously employed to analyze multicollinearity among factors. These are such as the Chi-square statistic (Kayastha 2015), Relief-F (Kutlug Sahin et al. 2017), the linear

50

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support vector machine method (LSVM) (Chen et al. 2017, Hong et al. 2017), and information gain (IG) (Azhagusundari and Thanamani 2013). For the Rwanda’s flood and landslide susceptibility study, the tolerance (TOL) and variance inflation factor (VIF) as applied in different studies (Meten et al. 2015, Chen et al. 2018) were also used and mathematically calculated using Eqs. (4.7) and (4.8): TOL = 1 - Y2i VIF =

ð4:7Þ

1 TOL

ð4:8Þ

where Y2i represents the regression value of i on other different variables in a dataset (Pourghasemi et al. 2018, Bui et al. 2019). The TOL less than 0.2 specifies the existence of multicollinearity between independent variables and becomes more serious when it is less than 0.1 (Menard 2002). In accordance with this study, Allison (2012) excluded factors with VIF greater than 2 and TOL less than 0.4 in the modeling process. Briefly, multicollinearity was analyzed as a more stable selection criterion for the influencing factors in order to evaluate conditional independence among the factors and hence, fit the applied models. Multicollinearity test for 14 influencing factors was done on training datasets using the TOL and VIF measures as presented in Table 4.3. For all the factors, VIF was found to be less than 2 and higher than 0.4 for the TOL as recommended (Allison 2012, Meten et al. 2015). The maximum VIF and minimum TOL values were valued as 1.61 and 0.62, respectively. These results implied no multicollinearity exists between any of the independent variables and hence, all the factors were all used in modeling process.

Table 4.3 Multicollinearity test of influencing factors revealed by the TOL and VIF

Collinearity test Influencing factors Altitude Slope gradient Aspect (slope orientation) Curvature (slope curve) Proximity to rivers Proximity to roads TWI SPI STI NDVI LCLU Soil texture Rainfall Lithology

TOL 0.83 0.75 0.86 0.65 0.8 0.94 0.62 0.95 0.81 0.69 0.88 0.85 0.99 0.91

VIF 1.2 1.3 1.16 1.54 1.25 1.06 1.61 1.05 1.23 1.45 1.14 1.18 1.01 1.09

References

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Ibrahim MI et al (2017) Fractures system within Qusaiba shale outcrop and its relationship to the lithological properties, Qasim area, Central Saudi Arabia. J Afr Earth Sci 133:104–122 Karamage F et al (2017) Modeling rainfall-runoff response to land use and land cover change in Rwanda (1990–2016). Water 9(2):147 Kayastha P (2015) Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from Garuwa sub-basin, East Nepal. Arabian J Geosci 8(10): 8601–8613 Kia MB et al (2012) An artificial neural network model for flood simulation using GIS: Johor River basin, Malaysia. Environ Earth Sci 67(1):251–264 Kutlug Sahin E, Colkesen I (2021) Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping. Geocarto Int 36(11):1253–1275 Kutlug Sahin E et al (2017) Investigation of automatic feature weighting methods (fisher, chi-square and Relief-F) for landslide susceptibility mapping. Geocarto Int 32(9):956–977 Li L et al (2022) Applicability and performance of statistical index, certain factor and frequency ratio models in mapping landslides susceptibility in Rwanda. Geocarto Int 37(2):638–656 Magliulo P et al (2008) Geomorphology and landslide susceptibility assessment using GIS and bivariate statistics: a case study in southern Italy. Nat Hazards 47(3):411–435 Menard S (2002) Applied logistic regression analysis. Sage Merz B et al (2007) Flood risk mapping at the local scale: concepts and challenges. Flood risk management. Europe Springer:231–251 Meten M et al (2015) GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in Central Ethiopia. J Mt Sci 12(6):1355–1372 Mind’je R et al (2021) Integrated geospatial analysis and hydrological Modeling for peak flow and volume simulation in Rwanda. Water 13(20):2926 MINIRENA (2015) Rwanda national water resources master plan, kigali Moore I, Burch G (1986) Modelling erosion and deposition: topographic effects. Transact ASAE 29(6):1624–1630 Moore ID, Wilson JP (1992) Length-slope factors for the revised universal soil loss equation: simplified method of estimation. J Soil Water Conserv 47(5):423–428 Muhire I et al (2015) Spatio-temporal variations of rainfall erosivity in Rwanda. J Soil Sci Environ Manag 6(4):72–83 Ndayisaba F et al (2016) Understanding the spatial temporal vegetation dynamics in Rwanda. Remote Sens 8(2):129 Nefeslioglu HA et al (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (eastern Black Sea region of Turkey). Geomorphology 94(3–4):401–418 Nsengiyumva JB et al (2018) Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. Int J Environ Res Public Health 15(2):243 Nzeyimana I et al (2013) Coffee farming and soil management in Rwanda. Outlook Agric 42(1): 47–52 Pawluszek K, Borkowski A (2017) Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Rożnów Lake, Poland. Nat Hazards 86(2): 919–952 Pham B et al (2020) GIS based hybrid computational approaches for flash flood susceptibility assessment. Water 12:683 Pourghasemi HR et al (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arab J Geosci 11(9):1–12 Pourghasemi H et al (2012) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz watershed, Iran. In: Terrigenous mass movements. Springer, pp 23–49 Pradhan B (2010) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2) Rahmati O et al (2018) Development of an automated GIS tool for reproducing the HAND terrain model. Environ Model Softw 102:1–12

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Chapter 5

Susceptibility Modeling and Mapping

The susceptibility modeling and mapping serve as a cornerstone for hydrogeological hazards prevention and disaster mitigation. It gives an essential indication of where future hazards are likely to occur based on the identification of zones of past hazards incidences and areas where similar or identical physical characteristics exist (Van Westen et al. 1997, Broeckx et al. 2018). Not only should flood or landslide susceptible zones be identified and demarcated to set in place quick and urgent adequate hydrological as well as land planning and management strategies (http:// www.oas.org/dsd/publications/unit/oea66e/ch06.htm). Modeling and mapping flood and landslide susceptible areas were conducted using historical events, influencing factors and different modeling approaches based on the hazard. Flood and landslide susceptibility were modeled using the multivariate logistic regression and probabilistic frequency ratio, respectively. Among numerous modeling approaches for susceptibility assessment, the option of the aforementioned models was driven by the availability and accessibility of the data (both in quantity and quality) in addition to the purpose and the scope of the study. Moreover, the models were selected on the basis of their simplicity in structure and operation, comprehensibility and easy for planners to use in policy making (González-Benito 2002).

5.1 5.1.1

Flood Susceptibility Modeling and Mapping Methods: The Multivariate Logistic Regression (LR) Model

LR has been used in various studies including vehicular pollution (Beydoun and Guldmann 2006), deposits transport in areas that faced wildfires (Rupert et al. 2008), flooding due to sedimentation of culverts (Wallerstein and Arthur 2012), and the study of biological agents (Berkson 1944). Among the aforementioned, LR has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_5

55

56

5 Susceptibility Modeling and Mapping

mostly been used for the prediction of different hazards especially hydrogeological hazards (flood and landslide) (Tehrany et al. 2013). For this research, it has been used to simulate flood susceptibility. According to Bai et al. (2012), LR calculates the variations in the probability of an event occurring in a class. It finds the most effective way to express the relationship between the hazards and influencing factors after their evaluation based on the highest generated numerical code. In this, the dependent variable describing the presence or absence of flood was represented by flood points (flood inventory) (Fig. 4.1). The conditional likelihood in LR model can be used to indicate the estimated likelihood of occurrence (P), which refers to any pixel that is vulnerable to flooding. The latter is computed by Eq. (5.1): Pr = log e

Pr 1 = 1 - Pr 1 þ e-ω

ð5:1Þ

where P denotes the likelihood of flood (represented by value between 0 and 1), and ω signifies the direct combination of dependent variables and it is the value determined from -1 to +1. LR involves the fitting of Eq. (5.2): ω = X0 þ X1 Y1 þ X2 Y2 þ X3 Y3 þ . . . þ Xn Yn

ð5:2Þ

where ω represents the linear combination of the dependent variables (absence or presence of flood), and variable values from -1 to +1, X0 is the intercept of the model, Xi (i = 0, 1, 2, 3, . . ., n) stands for the coefficients of LR model, and Yi (i = 1, 2, 3 . . . ., n) denotes the influencing factors (Lee and Sambath 2006). The produced influencing factors (Figs. 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 4.10, 4.11, 4.12, 4.13, 4.14, 4.15, and 4.16) were examined in R software environment for statistical computing for the calculation of the intercept and coefficients of LR then run the model for the final flood susceptibility map (FSM). The details on the applied scripts can be found at https://www.geeksforgeeks.org/logistic-regression-in-r-programming/. Finally, the susceptibility Index (SI) that shows the possibility of flooded areas was calculated using Eq. (5.3): SI =

exp½ω 1 þ exp½ω

ð5:3Þ

It should be noted that a positive LR coefficient value implies the presence of the factor in the area and increases the probability of flood occurrence, while a negative LR coefficient value indicates that the occurrence of flood is negatively correlated to that specific factor (Chauhan et al. 2010).

5.1

Flood Susceptibility Modeling and Mapping

5.1.2

57

Spatial Correlation Between Influencing Factors and Flood Occurrence

The spatial correlation between the applied parameters and the likelihood of flood incidence was revealed by the obtained coefficient of logistic regression displayed in the following Table 5.1: The calculated coefficients in Table 5.1 indicated that about 64% of the considered influencing factors showed a positive correlation with the flood probability of occurrence in the area. These factors with positive correlation are: LULC (0.72) > soil texture (0.64) > NDVI (0.60) > rainfall (0.52) > curvature (0.48) > lithology (0.024) > SPI (0.0012) > aspect (0.002) > altitude (0.0009). The higher positive coefficients expresses that these factors significantly affect the likelihood of flood occurrence in Rwanda. In contrast, the factors with negative correlation are: proximity to roads (-7.63), the slope gradient (-0.023), proximity to rivers (-0.00012), TWI (-0.0005), and STI (-0.017), which are equivalent to 36% of all the considered influencing factors (Table 5.1). The fact was testified by the collected past flood locations through a well mapped inventory (Fig. 4.1) where heavy rain-receiving sites, areas with possible land use change (detected via NDVI and LULC) and the types of soil sensitive to flood (clay loam) accounted for a big number of flood points. Besides, the proximity to roads and the slope gradient exhibited the lowest negative coefficients (-7.63 and -0.023, respectively). This means that water velocity and flood power are constrained by the topographic slope (Das 2020); and the road networks generally have a lower influence on flooding incidence in the area. These results were in coherence with those of previous studies (Tehrany et al. 2013, Khosravi et al. 2016, Shafapour Tehrany et al. 2017) which applied the multivariate statistical approaches such as LR and other models for flood susceptibility analysis and achieved a strong correlation between the amount of received rainfall, vegetation coverage (NDVI and LCLU factors), soil characteristics and flooding with high LR coefficients among the considered influencing factors in their study areas. The results evince those regions recording higher rainfall on an area covered by less vegetation, and soil mixture carrying more of clays than other sort of rock or minerals are envisaged to confront flood incidence, hence can be categorized as high susceptible.

5.1.3

Flood Susceptibility Mapping (FSM)

The model output has spatially mapped flood susceptibility with different classes in the study area. The modeled flood susceptibility (Fig. 5.1) was subdivided into five classes: very high, high, moderate, low, and very low susceptibility using the natural breaks method in ArcMap which is the most widely applied method in classifying susceptibility map (Papaioannou et al. 2015, Hong et al. 2018). The results exposed the eastern part to be less susceptible to flood whereas a big area of the southern part

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Table 5.1 The coefficients of LR per influencing factor No 1

Influencing factors Altitude

2

Slope gradient

3

Aspect (slope orientation)

4

Curvature

5

Proximity to rivers (m)

6

Proximity to roads (m)

7

TWI

8

SPI

Classification 921–1539 1539–1834 1834–2199 2199–2816 2816–4501 0–5 5–11 11–19 19–27 27–72 Flat area North Northeast East Southeast South Southwest West Northwest -14–-0.6 -0.6–-0.2 -0.2–0.1 0.1–0.4 0.4–23 0–100 100–200 200–300 300–400 > 400 0–100 100–200 200–300 300–400 > 400 2–5 5–6 6–9 9–12 12–24 -14–-7 -7–-2.3 -2.3–-0.3

Xi (coefficient of LR) 0.0009

-0.023

0.002

0.48

-0.00012

-7.63

-0.0005

0.0012

(continued)

5.1

Flood Susceptibility Modeling and Mapping

59

Table 5.1 (continued) No

Influencing factors

9

STI

10

NDVI

11

LULC

12

Soil texture

13

Rainfall

14

Lithology

Classification -0.3–2.2 2.2–14 0–1502 1502–7759 7759–21,275 21,275–39,797 39,797–63,825 -0.2–0.2 0.2–0.4 0.4–0.5 0.5–0.7 0.7–0.9 Forestland Grassland Cropland Built-up Wetland Water bodies Loam Sandy clay loam Clay loam Sandy clay Clay 796–956 956–1082 1082–1232 1232–1417 1417–1685 Schist rock Granite rock Basic igneous rock Basalt rock Volcanic ash rock Quartzite rock Colluvial rock Fluvial rock Organic rock

Xi (coefficient of LR)

-0.017

0.6

0.72

0.64

0.52

0.024

toward the Nyungwe national park (NNP) exhibited a very high susceptibility. Generally, the western and southern parts ranged from high to very highly susceptible to flooding while Kigali city, the most urbanized region showed a susceptibility level ranging from moderate to high. This situation can be justified by the climatic

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Fig. 5.1 Produced flood susceptibility map of Rwanda and its spatial distribution

condition, topographic nature and the geomorphological characteristic of the country’s landscape (REMA 2013, Asumadu-Sarkodie et al. 2017) in line with the considered influencing factors. The results of the present flood susceptibility modeling are in accordance with previous studies (MIDIMAR 2015, Nahayo et al. 2019) that concluded that the study area is prone and vulnerable to the recurrent flood hazard. However, the eastern region of the country was predicted as low flood-prone in spite that it covers a number of low-altitude water bodies. This could be attributed to its high rainfall deficit and late rainfall onsets (Fig. 4.15) over a long period of time (Nahayo et al. 2019). In the Northern part, the eroded soil from agricultural activities practiced on steep slopes eventually ends up in water channels, which reduces the drainage capacity to accommodate peak runoff and sediments accumulation, and consequently, it accelerates the likelihood of flooding (Uwera et al. 2020). On the other hand, the western part is mostly dominated by ridges and plateaus including the Congo Nile with a topographic feature that is entirely hilly (Muhire et al. 2015). Owing to the rising level of solid wastes from human actions that block drainage systems and culverts in the area, rainwater emanating from the hills and ridges falls towards valleys that cannot adequately accommodate or absorb all the water. In the southern part, heavy rains coupled with severe environmental damages ensuing from deforestation and poor land use practices have resulted in soil erosion and floods (REMA 2015). Additionally, the reason of this part being highly susceptible can be

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61

justified by the geomorphologic features of these areas whereby they cover around 90% of the major water catchments (Nyabarongo, Mukungwa, Sebeya, Akanyaru, Rusizi, etc.). The latter catchments are always saturated in areas where water is stagnant for a long period of rainy seasons which eventually result into severe flood events. Generally, flooding in urban zones (Kigali city) of the study area has been exacerbated by rapid urbanization in combination with extreme weather, poor drainage system, less infiltration that contributes to high runoff, rapid structural development and the presence of unplanned and poor urban settlement (Bizimana and Schilling 2009, Herve et al. 2015, Manyifika 2015). The latter have increased human-related activities that produce many wastes improperly disposed and consequently clog drainage systems and cause culverts’ siltation. Presented in Fig. 5.1, regions with steep slope in the study area showed to be highly susceptible when the slope gradient presented a negative spatial relationship with flood incidence. Scientifically, this can be ascribed to the magnitude, intensity and frequency of heavy downpours received in these areas. Rainfall was affirmed amongst the main factors driving flood probability of incidences. Vegetation cover-related factors (LULC and NDVI) influence flood incidence as a result of increased level of deforestation and fragmentation in the area (https://www.unep.org/news-and-stories/story/rwandasweet-alternative-deforestation). The above are accelerated by anthropogenic activities such as cropland expansion, urbanization process and the related infrastructure development in the highly susceptible regions. Additionally, the curvature was considered for flood susceptibility modeling due to the fact that its values denote several erosive settings of water, runoff conditions and topographical structures (Bui et al. 2019). It is, therefore, clear from the foregoing thorough discussion that Rwanda is prone to flooding. Previous studies (Bizimana and Schilling 2009, Asumadu-Sarkodie et al. 2017) stressed that during the rainy seasons, the regions with high rainfall are exposed to flood disaster, which corroborates with the results of the present study where regions with high amount of rainfall fell into high to very high susceptibility to flood probability of occurrence. Rwanda, as a developing country, has to take serious measures to control floods because once they happen, many different effects should be expected (Munyaneza et al. 2013). The impacts of flooding are generally put into three main categories. The first category (primary impacts) made by impacts involving physical destructions in a form of structure such as buildings, bridges, roadways, drainage and sewerage systems among others. The second category (secondary impacts) is composed of instances such as diseases, food and water supplies, and vegetation cover among others. Finally, the third category (tertiary impacts) made of long-term impacts such as education system. In parallel with the above impacts, many houses and different infrastructure have been destructed as consequences of previous flood incidences (MIDIMAR 2015). Additionally, cropland covers an immense portion of the study area, thus, floods devastate many hectares of land covered by crops. This consequently inhibit the productivity from agricultural activities and obstruct food security in the country. All these impacts should still be anticipated in locations that have been modeled as very high and high susceptible to flood and, therefore, drive the motive for urgent and immediate

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mitigation initiatives deployment gleaned from influencing factors that evinced a positive relationship with the likelihood of flood incidence. Among these initiatives, the monitoring and forecasting of meteorological and hydrological data have to feed flood early warning systems to forecast and anticipate extremes related events (Emerton et al. 2016). The proactive flood risk management requires an ongoing monitoring of indicators to assist the prediction of the flood occurrence and ascertain the response or recovery process in order to achieve flood resilience.

5.2 5.2.1

Landslide Susceptibility Modeling and Mapping Methods: The Bivariate Frequency Ratio (BFR) Model

The BFR model was applied to simulate the probability of landslide incidence known as “landslide susceptibility”. This is the ratio of the probability of landslide occurrence to the probability of a non-occurrence for given attributes (Lee and Sambath 2006). Generally, to forecast the occurrence of landslide event, it is usually significant to presume that landslide incidence is controlled by differing related influencing factors, and that upcoming incidences will strike under similar circumstances as the earlier ones. Therefore, the BFR approach is based on the observed correlation between the distribution of landslides and each related influencing factor, to reveal the correlation between landslide locations and the factors (Akgun et al. 2008, Mohammady et al. 2012). The BFR model revealed the spatial relationship between the sites of landslide incidence and the influencing factors. Subsequently, the fraction of each factor’s class was determined as the relationship between the hazard and each factor’s class value. The FR value of each factor class was computed by Eq. (5.4): Wpix ðLXi Þ m

LXi

FRρ =

i=1

Wpix ðXj Þ

m

W j=1

=

%Xρ %Yρ

ð5:4Þ

ð Þ

pix Xj

Where Wpix(LXi) denotes the proportion of pixel containing landslides in the class (i) of factor parameter (X), Wpix(Xj) signifies the pixels’ proportion in the factor parameter (Xj), m represents the available number of classes for the specific factor (Xi), and n indicates the number of considered influencing factors. FRρ stands for the FR per factor class; Xρ is the rate of landslides in a factor class out of all landslides and Yρ represents the location of the class as a rate of the whole map. In the relation analysis, the ratio is the proportion of the whole area to the area of landslide incidence. A value of 1 is an average value and if the value is greater than 1, it means a higher correlation, and value lower than 1 means lower correlation (Lee

5.2

Landslide Susceptibility Modeling and Mapping

63

2005). Thus, the greater the FR value, the higher the probability of landslide incidence and the smaller the FR value, the lower the probability of incidence. The Landslide Susceptibility Index (LSI) is then calculated by summating all the FRρ calculated for each factor class. FR is thus computed by Eq. (5.5): i

LSI =

FRρ

ð5:5Þ

j=1

Besides, the relative frequency (FRρ) representing the spatial relationship between the influencing variables and landslide was derived from the computed FRρ per factor Eq. (5.6). FRρ FRρ

RFρ =

ð5:6Þ

where RFρ represents the relative frequency per each factor class. The total of all the classes per each influencing factor is always equivalent to 1. Finally, to array the factors based on their level of influence in the area, the investigative network process (INP) was applied. This approach relies on decision-making to opt for the essential criteria and then arrange the considered influencing factors. These factors are ordered in a matrix in a hierarchical way. By applying the prioritized factors rating value (PFRV) method, the numerical value (Table 5.2) was assigned to the considered factors following the influence of each acquired from expert knowledge based upon the geomorphological features of the site, review of the literature and field observation (https://bpmsg.com/ahp/ahp-calc.php). A pairwise comparison matrix should be developed by arraying the factors hierarchically based on their relative significance (Table 5.4). Each factor is compared to every other factor when developing the pairwise comparison matrix by giving the crossing cell a relative dominating value on a scale of 1 to 9. The average of the hierarchically organized factors was applied to compute the prognostic weights and rating value (eigenvalue) along with the consistency ratio (CR) as proposed by Saaty (1980). Mathematically, the CR can be expressed by Eq. (5.7): CR =

CI RI

ð5:7Þ

Where, CI denotes the consistency index, while RI represents the random index which rests on how many factors were considered in the pairwise matrix. CI was computed following Eq. (5.8). CI =

θ-n n-1

ð5:8Þ

5 Susceptibility Modeling and Mapping

64

Table 5.2 Scale of preference between two variables in AHP representing the pairwise comparison of 9-point rating scale (Saaty and Vargas 2001) Importance 1 3 5 7 9 2, 4, 6, 8

Degree of preference Equal significance Moderate significance Strong significance Very strong significance Extremely high significance Intermediate values

Explanation Two variables equally influence the objective Judgment and experience slightly to moderately favor one variable over another Judgment and experience strongly or essentially favor one variable over another A variable is strongly favored over another and its dominance is showed in practice The evidence of favoring one variable over another is of the highest degree possible of an affirmation Used to represent compromises between the preferences in weights 1, 3, 5, 7, and 9

n indicates the number of factors while ɵ stands for the average value of the consistency vector. Note that the CI rule of thumb is that a CR ≤ 0.1 (10%) indicates an acceptable inconsistency or reciprocal matrix, whereas a ratio ≥ 0.1 indicates that there is a need to revise the subjective judgment (Saaty 1980).

5.2.2

Spatial Correlation Between Influencing Factors and Landslide Occurrence per Class

The spatial correlation between areas where landslide has occurred and influencing factors can be differentiated from the correlation between sites with no historical landslides and influencing factors (Samia et al. 2017). This difference is determined by the Frequency Ratio shown in Table 5.3. In concern to the altitude, higher FR values of 2.34 and 2.20 were respectively detected in 2199–2816 m and 1834–2199 m classes. This was followed by the class ranging from 1539–1834 with a FR value of 1.04; which specifies a greater correlation with the incidence of landslides. Distinctively, the lowermost class of altitude (921–1539 m) had the lowermost value of FR (0.23), which implicates an unsignificant or lower correlation. These results disclosed that the probability of landslide occurrence increases with the increasing altitude, which is in line with previous studies (Dou et al. 2015, Arabameri et al. 2020). However, with the altitude class ranging from 2816 to 4501 m, the case seemed slightly different due to the existence of dense forests and trees with profound roots; and resistant rock mass in the area, which eventually decrease landslide process (Meten et al. 2015). The relationship between landslide incidence and the slope gradient exposed a parallel increase between the FR values and the slope angle. This means that the greater the slope gradient, the larger is the likelihood of landslide incidence. The classes ranging from 27°–72°, 19°–27° and 11°–19° showed high correlations with

Curvature

Aspect

Slope gradient

Influencing factors Altitude

Classification 921–1539 1539–1834 1834–2199 2199–2816 2816–4501 Sum 0–5 5–11 11–19 19–27 27–72 Sum Flat area North Northeast East Southeast South Southwest West Northwest Sum Concave Flat Convex

#Points 31,513 88,811 109,820 69,712 955 300,811 75,442 42,018 43,928 84,036 55,387 300,811 38,198 43,928 25,784 19,099 29,604 30,558 48,703 31,514 33,423 300,811 83,692 131,516 85,603

#Pixels in domain 11,822,132 7,552,562 4,413,488 2,628,778 137,164 26,554,124 1,634,162 1,019,290 15,213,621 2,693,407 5,993,644 26,554,124 3,869,277 3,126,083 2,357,451 3,178,591 2,799,918 2,772,816 2,735,373 3,081,241 2,633,374 26,554,124 4,752,330 15,828,210 5,973,584

% Domain (Yρ) 44.52 28.44 16.62 9.9 0.52 100 57.29 22.57 6.15 10.14 3.85 100 14.57 11.77 8.88 11.98 10.54 10.44 10.3 11.6 9.92 100 17.90 59.61 22.50

Table 5.3 Frequency ratio of influencing factors activating landslide occurrences #Landslides 63 179 221 140 2 605 152 85 88 169 111 605 77 88 52 38 60 61 98 64 67 605 167 265 173

% Landslides (Xρ) 10.42 29.58 36.53 23.14 0.33 100 25.12 14.05 14.55 27.93 18.35 100 12.73 14.55 8.59 6.28 9.92 10.08 16.19 10.58 11.08 100 27.82 43.72 28.46

RF 0.04 0.16 0.34 0.36 0.10 1 0.04 0.06 0.22 0.25 0.44 1 0.10 0.14 0.11 0.06 0.10 0.11 0.17 0.10 0.12 1 0.44 0.21 0.36

Landslide Susceptibility Modeling and Mapping (continued)

FR 0.23 1.04* 2.20* 2.34* 0.63 6.44 0.44 0.62 2.37* 2.75* 4.77* 10.95 0.87 1.24* 0.97 0.52 0.94 0.97 1.57* 0.91 1.12* 9.11 1.55* 0.73 1.27*

5.2 65

SPI

TWI

Proximity to roads

Proximity to rivers

Influencing factors

Table 5.3 (continued)

Classification Sum 0–100 100–200 200–300 300–400 > 400 Sum 0–100 100–200 200–300 300–400 > 400 Sum 2–5 5–6 6–9 9–12 12–24 Sum -14–-7 -7–-2.3 -2.3–-0.3 -0.3–2.2 2.2–14 Sum

#Points 300,811 178,576 106,955 9550 4775 955 300,811 114,594 90,720 58,252 26,740 10,505 300,811 140,378 110,775 35,333 10,504 3821 300,811 89,766 20,054 84,036 21,009 85,946 300,811

#Pixels in domain 26,554,124 7,600,696 7,182,316 5,487,361 4,286,538 1,997,213 26,554,124 1,040,503 3,136,154 4,946,180 7,713,239 9,718,048 26,554,124 7,892,453 8,281,301 4,939,204 3,197,085 2,244,081 26,554,124 7,117,283 3,160,001 8,577,136 5,287,708 2,411,996 26,554,124

% Domain (Yρ) 100 36.6 29.05 18.63 11.8 3.92 100 7.52 16.14 20.66 27.05 28.63 100 29.72 31.19 18.60 12.04 8.45 100 33.41 27.92 21.03 4.62 13.02 100 #Landslides 605 359 215 19 10 2 605 230 182 117 54 22 605 282 223 71 21 8 605 181 40 169 42 173 605

% Landslides (Xρ) 100 59.34 35.54 3.14 1.65 0.33 100 38.02 30.08 19.34 8.92 3.64 100 46.61 36.86 11.74 3.47 1.32 100 29.92 6.61 27.93 6.94 28.6 100

FR 3.55 1.62* 1.22* 0.17 0.14 0.08 3.24 5.06* 1.86* 0.94 0.33 0.13 8.31 1.57* 1.18* 0.63 0.29 0.16 3.83 0.90 0.24 1.33* 1.50* 2.20* 6.16

RF 1 0.50 0.38 0.05 0.04 0.03 1 0.61 0.22 0.11 0.04 0.02 1 0.41 0.31 0.16 0.08 0.04 1 0.15 0.04 0.22 0.24 0.36 1

66 5 Susceptibility Modeling and Mapping

Rainfall

Soil texture

LULC

NDVI

STI

0–1502 1502–7759 7759–21,275 21,275–39,797 39,797–63,825 Sum -0.2–0.2 0.2–0.4 0.4–0.5 0.5–0.7 0.7–0.9 Sum Forestland Grassland Cropland Built-up Wetland Water bodies Sum Loam Sandy clay loam Clay loam Sandy clay Clay Sum 796–956 956–1082

21,282 25,782 137,383 85,182 31,182 300,811 5731 21,964 50,612 86,901 135,603 300,811 75,927 12,699 52,738 41,882 105,952 11,613 300,811 2559 3773 272,420 792 21,267 300,811 10,505 30,558

17,125,513 2,381,873 2,351,325 2,348,007 2,347,406 26,554,124 1,542,311 2,816,243 7,024,177 8,182,942 6,988,451 26,554,124 3,962,653 3,679,296 1,092,904 949,718 15,362,770 1,506,783 26,554,124 278,449 6,841,554 15,346,966 289,307 3,797,848 26,554,124 5,758,995 6,644,690

64.49 8.97 8.85 8.84 8.84 100 5.81 10.61 26.45 30.82 26.31 100 14.92 13.86 4.12 3.58 57.85 5.67 100 1.07 25.76 57.75 1.11 14.31 100 21.69 25.02

43 52 276 171 63 605 12 44 102 175 272 605 153 106 213 84 49 0 605 5 8 547 2 43 605 21 61

7.11 8.6 45.62 28.26 10.41 100 1.98 7.27 16.86 28.93 44.96 100 25.29 17.52 35.21 13.88 8.10 0 100 0.83 1.32 90.41 0.33 7.11 100 3.47 10.08

0.01 0.09 0.49 0.30 0.11 1 0.08 0.16 0.15 0.22 0.40 1 0.12 0.09 0.61 0.09 0.03 0.00 1 0.24 0.02 0.49 0.09 0.16 1 0.03 0.07

Landslide Susceptibility Modeling and Mapping (continued)

0.11 0.96 5.15* 3.20* 1.18* 10.59 0.34 0.69 0.64 0.94* 1.71* 4.31 1.69* 1.26 8.55* 2.01* 0.49 0.00 14.01 0.78 0.05 1.57* 0.30 0.50 3.19 0.16 0.40

5.2 67

Classification 1082–1232 1232–1417 1417–1685 Sum Schist rock Granite rock Basic igneous rock Basalt rock Volcanic ash rock Quartzite rock Colluvial rock Fluvial rock Organic rock Water bodies Sum

#Points 120,324 90,721 48,703 300,811 282,511 961 1173 6001 1336 961 2073 1332 2761 1702 300,811

# means number, * indicates classes with more influence

Lithology

Influencing factors

Table 5.3 (continued) #Pixels in domain 7,611,209 3,833,732 2,705,500 26,554,126 3,094,293 3,333,738 16,588,062 255,754 96,925 707,213 109,612 247,658 783,611 1,337,260 26,554,126

% Domain (Yρ) 28.66 14.44 10.19 100 11.65 12.55 62.47 0.96 0.37 2.66 0.41 0.93 2.95 5.04 100 #Landslides 243 182 98 605 522 28 19 12 16 6 0 0 2 0 605

% Landslides (Xρ) 40.17 30.08 16.2 100 86.28 4.63 3.14 1.98 2.64 1 0 0 0.33 0 100

FR 1.40* 2.08* 1.59* 5.64 7.40* 0.37 0.05 2.06* 7.23* 0.38 0.00 0.00 0.11 0.00 17.60

RF 0.25 0.37 0.28 1 0.42 0.02 0.00 0.12 0.41 0.02 0.00 0.00 0.01 0.00 1

68 5 Susceptibility Modeling and Mapping

5.2

Landslide Susceptibility Modeling and Mapping

69

landslide incidence as they portrayed higher values of FR (4.77, 2.75 and 2.37), respectively. Shortly, areas with slopes ranging between 11° and 72° are susceptible to the slope instability and result into landslide occurrence following its triggers. The above can be explained in such a way that the increase in slope angle results in the increase in shear stress of the material constituting the slope (especially soil and other unconsolidated material), thus making them susceptible to sliding (Regmi et al. 2014). At slope angles equal to or lower than 11°, the ratio was less than 1, which indicates a poor correlation and low possibility of landslide incidence. This result harmonizes with earlier studies arguing that the probability of landslide occurrence increases depending on the slope angle as the shear stress in the soil and different unconsolidated materials increase (Roback et al. 2018, Arabameri et al. 2020). In this regard, gentle slopes are forecasted to have a low frequency of landslides because the lower shear stresses are always related to low gradients (Jaafari et al. 2014). The significance of the aspect (slope orientation) in the likelihood of landslide incidences is discussed and debated. Some researchers consider the aspect as one of the most significant influencing factors for landslides (Yalcin and Bulut 2007, Galli et al. 2008) while others limit its importance to certain types of landslides (Luzi and Pergalani 1999, Capitani et al. 2013). With the aspect, only the slopes facing the southwest, north and northwest showed a strong connection with landslide incidences as they displayed higher values of FR (1.57, 1.24 and 1.12, respectively). Unsignificant correlations were noticed in the remaining slope orientations, particularly the east-oriented slope, which exhibited the lowermost value of FR (0.52). This trend is parallel to the one observed in a recent landslide susceptibility study (Nsengiyumva et al. 2019a, b) that was conducted in the same study area. Generally, these results might be ascribed to the local settings and with regards to the winds and fault direction, the structure of rocks, and soil erosion process. The likelihood of landslide incidence on the southwest oriented slope, is justified by the highest precipitation amount in the southwest facing slopes (Fig. 4.15). Moreover, since the aspect controls the soil moisture concentration with the effect of climate, greater correlation between landslide probability of occurrence and North and North-Westoriented slopes is probably due to their cooler, colder, and more humid conditions. In the zones where landslides transpire on the south sides (either southeast or southwest), a higher amount of solar insulation also befalls. Thus, on slopes with higher insulation and higher temperatures, soil erosion should also be highly expected and this is evident following Maniraho et al. (2021) who mapped this area as highly susceptible to soil erosion. The correlation between the possibility of landslide incidence and curvature divulged the concave slope to have a higher value of FR (1.55) than areas with convex slopes (1.27), while areas with flat slopes exhibited a lower value of FR (0.73). Generally, high extent of soil moisture can be associated to the instability of the slope in the curvature zones. As the moisture content of the soil increases, soil stability generally decreases (Jaafari et al. 2014, Razavizadeh et al. 2017). To rationalize the results, it should be noted that during the rainfall seasons, the concave slopes can grasp more water and retain it for long time; which consequently triggers

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the slope instability to activate landslide incidences. Besides, the instability of convex slopes is due to external forces which constantly expand; thus, making them susceptible to sliding (Regmi et al. 2014). The proximity of the slopes to river structures mostly identified by buffering is an important factor in terms of slope stability (Raja et al. 2017). Therefore, the study on landslides incidence should also examine the impact of river proximity on the likelihood of landslides incidence. The FR was more than 1 at a distance between 0 and 200 m, suggesting a high possibility to encounter landslide incidences. In contrast, the FR was less than 1 at a distance more than 200 m; which specifies a lesser risk of landslide. With this influencing variable, it was found that the likelihood of a landslide event increases with the closer a river network is near a specific slope. This probability can be attributed to the land’s alteration caused by hills excavation, slope strength modification, erosions and their related runoffs which stimulate the instigation of landslides in the study area (Intrawichian and Dasananda 2011). A certain road section may act as a barrier, a net source, a corridor for water flow, and based on its position, it typically contributes as a source of landslides (Pradhan 2010). Thus, in parallel to the effects of proximity to rivers, landslides and the proximity to roads were highly correlated at the distance equal or lower than 300 m while a low correlation was found at a distance greater than 300 m. The implication from this result is that the closer the road to a slope, the greater is its instability and the landslide probability due to the improper road cutting to connect different anthropogenic activities which in turn disturbs the natural topology and the stability of the slope in the study area (Jebur et al. 2014, Romer and Ferentinou 2016). For the TWI, higher values of FR are depicted in two classes including 5–6 (1.57) and 6–9 (1.18). This implies their likelihood to landslide occurrence, whereas the classes greater than 9 displayed lower correlations (FR values less than 1) with the class 12–24 having the lowest value (0.16) for landslide susceptibility. Following these results, it is verified that FR values decrease as the values of TWI increase (Table 5.3). As a measurement of the topographic control on hydrological processes, TWI quantifies the terrain driven variation in soil moisture. Generally, the smaller the TWI value, the lower the soil moisture while the higher TWI value symbolizes a higher order water channel (Sun et al. 2018). Explicitly, higher TWI classes are connected with the increasing water infiltration which often leads to increased pore water pressure and further lessens the soil strength which makes the terrain prone to slope failures (Kumar and Anbalagan 2016). However, the classes of TWI exposed to landslide denotes a minor order drainage prone to landslide. To discuss this, it should also be noted that most of the Rwandan catchments are located in the zones that correlated with the occurrence of landslide. As hydrological response of these catchments, high surface runoff from the top ridges influences the quick accumulation of pore pressure owing to the penetration on upslopes entailing the slope cutting which undermines stability, especially where settlements are within or located close to hollow and concave slope shapes (Nakileza and Nedala 2020). Moreover, SPI as an effective hydrological factor can influence the spatial variation of landslides (Shafizadeh-Moghadam et al. 2018). Generally, researches

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71

have shown that high ranges of SPI in many cases describe regions with high erosional potential and thus, linked to a high or very high probability of landslide occurrence in an area (Petrea et al. 2014, Gholami et al. 2019). This was confirmed in the current investigation where SPI and landslide exhibited a direct good correlation such that both variables increased synchronically. The highest association with the greatest value of FR (2.20) was noticed in the range of 2.2–14 (Table 5.3). As for STI, very high probability in influencing landslides’ incidence was depicted in different classes ranging between 7759–63,825 with the highest FR value being 5.15 in the range of 7759–21,275. In contrast, the classes ranging between 0 and 7759 exhibited a moderate to low relationship with landslide incidence having the least value of FR (0.11) in the class ranging between 0–1502. For this factor, it should be known that a high value is indicative of water contributions from upslope and high-water flow velocities, and the effect of topography on erosion, which are directly linked to landslide instigation (Pradhan and Kim 2014). For NDVI, the greatest value of FR (1.71) in the class ranging from 0.7 to 0.9 evinced a correlation with the probability of landslide incidence. This class is, thus, highly exposed to the incidence of landslide than less averaged classes. Besides, for NDVI values ranging between -0.2 and 0.2, the lowermost FR value (0.34) was depicted; indicating a lower probability of landslide. Generally, it is known that landslides occur in less vegetated areas (Reichenbach et al. 2014), and the rapid alteration in forest cover due to different human activities such as deforestation is a key contributing factor for landslide occurrence (Dahigamuwa et al. 2016). Nevertheless, areas covered by vegetation (though declined, according to previous studies) were detected in regions with thorough dominance of high topographic characteristics. This fact explains the probability of landslide incidences that is mostly activated by high rainfall (Fig. 4.15) and the related downpours combined with the fragile soil formation present in these areas (Fig. 4.14). In addition, the relationship between LULC and the likelihood of landslide incidence was also evaluated. Following the classified LULC types, a significant correlation between both variables (LULC and landslide incidence) was mostly depicted in built-up and agricultural regions since these areas portrayed respective greater values of FR (8.55 and 2.01). Considering a vegetation perspective, the relationship with landslide incidence in forest and grassland areas was also noted with FR values greater than 1 (1.69 and 1.26, respectively). Conversely, wetland regions showed a trivial correlation with landslide possibility of incidence with a low FR value equivalent to 0.49. Finally, it was obvious to find no correlation between water bodies and the probability of landslide incidence as the FR value was found to be zero. These results of susceptible LULC classes to landslide occurrence can be attributed to poor planning of urban and territorial infrastructure in built-up areas (Bizimana and Sönmez 2015) and the inappropriate cultivation on unstable steep slope (Maniraho et al. 2021). Majority of people in Rwanda rely on agriculture for their livelihood (Huggins 2009, Pritchard 2013) which, therefore, become a motive for the local community to engage into deforestation by converting vegetationcovered areas at the expense of expanding cropland areas. This condition, consequently, affect the stability of the slope leading to the incidences of landslide.

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The infiltration of water is mainly reliant upon soil properties, particularly soil texture (Zaibon et al. 2017). Thus, soil texture was also evaluated in correlation to the probability of landslide incidence in the research area. Clay loam with a FR value of 1.57 was revealed to be the most susceptible type of soil. This was followed by the loam and clay soil which exhibited a medium correlation with landslide occurrence (FR equals to 0.78 and 0.50, respectively). The remaining classes of soil texture, on the other hand, displayed lower values of FR, implying a lower relationship, and, thus, making it insignificant in landslide probability of incidence. This obtained result highly corroborates with a recent study (Nahayo et al. 2019) conducted using a knowledge-based approach along the study area. The investigation of the relationship between rainfall and landslides disclosed higher FR values in areas with high amount of rainfall (classes ranging between 1082 and 1685 mm) and, hence, is prone to future landslide incidences. Conversely, a poor relationship between the two variables was clear in the regions that receive a small rainfall quantity (796–1082 mm) with lower values of FR (Table 5.3). This result implies that the probability of landslide occurrence in the study area increases with rainfall amount that activates or triggers slope instability, as also indicated in past studies (Bizimana and Sönmez 2015, Piller 2016). Despite the lithological assortment in Rwanda, the correlation between the lithology and the probability of landslide incidence was only depicted in schist rocks, volcanic ash rock and basalt rocks with very high FR values of 7.40, 7.23, and 2.06, respectively whereas the remaining lithological units exhibited lower correlation. To discuss this correlation, lithology plays a significant role in the incidence of landslides because it affects the strength and permeability properties of the bedrock associated with slope (Henriques et al. 2015). The slope instability in this process is activated by instances such as the level of water infiltration in the soil, the functionality of drainage systems and the changes in soil materials. This is because the lithological unit can have different characteristics in terms of composition, structure, and cohesion of soil which produce different resistance to the motion (Silalahi et al. 2019).

5.2.3

Prognostic Weights and Ranking of the Factors Based on their Influence Level

Generally, the overall pairwise comparison matrix contains various trails by which the relative significance of the considered influencing factors is evaluated; thus, becomes credible to find out the degree of consistency employed for the judgments’ development. The pairwise comparison matrix was prepared for fourteen influencing factors (Table 5.4). The resulting CR value of the matrix for pairwise comparisons between the factors was 0.097, a value lower than the threshold (0.1). This divulged an acceptable good degree of consistency for the constructed pairwise comparison that is effective to identify a precise Prognostic Weight (PW). By computing the prognostic weights (PW) of influencing factors in percentage using the Investigative Network Process (INP) approach, the general analysis

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Landslide Susceptibility Modeling and Mapping

73

Table 5.4 Pairwise comparison matrix, prognostic weights, and consistency ratio for each factor IF

A

B

C

D

E

F

G

H

I

J

K

L

M

N

PW

Rank

A

1

1

9

7

9

7

9

9

9

7

2

1

2

9

18.9

2

B

1

1

9

9

7

5

9

7

9

5

3

1

5

7

19.3

1

C

0.1

0.11

1

0.33

2

0.1

0.3

1

2

0.2

0.1

0.1

0.14

0.2

1.2

11

D

0.11

0.14

3

1

5

0.1

0.5

5

2

0.1

0.1

0.1

0.14

0.5

2.1

10

E

0.14

0.11

0.5

0.2

1

0.1

0.2

1

1

0.1

0.1

0.1

0.14

0.2

1.1

12

F

0.2

0.14

7

7

7

1

7

7

9

1

0.5

0.5

0.33

2

6.9

7

G

0.11

0.11

3

2

5

0.1

1

5

7

0.1

0.1

0.2

0.2

1

2.8

9

H

0.14

0.11

1

0.2

1

0.1

0.2

1

1

0.1

0.1

0.1

0.2

0.2

1.1

13

I

0.11

0.11

0.5

0.5

1

0.1

0.1

1

1

0.1

0.1

0.1

0.2

0.14

1.1

14

J

0.2

0.14

5

7

7

1

7

7

7

1

1

0.3

0.33

5

7.4

6

K

0.33

0.5

9

9

7

2

9

9

9

1

1

1

2

9

12

4

L

1

1

9

9

9

2

5

7

7

3

1

1

2

9

9.7

5

M

0.2

0.5

7

7

7

3

5

5

5

3

0.5

0.5

1

7

13.4

3

n

0.14

0.11

5

2

5

0.5

1

5

7

0.2

0.1

0.1

0.14

1

3

8

CR = 9.7% Pev (Principal eigen value) = 15.98

Where IF is the influencing factors, Aaltitude, Bslope, Caspect, Dcurvature, Eproximity to rivers, F proximity to roads, GTWI, HSPI, ISTI, JNDVI, KLULC, Lsoil texture, Mrainfall, and Nlithology

Prognostic weight (%)

25 20

15 10 5 0

Influencing factors

Fig. 5.2 Factors by level of influence to incidence of landslide

unveiled factors that mostly influence the landslide’s possibility of incidence than others in the research area (Fig. 5.2). The level of influence to the incidence of landslide was put within 4 categories viz. very high influence, High influence,

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moderate influence and low influence (Table 5.4) based on their PW which ranged from 1.1 to 19.3 percent. The factors with very high influence on landslide probability of occurrence were found to be the slope (19.3%), altitude (18.9%), rainfall (13.4%), and LULC (12%), while the soil texture (9.7%), NDVI (7.4%) and the proximity to roads (6.9%) fell into factors with high influence. This was followed by factors with moderate influence namely the lithology (3%), the TWI (2.8%), and the curvature (2.1%). Finally, the aspect (1.2%), proximity to rivers, SPI, and STI have exhibited a low influence on the incidence of landslide with a PW equal to 1.1%. Finally, the landslide susceptibility was mapped and analyzed in ArcMap using the calculated prognostic weights (PW) of influence.

5.2.4

Susceptibility Mapping for Landslide

The landslide susceptibility map was engendered by combining the classified and rasterized layers of influencing factors and tallying each factor’s ratio value. The generated landslide susceptibility map was put into different classes including very low, low, moderate, high, and very high susceptibility (Fig. 5.3). The modeled landslide susceptibility mapping echoed the realism on the ground compared with the conducted field work in the research area (Fig. 4.2).

Fig. 5.3 Spatial distribution of landslide hazard susceptibility in Rwanda

5.2

Landslide Susceptibility Modeling and Mapping

Table 5.5 Landslide susceptible areas per category

Susceptibility category Very high High Moderate Low Very low Water Total

75 Area/km2 2830 3383 5398 8636 4479 1612 26,338

Susceptibility area (%) 11.45 13.68 21.83 34.93 18.11 0 100

Considering the generated susceptibility map, various parts displayed to be prone to future landslide incidences. The resulting map classified 34.93% of the entire area as low susceptibility, and 18.11% classified as very low susceptibility to landslide. The classes of high and moderate susceptibility were equivalent to 13.68% and 21.83%, respectively. At last, 11.45% fells into very high susceptibility class (Table 5.5). Previous studies (Walker and Shiels 2013, Gan et al. 2018) stipulated that the impacts from any landslide event will vary based upon different influencing factors such as the topography, source of water, location of water flow, rainfall amount, presence and effectiveness of landslide control systems, changes in land use, vegetation cover, soil type, etc. This was found to be consistent with the findings of the current study, which disclosed the areas with steep slopes (Fig. 4.4) to enclose high population exposed to landslides. The obtained results were compared with previous landslide incidences (from documented landslide events) considering their main losses and damages (Table 2.1). Accordingly, the Rwandan Ministry in Emergency Management, has reported 124 fatalities, 141 injuries, and 897 homes destruction were recorded between 2011 and May 2015. Also, in the rainy season of May 2016 itself, the report revealed 35 deaths, 26 injuries, 67 roads and 29 bridges destructed by landslide (Nsengiyumva et al. 2019a, b). Moreover, the 2018 MIDIMAR report (from January to October 2018) disclosed that landslide killed 234 people, injured 218, destroyed 15,264 houses and 9412 hectares of crops, damaged 31 roads and 52 bridges, destroyed 86 classrooms, and killed 797 livestock in different regions (Nsengiyumva et al. 2019a, b). Recently, between 07 and 09 May 2020, more heavy rainfall caused flooding followed by landslides in the northern province (specifically in Gakenke), and in Western Province (specifically in Ngororero, Nyabihu and Rubavu districts) with nearly 16,210 people (3242 households) affected across the four districts. Following the above landslides’ records and documentations, the western and northern regions in the whole study area showed to be the most susceptible owed to the repetitive incidences of landslides. This was in conformity with the findings of the current investigation and also complied with the 2001 FEMA’s (Federal Emergency Management Agency) conclusion stating that “the greatest indicator of upcoming landslide events is the previous incidences, as they have tendency of occurrence in similar locations”.

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In comparison with other studies in the study area, similar results on these areas more susceptible to landslide were revealed in Nahayo et al. (2019)‘s investigation using the bivariate statistical index approach. In addition, different influencing factors for instance the topographic setting (Fig. 4.3 and 4.4), LULC (Fig. 4.13) and soil properties (Fig. 4.14) among others are mostly triggered by heavy rainfall (Fig. 4.15) and consequently have a huge impact. This is more prevalent in districts including Nyabihu, Gakenke, Ngororero, Nyamagabe and Musanze which exhibited to range between very high to high susceptible class. Furthermore, districts such as Nyaruguru, Nyanza, Kamonyi and Ruhango in the southern province were found to be moderately susceptible to landslide incidence. The low and very low susceptible classes were dispersed in the southeastern part (with districts such as Nyamagabe, Gisagara and a portion of Huye); in Kigali city (with districts such as Nyarugenge and Kicukiro) and most especially in the eastern province (with districts such as Bugesera, Ngoma, Kayonza and Kirehe). The eastern and southeastern are relatively known to be flat with low elevation pattern (Li et al. 2021) which generates a mild and cool climate with a prolonged drought originating from its rainfall anomalies along the year (https://www.rema.gov.rw/soe/chap9.php). As a result of the on-going level of population growth (Fig. 3.3) and the pursue of agricultural land resource, people, particularly those residing in rural areas, have moved into locations that are prone to landslides in recent times. it is evident from comparing the generated susceptibility map (Fig. 5.3) and population density (Fig. 3.2) that a mass of people keeps inhabiting unstable areas (particularly ranging from moderate to high susceptible). Besides, people in these areas (rural) are known to be living in extreme poverty and mainly relying on agriculture for their livelihood (Muyombano 2019, Maniraho et al. 2021, Weatherspoon et al. 2021). This last explains why there is a limited ability to respond to a landslide disastrous incident. In Kigali, the capital city of Rwanda, three main districts (Nyarugenge, Kicukiro and Gasabo) entirely known as urban areas is highly populated because of the ruralurban migration but fortunately, the susceptibility analysis revealed it to be classified as low susceptible. The northern region of the country extended from moderate to very high susceptible location and was verified to have a population density level ranging from 250–2500 people/km2. On the other hand, high population density (2500–5000 people/km2) in the urbanized area (Kigali city) was thoroughly simulated as moderately susceptible to landslide. In comparison to other provinces, the eastern province was classified as a stable area for landslide. Notwithstanding, distinct regions of the country proved to be prone to landslide incidences, the degrees of people’ exposure vary based on each region’s unique characteristics, considering the influencing drivers. The spatial distribution of landslide susceptibility observed in highly prone areas has been attributed to a set of human actions that overexploit the natural environment such as deforestation for biomass fuels production, house constructions and other activities, inappropriate farming in delicate areas and different developmental activities like road constructions, which at the end contribute to slope failures (Bizimana and Sönmez 2015, Nahayo et al. 2019). These actions hasten the risks from suffering the damaging effects of the hazard such as fatalities and property destruction in the

5.3

Models’ Accuracy and Validation

77

exposed areas. Additionally, it has been highlighted that different members of local communities and immense hectares of cropland (Fig. 4.13) are situated in highly exposed areas. The population should, therefore, be informed about these areas to enable them build appropriate and effective mitigation efforts that could ultimately alleviate the effects by making them aware of the potential future landslides.

5.3

Models’ Accuracy and Validation

Scientists agree that appropriate methods need to be applied to evaluate the performance of the applied models in assessing any hazard susceptibility (Frattini et al. 2010, Fleuchaus et al. 2021). Given the regional variance, there is no clear consensus on the most appropriate technique to be utilized in testing the accuracy of the considered model. For this study, the area under the receiver operating characteristic (AUROC) that reveals the reliability and the capability of the model’s prediction rate considering the testing (validation) datasets from the inventory (Tehrany et al. 2015). The AUCROC plots various differing values entailing the obtained model’s accuracy versus the entire range of potential functions’ threshold values serving as a model’s overall accuracy metric. AUCROC portrays a specificity and sensitivity graph computed for various thresholds. The specificity displays the percentage of steady pixels below the intended threshold that the model properly predicted over all of the observed steady pixels whereas the sensitivity denotes the proportion of unsteady pixels above the intended threshold appropriately predicted by the model over the overall observed unsteady pixels (Chung and Fabbri 2003). The AUROC is mathematically expressed by Eq. (5.9): nþ1

AUROC = i=1

1 2

ðXi - Xiþ1 Þ2  ðYi þ Yiþ1 Þ

ð5:9Þ

Where, Xi denotes (1-specificity) and Yi represents the sensitivity at the threshold i when, X(n + 1) = 1 and Y(n + 1) = 1. The plot generally illustrates the false-positive rate on the x-axis (1-specificity) Eq. (5.10) and the true-positive rate on the y-axis (the sensitivity) Eq. (5.11). Specificity ðXÞ = 1 -

TN TN þ FP

ð5:10Þ

Sensitivity ðYÞ = 1 -

TN TP þ FN

ð5:11Þ

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where TP stands for true positives, TN for true negative, FP for false positives and FN for false negative. If the values of the above-mentioned criteria have the number of 1 in a model, then the model will be an appropriate/ideal model (Shirzadi et al. 2017). The true positive and false positive explain the percentage of pixels accurately identified as hazard and non-hazard, respectively. Meanwhile, TN and FN are the number of pixels classified correctly and incorrectly as non-flood/landslide, respectively (Shirzadi et al. 2018). Thus, sensitivity relates to the proportion of hazards (floods/landslides) that are appropriately classified per total predicted hazards while the specificity relates to the proportion of inappropriately classified hazard events per total predicted non-hazard events (Pham et al. 2016, Shirzadi et al. 2018). The prediction accuracy is known depending on the calculated percentage values extending from 0.5 to 1, and the model with the biggest AUCROC is the most accurate. The qualitative–quantitative correlation of AUROC and estimate evaluation is as follows: the excellent model produces a percentage ranging between 90 and 100, very good model (80–90), good model (70–80), an average performance of the model in the range of 60–70 and finally 50–60 for a weak or poor performance (Yesilnacar and Topal 2005, Abul Hasanat et al. 2010). After testing, the AUCROC showed the prediction rate of 80.4% and 84.6% for flood and landslide susceptibility modeling, respectively (Figs.5.4 and 5.5). These percentages are considered satisfactory despite the input data limitation and accuracy (Sajadi et al. 2022). They also explain how well was the performance of LR and FR models along with the influencing factors toward the prediction of flood and landslide.

Fig. 5.4 Performance evaluation for logistic regression model in predicting flood susceptibility

5.4

Limitations and Uncertainties

79

Fig. 5.5 Performance evaluation for frequency ratio model in predicting landslide susceptibility

5.4

Limitations and Uncertainties

Uncertainties in susceptibility-related researches are unescapable owing to difficulties to identify the best method of application and the quality of input datasets under diverse required conditions. Several existing methods or techniques for flood and landslide susceptibility assessment keep different uncertainties owing to the paucity of complete data, knowledge and variability (Ercanoglu and Gokceoglu 2002). Therefore, some limitations and uncertainties were identified in this study: • Generally, inventory maps for flood and landslide provide different information about their rate of recurrence (Pourghasemi et al. 2018). However, in this study, the complete and coherent historical data associated with flood and landslide rate of recurrence was deficient for some known exposed locations. Moreover, some errors were prevailing in the available database of these hazards especially for longstanding cases. • One of the most substantial data inputs for susceptibility modeling is the elements of the topography like the slope gradient, aspect, and curvature among others, which are computed using DEM (digital elevation model). The characteristics of these attributes also pose uncertainty owing to elevation error and this is indeed unavoidable with DEM data (Fisher 1991, Fisher and Tate 2006, Wechsler 2007, Wilson 2012). Hence, DEM error might cause some uncertainties and affect the

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produced map of flood or landslide susceptibility in the model-extrapolation and construction phases. • In mapping the set of influencing factors, satellite imageries at higher resolution were used knowing the research area to improve the identification of longstanding floods and landslides. Hence, the limited amount of inventory datasets used in the current research may contribute to some level of uncertainties based on informed rapid LULC change in the study area as argued by Li et al. (2021). • Compared to meteorological stations data, the gridded model data (as used in this research) are most of the time not powerful enough to seize climate variability (Obarein and Amanambu 2019). The paucity of effective and complete gauged meteorological data obliged the application of CHIRPS (climate hazards group infrared precipitation with station) data. This can to some extent increase uncertainties in susceptibility modeling but yet can be the best alternative for different regions with data shortage problem like Rwanda. • Earlier studies (Ahmed and Dewan 2017, Zêzere et al. 2017) have ascertained that in any study area, susceptibility-related studies have to be assessed individually for each type of flood or landslide because different types of floods or landslides have different spatial incidence related to distinct thresholds conditions concerning the influencing factors. However, this study has not considered flood or landslide per each type as it might be expensive and time consuming. Consequently, the study area’s diverse flood and landslide types may also contribute to some uncertainties in the produced flood and landslide susceptibility maps. Overall, for the purpose of this investigation, the uncertainties and their effects were assessed to be not very significant as long as this study does not intend to represent an exact and perfect situation but only wants to provide a thorough consideration and understanding of future scenarios of flood or landslide probability of incidence for both researchers and policy makers.

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Huggins C (2009) Agricultural policies and local grievances in rural Rwanda. Peace Rev 21(3): 296–303 Intrawichian N, Dasananda S (2011) Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed northern Thailand. Environ earth sci 64 (8): 2271–2285. J Geol Soc India 81(2):219231 Jaafari A et al (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926 Jebur MN et al (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165 Khosravi K et al (2016) Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ Monit Assess 188(12):1–21 Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286 Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491 Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855 Li C et al (2021) How will Rwandan land use/land cover change under high population pressure and changing climate? Appl Sci 11(12):5376 Luzi L, Pergalani F (1999) Slope instability in static and dynamic conditions for urban planning: the ‘Oltre Po Pavese’case history (Regione Lombardia–Italy). Nat Hazards 20(1):57–82 Maniraho AP et al (2021) Application of the adapted approach for crop management factor to assess soil erosion risk in an agricultural area of Rwanda. Land 10(10):1056 Manyifika M (2015) Diagnostic assessment on urban floods using satellite data and hydrologic models in Kigali. University of Twente, Rwanda Meten M et al (2015) GIS-based frequency ratio and logistic regression modelling for landslide susceptibility mapping of Debre Sina area in Central Ethiopia. J Mt Sci 12(6):1355–1372 MIDIMAR (2015) The National Risk Atlas of Rwanda. Nairobi, Ministry of Disaster Management and Refugee Affairs Mohammady, M., et al. (2012). "Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models." J Asian Earth Sci 61: 221-236 Muhire I et al (2015) Spatio-temporal variations of rainfall erosivity in Rwanda. Journal of Soil Science and Environmental Management 6(4):72–83 Munyaneza O et al (2013) Hydraulic structures Design for Flood Control in the Nyabugogo wetland, Rwanda. Kigali, Rwanda Muyombano E (2019) Livelihood and food security of vulnerable people with limited or no land in northern Rwanda: a land use consolidation programme analysis. Ghana Journal of Geography 11(2):103–126 Nahayo L et al (2019) Landslides hazard mapping in Rwanda using bivariate statistical index method. Environ Eng Sci 36(8):892–902 Nakileza BR, Nedala S (2020) Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda. Geoenvironmental Disasters 7(1):1–13 Nsengiyumva JB et al (2019a) Comparative analysis of deterministic and semiquantitative approaches for shallow landslide risk modeling in Rwanda. Risk Anal 39(11):2576–2595 Nsengiyumva JB et al (2019b) Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-eastern Africa. Sci Total Environ 659:1457–1472 Obarein OA, Amanambu AC (2019) Rainfall timing: variation, characteristics, coherence, and interrelationships in Nigeria. Theor Appl Climatol 137(3):2607–2621

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Papaioannou G et al (2015) Multi-criteria analysis framework for potential flood prone areas mapping. Water Resour Manag 29(2):399–418 Petrea D et al (2014) The determination of the landslide occurrence probability by GIS spatial analysis of the land morphometric characteristics (case study: the Transylvanian plateau). Carpathian Journal of Earth and Environmental Sciences 9(2):91–102 Pham BT et al (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84: 240–250 Piller, A. N. (2016). "Precipitation intensity required for landslide initiation in Rwanda." Pourghasemi HR et al (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arab J Geosci 11(9):1–12 Pradhan AMS, Kim Y-T (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-Ri Creek, South Korea. Nat Hazards 72(2): 1189–1217 Pradhan B (2010) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2) Pritchard MF (2013) Land, power and peace: tenure formalization, agricultural reform, and livelihood insecurity in rural Rwanda. Land Use Policy 30(1):186–196 Raja NB et al (2017) Landslide susceptibility mapping of the Sera River basin using logistic regression model. Nat Hazards 85(3):1323–1346 Razavizadeh S et al (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76(14):1–16 Regmi AD et al (2014) Landslide susceptibility mapping along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5): 1266–1285 Reichenbach P et al (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manag 54(6):1372–1384 REMA (2013) The assessment of economic impacts of the 2012 wet season flooding in Rwanda, Kigali REMA (2015) State of the environment and outlook report in Rwanda. Greening agriculture with resource efficient, low carbon and climate resilient practices. Government of Rwanda, Kigali Roback K et al (2018) The size, distribution, and mobility of landslides caused by the 2015 Mw7. 8 Gorkha earthquake, Nepal. Geomorphology 301:121–138 Romer C, Ferentinou M (2016) Shallow landslide susceptibility assessment in a semiarid environment—a quaternary catchment of KwaZulu-Natal, South Africa. Eng Geol 201:29–44 Rupert M et al (2008) Using logistic regression to predict the probability of debris flows in areas burned by wildfires, southern California, 2003-2006. US Geological Survey Washington, DC Saaty T (1980) The analytic hierarchy process: planning, priority setting resource allocation. N Y. McGraw-Hill." Saaty TL, Vargas L (2001) LG 2001. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, Kluwer, USA Sajadi P et al (2022) Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan plateau region by five learning algorithms. Geoscience Letters 9(1):1–25 Samia J et al (2017) Do landslides follow landslides? Insights in path dependency from a multitemporal landslide inventory. Landslides 14(2):547–558 Shafapour Tehrany M et al (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomat Nat Haz Risk 8(2):1538–1561 Shafizadeh-Moghadam H et al (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. J Environ Manag 217:1–11 Shirzadi A et al (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76(2):1–18

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Shirzadi A et al (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors 18(11):3777 Silalahi FES et al (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters 6(1):1–17 Sun X et al (2018) Landslide susceptibility mapping using logistic regression analysis along the Jinsha river and its tributaries close to Derong and Deqin County, southwestern China. ISPRS Int J Geo Inf 7(11):438 Tehrany MS et al (2013) Spatial prediction of flood susceptible areas using rule-based decision tree (DT) and a novel ensemble bivariate and multivariate statistical model in GIS. J Hydrol 504:69– 79 Tehrany MS et al (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101 Uwera M et al (2020) Contribution of green infrastructures on flood risk reduction in Kigali City of Rwanda. International Journal of Environmental Planning and Management 6(4):115–124 Van Westen CJ et al (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86(2):404–414 Walker LR, Shiels AB (2013) Physical causes and consequences for landslide ecology Wallerstein N, Arthur S (2012) Improved methods for predicting trash delivery to culverts protected by trash screens. Journal of Flood Risk Management 5(1):23–36 Weatherspoon DD et al (2021) Rwanda’s commercialization of smallholder agriculture: implications for rural food production and household food choices. Journal of Agricultural & Food Industrial Organization 19(1):51–62 Wechsler S (2007) Uncertainties associated with digital elevation models for hydrologic applications: a review. Hydrol Earth Syst Sci 11(4):1481–1500 Wilson JP (2012) Digital terrain modeling. Geomorphology 137(1):107–121 Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41(1):201–226 Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3-4):251–266 Zaibon S et al (2017) Soil water infiltration affected by topsoil thickness in row crop and switchgrass production systems. Geoderma 286:46–53 Zêzere J et al (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267

Part II

Community Perception on Flood and Landslide Risk

Chapter 6

Introduction

The latest edition of a global disaster risk perception survey puts the risks related to extreme weather events, natural disasters, and failure of climate change adaptation amongst the top ten risks respectively in 2nd, 6th and 7th place in terms of likelihood of occurrence (WEF 2015). Much research have been carried out about how people, both as individuals and community, react to disasters (Figueiredo et al. 2009; Misanya and Øyhus 2015). The aim has been to advance the response to community’ concerns as part of emergency readiness when response solutions are positioned. Hazards and their related disaster risk perceptions are commonly characterized by community’s knowledge of the hazard processes, own experience and time span since the last nearby hazardous or disaster event (van Manen 2014). Though these aspects don’t certainly correspond to those that influence risk perception or result in the adaptation of proper mitigation or risk reduction initiatives (Meheux and Parker 2006; Paton et al. 2008). For this, increasing number of research (Kelman and Mather 2008; Mercer et al. 2010; Bird et al. 2011) and intergovernmental organizations advocate the use and assimilation of community perception context into disaster risk reduction initiatives. Nevertheless, diminutive attempt has been carried out on understanding this aspect, and how does it influence the susceptibility of areas to the occurrence of natural hazards, particularly in Rwanda. Conferring to social scientists concentrating in social perception researches, local community’s perception and comprehension of natural hazards and their related disaster risks are socially built (Boholm 2003). The latter indicates that any apprehension of how the community perceive natural hazards must consider the context in which they are experienced (Renn 2017). For instance, Bempah and Øyhus (2017) argued that the community have been directly exposed to floods and landslides, and consequently, they perceive these hazardous events as having risk to a greater extent than the overall community who came to have less exposure. The perceptions of risk are a key issue when seeking to develop systems, practices and policies to protect local community based on their susceptibility to a hazard (Calvello et al. 2016). This is particularly evident when risk mitigation strategies involve non-structural measures such as relocation and warning systems © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_6

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which pre-suppose the active involvement of the communities in question (UNISDR 2006). Perceptions of risk are one of the fundamental elements that condition the behavior of the community (Tulloch and Lupton 2003) and thus, have a decisive impact on their resilience. Perceptions of flood and landslide risks are influenced by the ways in which decisions are implemented following processes of analysis and zoning, which imply that appropriate forms of communication are central to the success of risk management systems (Albanesi et al. 2011). Communication must be bilateral if it is to be effective and must involve listening to the opinions of local communities as well as understanding their views and perceptions. Hence, community perception on flood and landslide risk has been a significant factor in determining how the society will evolve and deal with the incident (Bempah and Øyhus 2017). However, the perception of risks related to natural hazards such as hydrogeological hazards has arguably not received enough attention in the scientific literature (Renn 1998; Wachinger et al. 2013). Consequently, there is a need of assessing local community perception on the risk caused by the hazards as far as disaster risk reduction is concern following the accepted practice within international disaster management stipulating that before getting on any disaster risk reduction project in a community, the perceptions must be documented.

References Albanesi C et al (2011) La comunicazione istituzionale dei rischi. Graph. Materiale reperibile sul sito www. rischio. unibo. it, Linee guida, Bologna Bempah SA, Øyhus AO (2017) The role of social perception in disaster risk reduction: Beliefs, perception, and attitudes regarding flood disasters in communities along the Volta River, Ghana. International journal of disaster risk reduction 23:104–108 Bird DK et al (2011) Different communities, different perspectives: issues affecting residents’ response to a volcanic eruption in southern Iceland. Bulletin of volcanology 73(9):1209–1227 Boholm Å (2003) Situated risk: An introduction. Ethnos 68(2):157–158 Calvello M et al (2016) Landslide risk perception: a case study in Southern Italy. Landslides 13(2): 349–360 Figueiredo E et al (2009) Coping with risk: analysis on the importance of integrating social perceptions on flood risk into management mechanisms–the case of the municipality of Águeda. Portugal. Journal of risk research 12(5):581–602 Kelman I, Mather TA (2008) Living with volcanoes: the sustainable livelihoods approach for volcano-related opportunities. Journal of Volcanology and Geothermal Research 172(3-4): 189–198 Meheux K, Parker E (2006) Tourist sector perceptions of natural hazards in Vanuatu and the implications for a small island developing state. Tourism Management 27(1):69–85 Mercer J et al (2010) Framework for integrating indigenous and scientific knowledge for disaster risk reduction. Disasters 34(1):214–239 Misanya D, Øyhus AO (2015) How communities’ perceptions of disasters influence disaster response: managing landslides on Mount Elgon. Uganda. Disasters 39(2):389–405 Paton D et al (2008) Risk perception and volcanic hazard mitigation: Individual and social perspectives. Journal of Volcanology and Geothermal Research 172(3-4):179–188

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Renn O (1998) Three decades of risk research: accomplishments and new challenges. Journal of risk research 1(1):49–71 Renn O (2017) Risk governance: coping with uncertainty in a complex world. Routledge Tulloch J, Lupton D (2003) Risk and everyday life. Sage UNISDR (2006) A guide to community-based disaster risk reduction in central Asia. Van Manen SM (2014) Hazard and risk perception at Turrialba volcano (Costa Rica); implications for disaster risk management. Applied Geography 50:63–73 Wachinger G et al (2013) The risk perception paradox—implications for governance and communication of natural hazards. Risk analysis 33(6):1049–1065 WEF (2015) Global Risks 2015. World Economic Forum

Chapter 7

Local Community Sampling and Questionnaire

After producing the flood and landslide susceptibility indices, the study also sought to link the obtained results with the perception of the community on the hazards. This was executed by conducting a field survey in the study area where questionnaires were distributed among the community members across districts sampled from each province based on the frequency of flood and landslide incidence (Figs. 4.1 and 4.2) rather than population size. Therefore, 11 districts were selected such that in the Western province (Nyabihu, Rubavu, Rusizi, Karongi, Rutsiro, Ngororero), Northern province (Musanze and Gakenke), Southern province (Nyaruguru), Eastern province (Gatsibo) and finally in Kigali city (Gasabo) were selected as part of the investigation. Generally, the designed questionnaires (Table 7.1) were administered to 550 respondents in a face-to-face manner such that in each selected district, 50 participants were simply and randomly nominated. The sampling’s randomness was executed by sampling with no replacement in which every selected participant possessed only one chance of being selected. It can be noted that the investigation was mostly conducted in rural areas (Fig. 7.1) because the hazards were mostly observed in these areas with different socioeconomic factors such as poverty, lack of enough education and inadequate warning system. It should also be noted that no interviews were conducted during the survey sessions but the questions & answers structured on questionnaires in the content analysis executed to collate insights and perceptions of the local communities and concerns. In summary, the questions have been asked to reveal the community’s experience, concern, their capacity and responsibility as well as mitigation initiatives and mechanisms. The collection of these data took place between November 2021 and February 2022. The sole inclusion criterion was to be a local community member of the districts (aged atleast from 18 year and above) in which the investigation is being done. Ethically, a letter introducing the process was sent to the head of each selected district to request their collaboration and the preparation of the local community for their active participation by elucidating the aim of the study with a descriptive view of the structured questionnaire. In case the respondents met any difficulty or confusion about the questions, the researchers took responsibility of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_7

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Table 7.1 The used questionnaire for the investigation No 1 2 3 4 5

Questions Have you ever experienced any flood/landslide event in your area? Do you believe that your residence is under risk of flooding/landslide? Do you believe that any action can be taken to mitigate the effects of flood/ landslide? Who do you think is responsible to mitigate flood and landslide? Which initiatives do you think can be taken to mitigate the effects of flood/ landslide?

Choice Yes / no Yes / no Yes / no Multiple choice Multiple choice

Fig. 7.1 Photographs taken on field during the survey sessions in different districts

explaining until the question becomes clear to the participant. All of them were granted the complete right to withdraw from the survey process or deny to take part. Furthermore, participants were informed that this was an anonymous survey which includes not only their names but also their contact information, so there would be no seepage of individual confidentiality. After the survey and questionnaire distribution process, the collated data were coded, edited and graphic created using IBM SPSS Statistics (SPSS version 20).

Chapter 8

Local Community Perception

8.1

Experience and Belief Toward the Risk and Mitigation

Preceding research have confirmed the importance of prior hazard incidents’ exposures and experience in local community perceptions (Kellens et al. 2011; Bempah and Øyhus 2017). As displayed in Fig. 8.1, majority of participants (76% and 81%) have already experienced flood and landslide, respectively. These high percentages on this question were indeed expected since the sampled areas or districts were selected on flood and landslide frequencies basis. Regarding the second question (Q2), high percentages (84% and 74%) of participants perceive their residential areas to be under landslide and flood risk, respectively. In parallel, earlier research disclosed that, based on their previous disasters’ experience, the community replies truthfully and that their discernment is influenced by qualitative factors including the frequency of the hazards’ incidence and the gravity of their impacts, their sense to withstand with the hazards and their current feelings (Slovic et al. 2004; Wachinger et al. 2013). In his research, Anilan and Yuksek (2017) argued that the perception of disaster risks is well forecasted by the experience of the local community on disaster incidences. Similarly, Lechowska (2018) and Adelekan and Asiyanbi (2016) inspected the community’ experiences in relation to the incidence of hazards in their residential areas, and the results showed a good correlation between community encounters with the levels of hazard risk perception. Self-experiences of the past hazards’ consequences such as fatalities and devastation of properties increase the community’ risk perception which, therefore, influences the way communities act in response to imminent potential hazardous incidences. Given that the obtained findings from Q2 on the analyzed hazards (floods and landslides) were completely not dissimilar from those in Q1, this is reasonable and harmonize with the reality of the current study. Lastly, majority of respondents (83% and 91%) regarding Q3, believed that different measures could be adopted for the mitigation of flood and landslide incidence, respectively. This perception may help to alleviate the damaging impacts of the hazards in the areas; which implicate an optimistic perspective toward © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_8

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Yes (%)

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Fig. 8.1 Community perception on their experience and belief toward the risk and mitigation

the reduction of future flood and landslide risk upon the right execution of the adopted measures.

8.2

Capacity, Concern and Responsibility

The community’s ability to manage the risks posed by the hazards greatly reduces their susceptibility and vulnerability to exposure (Adelekan 2011). While exploring the community’s point of view on mitigation measures responsibility, the results (Fig. 8.2) revealed that, with the exception of Gasabo district in urban areas, where 64% of respondents stressed the adoption and implementation of mitigation measures as a shared responsibility, the majority in the remaining considered districts perceived it as government’s responsibility. This was prevalent in districts such as Rutsiro (55%), Gakenke (51%), Musanze (50%), Rusizi (49%), and Nyabihu (48%). This latter was in disagreement with the United International Strategies for Disaster Risk and the Sendai Framework in its thirteen guiding principles, calling for the commitment of everyone including the central governmental and local authorities, different appropriate stakeholders, private institutions, and the community members. With the aforementioned, local communities and authorities should be empowered through the provision of resources, incentives and their active involvement in decision making. This is because the local community is most of the time not taken as the participant but the beneficiaries in the reduction of flood or landslide risks. This,therefore, affects them by only favoring governmental adopted measures, than those proposed by the community members who have their own means of adaptation in light of the realities unique to their immediate residential areas. (Nahayo et al. 2017).

8.2

Capacity, Concern and Responsibility

95

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41

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Fig. 8.2 Community perception toward flood and landslide mitigation responsibility. aNyabihu, b Gatsibo, cGakenke, dRubavu, eGasabo, fKarongi, gRutsiro, hMusanze, iNyaruguru, jNgororero, k Rusizi

As stressed by an earlier study (Adelekan 2011), the deleterious condition of disastrous events along with the background of residents’ risk to disaster is to a degree dependent upon the institutional potency in governing the community. In line with the above, it is probable that there might be a dearth of strong and effectual institutional aptitude in making extreme efforts regarding community education and awareness on their roles and concern toward the adoption and execution of possible disaster prevention and mitigation actions that alleviate the damaging effects of both hazards. This can be traced from Nahayo et al. (2017) study where information in relation to disaster risks are mostly disseminated via radio channels after than before their incidences; a fact which Contribute to the local community’s vulnerability. Instead, the community could generally be alerted before the occurrence for their readiness in terms of minimum or advanced preparedness actions. The substantial inference of most respondent’s perception on the question related to the responsibility of setting the coping actions for the hazards lies into the aspect where such perception gives the community a means of escaping the accountability and failing to take any steps remedial to the impacts of the hazards. The latter may, therefore, contribute to the increase in vulnerability and susceptibility of their region, making them less resilient toward hydrogeological disaster incidences. However, it is challenging to involve local community in disaster management practices, most particularly in nations with a top-down governance (Allen 2006; Hosseini et al. 2014) such as Rwanda. To make a factual difference to the capacities of local communities to mitigate and manage disaster risk, disaster risk reduction needs to be integrated in numerous sectors such as agriculture, urban planning, ecosystem management, health among others. This requires a good collaboration between actors (Governments, civil society, media and the private sector), yet such collaboration does not always come effortlessly or naturally as a result of different

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challenges. Among others, the main hurdle is frequently the communities’ lack of interest in and ability for disaster risk reduction and mitigation, which frequently reflects delicacy in the local government potential. The objective should be to establish a planning process where local community are given the power to plan and decide according to their abilities and resources with the local government authorities. Additionally, in a discussion with the local community, it has been disclosed that despite their local indigenous knowledge, they lack insufficient and scientific information and skills for disaster risk management, which implicates a weak partnership and collaboration between the local community and the department that serve them at governmental level. The community contended that the knowledge and abilities in relation to disaster risk management, acquired from weekly training courses are usually not apt and comprehensive to the local community, since the acquired information from the courses are typically theoretical and usually offered with less attention on the local and socio-economic settings. Most of the people are preoccupied by their daily economic-based activities and consequently provide less attention on issues related to disasters risk reduction and management.

8.3

Mitigation Initiatives and Mechanisms

This section’s goal was to determine community’s potential in coping with the hazards, learn more about their level of knowledge and awareness to take initiatives and perform mitigation actions toward floods and landslides. Majority of the local community argued that one of the best actions to be implemented to alleviate the damaging effects of floods and landslides on the community is relocation and resettlement from exposed to non-exposed areas based on the modeled susceptibility index. For flood (Fig. 8.3), this action was more widespread and mostly pronounced by the community in the western province with districts including Rubavu and Nyabihu then Gakenke district in the Northern province. For landslide (Fig. 8.4), the western province (specifically in Karongi, Nyabihu, and Rubavu districts) was where the action was mostly proclaimed by the community. These findings are in agreement with the inventory maps (Figs. 4.1 and 4.2) as the four districts display several points while the provinces covering these districts accounted many losses (Table 4.1) in the past events. Additionally, the findings were in parallel with the simulated susceptibility index such that its spatial dispersion (Figs. 5.1 and 5.3) displayed the four districts in the highly susceptible zones for both hazards. Moreover, the results engendered from the models unveiled influencing factors such as NDVI and LULC as substantial factors influencing the occurrence of floods and landslides in different regions. These factors implied that some districts are prone to the hazards due to the paucity of vegetation cover or even the loss of vegetation resulting from different human-related practices namely deforestation at the expense of cropland expansion, urbanization etc. Having in mind that most of Rwandan population especially in rural areas rely on agricultural activities for their

Mitigation Initiatives and Mechanisms

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Fig. 8.3 Community perception on flood mitigation initiatives and mechanisms

livelihood (Maniraho et al. 2021; Weatherspoon et al. 2021), community members have converted forest and grasslands into cultivated fields owed to the shortage of agriculture’s land and the rising demand for agricultural products. This loss of vegetation especially on steep topography was revealed by previous studies (Habiyaremye et al. 2011; Nahayo et al. 2013; Ordway 2015; Karamage et al. 2016; Ndayisaba et al. 2016) in the study area. This implicates that afforestation or forest landscape restoration may not come in the community’s best interest but rather damage their economy (economic loss). Thus, the agricultural sector remains exposed to the impacts of flood or landslide. (Table 4.1). We can say that community members are significantly increasing the factors driving the increase in flood and landslide susceptibility because of their economic interests. The above rationalizes the low percentage of local community who mentioned afforestation as an action to consider in study area. Comparatively, afforestation is the best approach for reducing flood risk, according to the majority (42%) of respondents in the Gatsibo district (Fig. 8.3). This district is located in the eastern province where a shortfall in rain has led to drought; reflecting a dearth in forest coverage; a factor that intensify flood risk whenever it rains seriously. Similar initiative has been mentioned by majority (43%) in Gakenke district as a good measure for landslide mitigation (Fig. 8.4). However, it has been stated by researchers (Zubir and Amirrol 2011; Nahayo et al. 2018) that the mitigation initiatives or practices for their hazards risk reduction may be less expensive if they are made known early and moved toward the community. In line with this, 43% and 38% of the participants argued direct participation of the

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Fig. 8.4 Community perception on landslide mitigation initiatives and mechanisms. aNyabihu, b Gatsibo, cGakenke, dRubavu, eGasabo, fKarongi, gRutsiro, hMusanze, iNyaruguru, jNgororero, k Rusizi. AF Afforestation, RE Resettlement and relocation, PA Public awareness, CP Community participation, CW Construction of waterways, LP & GFP Land use planning and good farming practices

community members as the best initiative for flood and landslide risk reduction in Gasabo district, respectively. This was in agreement with Nahayo et al. (2017) stipulating that one of the main factors influencing the magnitude of disasters is the negligible direct involvement of the community. This was followed by 24% of participant who mentioned the construction of waterways for flood reduction, a finding that inferred a dissimilar perception compared to that of rural population. Lastly, the findings also revealed that the local community particularly in rural regions do not actually take part in the process of lessening flood and landslide risk. This was in accordance with the findings where community leave mitigation actions, their adoption and implementation under the government responsibility (Fig. 8.3). In line with the aforementioned, Eiser et al. (2012) stipulated that disaster risk is influenced by human perceptions, their cultural attitudes, different conditioning factors, and decisions toward disaster risk reduction in addition to the physical conditions and processes. However, the intensity and severity of any disaster’s impacts depend on how the community members aspire to live and work in highrisk zones or feel forced to do so by setting effective mitigation actions. In short, the awareness level of community members (mainly in rural areas) become questionable as they lack sufficient understanding and information in regards to their roles and responsibilities in the process of lessening the risk of floods and landslides. Most of

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community members do not take steps to mitigate the damaging effects of the hazards; instead, rely on the government and local authorities to act on their behalf. This condition exposes their surrounding environment to a certain level of susceptibility to flood and landslide risk. Thus, despite the analysis of controlling factors, the provision of early warning, the full involvement and engagement of stakeholders (public as private), the active participation of the community by acknowledging their local indigenous knowledge should be reinforced toward the reduction of flood and landslide risk and strengthening the community’s resilience.

References Adelekan IO (2011) Vulnerability assessment of an urban flood in Nigeria: Abeokuta flood 2007. Nat Hazards 56(1):215–231 Adelekan IO, Asiyanbi AP (2016) Flood risk perception in flood-affected communities in Lagos, Nigeria. Nat Hazards 80(1):445–469 Allen KM (2006) Community-based disaster preparedness and climate adaptation: local capacitybuilding in The Philippines. Disasters 30(1):81–101 Anilan T, Yuksek O (2017) Perception of flood risk and mitigation: survey results from the eastern Black Sea Basin, Turkey. Nat Hazards Rev 18(2):05016006 Bempah SA, Øyhus AO (2017) The role of social perception in disaster risk reduction: beliefs, perception, and attitudes regarding flood disasters in communities along the Volta River, Ghana. Int J Disaster Risk Reduct 23:104–108 Eiser JR et al (2012) Risk interpretation and action: a conceptual framework for responses to natural hazards. Int J Disaster Risk Reduct 1:5–16 Habiyaremye G et al (2011) Demographic pressure impacts on forests in Rwanda. Afr J Agric Res 6(19):4533–4538 Hosseini KA et al (2014) Main challenges on community-based approaches in earthquake risk reduction: case study of Tehran, Iran. Int J Disaster Risk Reduct 8:114–124 Karamage F et al (2016) Deforestation effects on soil erosion in the Lake Kivu Basin, DR CongoRwanda. Forests 7(11):281 Kellens W et al (2011) An analysis of the public perception of flood risk on the Belgian coast. Risk Anal An Int J 31(7):1055–1068 Lechowska E (2018) What determines flood risk perception? A review of factors of flood risk perception and relations between its basic elements. Nat Hazards 94(3):1341–1366 Maniraho AP et al (2021) Application of the adapted approach for crop management factor to assess soil erosion risk in an agricultural area of Rwanda. Land 10(10):1056 Nahayo A et al (2013) Comparative study on charcoal yield produced by traditional and improved kilns: a case study of Nyaruguru and Nyamagabe districts in Southern Province of Rwanda. Energy Environ Res 3(1):40 Nahayo L et al (2017) Early alert and community involvement: approach for disaster risk reduction in Rwanda. Nat Hazards 86(2):505–517 Nahayo L et al (2018) Extent of disaster courses delivery for the risk reduction in Rwanda. Int J Disaster Risk Reduct 27:127–132 Ndayisaba F et al (2016) Understanding the spatial temporal vegetation dynamics in Rwanda. Remote Sens 8(2):129 Ordway EM (2015) Political shifts and changing forests: effects of armed conflict on forest conservation in Rwanda. Global Ecol Conserv 3:448–460

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Slovic P et al (2004) Risk as analysis and risk as feelings: some thoughts about affect, reason, risk, and rationality. Risk Anal Int J 24(2):311–322 Wachinger G et al (2013) The risk perception paradox—implications for governance and communication of natural hazards. Risk Anal 33(6):1049–1065 Weatherspoon DD et al (2021) Rwanda’s commercialization of smallholder agriculture: implications for rural food production and household food choices. J Agric Food Ind Organizat 19(1): 51–62 Zubir SS, Amirrol H (2011) Disaster risk reduction through community participation. WIT Trans Ecol Environ 148:195–206

Part III

General Conclusion and Recommendations

Chapter 9

Conclusion

Susceptibility analysis for hydrogeological hazards such as flood and landslide is very essential and pertinent for the demarcation of prone areas and evaluation of the mitigation initiatives. This study applied empirical statistical (logistic regression) and bivariate probabilistic (frequency ratio) models integrated with remote sensing techniques and geo-information system. An inventory and a set of different influencing factors (topographic, environmental, meteorological, and geological) were applied in the simulation step with the aim of analyzing and mapping the spatial distribution of the country’s susceptibility as well as detecting the factors that mostly influence the incidence of flood and landslide hazards known to be the most and common hydrogeological hazards in Rwanda. Moreover, the results of the spatial distribution of susceptibility have been linked with local community’s perceptions on both hazards countrywide. The main results and drawn conclusions are as follow: • It was revealed that provinces such as the western, southern and a portion of the northern are the most prone to flood occurrence while the eastern province was the part of the country with low flood susceptibility. The relationship between flood occurrence and the influencing variables revealed vegetation features (LULC and NDVI), soil texture and rainfall to have a substantial influence on the recurring floods in the study area. The latter led to a conclusion that less vegetated areas with higher rain falling in areas with a certain soil type (clayloam) will face future damaging effects of floods. • The same provinces (western, northern, and southern) have shown to be the most susceptible to landslide occurrence while the eastern part also exhibited low susceptibility. This was mostly influenced by the slope, LULC, rainfall and elevation. From these results, it was concluded that regions receiving higher amount of rainfall at unvegetated higher (steepe) slopes and elevation will suffer the damaging effects of landslides. • The study also assessed community’s perception on flood and landslide risk in relation to the produced spatial distribution of susceptibility to both hazards and was, therefore, noted that majority of local community members perceive the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_9

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government as the key actor and implementor of mitigation initiatives disregarding that disaster risk reduction has to be each and everyone’s responsibility following the report of the United Nation International Strategies for Disaster Risk. This perception implied a contribution to the risk and increase in the susceptibility of different area. This led to a conclusion implicating the need of establishing programs to inculcate a sense of mitigation and prevention in local communities, especially in rural areas. The information from this research will be used as a baseline and benchmark for planners, future researchers, disaster risk managers and be used as a supplementary decision-making tool in the country as far as flood and landslide risk management is concerned. Moreover, findings from this study can be used as a stimulation to suggest a continuing early alert information on regions that are expected to be highly affected by future flood and landslide disasters and, therefore, assist in the improvement of the preparedness, mitigation initiatives and finally help in updating the current environmental and land use policies and plans for environmental sustainability achievement.

Chapter 10

Recommendations

Natural disasters are considered as crosscutting issue to community’s socioeconomic development. Various factors influence the increase of hydrogeological hazards (flood and landslide) occurrences in developing countries due to insufficient and untimely mitigation and prevention initiatives. For Rwanda, the topographic landscape, the weak enforcement of building codes and land use regulations combined with the population’s socio-economic condition are amplifying the risk of flood and landslide. Therefore, to reduce the incidence of these hazards, susceptibility and vulnerability, the government and local communities are recommended below:

10.1

The Government (Ministry in Charge)

• The noticed population density requires strong control measures. The government is suggested to increase efforts in the expropriation process of the local community inhabiting from very high susceptible to safe areas for their lives and properties safety. It is also recommended to determine and enforce acceptable land uses to assuage the risk of damage by restraining exposure in prone areas for both hazards. • Put in practice the land use planning and building zoning in different area of the country followed by initiatives such as prohibiting inappropriate agricultural encroachments (techniques) towards rivers and steep slopes but rather promote the sustainable agriculture practices such as the green agriculture, bench terraces, agroforestry among others. • Planning and setting an effective approach for community involvement and awareness rise campaign particularly on the frequency and magnitude of the events for early preparedness, mitigation and adaptation. This should also involve the promotion of participatory activities for hydrogeological disaster management by strengthening the capacities for emergency response. In other words, flood and landslide-prone community should be empowered with proper training, drills, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 L. Li, R. Mind’je, Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda, https://doi.org/10.1007/978-981-99-1751-8_10

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• • • • •

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Recommendations

and provide necessary information at both pre-disaster and post disaster phases through effective community participation strategies. Initiating the collaborations and network between local community and the local government. Provide sufficient financial assistance to empower the scientific and technological progress of the education program. Increasing the number of meteorological stations for regular rainfall updates. For flood: plan for the management of soil and water conservation to regulate the downstream discharge and thus, adopt a comprehensive planning for floodplain management. There is a paucity of flood and landslide database and past records for Rwanda. This gap has to be addressed by adopting an adequate and consistent structure for recording flood and landslide events to assist as a database for future national inventory construction and quantitative modeling since the resulting susceptibility maps powerfully depend on these input datasets.

10.2

The Local Community

• Similar to the government, the local community is recommended to keep initiating, maintaining and strengthening the practices of agroforestry, bench terraces and rain harvest to curtail the associated impacts of both flood and landslide. On this, the members of community in each district are also required to preserve and plant more trees in landscape designs to lessen the amount of stormwater runoff and sustain the steep slopes. • Establishing a citizen plan implementation steering committee to monitor progress on local mitigation actions per districts and include a fusion of representatives from neighborhoods, local businesses, among others. • Execute regular maintenance of drainage systems, including the debris and sediment clearance, as well as detect and prevent discharges into stormwater and sewer systems from residence footing drains and downspouts. • Voluntarily engage into community work, disaster management-related meeting and awareness programs.

10.3

Future Research

In the future, more studies should be conducted to improve the generalizability and accuracy of the results. For this, future research on hydrogeological hazards such as flood and landslide should consider additional influencing factors missing in this study using a combination of multiple current modeling approaches as well as their comparison for the sake of best approach in predicting future flood and landslide occurrence. For these studies, it is also suggested that upon their availability and

10.3

Future Research

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accessibility, use high resolution satellite images interpretation in mapping and assessing flood and landslide susceptibility. Finally, the morphometric and engineering analysis on the floods and landslides in the modeled susceptible areas is needed.