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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
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
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
iii
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.
iv
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.
v
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
vi
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.
vii
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 ....................................................................................................................... XIII
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
viii
3.1.2. Landscape, soil and vegetation ....................................................................................... 18 3.1.3. Climate and Hydrology .................................................................................................. 20 3.1.4. Population and Economic Activities ............................................................................... 20
3.2. METHODS ............................................................................................................................. 20 3.2.1. Study Population and sampling ...................................................................................... 21 3.2.2. Selected Vulnerability Conceptual Frameworks ............................................................. 21 3.2.3. Flood Vulnerability Indicator Development ................................................................... 22
3.3. DATA COLLECTION AND ANALYSIS ....................................................................................... 24 3.3.1. Primary Data Collection ................................................................................................ 24 3.3.2. Secondary Data .............................................................................................................. 24 3.3.3. Data Analysis ................................................................................................................. 25
3.3.3.1. Trend Analysis of Rainfall And River Discharge to Assess Climate Change............. 25 3.3.3.2. Analysis of the determinants of communities' vulnerability to flood ......................... 26 3.3.3.3 Analysis of human-environmental condition ............................................................. 28 3.3.3.4 Computation of Flood Vulnerability Index (FVI) ...................................................... 28
CHAPTER IV: PRESENTATION AND DISCUSSION OF RESULTS ..................................... 32
4.1. EMPIRICAL FINDING ON TREND AND VARIABILITY ANALYSIS............................................... 32 4.1.1. Precipitation Time Series Analysis ................................................................................. 32 4.1.2. Discharge Time Series Analysis ...................................................................................... 34
4.2. DETERMINANTS OF COMMUNITIES' VULNERABILITY TO FLOODS ........................................... 37 4.2.1. Flood Frequency and Magnitude Analysis ...................................................................... 37 4.2.4. Assessment of communities' vulnerability : Human-environmental conditions ................ 41
4.2.4.1. Socio-Demographic Characteristics of Households .................................................. 41 4.2.4.2 Location of settlement and type of construction ........................................................ 42 4.2.4.3. Livelihood patterns of respondents .......................................................................... 43
4.2.4.4. Awareness and impacts of flood ................................................................................... 43 4.2.4.5 Environmental conditions ........................................................................................ 44 4.2.4.6. Household coping mechanisms ................................................................................ 45 4.2.4.7. Anticipative measures of flood occurrence ............................................................... 45 4.2.4.8. Training on flood hazard management ..................................................................... 46 4.2.4.9. Household recovery time and positive effects of flood on household ....................... 46
4.2.5 Household adaptation options ......................................................................................... 47 4.2.5.1 Household's perception of Government and NGOs role in flood management ........... 47 4.2.5.2. Household `s perception of communities role in flood management ........................ 47
4.3 COMPUTATION OF FLOOD VULNERABILITY INDEX ................................................................. 48 4.3.1 Identifying key indicators of developed FVI ............................................................... 48 4.3.2. Normalised Scores and Weight Values of Indicators ................................................... 48
4.3.4. Composite vulnerability index of vulnerability factors ................................................... 49 4.3.4.1. Exposure factor ....................................................................................................... 49 4.3.4.2 Susceptibility Factor ................................................................................................. 50 4.3.4.3 Resilience Factor ...................................................................................................... 52
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CHAPTER V: CONCLUSION AND POLICY RECOMMENDATION .................................... 55
REFERENCES .............................................................................................................................. 57
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 - 4 -
ANNEXE 5: Calculation for return period of 2010 flood, Mono river ...................................... - 5 -
ANNEXE 6:Normalised scores of flood vulnerability indicators of each village ....................... - 6 -
ANNEXE 7: Calculated weights of flood vulnerability indicators ................................................. 7
ANNEXE 8: Questionnaire for household interview ...................................................................... 8
ANNEXE 9: Key informants interview guide. .............................................................................. 14
VITA . ............................................................................................................................................ 14
x
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
xi
LIST OF TABLES
TABLE 1: FLOOD INDICATORS INFORMATION ................................................................................... 13
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
xii
LIST OF MAPS
MAP 1: MAP SHOWING THE TARGETED VILLAGES ................................................................. 19
MAP 2: MAP OF YOTO DISTRICT SHOWING THE SURVEYED VILLAGES IN LOW ALTITUDE ......... 40
MAP 3: FLOOD EXPOSURE MAP OF THE STUDY AREA ............................................................. 50
MAP 4: FLOOD SUSCEPTIBILITY MAP OF THE STUDY AREA .................................................... 51
MAP 5: FLOOD RESILIENCE MAP OF THE STUDY AREA ............................................................ 52
MAP 6 FLOOD VULNERABILITY MAP OF THE STUDY AREA ..................................................... 53
xiii
LIST OF PHOTO
PHOTO 1: PALM TREES FARM UNDER WATER SINCE THE 2010 FLOOD, PHOTOGRAPH TAKEN
DURING FIELD WORK ......................................................................................................... 41
PHOTO 2: USE OF WATER IN THE COMMUNITY, PHOTOGRAPH TAKEN DURING FIELD WORK ....... 42
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.................................................................. .............................................................. 42
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.......................................... 44
PHOTO 8: IMPLANTATION OF SIGN POST MARKING POSSIBLE FLOODING LEVELS (EARLY WARNING
SYSTEM) ........................................................................................................................... 46
xiv
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
1
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
2
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.
3
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).
4
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?
5
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.
6
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.
7
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
8
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).
9
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.
10
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
11
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
12
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"
13
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)
14
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)
15
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.
16
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
17
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.
18
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.
Map 1: Map showing the targeted villages
Plateau Region
Zio District
Vo District
Bas-Mono District
20
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
21
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
22
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
23
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 (-)
24
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
25
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
26
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 α (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
27
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.
28
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; Σ Wi = 1
29
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.
30
Yi= ∑Wj Yij
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:
(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)
31
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.
32
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.
33
1970 1980 1990 2000 2010600
700
800
900
1000
1100
1200
1300
1400
Rainf
all (
mm
)
Years
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.
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
0
20
40
60
80
100
120
140
160
180
Rainfall (mm)
Months
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
Rainfall (mm) Linear fit
Monthly Average Rainfall
y = 3.434x + 943.6 R² = 0.053
Annual rainfall-plot
34
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.
1975 1980 1985 1990 1995 2000 2005 2010
-300
-200
-100
0
100
200
300
Rainfall (mm)
years
Rainfall
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,
Annual Rainfall Cumulative Deviation
35
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.
1970 1980 1990 2000 2010
0
50
100
150
200
250
300
Disch
arge
(m3/s)
Years
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)
Jan Feb Mar Apr May June Jul Aug Sept Oct Nov Dec0
50
100
150
200
250
300
350
Disch
arge
(m3/s)
Months
Discharge
Figure 10: Monthly discharge Average(1971-2010)
Annual discharge plot Y= 2.625x + 61.173 R2 = 0.2706
Discharge Linear fit
Monthly Average Discharge
36
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".
1975 1980 1985 1990 1995 2000 2005 2010
-100
-50
0
50
100
150
Dis
char
ge (m
3/s)
Years
Discharge
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
Annual Discharge Cumulative Deviation
37
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.
38
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.
39
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?????
High Hazard Moderate hazard Low hazard
40
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
41
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%
42
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
43
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
44
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
45
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
46
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%)
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
Low level
Medium level
High level
47
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%);
48
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
49
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".
50
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
51
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
52
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
53
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
54
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.
55
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).
56
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.
57
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78. Yu, Y. S., S. Zou, and D., Whittemore .1993. Non-parametric trend analysis of water quality data
of rivers in Kansas. J. Hydrol 150: 61–80.
- 1
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
- 2
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
- 3
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
- 4
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
- 5
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
- 6
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
7
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
8
Annexe 8: Questionnaire for Household Interview
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)
..............................................................................................................................................
Date: ___ ___ / ___ ___ / 2014
County : ___________________
Team ID:____________
Village:____________
Questionnaire No!____ ! !____ ! !____ !
9
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 |__|
10
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?
11
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?
12
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?
..................................................................................................................................................................
13
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?
14
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?
15
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 [email protected]
Tel: 00228 92 60 94 25/ 99 40 77 48