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Flood vulnerability assessment of donstream area in Mono basin in Yoto district, south-eastern Togo


par Abravi Essenam KISSI
University of Lome - Master 2014
  

Disponible en mode multipage

    UNIVERSITE DE LOME

    West Africa Science service Center on

    Climate Change and Human Security

    FLOOD VULNERABILITY ASSESSMENT IN DOWNSTREAM AREA OF MONO BASIN, SOUTH-EASTERN TOGO: YOTO DISTRICT

    A Thesis

    by

    KISSI Abravi Esssenam

    Submitted to

    West African Science Service Center on Climate Change and Adapted Land Use

    Université de Lomé, Togo

    in partial fulfilment of the requirements for the degree of

    MASTER OF SCIENCE

    November, 2014

    Major Subject: Climate Change and Human Security

    FLOOD VULNERABILITY ASSESSMENT IN DOWNSTREAM AREA OF MONO BASIN, SOUTH-EASTERN TOGO: YOTO DISTRICT

    A Thesis

    by

    KISSI Abravi Esssenam

    Submitted to

    West African Science Service Center on Climate Change and Adapted Land Use

    Université de Lomé, Togo

    in partial fulfilment of the requirements for the degree of

    MASTER OF SCIENCE

    Approved by:

    Chair of Committee, Georges Abbevi ABBEY

    Committee Members, Amadou Thierno GAYE

    Komi AGBOKA

    Director of Program, Kouami KOKOU

    November 2014

    Major Subject: Climate Change and Human Security

    ABSTRACT

    Flood Vulnerability Assessment in Downstream Area of Mono Basin,

    South-Eastern Togo: Yoto District. (November, 2014)

    KISSI Abravi Essenam.

    B.S., Université de Lomé

    Chair of Advisory Committee: Dr. Georges Abbevi ABBEY

    The Mono River in the Yoto district, presents a challenge in terms of repeated flood hazard. The eight selected communities lie in majority in the floodplains of the Mono River and experience year after year flood disaster. This study focuses on flood vulnerability assessment of the downstream part in the Mono River basin in the Yoto district. It analyses the trend in rainfall and river discharge series (1971-2010); it assesses the determinants of flood vulnerability; and it equally computes Flood Vulnerability Index (FVI).

    The result reveals a clear evidence of change in precipitation and river discharge patterns during the period of record. It shows an extreme variability in terms of flood magnitude and frequency in the Mono River. Besides, the closeness of households' farmlands to the river body, the type of construction and the position of settlements, the household size, the low level education of household head, the lack of diversification of livelihood strategies, the lack of adequate flood warning system and lack of willingness and ability to take responsive actions coupled with inadequate emergency services, are identified as main determinants increasing communities' vulnerability to flood disaster. Furthermore, FVI offers easy comparison of communities' vulnerability to flood disaster.

    Keywords: Trend analysis, Determinant of flood vulnerability, Flood Vulnerability Index, Downstream part of the Mono River basin.

    RESUME

    Le fleuve Mono, présente un défi majeur en terme d'inondations qui constituent un phénomène récurent dans la préfecture de Yoto. La majeur partie de la population des huit villages est localisée dans le lit supérieur et moyen du fleuve et fait face année après année aux inondations. A cet effet, cette étude a été initiée pour analyser la vulnérabilité des populations aux inondations dans la basse vallée du fleuve Mono dans la préfecture de Yoto. L'analyse a portée sur la variation des précipitations et débits du fleuve de 1971-2010, l'identification des facteurs de vulnérabilités et le calcul des indices de la vulnérabilité d'inondation

    Le résultat révèle une claire évidence de la variabilité pluviométrique et des débits pendant la période considérée. Il montre une variabilité extrême quant à la fréquence et l'intensité des inondations. La proximité des terrains agricoles par rapport au fleuve, le type de construction, le faible niveau d'éducation, l'absence de système d'alerte précoce adéquat, et la faible capacité de la population à prendre des mesures appropriées pour faire face aux impacts des inondations sont identifiés comme les principaux facteurs de la vulnérabilité de la population aux inondations. En outre, le calcul des indices de vulnérabilité offre une comparaison facile de la vulnérabilité des communautés aux inondations.

    Mots clés: Analyse de la variation; Les facteurs de vulnérabilité; Indice de vulnérabilité d'inondation; Basse vallée du fleuve Mono.

    Dedication

    To

    The Living God Almighty

    who by his grace has seen and guided me in my entire life and through my academic course of study to this level, the glory be to him.

    To

    My father Kissi Kodjo, my uncle Houngbedji Clement and my aunt

    Houngbedji Clotilde, brothers and sisters

    Thank you for inspiring me always to look higher

    May the Lord reward you

    ACKNOWLEDGEMENTS

    I would like to start by thanking to WASCAL ( West African Science Service Centre on Climate Change and Adapted Land Use) for offering me a scholarship, which made it possible for me to participate in the Master Programme in Climate Change and Human Security (MRP CCHS) at University of Lomé.

    My special thanks go to my committee chair, Dr. Georges Abbevi ABBEY, and my committee members, Prof Amadou Thierno GAYE, and Dr. Komi AGBOKA, for his patience, unceasing and tireless efforts, his guidance and mentorship, and for his encouragement, constructive comments, remarks, suggestions and support during the writing process of this thesis.

    I would like to thank our former Director Professor Adote Blim Blivi for his constructive comments and advices to give us the will and strength to be best.

    I would also like to express my gratitude to my lecturers and colleagues who helped me and kept me during the two years of this Master.

    I would like to sincerely thank our current Director Professor Kokou Kouami and our coordinator Dr Aklesso Egbendewe-Mondzodzo for their encouragement and support.

    I would like to express my sincere gratitude to Togo Red Cross Society, Mr André Akpadja , to the team of research assistants for their effort during the process of collecting data for this study and to all the communities and households where the questionnaires were administered without whom this research would not have been possible.

    I am grateful to all those who have helped directly or indirectly in the production of this thesis . I render my special thanks to my entire family especially, to my young sister, Ms Kissi Esther, to all my friends, especially, Mr Batadjaga Magloire, Mr Adjaho Iréné, Mr Kpotor Edguard, Mr Wilson-Bahun Noah, Mr Bruce Michel, Mr Etoh Kudzo Sena who listened to my complains, gave me advice and the will to go on, and made me laugh when I needed to. To you, all I owe my gratitude.

    May the Almighty Bless You.

    TABLE OF CONTENTS

    ABSTRACT III

    RESUME IV

    DEDICATION V

    ACKNOWLEDGEMENTS VI

    TABLE OF CONTENTS VII

    LIST OF FIGURES X

    LIST OF TABLES XI

    LIST OF MAPS XII

    LIST OF PHOTO XII

    CHAPTER I: INTRODUCTION 1

    1.1. PROBLEM STATEMENT 1

    1.2 RESEARCH OBJECTIVES 4

    1.3. RESEARCH QUESTIONS 4

    1.4. RESEARCH HYPOTHESIS 5

    1.5. THESIS STRUCTURE 5

    CHAPTER II: LITERATURE REVIEW 6

    2.1. HAZARDS, DISASTERS, AND VULNERABILITY 6

    2.2. FLOOD VULNERABILITY FACTORS 8

    2.3. METHODOLOGY FOR MEASUREMENT OF VULNERABILITY TO NATURAL HAZARDS 10

    2.3.1. Theoretical and Conceptual Frameworks of Vulnerability 10

    2.3.2 Indicators for Measuring Vulnerability 11

    2.4. THE INDEX APPROACH TO STUDY VULNERABILITY 15

    2.4.1.Existing Flood Vulnerability Index 16

    CHAPTER III: RESEARCH METHODOLOGY 18

    3.1 THE AREA OF STUDY 18

    3.1.1 Localisation 18

    3.1.2. Landscape, soil and vegetation 18

    3.1.3. Climate and Hydrology 21

    3.1.4. Population and Economic Activities 21

    3.2. METHODS 21

    3.2.1. Study Population and sampling 22

    3.2.2. Selected Vulnerability Conceptual Frameworks 22

    3.2.3. Flood Vulnerability Indicator Development 23

    3.3. DATA COLLECTION AND ANALYSIS 25

    3.3.1. Primary Data Collection 25

    3.3.2. Secondary Data 25

    3.3.3. Data Analysis 26

    3.3.3.1. Trend Analysis of Rainfall And River Discharge to Assess Climate Change. 26

    3.3.3.2. Analysis of the determinants of communities' vulnerability to flood 27

    3.3.3.3 Analysis of human-environmental condition 29

    3.3.3.4 Computation of Flood Vulnerability Index (FVI) 29

    CHAPTER IV: PRESENTATION AND DISCUSSION OF RESULTS 33

    4.1. EMPIRICAL FINDING ON TREND AND VARIABILITY ANALYSIS 33

    4.1.1. Precipitation Time Series Analysis 33

    4.1.2. Discharge Time Series Analysis 35

    4.2. DETERMINANTS OF COMMUNITIES' VULNERABILITY TO FLOODS 38

    4.2.1. Flood Frequency and Magnitude Analysis 38

    4.2.4. Assessment of communities' vulnerability : Human-environmental conditions 42

    4.2.4.1. Socio-Demographic Characteristics of Households 42

    4.2.4.2 Location of settlement and type of construction 43

    4.2.4.3. Livelihood patterns of respondents 44

    4.2.4.4. Awareness and impacts of flood 44

    4.2.4.5 Environmental conditions 45

    4.2.4.6. Household coping mechanisms 46

    4.2.4.7. Anticipative measures of flood occurrence 46

    4.2.4.8. Training on flood hazard management 47

    4.2.4.9. Household recovery time and positive effects of flood on household 47

    4.2.5 Household adaptation options 48

    4.2.5.1 Household's perception of Government and NGOs role in flood management 48

    4.2.5.2. Household `s perception of communities role in flood management 48

    4.3 COMPUTATION OF FLOOD VULNERABILITY INDEX 49

    4.3.1 Identifying key indicators of developed FVI 49

    4.3.2. Normalised Scores and Weight Values of Indicators 49

    4.3.4. Composite vulnerability index of vulnerability factors 50

    4.3.4.1. Exposure factor 50

    4.3.4.2 Susceptibility Factor 51

    4.3.4.3 Resilience Factor 53

    CHAPTER V: CONCLUSION AND POLICY RECOMMENDATION 56

    REFERENCES 58

    ANNEXE 1 : Statistical summary of annual and monthly precipitation for Tabligbo - 1 -

    ANNEXE 2: Mann-Kendall test results of annual, monthly and seasonal precipitation - 2 -

    ANNEXE 3 : Statistical summary of annual and monthly flow for Athieme - 3 -

    ANNEXE 4: Mann-Kendall results of annual , monthly and seasonal flow for the study area - 5 -

    ANNEXE 5: Calculation for return period of 2010 flood, Mono river - 6 -

    ANNEXE 6:Normalised scores of flood vulnerability indicators of each village - 7 -

    ANNEXE 7: Calculated weights of flood vulnerability indicators 8

    ANNEXE 8: Questionnaire for household interview 9

    ANNEXE 9: Key informants interview guide. 15

    VITA . 16

    LIST OF FIGURES

    FIGURE 1: NUMBER OF PEOPLE AFFECTED BY FLOOD DURING THE PERIOD OF (1994-2010) 3

    FIGURE 2 SUSCEPTIBILITY FRAMEWORK 9

    FIGURE 3 RESILIENCE FRAMEWORK 10

    FIGURE 4: TURNER ET AL'S VULNERABILITY FRAMEWORK; SOURCE: TURNER ET AL., 2003, P 8076 21

    FIGURE 5: VULNERABILITY COMPONENTS EXTRACTED FROM TURNER ET AL., 2003 FRAMEWORK 22

    FIGURE 6: LINEAR TREND LINE CORRESPONDING TO RAINFALL DATA (1971-2010) 33

    FIGURE 7: MONTHLY AVERAGE RAINFALL (1971-2010) 33

    FIGURE 8: ANNUAL RAINFALL CUMULATIVE DEVIATION (1971-2010) (TABLIGBO STATION) 34

    FIGURE 9: AVERAGE ANNUAL DISCHARGE VARIATION (1971-2010) 35

    FIGURE 10: MONTHLY DISCHARGE AVERAGE(1971-2010) 35

    FIGURE 11: ANNUAL DISCHARGE CUMULATIVE DEVIATION (1971-2010) 36

    FIGURE 12:FLOOD FREQUENCY DISTRIBUTION 39

    FIGURE 13: LIVELIHOOD STRATEGIES BY MARITAL STATUS OF HEADS OF HOUSEHOLDS 43

    LIST OF TABLES

    TABLE 1: FLOOD INDICATORS INFORMATION 3

    TABLE 2: SELECTED INDICATORS FOR FLOOD VULNERABILITY 23

    TABLE 3: DESCRIPTION OF SECONDARY DATA USE 24

    TABLE 4: REFERENCE OF METEOROLOGICAL AND HYDROLOGICAL STATIONS 25

    TABLE 5: ANNUAL MAXIMUM FLOW BASIC STATISTICS 38

    TABLE 6: DEPTHS OF FLOOD WATER (2010) AS REVEALED BY MARKS ON BUILDING WALLS AND AVERAGE FLOOD DURATION (2010) FROM HOUSEHOLD 40

    LIST OF MAPS

    MAP 1: MAP SHOWING THE TARGETED VILLAGES 3

    MAP 2: MAP OF YOTO DISTRICT SHOWING THE SURVEYED VILLAGES IN LOW ALTITUDE 43

    MAP 3: FLOOD EXPOSURE MAP OF THE STUDY AREA 53

    MAP 4: FLOOD SUSCEPTIBILITY MAP OF THE STUDY AREA 54

    MAP 5: FLOOD RESILIENCE MAP OF THE STUDY AREA 55

    MAP 6 FLOOD VULNERABILITY MAP OF THE STUDY AREA 56

    LIST OF PHOTO

    PHOTO 1: PALM TREES FARM UNDER WATER SINCE THE 2010 FLOOD, PHOTOGRAPH TAKEN DURING FIELD WORK 3

    PHOTO 2: USE OF WATER IN THE COMMUNITY, PHOTOGRAPH TAKEN DURING FIELD WORK 45

    PHOTO 3: HOUSE MADE IN BANCO AND STRAW. 46 PHOTO 4: HOUSE MADE BY CLAY WALL WITH DESTROYED BY THE 2010 FLOOD IN TOKPLI COUNTY THATCHED ROOF, PHOTOGRAPH TAKEN DURING SOURCE: PDNA, 2010 FIELD WORK.................................................................. 46

    PHOTO 6: DEHYDRATE SOIL IN BATOE VILLAGE...................................................................48 PHOTO 7: MONO RIVER BANK FRAGMENTATION IN PHOTOGRAPH TAKEN DURING FIELD WORK VILLAGE MAWUSSOU, PHOTOGRAPH TAKEN DURING FIELD WORK 48

    PHOTO 8: IMPLANTATION OF SIGN POST MARKING POSSIBLE FLOODING LEVELS (EARLY WARNING SYSTEM) 49

    ACRONYMS

    CCA: Climate Change Adaptation

    CRED: Centre for Research on Epidemiology of Disasters

    CV: Coefficient of Variation

    DNM: Direction National de la Météorologie

    DRM: Disaster Risk Management

    EM-DAT: Emergency Events Data Base

    EVI: Extreme Value Type 1 distribution

    FAO: United Nations Food and Agriculture Organization

    FVI: Flood Vulnerability Index

    GDP: Gross Domestic Product

    GIS: Geographical Information System

    HFA: Hyogo Framework for Action

    IMF: International Monetary Fund

    IPCC: Intergovernmental Panel on Climate Change

    MERF: Ministère de l'Environnement et des Ressources Forestières

    NGO: Non Government Organisation

    LP3: Log Pearson Type 3 distribution

    PAR: Pressure and Release Model

    PDNA: Post Disaster Needs Assessment

    OCHA: Office for the Coordination of Humanitarian Affairs

    SREX: IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

    UN: United Nations

    UNDP: United Nations Development Programme

    UN/ISDR: United Nations International Strategy for Disaster Reduction

    UNU-EHS: United Nations University-Institute for Environment and Human security

    CHAPTER I. INTRODUCTION

    1.1. Problem Statement

    Many countries worldwide, whether in Europe, America, Asia, Oceania, Australia or in Africa, are experiencing heavy rains, river overflows, hurricanes, typhoons, tsunamis causing unexpected floods which decimate entirely or partly some localities in all over the world. Floods are among the most recurring and devastating natural hazards, impacting human lives and causing severe economic damage throughout the world (Sadiq et al, 2011, p 85). Floods can be defined as hydrological events characterised by a rapid rise of water flow in the river. They are characterised by long, short and no warning, depending on the type of floods, speed or onset which may be gradual or sudden (Carter 1991, p1). Various elements either climatic or non-climatic influence flood processes resulting in different types of flood. Six types of floods are distinguished: coastal, flash, river, flood due to drainage problems, tsunamis, and tidal wave/bore floods (Jonkman, 2005).

    Flood disasters are occurring as a consequence of either natural factors, such as climate change and climate variability or anthropogenic factors, such as socio-economic and land-use developments (Balica, 2009, p 2571). The frequency of those disasters has been increasing over the years, resulting in loss of life, damage to property and destruction of the environment.

    Over the last 50 years, there has been a growing body of evidence pointing to the effect of human behaviour on the global natural environment and on the possibility that certain types of natural disasters such as floods may be increasing as a direct consequence of human activity (Guha-Sapir et al, 2004, p 15). Equally, the effects associated with global warming such as sea level rise, more intensive precipitation levels and higher river discharges may be consequences of this as well. Those effects may increase the frequency and the extent of flood hazards on a worldwide scale and make the number of people at risk in developing countries more vulnerable to flood disasters due to high poverty level.

    In Africa, floods of different kinds are one of the most common type of disastrous events, and they account for the biggest losses inflicted by natural disasters. The UN Office for the Coordination of Humanitarian Affairs (OCHA) recently stated that, compared with previous years, 2010 has seen the largest number of people affected and dying from flooding. This is consistent with the dramatic rise in flood events that have battered the world, with West Africa being a case in point.

    It is understood that flood risks will not subside in the future, and with the onset of climate change, flood intensity and frequency will threaten many regions of the world (Sadiq, 2011, p 85). The Fourth Assessment Report (AR4) by the Intergovernmental Panel on Climate Change (IPCC, 2007) projects that warming in Africa in the 21st century is likely to be greater than the average global warming and does find that extremely wet seasons, high intensity rainfall events, and associated flooding in West Africa are expected to increase by 20% over the next decades. However, it is noted that responses of local communities to the impact of extreme climatic events in many cases in West Africa have mostly been reactive instead of proactive.

    Between 1925 and 1992, Togo has recorded 60 urban and rural floods that caused damages and casualties (MERF, 2013, p 13). Flood disasters are not then a recent phenomenon in the country but have become a frequently recurring problem that occur mainly between July and October which inflicts significant environmental, social, and economic damages and affects population safety. According to "EM-DAT" (Emergency Events Database) of the Centre for Research on the Epidemiology (CRED), a number of flood disasters have been recorded during the last 20 years, with particularly severe events occurring in 2007, 2008 and most recently in 2010 "figure 2". According to MERF (2010), the 2010 flood, in Togo has impacted both urban and rural areas throughout the entire country, affecting 82,767 people; 21 persons were reported to have lost their lives, 85 to have been injured, 12,382 houses have been impacted and 7744,24 hectares of land to have been destroyed. Damage and losses were amounted to an estimated 1.1 percent of GDP, amounting to US$38 million.

    Figure 1: Number of people affected by flood during the period of (1994-2010)

    Source: EM-DAT: The OFDA/CRED International Disaster Database

    The study area is made up of eight villages (Mawussou, Djrekpon, Batoe, Tofacope, Atikpatafo, Logokpo, Tchakponou-kondji, Kpodji) from three counties Sedome, Esse-Godjin and Tokpli located along the river in a severely flood prone area. The population has experienced occurrence of floods. Flood events were frequent during the last decade, causing loss of lives, extensive damages to property, including houses, destruction of transport infrastructures, agricultural land, breakdown in education system and food production. In sum flood affects human security in these communities. The number of reported flood disasters during the last 20 years in the Yoto area, occurred mainly in 1995, 1999, 2007, 2008 and 2010, with the 2010 flood being the most severe (UN, 2010) and recently in October 2014. During the 2010 flood, six counties, 35 villages, including the study villages, were impacted; 2081 people were affected and 1496 hectares of crops were destroyed in the area. Supplied by a set of sub-branches, the Mono River with 21,300 km² often undergoes during torrential rains period, the rising of water level followed by high flows causing the overflowing of the river which inundates the selected villages and makes the population more vulnerable to flood disasters. To this are added environmental factors such as fragmentation of the river banks due to erosion effect digging and widening the river channel, the anthropogenic pressure like the construction of Nangbeto dam at the upstream of the Mono basin, deforestation, the demographic explosion and the socio-economic constraints that exacerbate the vulnerability of the population located at the downstream part of the basin (AGO et al., 2005, p 1).

    The country's vulnerability is expected to increase as a result of climate change. It follows that both the frequency and the severity of flood hazards have the potential to increase (MERF, 2009). Regardless of the current and the future trend of flood hazards combined with socio-economic constraints of the area, about 74.8% of people are below the poverty line (IMF, 2010, p 17), the occupancy or use of flood-prone areas may involve a degree of vulnerability.

    For communities to be protected against damage due to floods, it exists four main type of flood measures that have to be taken into account: the non-structural measures, the structural measures, land planning measures and conducting flood vulnerability and risks assessments, the latter being the first step in disaster risk reduction process. Therefore, to enable decision makers to implement appropriate flood policies in the right place, there is a need to conduct flood vulnerability assessment with a vulnerability score for a systematic understanding of an area; its characteristics related to flood disasters and easily interpret and compare vulnerability of different communities. Thus, the focus of this study is to conduct flood vulnerability assessment of the downstream area in the Mono River basin in the Yoto district through indicator-based vulnerability assessment as proactive response to floods.

    1.2 Research Objectives

    The overall objective of the current study is to conduct indicator-based flood vulnerability assessment of the downstream part in the Mono River basin in the Yoto district to compute a Flood Vulnerability Index in order to assess the conditions which influence flood damage in the study area and pinpoint the most vulnerable villages to flood for an effective flood risk reduction. More specifically, the present study attempts to:

    1. examine the long term trends in rainfall and discharge data for a record period of (1971-2010);

    2. identify the determinants of communities' vulnerability to floods under the three factors of vulnerability (Exposure, Susceptibility and resilience);

    3. apply FVI methodology to compute Flood Vulnerability Index of the target area.

    1.3. Research Questions

    This case study strives to answer the following questions:

    · Do the occurrence of floods hazards relate to change in rainfall and river discharge?

    · what are the conditions which influence flood damage in the study area ?

    · what are the most vulnerable villages to flood in the study area?

    1.4. Research Hypothesis

    While the frequency and the intensity of flood hazards in the study area may be related to change in rainfall and river discharge patterns, interaction between the human-environment or socio-ecological system could be the major determinant of households, and communities' vulnerability to such hazard. Analysing trend in rainfall and discharge time series and understanding the conditions which influence flood disaster in the study area should be reliable information to pinpoint local hotspots of flood vulnerability.

    1.5. Thesis Structure

    Chapter I covers the background information, the problem statement, the objectives, the justification, significance of the study and its objectives and the scope of the study. Chapter II discusses the definition of related concepts to the research topic and literature review on index approach to measure vulnerability to natural hazards. Chapter III includes both the research methodology and data collection process to answer the research questions and to test the research hypothesis. Chapter IV explains the empirical findings on the assessment of climate change, flood frequency analysis and flood vulnerability assessment. Finally, the last chapter includes conclusions as well as way forward for future research and the limitation of the current study.

    CHAPTER II: LITERATURE REVIEW

    2.1. Hazards, Disasters, and Vulnerability

    The concepts of hazard, disaster and vulnerability have been extensively used in various disciplines with different meanings. Even for natural hazards, such as floods, no unique definitions and assessment procedures have been widely accepted (Pistrika and Tsakiris, 2007, p 1). Hazard is the probability of occurrence within a specified period of time and within a given area of a potentially damaging phenomenon (Maiti, 2007, p 10). This definition adds both spatial and temporal components to the definition of hazards while another definition from UNISDR (2009, p 17) refers hazard to "a dangerous phenomenon, substance, human activity or condition that may cause loss of life, injury or other health impacts, property damage, loss of livelihoods and services, social and economic disruption, or environmental damage." Hazard is, in the case of river-floods, a natural event that is perceived as a threat and not as a resource by humans (Fekete, 2010, p 31). For the author, hazard is revealed in the state of exposure, when the natural event actually hits the vulnerable elements. In technical settings, hazards are described quantitatively by the likely frequency of occurrence of different intensities for different areas, as determined from historical data or scientific analysis.

    Hazard becomes a disaster when it hits a vulnerable community. It causes disaster when large numbers of people are killed, injured or affected in some ways (Maiti, 2007, p 10). In the same line of thought FAO (2008, p 16) points out that disasters of all kinds happen when hazards seriously affect communities and destroy temporarily or for many years the livelihood security of their members. Another definition from ISDR refers disaster to «a serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses which exceed the ability of the affected community or society to cope using its own resources". A disaster results then from the combination of exposure to a hazard, socio-ecological vulnerability that are present, and the limited capacities of households or communities to reduce or cope with the potential negative impacts of the hazard.

    Assessing and measuring vulnerability in the context of natural hazards and climate change requires first and foremost a clear understanding of the concept (s) of vulnerability (Birkmann, 2013, p 9 ). Vulnerability is an important concept in human environment research, its conceptualization has been interpreted in many different ways, according to the perception of the researchers. The word "vulnerability" has created important links between different research communities, particularly disaster risk management (DRM), climate change adaptation (CCA), development and resilience research (Birkmann ,2013, p 9).

    Cannon (1990) refers vulnerability only to biophysical exposure, where vulnerability is described as a measure of the degree and type of exposure to risk generated by different societies in relation to hazards.

    Some studies found that vulnerability only refers to the susceptibility of a given system; United Nation/ISDR (2004) and the United Nation Development Programme (UNDP, 2004) view vulnerability as a human condition or process resulting from physical, social, economic and environmental factors, which increase the susceptibility of a system to be damaged from impact of a given hazard.

    Other authors, like Blaikie et al.(1994) and Wisner, et al. (2004) relate vulnerability of a system or a community only to its capacity to anticipate, cope with, resist and recover from the impact of a hazard.

    Adger (1999) views vulnerability as a function of two components: the effect that an event may have on humans, referred to as social vulnerability and the risk that such an event may occur, often referred to as exposure.

    According to Chamber (1983), vulnerability has two sides: an external side of risks, shocks to which an individual or household is subjected to climate change and an internal side, which is defencelessness, meaning a lack of means to cope without damaging loss.

    Numerous studies define vulnerability as being a function of exposure, susceptibility or sensitivity, coping capacity or resilience. Watson et al. (1996), defines vulnerability as the extent to which climate change may damage or harm a system, depending not only on a system's sensitivity but also on its ability to adapt to new climatic conditions. Kasperson et al., (2000) defines vulnerability as the degree to which an exposure unit is susceptible to harm due to exposure to a perturbation or stress and the ability or lack of the exposure unit to cope, recover or fundamentally adapt to become a new system or to become extinct. According to Tuner et al. (2003, p 8075), vulnerability refers to the degree to which a system, subsystem or system component is likely to experience harm due to exposure to a hazard be it perturbation or stressor. For Balica (2007 p 26), vulnerability is the extent to harms, which can be experienced by a system under certain conditions of exposure, susceptibility and resilience. For Damm, (2010), the term vulnerability is taken as a function of exposure, susceptibility, and capacities. According to Fekete (2010, p 31), vulnerability is both a state and a degree: everyone is vulnerable in the state of exposure to a hazard and is vulnerable to a certain degree: vulnerability changes in time and space and aims at identifying and explaining why the object of research is at risk and how risk can be mitigated.

    While, IPCC (2007) relates vulnerability to the character, the magnitude and the rate of climate change and variation in addition to the susceptibility and limited coping capacity of a system and IPCC (2012a, p 32) shows how the concept of vulnerability has served as a guiding element to address disaster risk in the context of climate change and climate variability.

    The similarity between all of these studies is that they agree on the three factors that define vulnerability. Thus, the vulnerability of a system is not only a function of exposure to hazards, perturbations and stresses alone but also resides in the sensitivity or susceptibility and in resilience or capacity of the system experiencing such hazards. Birkmann (2013, p 10) reviews vulnerability concept from various researchers and concludes that the concept of vulnerability stresses the fundamental importance of examining the preconditions and the context of societies and communities and elements at risk to effectively promote risk reduction and climate change adaptation.

    Based on the various views on vulnerability shown above, flood vulnerability in the current study is viewed as the degree of experienced flood harms under certain condition of exposure, susceptibility and resilience factors within the human-environment systems. Therefore, flood vulnerability is taken here as a function of exposure, susceptibility and resilience.

    2.2. Flood Vulnerability Factors

    The vulnerability of any system (at any scale) is a function of the exposure and susceptibility of that system to hazardous conditions and the ability, capacity or resilience of the system to cope, adapt and/or recover from the effects of those conditions (Smit and Wandel, 2006). Core factors of vulnerability encompass exposure, susceptibility or sensitivity and resilience or coping and adaptive capacities. Exposure generally refers to the extent to which a unit or a system of the assessment (community, city, building) falls within the geographical range of a hazard event (Birkman, 2013, p 25).

    According to IPCC (2012a, p 559), exposure describes the presence of people, livelihoods, environmental services, resources and infrastructures or other valuable items in place that could be affected. Exposure to floods could be understood, then, as the presence of valuable items of human-environment, or socio-ecological systems that are present in floods-prone areas. The indicators for this component can be put in two categories; the first one covers the exposure of different elements at risk and the second one gives details on the general characteristics of the flood. While the first category of indicators supplies information about the location, elevation, population density, land-use, their proximity to the river, their closeness to inundation areas, the second category provides information about the frequency of floods in floodplains, their duration and magnitude (Balica, 2007, p 31).

    Penning-Rowsell and Chatterton (1977) defines susceptibility as the relative damageability of property and materials during floods or other hazardous events. According to Turner et al. (2003), susceptibility is mainly defined by cross-scale interactions of multiple internal stresses and perturbations. The concept of susceptibility or sensitivity is the vulnerability factor that describes the human-environmental or socio-ecological conditions or current state that can worsen the hazard, or trigger an impact. So, flood susceptibility indicators evaluate the sensitivity of an element at risk before and during a flood event

    "figure 3" .

    Figure 2 Susceptibility framework

    Source: Balica (2007, p 33)

    Buckle (1998) defines resilience as "the capacity that people or groups may possess to withstand or recover from emergencies and which can stand as a counterbalance to vulnerability". According to UN/ISDR (2004), resilience is determined by the degree to which the social system is capable of organizing itself to increase its capacity for learning from past disasters for better future protection and to improve risk reduction measures.

    For Turner et al (2003), resilience of the system is often evaluated in terms of the amount of change a given system can undergo and still remain within the set of natural or desirable states.

    Based on the above definitions, flood resilience can be seen as the ability of a system or a community to mitigate or minimize threats of floods on itself. Resilience of a system to flood disasters can only be considered with past flood events as it focuses on elements encountered during and after the floods "figure 4"

    Figure 3 Resilience framework

    Source: Balica (2007, p 35)

    2.3. Methodology for Measurement of Vulnerability to Natural Hazards

    2.3.1. Theoretical and Conceptual Frameworks of Vulnerability

    The different views on vulnerability are displayed in various concepts and frameworks on how to systematize it (Birkmann, 2013, p 41). The measurement of vulnerability requires for a model, which delivers the structure, context and objectives of the analysis (Fekete, 2010). The different concepts and models are essential to the development of methods for measuring and identifying relevant indicators of vulnerability (Downing, 2004).

    According to Birkmann (2013, p 62), the different conceptual frameworks can be classified into at least six different schools of thought: (a) school of vulnerability frameworks that is rooted in political economy and particularly addresses issues of the wider political economy, such as root causes, dynamic pressures and unsafe conditions that determine vulnerability. It can be illustrated by , for example, the pressure and release (PAR) model published in Blaikie et al. (1994) and Wisner et al. (2004); (b) school of vulnerability that focus on the notion of coupled human -environmental systems and are linked to a socio-ecological perspective and socio-ecology as research school. The social-ecology perspective compared to political-economy, puts the coupled human-environmental system at the centre of the vulnerability analysis and stresses the transformative qualities of society with regard to nature. It can be represented by the framework developed and published by Turner et al.(2003); (c) school of vulnerability that sees vulnerability and disaster risk assessment from a holistic view. It has tried to develop an integrated explanation of risk and particularly differentiate exposure, susceptibility and societal response capacities. A core element of this approaches is a feedback-loop system that claims that vulnerability is dynamic and that vulnerability assessment cannot be limited to the identification of deficiencies. It can be represented by BBC framework published by Birkmann (2006a); (d) school of vulnerability that emerged within the context of climate change science and adaptation research. It focuses on exposure, sensitivity and adaptive capacities as key determinants of vulnerability including physical characteristics of climate change and climate variability. It can be illustrated by (Fussel and Klein, 2006); (e) school of vulnerability that integrates adaptation and coupling processes into a feedback-loop system and process-oriented perspective of vulnerability. It can be illustrated by Move framework published by Birkmann et al. (2013) and finally (f) the school of vulnerability that combines framework of disaster risk research and climate change adaptation represented by the IPCC SREX concept (IPCC, 2012a). It stresses the need to differentiate the physical event from vulnerability in order to maintain the analytic power of the concept vulnerability as a way to show and examine the social construction risk.

    Despite the different points of views reveal by the different schools of thought, it is important to acknowledge that they also represent some similarities, such as the understanding that vulnerability is mainly concerned with the preconditions of a society or community that make it liable to experience harm and damage from a given hazard.

    2.3.2 Indicators for Measuring Vulnerability

    Indicators are widely recognized as useful measurement tools in distinct fields of research (Damm 2010, p 42) but researchers disagree on their definitions. According to Gallopin (1997, p 14) indicator is defined as a sigh that summarizes information relevant to a particular phenomenon. Some authors (Adriaanse, 1995) define indicators in relation to an aggregation process starting with variables or basic data, followed by processed information and indicators, finally ending up with highly aggregated indices. While others view them as a single variable or an output value from a set of data that describes a system or process. According to Birkman ( 2013, p 88), defining indicator in terms of the level of aggregation neglects an essential aspect: goals. For this researcher, every indicator-development process needs to be related to goals, or at least to a vision which serves as a basis for defining the state or characteristic of interest.

    The Hyogo Framework for Action (HFA) 2005-2015 stresses the need to develop systems of indicators of disaster risk and vulnerability at national and sub-national levels that will enable decision-makers to assess the impact of disasters (UN/ISDR 2005). An indicator, or set of indicators, can be defined as an inherent characteristic that quantitatively estimates the condition of a system (Balica et al. 2012). «Indicators necessarily limit themselves to the sphere of the measurable» (Moldan and Dahl 2007: 9). A vulnerability indicator can be defined as a variable which is an operational representation of a characteristic or quality of an object or subject able to provide information regarding the susceptibility, coping and adaptive capacity and resilience of a system (Birkman, 2013, p 87).

    Vulnerability indicators are widely used in vulnerability assessment. The first step in an indicator-based vulnerability assessment is the selection of the study area; second, one has to select indicators based on criteria, such as the availability of data, personal judgement or previous research. The procedures for indicator selection follow two general approaches. These are deductive and inductive approaches (Adger et al.,2004). In deductive approach, indicators are selected based on relationships established from theories and conceptual frameworks, whilst inductive approach involves statistical procedures to relate a large number of variables to vulnerability in order to identify the factors that are statistically significant. While a range of widely-accepted relevant characteristics and indicators is being presented in literature, (Adriaanse, 1995; World Bank, 2005.), the actual conditions that determine flood vulnerability are, to a certain degree, very site-specific, location, and hazard-dependent (Muller et al, 2011, p 2113). It can be expressed in terms of functional relationships between expected damages regarding all systems and exposure, susceptibility and resilience characteristics of the affected system, referring to all the different types of possible flood hazards (Balica, 2007).

    A total of 30 indicators have been identified under the three factors of vulnerability through various literature. Exposure and susceptibility both have a positive influence on vulnerability, and resilience has a negative influence on vulnerability "Table 1"

    Table 1: Flood indicators information

     

    No

    Defined indicator

    Factors

    Unit

    Functional relationship with vulnerability (+ or -)

    References

    1

    Flood frequency

    Exposure

    year

    Higher is the number of flood events, higher is the vulnerability (+)

    Balica (2007)

    2

    Flood duration

    Exposure

    days

    The higher the flood duration, the higher the vulnerability (+)

    Balica (2007)

    4

    Flood water depth

    Exposure

    m

    The higher the flood water level, the higher the vulnerability (+)

    Balica (2007)

    5

    Proximity of the village to the water body

    Exposure

    m

    The Closer is the place to the river, the higher is the vulnerability (+)

    Balica (2007)

    7

    population in the flood area

    Exposure

    #

    The higher the number of population, the higher the vulnerability (+)

    Balica (2012); Fekete (2009);

    8

    Heavy rainfall

    Exposure

    mm

    The higher the value of the variance, the higher the vulnerability (+)

    Balica (2012)

    9

    Maximum discharge in the past ten years

    Exposure

    m3/s

    The higher the discharge, the higher the vulnerability (+)

    Balica (2012);

    10

    Land use: Farmland

    Exposure

    %

    The higher the %, the higher the vulnerability (+)

    Balica (2012); Fekete (2010); Bowen and Riley (2003)

    11

    Gender

    Susceptibility

    %

    The higher the % of women, the higher the vulnerability (+)

    Wisner et al. (2004); Haki et al. (2004); Cutter et al. (2003); Muller et al. (2011)

    12

    Elderly

    Susceptibility

    %

    The higher the % of elderly, the higher the vulnerability (+)

    Clark et al. (1998); Muller et al (2011); Steinführer and Kuhlicke (2007); Thieken et al. (2007); Birkmann et al. (2008)

    13

    Children under 15

    Susceptibility

    %

    The higher the % of children, the higher the vulnerability (+)

    Schneiderbauer (2007); Cutter et al. (2003); Muller et al. (2011); Birkmann et al. (2008)

    14

    Agriculture workers

    Susceptibility

    %

    The higher the % of household having agriculture activity the higher the vulnerability (+)

    Fekete (2010)

    15

    Female headed household

    Susceptibility

    %

    The higher the %, the higher the vulnerability (+)

    McLanahan (1983); Snyder et al. (2006);

    16

    Literacy Level

    Susceptibility

    %

    The higher the %, the higher the vulnerability (+)

    Fekete (2010); Schneiderbauer (2007); Haki et al. (2004); Steinführer and Kuhlicke 2007

    17

    Household size

    Susceptibility

    %

    The higher the %, the higher the vulnerability (+)

    Haki et al. (2004); Cutter et al. (2003); Muller et al. (2011); Martens and Ramm (2007)

    18

    Number of houses with poor material (wall, roof, floor)

    Susceptibility

    #

    The higher the number of houses with poor material, the higher is the vulnerability (+)

    Schneiderbauer (2007); Clark et al. (1998);

    Cutter et al. (2003); Muller et al (2011)

    19

    Past experience

    Susceptibility

    %

    The lower the %, the higher the vulnerability (+)

    Balica (2007); Birkmann (2005a); Velasquez and Tanhueco (2005); Wisner et al (2004); Muller (2011)

    20

    Preparedness

    Susceptibility

    %

    The lower the % of people with flood experience, the higher the vulnerability (+)

    Balica (2012); Birkmann (2005a); Velasquez and Tanhueco (2005); Wisner et al. (2004); Cardona (2003); Muller (2011)

    21

    Awareness

    Susceptibility

    %

    The lower the % of people, the higher the vulnerability (+)

    Balica (2007)

    22

    Emergency services

    Resilience

    %

    The higher the % of people reported to get help from government or institution during and after flood, the lower the vulnerability (-)

    Balica (2007)

    23

    Ability to evacuate

    Resilience

    %

    The higher the %, the lower the vulnerability (-)

    Cardona (2003); Muller (2011); Balica (2012); Birkman et al (2013)

    24

    Knowledge about private protection measures

    Resilience

    %

    The higher the %, the lower the vulnerability (-)

    Muller et al (2011)

    25

    Knowledge about flood hazard

    Resilience

    %

    The higher the percentage, the lower the vulnerability (-)

    Cardona (2003); Muller (2011)

    26

    Warning system

    Resilience

    %

    The existence of warning system lowers the vulnerability (-)

    Balica (2007); Balica(2012); Veenstra (2013)

    27

    Recovery Time to flood

    Resilience

    %

    The faster the recovery time, the lower the vulnerability (-)

    Balica (2012)

    28

    Emergency service

    Resilience

    %

    The higher the %, the Lower the vulnerability (-)

    Balica (2012); Aall and Norland (2005); Veenstra (2013)

    29

    Long term residents

    Resilience

    %

    The higher the %, the lower the vulnerability (-)

    Fekete (2010)

    30

    Environmental recovery

    Resilience

    %

    The higher the %, the lower the vulnerability (-)

    Balica (2007)

    2.4. The Index Approach to Study Vulnerability

    In literature, quantitative assessment of vulnerability is usually done by constructing a vulnerability index. This index is based on several sets of indicators that result in the vulnerability of a region. It produces a single number, which can be used to compare different regions. Literature on index number construction specifies that there should be good internal correlations between these indicators.

    Different methodologies have been used to compute a Flood Vulnerability Index (FVI). All FVI equations have factors for exposure to hazard, sensitivity or susceptibility of the people, and their resilience or coping capacity to the hazard. Vulnerability is the result of the combination of exposure, susceptibility and resilience.

    Atkins et al. (1998) studied the methodology for measurement of vulnerability and constructed a suitable composite vulnerability index for developing countries and island states. Their composite vulnerability indices were presented for a sample of 110 developing countries for which appropriate data were available. The index suggests that small states are especially prone to vulnerable events when compared to large states. Among the small states, Cape Verde and Trinidad and Tobago are estimated to suffer relatively low levels of vulnerability and majority of the states estimated to experience relatively high vulnerability; and the states like Tonga, Antigua and Barbados being more vulnerable to external economic and environmental factors.

    Chris Easter (2000) constructed a vulnerability index for the commonwealth countries, which is based on two principles. First, the impact of external shocks over which the country was affected and, second, the resilience of a country to withstand and recover from such shocks. The analysis used a sample of 111 developing countries of which 37 small and 74 large for which relevant data were available. The results indicate that among the 50 most vulnerable countries, 33 were small states with 27 being least developed among them.

    Moss et al. (2001) identified ten proxies for five sectors of climate sensitivities which are settlement sensitivity, food security, human health sensitivity, ecosystem sensitivity and water availability. They equally established seven proxies for three sectors of coping and adaptive capacity: economic capacity, human resources and environmental or natural resources capacity. These proxies are aggregated into sectoral indicators, sensitivity indicators and coping or adaptive capacity indicators and finally help in constructing vulnerability resilience indicators to climate change.

    Dolan and Walker (2003) discussed the concept of vulnerability and presented a multi-scaled, integrated framework for assessing vulnerabilities and adaptive capacity. Determinants of adaptive capacity include access to and distribution of wealth, technology and information, risk perception and awareness, social capital and critical institutional frameworks to address climate change hazards. These are identified at the individual and community levels and situated within larger regional, national and international settings.

    Katharine Vincent (2004) created an index to empirically assess relative levels of social vulnerability to climate change-induced variations in water availability that allow cross-country comparison in Africa. An aggregated index of social vulnerability was formed through the weighted average of five composite sub indices, which are economic well-being and stability, demographic structure, institutional and strength of public infrastructure, global interconnectivity and dependence on natural resources. The results indicate that using the current data, Niger, Sierra-Leone, Burundi, Madagascar and Burkina-Faso are the most vulnerable countries in Africa.

    2.4.1.Existing Flood Vulnerability Index

    Connor and Hiroki (2005) presented a methodology to calculate a Flood Vulnerability Index (FVI) for river basins, using eleven indicators grouped into four components. The index uses two sub-indices for its computation: the human index, which corresponds to the social effects of floods; and the material one, which covers the economic effects of floods. The purpose of the FVI is to serve as a tool for assessing flood risks due to climate change in relation to underlying socio-economic conditions and management policies.

    An elaborated methodology to calculate FVI was developed by Balica (2007), using indicators which aims at assessing the condition that favour flood damages at various levels: river basin, sub-catchments and urban area. The methodology focused on two concepts: factors of vulnerability based on three elements, including exposure, susceptibility and resilience on one hand, and components of vulnerability including actual flooding and establishing the elements of a system that suffer from this natural disaster on the other hand. The methodology has been applied at different scales and has resulted in interesting observations as to how quantifiable indicators can reflect backs. Balica defines vulnerability as a function of exposure, susceptibility, and resilience.

    The Seventh Framework Programme (2011) defined the FVI in terms of the following factors: exposure, susceptibility, and lack of coping capacity. The methodology included a step of converting the indicators into non-dimensional units, by interpolating the maximum and minimum of the series of data obtained. The FVI values oscillate between 0 and 1, where 1 means the highest flood vulnerability and 0 represents the lowest vulnerability to floods. The methodology was tested in Japan river basins and in 18 river basins in Philippines.

    Depending on the equation used, the indicators will have to have a different format, but the result of the FVI remains the same. The goal of the equation of the FVI is to compare different communes to one another in overall vulnerability, but also in its separate factors exposure, susceptibility and resilience. To make it possible to visualize these separated factors, a summation relationship is more useful. Also, it is preferred if the resilience is negatively formulated, and a higher score causes the vulnerability to be higher, conform other factors. With the chosen equation, the indicators have to be measured on a scale from 0-100% or 0-1, like Balica et al. (2012). Then, the indicators have to be normalised. The method of normalization has to take into account the functional relationship between the variable and vulnerability. If the functional relation is ignored and if the variables are normalized simply, the resulting index will be misleading. After computing the normalized scores the index is constructed by giving either equal weights to all indicators/components or unequal weights. These factors are then summed up according to the equation, and the result is a 0-100% or

    0-1 number for vulnerability.

    CHAPTER III: RESEARCH METHODOLOGY

    3.1 The area of study

    3.1.1 Localisation

    Mono River system is the largest river system in Togo with catchments area of 21500 km2; it serves as eastern boundary between the Yoto district and the Republic of Benin. The district is located in South-Eastern Togo, North-East of the Maritime region. It is geographically bound by latitude 6°30` and 6°60`N, longitude 1°20` and 1°35`E. It is bordered by the Haho district to the north, Bas-Mono and the Vo districts to the south, the Zio districts to the west and Republic of Benin to the east. The study was conducted in the downstream area of the Mono River basin in eight villages of Sedome (Mawussou, Djrekpon, Batoe), Esse-Godjin (Tofakope, Atikpatafo), and Tokpli (Kpodji, Tchakponou-kondji and Logokpo) counties, in the Yoto district "Map 1" .The selected villages fall under the hazard prone area, where populations have been affected, especially during 2010 flood event, providing then a better study population who can help us to generate a better view on the assessment conducted.

    3.1.2. Landscape, soil and vegetation

    The study area is formed by hydromorphous soils which are rapidly saturates of water. The sand contents decreases, depending on the closeness of the area to the river. The geology consists of the continental shelf called the terminal plate which extends from Kouvé area to the north-western of Sedome.

    The vegetation is a savannah and is composed of the classified and gallery forests and various grassland grasses.

    The fauna consists of mammals (buffalo, warthogs, monkeys, deer, agouti etc.) and various birds of prey, aquatic life, crocodiles and hippos.

    Plateau Region

    Zio District

    Bas-Mono

    District

    Vo District

    Map 1: Map showing the targeted villages

    3.1.3. Climate and Hydrology

    The study area, which is at an altitude that ranges from 17 to 55 meters above sea level, has Guinean sub-equatorial climate with two distinct rainy seasons separated by dry periods which are influenced by the movement of two (2) types of winds at different times of the year. The mean annual temperature ranges from 22°C to 30°C and precipitation varies between 800mm and 1200 mm/year; this usually peaks in May-June and September-October. The Mono River has a pluvial law which has changed in the downstream part of the basin due to the construction of Nangbéto dam in 1987 for hydroelectric purposes. Thus, it passed from the irregular to a relatively regular flow due to the release of water of the dam. Before the construction of the Nangbéto dam, the Mono River presented the phases of low water with null flow and height from mid-December to the third week of May, whereas from May until December the river experienced high flow with average maximum of (450 m3/s) in September. This is changed after the construction of the dam with a relatively permanent out-flow at the downstream part ( Ago et al 2005).

    3.1.4. Population and Economic Activities

    The study area is made up of three counties (Sedome, Esse-Godjin and Tokpli). According to the Togo Population and Housing Census Report in 2010, the total population of the three counties was estimated at about 34918 with 10803 in Sedome, 9261 in Esse-Godjin and 14854 in Tokpli. The majority of the population is located in the River floodplains. Agriculture is the most important activity being carried out in the area with a majority of the people living practising subsistence farming.

    The fertile soils coupled with the abundant rainfall per year ensure ample yields of food crops. The main crops grown in the area include maize, cassava, sugarcane, beans, groundnut, palm trees and some vegetables.

    The people in the targeted area also keep animals such as goats, cattle, pigs and chicken. Other activities in the targeted area include trading, fishing, palm oil production etc.

    3.2. Methods

    This chapter describes the methods that are used in executing this study. Construction of vulnerability index consists of several steps. First is the selection of study area which consists of several villages. In each village a set of indicators are selected for each of the three components of vulnerability.

    3.2.1. Study Population and sampling

    This study is carried out in eight villages (Mawussou, Drekpon, Batoe, Atikpatafo, Tofakope, Tchakponou-kondji, Logokpo and Kpodji) from three counties (Sedome, Esse-godjin, Tokpli) in the Yoto district, popularly known to be associated with flood. The choice of the counties and the villages is based on information obtained from literature and further confirmed from Togolese Red Cross institution which is highly involved in Disaster Risk Reduction and Adaptation to Climate Change in the Yoto district.

    Data were collected through personal interviews from two hundred and twenty one (221) households randomly sampled from the selected villages.

    3.2.2. Selected Vulnerability Conceptual Frameworks

    The current study relies on Turner et al's vulnerability framework . It focuses only on the vulnerability part of the framework in red "figure 5"

    Figure 4: Turner et al's Vulnerability Framework; Source: Turner et al., 2003, p 8076

    The Turner et al's vulnerability framework is selected for the present study for many reasons. It illustrates the interactions involved in vulnerability analysis, drawing attention to the array of factors and linkages that potentially affect the vulnerability of the coupled human-environment system in a place. It facilitates the identification of critical interactions in the human-environment system that suggest response opportunities for decision makers. It is opened to the use of both quantitative and qualitative data. It also illuminates the nested scales of the vulnerability problem but provides an understanding of the vulnerability of a particular place. This study focused on the local level `village' as a unit of analysis. The main factors of the framework that were tackled in the present study are presented in "figure 6".

    Figure 5: Vulnerability components extracted from Turner et al., 2003 framework

    3.2.3. Flood Vulnerability Indicator Development

    In this study, only the deductive approaches were used to select indicators to serve as proxies of human-environment vulnerability to flood disasters. The field survey and interviews that were carried out in the scope of this research showed whether the selected indicators are most relevant for flood vulnerability analysis in the study area taking into account the local knowledge and perception of the affected people. Those indicators which fitted the local conditions best were combined into the composite vulnerability index "Table 2".

    Table 2: Selected indicators for flood vulnerability

    No

    Defined Indicators

    Factors

    Abbr

    Functional relationship

    1

    Population in flooded area

    Exposure

    E1

    (+)

    2

    Women (%)

    Exposure

    E2

    (+)

    3

    Children (%)

    Exposure

    E3

    (+)

    4

    Elderly (%)

    Exposure

    E4

    (+)

    5

    Return period ( year)

    Exposure

    E5

    (+)

    6

    Flood duration (days)

    Exposure

    E6

    (+)

    7

    Flood depth (m)

    Exposure

    E7

    (+)

    8

    Flood magnitude (m3/s)

    Exposure

    E8

    (+)

    9

    Village proximity (m)

    Exposure

    E8

    (+)

    10

    Farmland in flood area (ha)

    Exposure

    E10

    (+)

    12

    Education : no schooling (%)

    Susceptibility

    S1

    (+)

    13

    Household size (more than 10)%

    Susceptibility

    S2

    (+)

    14

    Female headed (%)

    Susceptibility

    S3

    (+)

    15

    Farmers (Solely) (%)

    Susceptibility

    S4

    (+)

    16

    Poor building material (%)

    Susceptibility

    S5

    (+)

    17

    Household with affected land (%)

    Susceptibility

    S6

    (+)

    18

    Community Awareness (%)

    Susceptibility

    S7

    (+)

    19

    Household Coping mechanisms (%)

    Susceptibility

    S8

    (+)

    20

    Emergency service (%)

    Susceptibility

    S9

    (+)

    21

    Household Past experience (%)

    Susceptibility

    S10

    (-)

    22

    Household Preparedness (%)

    Susceptibility

    S11

    (-)

    23

    Warning system (%)

    Resilience

    R1

    (-)

    24

    Household perception on flood risk(%)

    Resilience

    R2

    (-)

    25

    Household Evacuation capability (%)

    Resilience

    R3

    (-)

    26

    Household flood Training (%)

    Resilience

    R4

    (-)

    27

    Recovery capacity (%)

    Resilience

    R5

    (-)

    28

    Recovery Time (%)

    Resilience

    R6

    (-)

    29

    Long term resident 10 years + (%)

    Resilience

    R7

    (-)

    30

    Environmental recovery (%)

    Resilience

    R8

    (-)

    3.3. Data collection and Analysis

    This part describes data collection processes as well as data analysis methods.

    3.3.1. Primary Data Collection

    Field work plays a very important role in collecting primary data. By applying simple random sampling technique, information were collected through questionnaire-based interviews at household levels and personal observation. The questionnaires are designed based on selected indicators developed under Turner et al (2003) vulnerability framework "Table 2". One question for every indicator under each factor is displayed "Annexe 8".

    They were obtained by directly talking to the interviewees at household level so as to get very reliable and accurate information because they were the ones directly affected by the flood disasters and whose livelihood was being disrupted. The households were interviewed from their individual homes.

    To ensure the primary data quality, research assistants were recruited and trained on how to administer questionnaires and collect quality data. They were familiar with the study area and fluent in the local language (Ewe) and French. The questionnaires were pre-tested and edited to cover identified gaps. The researcher and the research assistants were together in the field during the data collection period. Additionally, supervision was done continuously and meetings were held with research assistants on a daily basis to address any challenges that were met during the data collection process.

    3.3.2. Secondary Data

    The secondary data included rainfall and river discharge data; topographical sheet. "Table 3 ".

    Table 3: Description of secondary data use

    Data

    Sources

    Zone

    Data period

    Documents, articles, reports; theses

    Library; Different offices (e.g: MERF); Online sites

    Mono basin; Yoto district

    Not determined

    Monthly and annual rainfall data

    Meteorological service of Togo (DNM)

    Tabligbo

    1971-2010

    Monthly and annual Mono Discharge data

    Hydrological service of Lomé

    Athieme

    1971-2010

    Topographical Sheets

    Cartography service of Lomé

    The Yoto district

    IGN 1984

    3.3.3. Data Analysis

    Articles, theses, reports etc... processing consisted in the reading of documents in order to come up with a consistent literature review. After this first step, the trend detection analysis in the annual and seasonal datasets was accomplished to assess climate change over the study area. In addition, the analysis of vulnerability's determinants under exposure, susceptibility and resilience and computation of flood vulnerability index were carried out. Moreover, an accurate flood vulnerability maps were created using ArcGIS techniques.

    3.3.3.1. Trend Analysis of Rainfall And River Discharge to Assess Climate Change.

    This study examines trend in river discharge and rainfall in the downstream part of the Mono basin, using Mann-Kendall statistic test. Athieme flow gauging station (downstream) and Tabligbo rainfall gauging station were selected "Table 4". Each station had a long record of 40 years (1971-2010) of data to determine whether or not there have been any significant changes in those variables over the downstream part of the river basin using Mann-Kendall test run at 5% significance level on time series data. Available monthly rainfall and daily river discharge data were first grouped into monthly, seasonal and annual average data. Missing data were filled through linear interpolation of the same months data of the contiguous years on either side of the missing value.

    Table 4: Reference of meteorological and hydrological stations

    Stations

    Latitude

    Longitude

    Altitude

    Creation date

    Data period

    Tabligbo

    06°30' N

    01°37' E

    70 m

    1937

    1971-2010

    Athieme

    06°34'44'' N

    01°39'53 E

    8.2 m

    1944 

    1971-2010

    - Mann-Kendall Test

    Mann-Kendall test was formulated by Mann (1945) as non-parametric test for trend detection and the test statistic distribution was given by Kendall (1975) for testing non-linear trend and turning point. This test, is widely employed in various studies to ascertain the presence of statistically significant trend in hydrologic and climatic variables with reference to climate change (Yu et al.1993; Douglas et al. 2000; Hess et al.2001; Burn and Elnur 2002; Yue et al. 2003; Burn et al.2004; De Toffol et al., 2008; Singh et al. 2008). There are two advantages of using this test. First, it is a non-parametric test and does not require the data to be normally distributed. Second, the test has low sensitivity to abrupt breaks due to inhomogeneous time series. According to this test, the null hypothesis H0 assumes that there is no trend and under the alternate hypothesis, it is assumed that a significant change has occurred over time, or that an increasing or decreasing trend is evident in the time series.

    In this study, trend analysis has been done by using non-parametric Man-Kendall test together with the Sen's Slope Estimator (Qi) for the determination of trend and slope magnitude to find out the annual and monthly variability of rainfall and discharge data over the Mono basin.

    The null hypothesis is tested at 95% confidence level for both rainfall and discharge data. If the p value is less than the significance level á (alpha) = 0.05, H0 is rejected. Rejecting H0 indicates that there is a trend in the time series, while accepting H0 indicates no trend was obtained.

    Positive value of Qi indicates an upward or increasing trend and a negative value of Qi gives a downward or decreasing trend in the time series. Statistical Mann-Kendall test and Sen's Slope Estimator Test were performed, using Addinsoft's XLSTAT 2014 software.

    3.3.3.2. Analysis of the determinants of communities' vulnerability to flood

    1) Analysis of Flood Characteristics

    a) Flood frequency and magnitude analysis

    The magnitude of an extreme event is inversely related to its frequency of occurrence, very severe events occurring less frequently than more moderate events (Maiti, 2007, p 44). The objective of frequency of occurrence is obtained through the use of probability distributions. Some of the commonly used probability distributions are: Gumbel's or Extreme Value type 1 distribution (EV1); Log-Normal distribution; Log-Pearson type III distribution (LP3), and Method of plotting position.

    For this study, further insight into flood frequency is provided by the return period analysis. The return period was obtained using the most efficient formula for computing plotting positions for unspecified distributions and now commonly used for most sample data: the Weibull equation (1). The objective of the method is to build the relation between the probability of the occurrence (return period) of a certain event and its magnitude. Frequency is how often an event of a given magnitude may be expected to occur in the log-run average.

    The annual peak discharge data of the Mono River at Athieme station (1971-2010, N= 40 years) is selected for flood frequency analysis. A simple technique was to arrange the given peak in descending order of magnitude and assigned an order number (m). The probability of occurrence for each observation is given by:

    (1) (Sreyasi Maiti 2007, p 45)

    Where: P= Probability of occurrence; m= order number of the event; N= Total number of events in the data; The return period for each observation was determined using the following formula:

    (2) (Sreyasi Maiti 2007, p 45)

    Where: T = return period (Recurrence interval or frequency)

    Depending on the flood peaks recorded in 2010 for the study area and the average flood peaks for the examined period, floods are classified according to their magnitude.

    b) Flood duration and flood water level assessment

    Data on flood duration and flood water levels were obtained from each household from interview. The interviewed household could recall the peak duration of flooding during the latest more severe flood (2010). The average days recorded from household interviews was calculated for each village. Flood water levels were measured inside the house as revealed by marks on building walls with reference to the ground floor during the interviews. Only houses in the main village (populated area) were considered. The flood water levels were ranged from the lowest level to the highest level for each village.

    3.3.3.3 Analysis of human-environmental condition

    Statistical analyses were used as the methods for human-environmental condition components analysis. It includes descriptive statistics to describe all the data in general.

    3.3.3.4 Computation of Flood Vulnerability Index (FVI)

    The collected data were arranged in the form of a rectangular matrix with rows representing villages and columns representing indicators. In order to obtain figures which are free from the units and also to standardize their values, the indicators were normalized so that they all lie between 0 and 1. After computing the normalized scores the index is constructed by giving unequal weights to all indicators.

    1) Normalisation of Indicators Using Functional Relationship

    Two types of functional relationships are possible: vulnerability increases with increase (decrease) in the value of the indicator. The study used then two formula to normalise indicator, depending on their functional relationship with vulnerability. Then, in case that the indicator has an increase functional relationship with vulnerability (positive indicators), the normalisation is done using the following formula:

    (3)

    On the other hand, in case that the indicator has a decrease functional relationship with vulnerability (negative indicators), the normalized score is computed using the formula:

    (4)

    Xij denotes the value of j indicator (j=1, 2, .........30) in the i village (i=1, 2-, ....8).

    Yij is the matrix corresponding to the normalised score;

    Wj and Yij lie between 0 and 1; Ó Wi = 1

    It is obvious that the scaled values of Yij lies between 0 and 1. The value 1 corresponds to that village with maximum value and 0 corresponds to the village with minimum value. Through those formula the normalised scores for each indicator were obtained using MS-EXCEL Max() and Min() functions.

    2) Method of Weighting and Aggregation of Indicators into Vulnerability Index

    After computing the normalized scores, the index is constructed by giving an unequal weight to all indicators. In literature, several methods are used to give weight to indicators either equal weights (simple average of the scores and Patnaik and Narain Methods) or unequal weights (Expert judgement and Iyengar and Sudarshan's methods) or multivariate statistical techniques (Principal components and cluster analysis method).

    The present study uses an unequal method of Iyengar and Sudarshan's to give weight to all indicators. Iyengar and Sudarshan (1982) developed a method to work out a composite index from multivariate data and it was used to rank the districts in terms of their economic performance. This methodology is statistically sound and equally suited for the development of composite index of vulnerability to climate change. In Iyengar and Sudarshan's method, the weights are assumed to vary inversely as the variance over the regions in the respective indicators of vulnerability.

    That is, the weight wj is determined by:

    (5)

    where c is a normalizing constant: equation 6

    (6)

    The choice of the weights in this manner would ensure that large variation in any one of the indicators would not unduly dominate the contribution of the rest of the indicators and distort inter regional comparisons. It is well known that, in statistical comparisons, it is more efficient to compare two or more means after equalizing their variances.

    The overall village index, Yi , also varies from zero (0) to one (1) with 1 indicating maximum vulnerability and 0 indicating no vulnerability at all. the higher the district index, the more the level of vulnerability .

    The composite indicator for flood vulnerability factors (exposure, susceptibility and resilience) for the ith village was obtained as:

    Yi= ?Wj Yij

    (7)

    where: Yi is the composite indicator of ith village; Wj is the weight for each indicator lies between 0 and 1; ?Wj= 1; and Yij is the normalised scores of indicators.

    To ensure that the indices calculated for each vulnerability factor can be compared, the sum for each factor of exposure, susceptibility and resilience are divided by their respective number of indicators that describe each vulnerability factor. The composite vulnerability index for exposure factor is given as:

    (8)

    Where: is the composite vulnerability index of exposure factor,

    Wj is the weight a single indicator, ei is exposure indicators; Yij is the normalised value of exposure indicator; n is the number of indicators.

    Susceptibility and resilience factors can all be represented in similar way.

    Any flood vulnerability analysis requires information regarding these factors, which can be specified in terms of exposure indicators, sensitivity indicators and resilience indicators. Finally, the vulnerability of a system to flood events can be expressed with the following general equation (Balica, 2007, p 37). This equation is used in the present study to compute Flood Vulnerability Index (FVI).

    Vulnerability = Exposure + Susceptibility - Resilience (9)

    3.3.3.5. Flood vulnerability maps

    The composite index values of the three factors of vulnerability and total flood vulnerability index values were integrated in ArcGIS 10.1 software with all relevant input data being available in a digital spatial database (polygon shape file) to produce exposure, susceptibility, resilience and vulnerability maps. The maps were classified and colour coded green-yellow-red, indicating low-moderate-high areas, respectively.

    CHAPTER IV: PRESENTATION AND DISCUSSION OF RESULTS

    This chapter presents and discusses results of the research based on the primary and secondary data collected. The results presented in this chapter have been arranged in sections which include finding on trend in rainfall and river discharge of the study area, determinants of vulnerability and Flood Vulnerability index and mapping of vulnerable areas results.

    4.1. Empirical Finding on Trend and Variability Analysis

    4.1.1. Precipitation Time Series Analysis

    Statistical properties of the annual and monthly rainfall series were tested and presented in "Annexe 1". The result shows that April, May, June and October represent the smallest Coefficient of Variation (CV): 0.486, 0.388, 0.353, and 0.439, respectively which means that they were the homogenous months in terms of rainfall variations during the period of record. On the other hand, December, January and February show the largest CV with 2.122, 1.586 and 1.06, respectively. The rest of the months present similar rainfall pattern representing similar variation during the study period. The annual maximum rainfall occurred in the year 1999 with the total precipitation of around 1341.5 mm approximately and the minimum rainfall occurred in the year 1977 with the total of around 674 mm.

    On running the Mann-Kendall test on precipitation data, the Sen's slope shows an evidence of a positive trend in annual series. The rate of annual rainfall change is about 3.434 mm/year. The result indicates that the null hypothesis was accepted for the annual rainfall trend (p-value= 0.159). Thus, statistically significant positive trend is not found for annual rainfall over the time period.

    On plotting the linear trend line for the 40 years rainfall data, the following results in "Figure 7" were obtained.

    Annual rainfall-plot

    y = 3.434x + 943.6
    R² = 0.053

    Rainfall (mm)

    Linear fit

    Figure 6: Linear trend line corresponding to rainfall data (1971-2010:Tabligbo station)

    The "figure 7" represents the graph for the twelve (12) months average rainfall for the time period (1971-2010). It shows two peaks in the year one in June (159.647mm) and another one in October (130.06 mm) which reveals the bimodal pattern of rainfall in the study area.

    Monthly Average Rainfall

    Figure 7: Monthly Average Rainfall (1971-2010)

    In the Mann-Kendall test, the Sen's slope estimator reveals the trend of the series for 40 years for individual 12 months from January to December which are -0.073, -0.560, -0.069, 0.353, 0.285, 1.134, 0.980, 0.153, -0.480, 1.307, 0.699, and -0.191, respectively. For April, May, June, July, August, October and November, there is an evidence of a rising trend while the result is displaying negative trend in January, February, March, September and December. Thus Sen's slope estimator shows a positive trend for eight months and for other four months it shows a negative one representing almost non-significant condition. The Null hypothesis was accepted for all the twelve (12) months "annexe 2". Therefore, statistically significant trends are not found for precipitation on monthly basis, at 95 % confidence level, even though there are negative and positive trends for the record of period (1971-2010) considered.

    Annual Rainfall Cumulative Deviation

    Figure 8: Annual rainfall cumulative deviation (1971-2010) (Tabligbo station)

    To be able to determine normal, wet and dry years, cumulative deviation from mean of rainfall pattern were computed for the periods of record. "The figure 8" reveals that a cyclic pattern of variations with alternating drier and wetter years is suggested. Two main phases of different lengths were detected (1971-1977 and 1978-2010). The first phase covering eight (8) years shows six negatives anomalies that let to conclude a dry phase. It is followed by a long period from 1978-2010 characterized by variations with alternating drier and wetter years. This result explains rainfall variability over the study area during the period under examination.

    4.1.2. Discharge Time Series Analysis

    Trend analysis of the downstream part of the Mono River basin has been done also with 40 years of river discharge data from 1971 to 2010. Statistical properties of the annual and monthly flow series were tested and presented in "annexe 3". The results show positive skewness which means the data were normally distributed. According to the results, all the individual months show the largest CV representing similar variation during the study period. The annual average discharge for these 40 years is 114.985 m3/s. During the record period,

    the maximum discharge occurred in the year 2001 with the total discharge of 262.408 m3/s approximately and a minimum discharge in the year 1984 with the total of around 19.09 m3/s.

    On running the Mann-Kendall test on river discharge data, the Sen's slope shows an evidence of a positive trend in annual series. The rate of annual rainfall change is about 2.462m3/s/year. The result indicates that the null hypothesis was rejected for the annual discharge trend (p-value= 0.002) "annexe 4". Thus, statistically significant positive trend is found for annual river discharge over the time period.

    On plotting the linear trend line for the 40 years river discharge data, the following results in "Figure 9 " were obtained.

    Annual discharge plot

    Discharge

    Linear fit

    Y= 2.625x + 61.173

    R2 = 0.2706

    Figure 9: Average annual discharge variation (1971-2010)

    The "figure 10" shows the monthly discharge distribution of 40 years. It shows one peak in September (333.442 m3/s)

    Monthly Average Discharge

    Figure 10: Monthly discharge Average(1971-2010)

    In the non-parametric Mann-Kendall test, monthly trends of river flow for 40 years have been calculated for each month individually together with the Sen's magnitude of slope (Q). The Sen's slope reveals the trend of the series for 40 years for individual 12 months from January to December which are 2.653, 2.13, 2.233, 2.031, 2.289, 0.910, -1.354, -3.391, -4.697, -0.047, 2.295, and 3.433 respectively. While July, August, September and October show an evidence of negative trend, the others months show evidence of positive trend. The Null hypothesis was accepted for July, August and October months and rejected for the others months "annexe 4".

    Annual Discharge Cumulative Deviation

    Figure 11: Annual discharge cumulative deviation (1971-2010)

    The "figure 11" shows the cumulative deviation from mean that reveals a cyclic pattern of variations with alternating low and high discharge years. Three main phases of different lengths were detected (1971-1978, 1979-1997 and 1998-2010). The first phase shows negative anomalies that let to conclude a low discharge phase. It is followed by a period (1979-1997) characterized by variations with alternating low and high discharge years. The last period (1998-2010) shows a phase of high discharge years. This result explains river discharge variability over the study area during the period of record.

    The application of the trend analysis reveals an overall upward trend in annual rainfall and river discharge. It is well known that rainfall is one of the major inputs into runoff processes while river discharge shows a composite response of the whole basin. The upward trend in the two variables may show a causal effect of rainfall on river discharge and an evidence of climate variability. The evidence of positive trend in the river discharge and the rainfall characteristics suggest that the study area may be exposed to either river flood or flash flood.

    The result equally shows that even though there is an evidence of upward trend in annual rainfall and river discharge in the study area , the trend in the rainfall series is not significant compared to the one of the river discharge. This may be explained by these studies (Rossi 1996, Klassou 1996, Amoussou and al.2012, ) which show that the Mono River is under the influence of downstream and upstream rainfall and besides, under the effect of the Nangbéto dam put in service since 1988 for hydropower production reasons which confers to the river an artificial character at the downstream part. According to the interview carried out in the scope of this study, the increase of flood hazards in the area is not only due to change in precipitation patterns causing overflow of the river but also to man-made actions such as: the regular opening of Nangbeto dam at the upstream of the basin. Clearly, flood hazard in the study area is a natural phenomenon which was exacerbated by anthropogenic factors.

    From the result, river flood peaks may occur in September. The increase of river discharge causing flood hazards in the study area calls up the need to describe past floods magnitudes in order to predict design floods for the targeted area. To this end, calculation of the return period as well as the probability of occurrence of past flood magnitude to estimate the likely values of discharges to expect in the river at various recurrence intervals based on the available historical record was carried out.

    4.2. Determinants of Communities' Vulnerability to Floods

    4.2.1. Flood Frequency and Magnitude Analysis

    The maximum instantaneous flow of 736.70 m3/s was recorded at Athieme during the 1999/ 2000 hydrological year while the lowest flood flow of 69.16m3/s was recorded in 1983/1984 hydrological year. The 40-years mean instantaneous maximum flood flow is 372.34m3/s with a CV of 40.6% and a standard variate of 182.757m3/s "table 5". The coefficient of variation was applied to measure the consistency and the steepness for the frequency curves in the river flow data. The CV value obtained indicated that the distribution of flood flows was not highly variable.

    Table 5: Annual Maximum flow basic statistics

    Basic statistics

    Values

    Mean

    372.34

    Maximum

    736.70

    Minimum

    69.16

    Std. Deviation

    182.757

    Coef. of Variation (CV)

    0.491

    Skewness

    0.406

    The return period and the probability of occurrence for each observation of the Mono River have been computed, using Weibull's formula, for the period 1971- 2010 which generally starts to peak in the month of July with the maximum in the month of September. according to flood hydrology data of Athieme station.

    The Mono River discharge at the downstream reveals that the study area has been affected 22 times by low intensity flood with return periods of 1 or 2 years with high probability of occurrence. The low intensity is ranged between (69.16-372.33m3/s). The study area experienced nine times moderate intensity flood with return period of 2, 3 or 4 years with probability of occurrence less than 50% and magnitude between (372.34-549.64 m3/s). The study area was challenged with high flood event nine times ranging between (549.65-736.70 m3/s) "figure 18" and "table 6".

    The latest more severe flood for the downstream part of the Mono basin is the one that occurred in 2010. Its return period is 5 years and the probability of occurrence of the 2010 flood (as a same magnitude) would be once in five years (Probability=0.22) "annexe 5". During the period, the recurrence interval of high flood based on the 2010 flood magnitude has ranged from 5 to 41 years. There are eight recurrence intervals covering a total period of 40 years between the first and the last occurrence of high flood events.

    Low hazard

    High Hazard

    Moderate hazard

    Discharge

    Frequency

    Magnitude of flood

    549.65-736.70

    9

    High

    372.34-549.64

    9

    Moderate

    69.16-372.33

    22

    Low

    Figure 12:Flood Frequency and Flood Magnitude

    4.2.2 Flood Duration and Flood Water Level Assessment

    The water level as well as flood duration were different for the targeted villages "table 7". The result reveals that the higher the flood water level, the higher the flood duration.

    It was noticed that villages such as Kpodji, Tchakponou and Logokpo have the highest flood level and highest flood duration although they are the most distant of the Mono River. This can be explained by the fact that these villages are surrounded by Mono River's sub-branches. They are not directly inundated by the Mono river itself but rather by the Mono's sub-branches. Then, when water comes from all the sub-branches, the total areas is highly inundated. In addition, the area is made up by heavy soil which can decrease the flood water infiltration capacity and increase the duration of flood water in the area.What about the soil types in the affected villages?????

    Table 6: Depths of flood water (2010) as revealed by marks on building walls and average flood duration (2010) from household

    Flooded areas

    Depth of flood marks on walls (2010)

    Proximity to the river body (m)

    Flood duration (days) (2010)

    Djrekpon

    63- 99cm

    470.35

    58

    Batoe

    50-118 cm

    303.79

    82

    Logokpo

    70-100cm

    909.28

    88

    Tchakponou

    50-106cm

    626.13

    80

    Kpodji

    57-170 cm

    1874.22

    95

    Tofacope

    30-45cm

    112.54

    41

    Atikpatafo

    65- 70 cm

    165.55

    71

    Mawussou

    45-65cm

    435.55

    51

    In addition, all the eight surveyed villages lie in the low altitude level of the Yoto district comprised between (12-58m) which makes them to be highly exposed to flood hazards "Map 2".

    Map 2: Map of Yoto district showing the surveyed villages in low altitude

    4.2.3. Elements at Risks

    The study focuses on two main elements at risks: households and their agriculture croplands. The total population from the surveyed sample is about 2124 composed of children, young, elderly, and adult. 42.74% of the total population are children, 4.61% are elderly and 17.14% are women. As it is shown in various studies and confirmed by the majority of the respondents 72.36%, women, children and elderly are considered as the most exposed to flood due to their fragility. The little high proportion of children under 15 years in the study area may increase the communities' vulnerability to flood disasters.

    Based on information collected through the simple random survey, it was found that those surveyed households were having agriculture croplands of about 506 ha of which the majority is very closer to the river body (less or equal to 200m). The crop production activity in the study area depends on rainfall and practices which is still traditional with rudimentary tools. Farmers are faced with problems of storage and preservation of harvested crops. Thus, when flood came, before and even after harvesting period, the majority of crops are destroyed.

    Photo 1: Oil palm farm under water since the 2010 flood, Photograph taken during field work

    4.2.4. Assessment of communities' vulnerability : Human-environmental conditions

    This section consists in identifying the different characteristics of a Human-Environment system which would make the surveyed villages vulnerable to floods.

    4.2.4.1. Socio-Demographic Characteristics of Households

    The total sample comprised of 221 respondents who were household heads. The social demographic features of households are as shown in (Table 7). The majority of the respondents were male (86.9%) while females constituted 12.2%. Most of the respondents were aged between 40 and 59 years (47.1%). The level of education was assessed because it was an important factor in understanding household vulnerability to disasters. The majority of the respondents (39.8%) attained only primary education as their highest level with 38.9% having no schooling at all. 14.5% had secondary level education and only 0.09% had attained tertiary level of education. The majority of the households heads are married (90.5%) with 7.2% of widowed. Most of households that is 62.90% had more than 10 members.

    4.2.4.2 Location of settlement and type of construction

    The location of settlements in the targeted area led to increase household vulnerability to flood disasters. As observed during the field survey, most of the settlements are located near the river as the river is the main source of water in the study area. The proximity to the river body facilitates the access to water for their various activities.

    Photo 2: Use of water in the community, Photograph taken during field work

    The field survey carried in the scope of this study shows that the vulnerability of the building structure depends on the building materials. A total sample comprised of 221 respondents were household heads .The majority of the households interviewed (74.7%) lived in building made up of clay walls with thatched roof. Of the households, 13.1% lived in clay walls with iron/tiles sheet roof's building, while 4.5% and 5.9% of the respondents lived in brick walls with iron/tiles sheet roof and hurdle or banco walls with thatched roof buildings, respectively. The majority of households therefore lived in the type of houses that make them susceptible to floods.

    Photo 3: House made in Banco and straw Photo 4: House made by clay wall with destroyed by the 2010 flood in Tokpli county thatched roof, Photograph taken during Source: PDNA, 2010 field work

    4.2.4.3. Livelihood patterns of respondents

    The socio-economic status of this community constitutes another source of vulnerability. The social economic status of households is an important factor in assessing their vulnerabilities to disasters (Wisner, et al. 2004:12). Almost all people of the community in the study area depend on agriculture. The interview reveals that the main source of income for the assessed households are agriculture activities (crop production) 90% and 7.2 % of respondents who do not have agriculture as main activities have it as secondary activities. 65.05% of the total respondents depend solely on agriculture activities. Most of the surveyed households have a limited livelihood options, for most of them indicate having no secondary livelihood sources. Those who have a secondary activity mention second livelihood sources such as trading, breeding, fishing, hunting, palm oil production.

    The marital status of household head played an important role in determining the livelihood strategy. Those who are married have a diversity of livelihoods as opposed to singles, and widowed household heads "figure ".

    Figure 13: Livelihood Strategies by Marital Status of Heads of Households

    4.2.4.4. Awareness and impacts of flood

    The awareness to flood hazards may raise the attention of population on how to manage flood risks. According to the key informants (NGOs and Public institutions) that were interviewed in the field, the occurrence of flood in the area does not take the residents by surprise because even though it is a natural phenomenon, the residents know when the river may overflow and they received warning on radio and through the sign posts implanted to indicate the water level. Equally, they are aware of flood-related risks in the area. The analysis shows that the majority, 94.1% is aware of the flood risks, while only 4.5% said they are not aware. A majority of the households (84.6%) said they had warnings about the threat of flood and how to handle its effects, while 13.6% said they did not have any prior warning about the impending threat of flood. Looking at the 2010 flood which was exceptional in the area according to the population, 86.9% were aware of the 2010 flood occurrence while only 9.5% said they were not aware.

    Regarding the different ways for passing information on flood threat, 77.8% of the total respondents said they received information by radio, while 2.7% and 0.09% said they received warnings about floods by traditional ways and through volunteers of the Red Cross, respectively. Furthermore, despite the threats caused by flood in those communities, they still live in the same area for several reasons. The majority of the respondents (47.5%) reported they have nowhere to go, 22.2%, 12.7%, and 8.6% reported respectively that it is hard to find farmland elsewhere, that the lands are for their ancestors, and the lands of the area are fertile.

    In terms of the impact of 2010 flood, 96.4% of the respondents had been affected in various ways (loss of crops, destruction of houses, loss of animals, loss of important assets etc.) and 2.7% said they had not been affected.

    4.2.4.5 Environmental conditions

    The extent of environmental degradation has also an important role to play in exacerbating community vulnerability. The field survey revealed that the study area is subjected to the fragmentation of the river bank digging and widening the river channel, the soil degradation "photo 6 and 7" and removing of vegetation along the river bank which leaves the soil exposed and increase surface runoff and then flood extent. AGO et al (2005) observed that, the soil degradation, the deforestation of the floodplain, the increase in the number of human settlements in the river boundary increases the vulnerability of population to flood hazard.

    Photo 5: Dehydrate soil in Batoe village, Photo 6: Mono River bank fragmentation Photograph taken during field work in Mawussou village, Photograph taken during field work

    4.2.4.6. Household coping mechanisms

    The occurrence of flood hazard affects people in different ways. The affected people are forced to employ coping strategies. Various preparedness and coping mechanism are adopted by the surveyed households. Advanced preparation, training and planning will facilitate the evacuation processes (Ekotu, 2012:101). The majority of the respondents of the total sample (58.4%), reported they employed preparedness measures to reduce flood impact and (48%) have used reactive measures to cope during. They employed pro-active measures such as the use of short cycle seeds (6.3%), early harvest (13.1%), preparation of evacuation place (9%), protection of food supply (5%), putting of important assets in safe place (12.2%). Respondents reported to employ reactive measures, such as tire track (1.8%), sandbag track (11.8%), backfilling with sand (22.6%), backfilling with palm kernel shells (5.9%), and pumping (0.09%). The use of those pro-active measures as well as the reactive measures by the population were confirmed by Red cross institution of the Yoto district that is highly involved in disaster risk reduction in the targeted areas.

    During and after flood disasters, most of households do not have any assistance from government and there is no policy to help the communities out. Each household, then, relies on itself and on their families and relatives in non-flooded area to cope with flood.

    Despite the existing anticipatory information in the study area there is no adequate strategy to increase population's resilience to flood disaster. This calls to think on alternative solution to increase adaptive capacities of population. According to the key informant interview, population in vulnerable areas should firstly change their mentality in order to accept their relocation in a safe areas, they should build strong houses and adopt an appropriate agricultural practices adapted to their area and the use of short cycle seeds to increase their resilience.

    4.2.4.7. Anticipative measures of flood occurrence

    The anticipative measures play a great role in the reduction of flood impacts. The field survey reveals some anticipative measures used by the population to predict the occurrence of flood. The population combines sign post markers to control the level of water with local indicators of flooding (massive presence of ants coming out from ground, and snail climbing trees, transport of mud, presence of hippopotamus and some type of bird so called "Tolem" in local language) to anticipate the occurrence of flood. The majority of the respondents of the total sample (82.8%) reported they are aware of anticipative measures such as local indicators (78.7%) and sign post marking possible flooding levels (4.5%)

    Low level

    Medium level

    High level

    Photo 7: Implantation of Sign post marking possible flooding levels

    (Early Warning system)

    4.2.4.8. Training on flood hazard management

    In terms of training on flood hazard management, 82.8% of the total respondents said they have received some training on flood hazard management while, 14% have never received any training . Of those who have some training, 1.8% indicated they received information on how to control river water level, 20% reported they received information on hygiene and water purification measures, 12.21% reported they received information on local indicators and a small number 3.1% said they were informed on evacuation areas.

    4.2.4.9. Household recovery time and positive effects of flood on household

    The majority of respondents (36.2%) said it takes long time for them to recover from flood disasters, while 24.9% reported they recover quick from flood disasters. 75.11% of respondents of the total sample said flood had no positive effects on their household while 24.9% reported flood had some positive effects on them. Of all those who reported having experienced positive effect of flood, 11.31% said their farmlands become more fertile after flood, 7.2 % said they experienced increase of crop yield after flood, 4.07% said they were able to practice garden activity after flood because the soil become suitable for such activity and a small number 2.3% said they experienced increase in fish population, which is good for fishing activity.

    4.2.5 Household adaptation options

    4.2.5.1 Household's perception of Government and NGOs role in flood management

    The perception of households on the role of Government and NGOs in flood management was assessed, based on the three phases of disaster management cycle. The majority (15.83%) of the total respondents interviewed indicated that government and non government organisations should increase awareness and educate people about the causes, risks and warning signs of floods; 11.31% said government and NGOs should build dams and reservoirs or dikes and levees, and health centres. Others reported that government and non government organisations should build retaining ponds, flood channels, safe heaven places, construct roads, distribute boats; install more sign posts marking possible flooding levels in the community, plant trees along the river bank and control of Nangbeto dam opening, as mitigation and preparedness measures to control flood disasters. As response measures during flood, the majority (54.30%) of respondents reported that government and NGOs should assist the affected communities with food and non-food -items, however 36.20% and 9.50% said the government and NGOs should take appropriate measures to evacuate affected people in the safe havens and provide health assistance. 52.03% respondents of the total sample reported government and NGOs should provide seeds, fertilizers and animals; 13.57% suggested assisting people in returning to their home and distribution of building materials while 11.31% said government and NGOS should provide advice and training to flood victims and 9.50% proposed to assist affected people with financial support for their recovery after floods.

    4.2.5.2. Household `s perception of communities role in flood management

    A part from the role of government and NGOs in flood management, the interviewed households recognized the role of communities in managing flood disasters. 33.94% reported they should adopt early harvest option in order to reduce impact of flood on their livelihood; 22.63% said the importance of planting trees in reducing flood extent, while 13.57% reported they should install collective food storage in order to assist affected people with food items. Others suggested diversification of economic activities (11.31%), group farming (7.24%); building of strong house (3.16%), avoiding cultivation close to the river (2.26%), flood management committee (2.26%), collective saving (3.62%).

    To sum up, besides the extreme variability in terms of flood magnitude and frequency in the Mono River in the study area, which may be due to the increasing in the precipitation and the river discharge patterns, the proximity of the villages and the closeness of households' farmlands to the river body, the type of construction and the position of settlements, the structure of the populations (high number of children; high household size), low level education of household, the lack of the diversification of livelihood strategies, the lack of adequate flood warning system and lack of willingness and ability to take responsive actions coupled with inadequate emergency services during and after flood, may increase the communities' vulnerability to flood disasters.

    The low level education coupled with the limited livelihood strategies and the low incomes have resulted in poor agricultural practices. In addition, since the crop production is the main source of income and food added to the high number household member, increased exposure to floods will exacerbate the population vulnerabilities to flood hazards by compromising their food security. This situation proves that the research hypothesis is verified.

    4.3 Computation of Flood Vulnerability Index

    The Flood Vulnerability Index (FVI), in the present study, aimed to identify the most vulnerable village related to flood events in the three selected counties in the downstream area of the Mono River basin in the Yoto district.

    4.3.1 Identifying key indicators of developed FVI

    Thirty (30) indicators were used in the present study. Those indicators were categorised under the three factors of vulnerability and were included in the FVI computation.

    4.3.2. Normalised Scores and Weight Values of Indicators

    A system at risk is more vulnerable when it is more exposed to a hazard. However, it will be less vulnerable the more resilient it is. From the vulnerability equation, high exposure and high susceptibility lead to increases in vulnerability. On the other hand, high resilience levels decreases vulnerability. To this end, the normalization method takes into account the functional relationship between the variable and vulnerability in order to avoid misleading issue in the construction of the indices. The normalised values of each indicator is given in annexe (6). Iyengar and Sudarshan (1982) method were used to calculate weight of each indicator. The calculated weights for each of the flood vulnerability indicators are given in "annexe "7.

    4.3.4. Composite vulnerability index of vulnerability factors

    4.3.4.1. Exposure factor

    Exposure considers the indicators which explain how social entities such as households or economic activities like agriculture, etc., are exposed to flood events. Ten (10) indicators are used to explain the determinant of communities' vulnerability to flood disaster under exposure factor. Two main determinants are found: flood characteristics composed of flood frequency, magnitude, depth and duration as well as elements at risks composed of households and their farmland. Flood characteristics are quite the same for all the surveyed village but the difference is related to the elements at risk in each village. The composite vulnerability index of this factor is calculated for each village. By considering the composite index of exposure factor, the most exposed villages are Djrekpon and Kpodji with high indices ranging from 0.0451 to 0.0651 "Map 3". The most exposed villages have the highest scores for most of the considered indicators under the elements at risk compared to the other villages : high population and farmland in flooded area, high percentage of women and children and elderly in flooded area "annexe 6".

    Map 3: Flood Exposure map of the study area

    4.3.4.2 Susceptibility Factor

    Susceptibility considers the indicators which evaluate the sensitivity of an element at risk before and during a flood event. Eleven (11) indicators are also equally used to explain the determinants of communities' vulnerability to flood disasters under susceptibility factors. The composite index of susceptibility factor for each village is computed. By considering the composite index of susceptibility factor, the most susceptible villages are Batoe and Atikpatafo with high indices ranging from 0.0058 to0.0065 "Map 4". The most susceptible villages have the highest scores for most of the considered indicators :high female headed household, low education level, limited livelihood strategies, high household size, low coping capability, low access to emergency service, low preparedness capability "annexe 6".

    Map 4: Flood Susceptibility Map of the study area

    4.3.4.3 Resilience Factor

    Resilience factor considers indicators which clarify the ability of a Human-Environment system to persist if exposed to flood by recovering during and after the event. Eight (08) indicators have been used to explain the determinants of communities' vulnerability to flood disasters under resilience factor. The composite index of susceptibility factor for each village is computed. By considering the composite index of resilience factor, the least resilient villages are Djrekpon, Tchakponou and Kpodji with indices ranging from (0.0059- 0.0069 ) "Map 5". The least resilient villages have the lowest scores for most of the considered indicators :Low knowledge on warning system, low evacuation capability, low recovery capacity "annexe 6".

    Map 5: Flood resilience map of the study area

    4. Composite vulnerability index of components

    The values of the indicators were used in the following general equation of vulnerability to determine the overall flood vulnerability index. Among the eight villages surveyed, Djrekpon and Kpodji are found to experience relatively high vulnerability with indices ranging from (0.0048-0.0225) , Batoe, Atikpatafo, Logokpo and Tchakponou are found to be moderately vulnerable with indices ranging from (0.0256-0.461) and Mawussou and Tofacope are estimated to suffer relatively low level of vulnerability to flood disaster with indices ranging from (0.462-0.668) "Map 6".

    Map 6 Flood vulnerability map of the study area

    This section compares the vulnerability of the selected villages by computing the composite vulnerability index of the three factors of vulnerability, using indicators identified under the different determinants of Human-Environment system and the overall flood vulnerability index. The overall FVI has shown two most vulnerable villages: Djrekpon and Kpodji. It is found that Djrekpon and Kpodji villages were highly exposed, Djrekpon was highly susceptible and Kpodji was moderately susceptible while the two villages were found to be least resilient. Some justification can be found in these results by looking at the number of households affected by floods during the last ten years, the high percentage of household heads with no education level, the lack of livelihood strategies option of those households, the highly susceptible building materials, the lack of adequate coping capacity and recovery capacity from floods. However, the low values found in the different results of the three factors of flood vulnerability as well as in the overall vulnerability index for some villages can be misinterpreted as not being vulnerable to floods. This may not be the case since all determinants of human-environment or socio-ecological system can be damaged under certain conditions.

    CHAPTER V: CONCLUSION AND POLICY RECOMMENDATION

    This chapter presents the conclusions, and policy recommendation, of the current study and areas for further study based on the discussion of results presented in Chapters 4. This study was conducted in the district of Yoto, South-eastern Togo, in three counties: Sedome, Esse-godjin and Tokpli. The study covered the villages of Mawussou, Djrekpon, Batoe, Tofacope, Atikpatafo, Logokpo, Tchakponou-kondji and Kpodji. The main objective of the study was to analyse the long term trend of rainfall and river discharge series for the study area, to identify the determinant of communities' vulnerability to flood disasters and to compute flood vulnerability index in order to easily understand and compare the vulnerability of the different villages.

    The data analysis results suggested the evidence of change in precipitation and in the river discharge which may be the major contributing factor of vulnerability to flood hazards. The increase of river discharge over the years calls up the need to describe flood hazards by computing the return period of the different intensities recorded over the record period of 1971-2010 and especially for 2010 flood which was exceptional in the targeted area and in the whole country. Flood disasters in the target area is not only due to the increase of precipitation and river discharge, but also to the interaction between human and the environment; the vulnerability analysis reveals that the communities' vulnerability to flood in the targeted area may mostly be caused by lack of coping mechanisms, the insufficient emergency response or service during and after floods from public and private institutions, the closeness of households' farmlands and settlements to the river body, lack of diversification of livelihood strategies, poor building materials, low education level etc...

    The computation of Flood Vulnerability Index (FVI) suggests that communities' vulnerability to flood can be reflected by the three factors (exposure, susceptibility and resilience). The FVI offers easy comprehensive results, with the use of a composite values to characterise high, moderate and low vulnerability communities. It is found that out of the eight surveyed village, two were highly vulnerable (Djrekpon and Kpodji); Four were moderately vulnerable (Batoe, Atikpatafo, Logokpo and Tchakponou-kondji) while two were least vulnerable (Mawusssou and Tofacope).

    To alleviate the vulnerability of those surveyed villages, the study recommends some prior actions that can be taken to prevent homes and communities from the damage caused by flooding. Based on the current research, it is recommended to build awareness, preparedness and knowledge of communities about the importance of flood risk management and particularly, what actions should be taken as preparation and coping mechanisms. It recommends to educate the populations in risk reduction by putting in a place local flood management committee in order to enable preparation of community action plans that explain what to do in flood case. The study recommends putting in place a good early warning system, that local and regional weather information can use to sensitize about flooding. Regarding advance warning, it is suggested to associate the public and private media in the diffusion of the alerts related to the seasonal forecasting of flood hazards, to install sign posts marking possible flooding levels in the community and educate people about warning signs of floods in order to limit the impacts of flood in the communities prone to flood disasters.

    This study is an attempt to assess communities' vulnerability to flood in the Yoto district. For timing and limited funding purposes, it was difficult to expand the area of assessment beyond the eight selected villages. The results may not be totally extrapolated for the whole district as each community has its own conditions. Since the methodology is based on indicators, its main weakness is the accuracy of data to compute the equation. The results of Flood vulnerability index in this study depend in majority on information from communities. Some information was derived from sources that can be considered as non-reliable, for example the village distance of contact with a river, which was taken from Google Earth, by computing the distance using the ruler tool in the software. For the results to be valid, all data must be derived from other reliable sources.

    Based on the present study, there is clearly a need for more research into communities resilience, and adaptive options to the flood hazard, particularly communities traditional knowledge and perceptions, experiences and historical processes used to mitigate floods. Also, the aspect of communities' behavioural response toward flood awareness especially household private decisions in flood risk management should be investigated. It is also recommended to conduct local risk assessment for building and agriculture croplands using the engaging technique of flood-depth-analysis to work out the structural and agricultural vulnerability that different levels of flood water could bring.

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    ANNEXES

    Annexe 1 : Statistical summary of Annual and monthly precipitation for Tabligbo

    Tabligbo station

    Minimum

    Maximum

    Median

    Mean

    Stdev

    Variance

    CV

    Skewness

    Annual

    674

    1341.5

    1005.1

    1016.258

    179.610

    32259.59

    0.177

    -0.165

    Jan

    0

    50

    1.45

    7.967

    12.64

    159.77

    1.586

    1.851

    Feb

    0

    121.5

    24.15

    31.8

    33.716

    1136.79

    1.06

    1.313

    Mar

    0.1

    309.6

    93.850

    96.262

    60.612

    3673.82

    0.630

    1.050

    Apr

    24.3

    264.3

    107.3

    118.378

    57.584

    3315.97

    0.486

    0.641

    May

    45.3

    325.5

    142.75

    152.712

    59.196

    3504.16

    0.388

    0.514

    Jun

    44.8

    288.9

    165.95

    159.647

    56.391

    3179.93

    0.353

    0.015

    Jul

    4.8

    209.7

    79.75

    89.19

    52.361

    2741.71

    0.587

    0.526

    Aug

    2.2

    213.6

    46.55

    53.655

    44.726

    2000.44

    0.834

    1.562

    Sept

    0.2

    291.3

    109.500

    120.9

    69.310

    57.122

    35.206

    33.334

    Oct

    28

    313.3

    135.55

    130.06

    57.122

    3262.92

    0.439

    0.701

    Nov

    0

    161.4

    29.950

    39.980

    35.206

    1239.45

    0.881

    1.347

    Dec

    0

    194.1

    3.5

    15.705

    33.334

    111.123

    2.122

    4.263

    Annexe 2: Mann-Kendall test results of annual, monthly and seasonal precipitation

     

    Mann Kendall

    Statistic (S)

    p-value (two tailed test)

    alpha

    Sen's slope estimate

    Test Interpretation

    Jan

    10

    0.153

    0.05

    -0.073

    Accept H0

    Feb

    -123

    0.291

    0.05

    -0.560

    Accept H0

    Mar

    - 31

    0.651

    0.05

    -0.069

    Accept H0

    Apr

    39

    1.000

    0.05

    0.353

    Accept H0

    May

    53

    1.000

    0.05

    0.285

    Accept H0

    Jun

    114

    0.291

    0.05

    1.134

    Accept H0

    Jul

    115

    1.000

    0.05

    0.980

    Accept H0

    Aug

    57

    0.651

    0.05

    0.153

    Accept H0

    sept

    -20

    0.451

    0.05

    -0.480

    Accept H0

    Oct

    122

    0.175

    0.05

    1.307

    Accept H0

    Nov

    119

    0.760

    0.05

    0.699

    Accept H0

    Dec

    -33

    0.532

    0.05

    -0.191

    Accept H0

    Annual

    122

    0.159

    0.05

    3.434

    Accept H0

    High rainy season

    -16.

    0.861

    0.05

    -0.063

    Accept H0

    High dry season

    119

    0.169

    0.05

    0.703

    Accept H0

    Small dry season

    61

    0.484

    0.05

    0.349

    Accept H0

    Small rainy season

    52

    0.552

    0.05

    0.468

    Accept H0

    Annexe 3 : Statistical summary of Annual and monthly Flow for Athieme

    Athieme Station

    Minimum

    Maximum

    Median

    Mean

    stdev

    Variance

    CV

    skewness

    Annual

    19.089

    262.408

    102.970

    114.985

    58.998

    3480.749

    0.513

    0.725

    Mar

    0.180

    132.952

    20.762

    40.509

    43.749

    1913.998

    1.080

    0.662

    Apr

    0.380

    136.863

    21.648

    369.902

    39.073

    1526.688

    0.979

    0.560

    May

    0.790

    141.390

    28.752

    47.229

    47.705

    2275.808

    1.010

    0.779

    Jun

    1.928

    257.758

    56.581

    67.245

    59.815

    3577.842

    0.890

    1.213

    Jul

    6.094

    359.385

    105.551

    128.895

    91.771

    8421.840

    0.712

    1.055

    Aug

    21.945

    588.352

    221.125

    230.096

    141.150

    19923.299

    0.613

    0.953

    Sept

    35.190

    731.677

    302.805

    333.442

    189.934

    36074.800

    0.570

    0.435

    Oct

    19.369

    736.705

    214.869

    237.667

    155.241

    24099.804

    0.653

    1.153

    Nov

    1.589

    281.567

    75.072

    91.686

    75.570

    5710.826

    0.882

    0.867

    Dec

    0.966

    490.532

    31.336

    66.880

    89.258

    7967.078

    1.335

    2.934

    Jan

    0.429

    248.383

    35.203

    51.203

    56.458

    3187.500

    1.094

    1.269

    Feb

    0.389

    225.486

    37.751

    44.683

    49.629

    2463.063

    1.111

    1.409

    Annexe 4: Mann-Kendall results of annual , monthly and seasonal flow for the study area

    Months

    Mann Kendall test

     

    Mann Kendall

    Statistic (Zc)

    p-value (two tailed test)

    alpha

    Sen's slope estimate

    Test Interpretation

    Mar

    62.000

    < 0,0001

    0.05

    2.233

    Reject H0

    Apr

    50.000

    < 0,0001

    0.05

    2.031

    Reject H0

    May

    52.000

    < 0,0001

    0.05

    2.289

    Reject H0

    June

    26.000

    0.014

    0.05

    0.910

    Reject H0

    Jul

    14.000

    0.202

    0.05

    -1.354

    Accept H0

    Aug

    -14.000

    0.202

    0.05

    -3.391

    Accept H0

    Sept

    -26

    0.014

    0.05

    -4.697

    Reject H0

    Oct

    6.000

    0.624

    0.05

    -0.047

    Accept H0

    Nov

    30.000

    0.0004

    0.05

    2.295

    Reject H0

    Dec

    48.000

    < 0,0001

    0.05

    3.433

    Reject H0

    Jan

    50.000

    < 0,0001

    0.05

    2.653

    Reject H0

    Feb

    56.000

    < 0,0001

    0.05

    2.135

    Reject H0

    Annual

    262

    0.002

    0.05

    2.462

    Reject H0

    High rainy season

    456

    0.000

    0.05

    3.416

    Reject H0

    High dry season

    436

    0.000

    0.05

    2.663

    Reject H0

    Small dry season

    80

    0.357

    0.05

    1.392

    Accept H0

    Small rainy season

    54

    0.537

    0.05

    1.125

    Accept H0

    Feb

    56.000

    < 0,0001

    0.05

    2.135

    Reject H0

    Annual

    262

    0.002

    0.05

    2.462

    Reject H0

    High rainy season

    456

    0.000

    0.05

    3.416

    Reject H0

    High dry season

    436

    0.000

    0.05

    2.663

    Reject H0

    Small dry season

    80

    0.357

    0.05

    1.392

    Accept H0

    Small rainy season

    54

    0.537

    0.05

    1.125

    Accept H0

    Annexe 5: calculation for Return Period of 2010 Flood, Mono River

    Water Year

    Rank

    Ranked discharge

    Return period

    Exceedence probability

    2000

    1

    736.70

    41

    0.02

    1971

    2

    731.68

    21

    0.05

    2007

    3

    709.11

    14

    0.07

    2001

    4

    665.12

    10

    0.10

    2003

    5

    615.93

    8

    0.12

    1981

    6

    591.77

    7

    0.15

    1986

    7

    588.35

    6

    0.17

    1980

    8

    552.91

    5

    0.20

    2010

    9

    549.65

    5

    0.22

    1999

    10

    526.23

    4

    0.24

    2005

    11

    471.37

    4

    0.27

    1992

    12

    463.27

    3

    0.29

    1975

    13

    444.23

    3

    0.32

    1988

    14

    440.94

    3

    0.34

    1990

    15

    436.73

    3

    0.37

    1972

    16

    425.99

    3

    0.39

    1989

    17

    424.81

    2

    0.41

    2008

    18

    402.60

    2

    0.44

    1982

    19

    350.72

    2

    0.46

    1976

    20

    337.38

    2

    0.49

    2002

    21

    331.09

    2

    0.51

    1974

    22

    329.98

    2

    0.54

    2006

    23

    315.35

    2

    0.56

    1978

    24

    289.35

    2

    0.59

    1977

    25

    257.10

    2

    0.61

    2004

    26

    256.97

    2

    0.63

    1979

    27

    252.48

    2

    0.66

    1995

    28

    251.82

    1

    0.68

    1973

    29

    247.67

    1

    0.71

    1985

    30

    240.60

    1

    0.73

    1991

    31

    227.13

    1

    0.76

    1998

    32

    225.49

    1

    0.78

    1983

    33

    212.32

    1

    0.80

    1987

    34

    200.91

    1

    0.83

    1994

    35

    196.56

    1

    0.85

    1993

    36

    166.30

    1

    0.88

    1997

    37

    139.64

    1

    0.90

    1996

    38

    137.94

    1

    0.93

    2009

    39

    80.17

    1

    0.95

    1984

    40

    69.16

    1

    0.98

    Annexe 6:normalised scores of flood vulnerability indicators of each village

    Indicators

    Djrekpon

    Batoe

    Logokpo

    Tchakponou

    Kpodji

    Tofacope

    Atikpatafo

    Mawussou

    E1

    0.9007

    1.0000

    0.8278

    0.0000

    0.6821

    0.4967

    0.4967

    0.8675

    E2

    0.6855

    0.3918

    0.4378

    0.0000

    0.3877

    0.1526

    0.4127

    1.0000

    E3

    1.0000

    0.2454

    0.3152

    0.2179

    0.3865

    0.9689

    0.5672

    0.0000

    E4

    0.0946

    0.0054

    0.0000

    1.0000

    0.4482

    0.0518

    0.2750

    0.1679

    E5

    1.0000

    0.5000

    0.0000

    0.0000

    0.5000

    1.0000

    1.0000

    0.0000

    E6

    0.0000

    0.7593

    0.8704

    0.7222

    1.0000

    0.3148

    0.5556

    0.1852

    E7

    1.0000

    0.5932

    0.2712

    0.3729

    0.8729

    0.0508

    0.2542

    0.0000

    E8

    0.2031

    0.1080

    0.4523

    1.0000

    0.2915

    0.0000

    0.0301

    0.1834

    E9

    0.5986

    0.9010

    1.0000

    0.7790

    0.8236

    0.8605

    0.0000

    0.0906

    E10

    0.8581

    1.0000

    0.6782

    0.0000

    0.6910

    0.4334

    0.0800

    0.1172

    S1

    0.5101

    0.1376

    0.5694

    0.1376

    1.0000

    0.0000

    0.7974

    0.5926

    S2

    0.5101

    0.1376

    0.5694

    0.1376

    1.0000

    0.0000

    0.7974

    0.5926

    S3

    0.1601

    0.8204

    0.8521

    0.4392

    0.0000

    0.6931

    0.7949

    1.0000

    S4

    0.3811

    0.0000

    0.8147

    0.4072

    1.0000

    0.2264

    0.2769

    0.0983

    S5

    0.0645

    0.5163

    0.7737

    1.0000

    1.0000

    0.4627

    0.7099

    0.0000

    S6

    0.7921

    0.7850

    1.0000

    0.7850

    1.0000

    0.8808

    0.8711

    0.0000

    S7

    0.6235

    0.4833

    0.0000

    0.0000

    0.0000

    0.1788

    0.0000

    1.0000

    S8

    0.0000

    0.6071

    0.3928

    0.7500

    0.5714

    0.3928

    1.0000

    0.5357

    S9

    0.0000

    0.6071

    0.3928

    0.7500

    0.5714

    0.3928

    1.0000

    0.5357

    S10

    0.8645

    1.0000

    0.0000

    0.5803

    0.0939

    0.8252

    0.8321

    0.4392

    S11

    0.7742

    1.0000

    0.0624

    0.1334

    0.0000

    0.0740

    0.0000

    0.3448

    R1

    0.5913

    0.6613

    0.0471

    0.0233

    0.0000

    0.4768

    0.9418

    1.0000

    R2

    0.8461

    1.0000

    0.3903

    0.5435

    0.0000

    0.7925

    0.5102

    0.5205

    R3

    0.9033

    1.0000

    0.2031

    0.0000

    0.6364

    1.0000

    0.0400

    0.3276

    R4

    0.9033

    1.0000

    0.2031

    0.0000

    0.6364

    1.0000

    0.0400

    0.3276

    R5

    0.5482

    1.0000

    0.0000

    0.4712

    0.5672

    0.3538

    0.6299

    0.9361

    R6

    0.2944

    0.1667

    0.4141

    0.0625

    0.2898

    1.0000

    0.0000

    0.2995

    R7

    1.0000

    0.8307

    0.1272

    0.1538

    0.0000

    1.0000

    0.7969

    0.7375

    R8

    0.9240

    0.8821

    0.4475

    0.4500

    0.0000

    0.9122

    1.0000

    1.0000

    R9

    0.4300

    1.0000

    0.1040

    0.0000

    0.0000

    0.6173

    0.4000

    0.9197

    Annexe 7: Calculated weights of flood vulnerability indicators

    No

    Indicators

    Weight

    1

    E1

    0.00234

    2

    E2

    0.03905

    3

    E3

    0.0162

    4

    E4

    0.06061

    5

    E5

    0.12353

    6

    E6

    0.00601

    7

    E7

    0.5302

    8

    E8

    0.0002

    9

    E9

    0.01099

    10

    E10

    0.00449

    11

    S1

    0.01011

    12

    S2

    0.01217

    13

    S3

    0.01398

    14

    S4

    0.00812

    15

    S5

    0.02167

    16

    S6

    0.01148

    17

    S7

    0.01462

    18

    S8

    0.00434

    19

    S9

    0.00483

    20

    S10

    0.00596

    21

    S11

    0.01078

    22

    R1

    0.00459

    23

    R2

    0.00404

    24

    R3

    0.0143

    25

    R4

    0.0058

    26

    R5

    0.00698

    27

    R6

    0.00371

    28

    R7

    0.00972

    29

    R8

    0.00846

    30

    R9

    0.02333

    Annexe 8: Questionnaire for Household Interview

     

    Date:

    ___ ___ / ___ ___ / 2014

    County : ___________________  

    Team ID:____________    

    Village:____________  

    Questionnaire No!____ !   !____ !  !____ !  

    INTRODUCTION

    I am a student at the University of Lome , Togo pursuing a Masters in Climate Change and Human Security. I am collecting data for a research study in Yoto District. The study focuses on assessing population vulnerability to flood in the area. I would like to ask you some questions about your family. The data that you provide is for academic purpose and it will be kept strictly confidential. This is voluntary, you can refuse to answer to some of the questions but I hope you will accept as your views are very important.

    Section 1: Exposure

    1.What are the main climate hazards that have affected your community during the last ten years?

    1. Flooding |__| 2. Drought |__|; 3. Storm |__|; 4. Bushfire |__|

    2.Among those hazard, what was the most damageous?

    1. flooding |__|; 2. drought |__|; 3. Storm |__|; 4. Bushfire |__|

    3.What are the causes of flood in your locality?

    1. heavy rainfall |__|; 2. overflow of Mono river |__|; 3. Other, specify..... ........................|__|

    4.Do you think the frequency of occurrence and impacts of flooding have increased during this decade compared to previous decades ?

    1. Yes |__|; 2. No |__|

    5. Number of flood event during the past ten years

    .................................................................. ..................................................................................

    6. Was your household affected by the 2010 flood?

    1. Yes |__|; 2. No |__|

    7. Flood duration (the number of flood days during the 2010 flood)

    ..............................................................................................................................................

    8. What is the height of the 2010 flood in your household? (Flood depth)

    ............................................................................................................................................

    9.How could you appreciate the 2010 flood magnitude compared to the others floods?

    |__| 1. Less; 2. Equal |__| ; 3. more |__|

    10. The size of household's agricultural land

    .............................................................................................................................................

    11. Does your household's farmlands often affected by floods?

    1. Yes |__|; 2. No |__|

    12. Proximity of the farmland to the water body

    1. <1km |__|; 2. 1-2km |__|; 3. >2km |__|

    13. Number of women in your household

    ................................................................................................................................................

    14. Number of children under 15 years in your household

    ................................................................................................................................................

    15. Number of elderly in your household

    ..............................................................................................................................................

    Section 2 : Susceptibility

    14.Sex of household head

    |__| 1. Male; 2. Female |__|

    15.Age of head of household

    1. <20ans |__|; 2. 20-39 |__|; 3. 40-59 |__|; 4. 60+ |__|

    16.Marietal Status of household head

    1. Single|__|; 2. Married |__|; 3. Widowed |__|

    17.Education status: highest level of education attained

    1. No schooling |__| ; 2. Functional literacy |__|; 3. Primary schooling |__|; 4. Secondary schooling |__|; 5. Tertiary schooling|__|; 6. University |__|

    18.Household size

    .................................................................................................................................................................

    19.Type of dwelling for the household

    1. brick walls with iron/tiles sheet roof |__|; 2. Mud walls with iron/tiles sheet roof |__|;

    3. Mud walls with thatched roof |__| ; 4. hurdle walls with thatched roof |__|

    20. What are the main sources of income for the household?

    1. None |___|; 2. Agriculture |___|; 3. breeding |___| ; 4. fishing |___|; 5. handcraft |___|

    6. Palm oil production |___|; 7. Trading |___| other specify ...................................... |___|

    21. What are the secondary source of income of the household?

    1. None |___|; 2. Agriculture |___|; 3. breeding |___| ; 4. fishing |___|; 5. handcraft |___|

    6. Palm oil production |___|; 7. Trading |___| other specify .................................. |___|

    22.Are you aware of the risk of floods in your locality?

    1. yes |__|; 2. No |__|

    23.if yes why do you still live in such an area?

    ......................................................................................................................................................

    24.Was there any information or announcement or warning about the threat of floods?

    1. yes |__|; 2. No |__|

    25.if yes, from which ways the information is passed

    1. TV|__|; 2. Radio|__|; 3. traditional ways|__|; 4. volunteers|__|

    26. Were you aware of the 2010 flood:

    1. yes |__|; 2. No |__|

    27. Were you affected by the 2010 flood or any other flood in your locality?

    1. yes |__|; 2. No |__|

    27.Are you prepared for floods?

    1. Yes |__|; 2. No |__|

    28.If yes what are the methods used?

    29. Do you use any method to reduce the effect of flood disasters to your household when they occur?

    1. yes |__|; 2. No|__|

    30.If yes, what are those methods?

    ..................................................................................................................................................................

    31.Do you get help from the government or other institutions during floods?

    |__| 1. yes; |__| 2. No

    32.If yes what type of help?

    ....................................................................................................................................................................

    33.How do you value the government response during and after the flooding in your area?

    1. belated|__| ; 2. immediat|__|; 3. inadequate|__|; 4. adaquate|__|, other specify.................. |__|

    Section 3: Resilience

    34.Did you attend any training on flood management?

    1. yes |__|; 2. No |__|

    35.If yes, what information did you receive during the training?

    ..............................................................................................................................................................

    36.Which structures provide the information/warning/training?

    |__| 1. Croix rouge; |__| 2. NGOs; |__| 3. locale government

    37.Was the information you received useful during and after flood disaster?

    1. yes |__|; 2. No|__|

    38. Is there a committee of flood management in your community?

    1. yes; |__| 2. No|__|

    39.if yes, are you member?

    1. yes; |__| 2. No |__|

    40.Are you able to anticipate the occurrence of the floods?

    1. yes |__|2. No|__|

    41.if yes how?

    1. Local indicators|__|; 2. early warning system |__|; 3 other specify................................................ |__|

    42.Is your household able to evacuate, in case of a flood?

    1. yes|__|; 2. No|__|

    43.Are there any place where you can seek shelter during flood?

    1. yes; |__| 2. No |__|

    44.If yes where is that area?

    1. Public school building |__|; 2. Neighbours or relatives in non flooded area |__|;

    3. church building|__| 4. public evacuation site |__|; 5. migrate temporarily to other areas less vulnerable |__|; 6. Other Specify...................................................................................... |__|

    45.Does the government or others institutions provide prevention and protection measures?

    1. yes |__| ; 2. No |__|

    46.if yes what are those measures?

    .......................................................................................................................................................

    47.How do you value the ability of anticipation and preparation of the government or other institution to floods?

    1. bad|__|; 2. inadequate|__|; 3. Good|__|

    48.Do you get help from the government or other institution after the flood?

    1. Yes|__|; 2. No|__|

    49.If yes what type of help?

    ..................................................................................................................................................................

    50.Do you have community's support mechanisms to address the flood risks?

    1. yes |__|; 2. No |__|

    51.If yes what are those mechanisms?

    ................................................................................................................................................................

    52.Are you able to recover to the previous efficient state

    1. yes |__|; 2. No |__|

    53.Do experience environment recovery after flood ? ( positive effect of flood on the environment)?

    1. yes |__|; 2. No|__|

    54.if yes what are those effects

    ...................................................................................................................................................................

    55.What do you think the government or NGOs should do as prevention measures to reduce flood impacts on your community?

    ................................................................................................................................................................

    56.What do you think the government or NGOs should do to respond to flood during flood disaster

    .................................................................................................................................................................

    57.What do you think government or NGOs should to after flood to help you recover?

    ....................................................................................................................................................................

    58.What do you think the government or other institution should do to control flood disaster

    ....................................................................................................................................................................

    59.What do you think your community itself should do to reduce flood impact on your locality?

    Annexe 9: Key Informants Interview Guide.

    1. What do you think are the causes of flood? Why?

    2. How do you usually deal with flood occurrences and the effects?

    3. In which areas of life have floods affected you?

    4. How were you affected?

    5. Why do you think your community was more affected than any other communities?

    6. Were you able to tell that flood would occur?

    7. How did you know?

    8. Is there any way you are being prepared to deal with hazards

    9.Which relief organizations assisted you to deal with floods?

    10. Do you think relief organizations are important during disaster situations?

    11. How and when do they usually help during floods?

    12. Do they ever seek your ideas before, during and after helping in disaster situations?

    13. Does the district have a disaster management committee?

    14. How do you think the community can help itself in managing floods?

    VITA

    KISSI Abravi Essenam received his Bachelor of Science degree in Environment Sciences at the Faculty of Sciences from Université de Lomé (Togo) in 2011. After three months of Proficiency in English at University of Cape Coast (Ghana) in 2012, she entered the Climate Change and Human Security program at Université de Lomé October 2012 and received her Master of Science degree in November 2014.

    Her research interests include Environmental impact assessment, Disaster Risk Reduction, Risk and Vulnerability Assessment, Climate Change, Human security and Mapping.

    Her email is elvire.kissi@yahoo.com

    Tel: 00228 92 60 94 25/ 99 40 77 48