How to cite: Z.A. Ndanusa, I.J. Musa, A.A. Hudu and M. Isma’il. 2022.Multi-dimensional Model for Flood Vulnerability Assessment in Mokwa: A Case of Downstream Communities of Kainji Dam, Niger State, Nigeria. Journal of Inclusive cities and Built environment. Vol. 2 Issue 3, Pg 69-86 Published by the University of KwaZulu-Natal
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Journal of Inclusive cities and Built environment. Vol. 2 Issue 3
Ndanusa, Z. A: Centre for Disaster Risk Management and Development Studies, Ahmadu Bello University, Zaria Corresponding Author: Email: [email protected] | Phone Number: 08037861394
Musa, I.J and Isma’il, M.: Department of Geography and Environmental Management, Ahmadu Bello University, Zaria Hudu, A.A.: Department of Political Science and International Studies, Ahmadu Bello University, Zaria
Published 11 July 2022
By Z. A. Ndanusa, I.J. Musa, A.A. Hudu and M. Isma’il
MULTI-DIMENSIONAL MODEL FOR FLOOD VULNERABILITY ASSESSMENT IN MOKWA: A CASE OF DOWNSTREAM COMMUNITIES OF KAINJI DAM, NIGER STATE, NIGERIA
ABSTRACT
One of the most devastating and expensive natural hazards in the world today is flooding. Hence, several attempts have been made by different scholars and researchers across the globe and in Nigeria to study flood vulnerability.
These studies focused on assessing either the physical or social components of vulnerability without a holistic assessment of all vulnerability components. A multi-dimensional approach to flood risk assessment is required to provide a holistic view of residents’ degree of vulnerability to flooding. However, where the multidimensional approach was adopted the result were aggregated and not localized to specific areas. Therefore, this study attempts to quantify the vulnerability indicators using the participatory approach and develop a multi-dimensional approach for flood vulnerability assessment in Mokwa, Nigeria. Vulnerability was explored through the lens of four dimensions (economic, environmental, physical, and social) and eighteen indicators. The indicators were scrutinized and standardized for easy aggregation and comparability. The indicators were weighted unequally using Analytical Hierarchical Process (AHP). Nine communities and 382 households were selected purposively from the downstream area of the Kainji dam for sampling. The data collected were subjected to descriptive and inferential statistics using XLSTAT (2014) and spatial analysis in ARCGIS 10.7 environment. The flood vulnerability index revealed that the communities experienced high flood vulnerability from all dimensions; economic (0.71), physical (0.66), social (0.62), and environmental (0.57). The study reported a multi-dimensional flood vulnerability index of 0.65, which implies a high level of vulnerability to flooding. This study has found significant variations in all dimensions of vulnerability among the communities. The study concludes that the multi-dimensional approach to flood vulnerability provides information on the vulnerable population as well as the factors driving vulnerability in the area. The study recommends the use of a multi-dimensional approach, sophisticated models, site-specific indicators, and fine-resolution satellite data for future vulnerability assessment.
KEY WORDS Flood, Indicators, Vulnerability, Model and GIS
1. INTRODUCTION
Flooding has become a common occurrence in many places throughout the world, and Nigeria is no different. The world’s attention has turned in recent years from flood hazard control to flood vulnerability/risk assessment (Bubeck et al., 2011). Assessing vulnerability is highly important in sustainability research and it is a primary component of disaster risk mitigation (Zhou et al., 2015). For global environmental change, disaster risk management, and climate change adaptation, vulnerability has become a broadly applied concept (Gain et al., 2015). The assessment of vulnerability entails determining and reducing the susceptibilities of the exposed elements. It is seen as a crucial step in decreasing the impact of natural disasters and risks (Fuchs et al., 2012). In flood-prone locations, city officials must manage floods to maintain citizens’ safety and well-being while also protecting the environment. To achieve this goal, a thorough understanding of flood vulnerability drivers is required, as well as the selection of effective flood response techniques.
Floodplain areas in Nigeria have traditionally been a magnet for settlement, particularly in the north, where the population is predominantly agrarian (Komolafe, 2015). Fishers and other people who engage in agricultural activities typically settle along or near river banks, which are natural flood-prone areas (Anunobi, 2013). One of Nigeria’s most destructive floods since 1935 is the 2012 flood (West Africa Insight, 2012).
Flooding affected 7.7 million people in 32 of Nigeria’s 36 states in March 2012 (Niger State Emergency Management Agency, 2012), killing over 360 persons and displacing about 2 million people (Rana and Routray, 2018).
Nigeria has continued to report flood cases in several states and local government areas over the last ten years, and Niger State is no exception.
Between 2009 and 2018, flooding was reported in numerous local government areas (LGAs) in Niger State (NEMA,
2018). In 2018, eighteen (18) of the (27) LGAs were affected by floods; notable among these LGAs are Kontagora, Mariga, Shiroro, Lavun, Mokwa, Gbako, Lapai, Bosso, Suleja, Tafa, and Edati.
Several communities were displaced while valuable properties were destroyed (National Emergency Management Agency (NEMA), 2018). The worst affected LGAs and villages are those located downstream of the Niger River and the Shiroro Dam.
The magnitude of the flood calamity in the country and around the world has drawn the attention of international organisations and academics around the world. As a result, studies on flood vulnerability assessment and methodologies abound (Balica et al., 2009; Behanzin et al., 2015; Muller et al., 2011; Yoon, 2012; Ismail and Saanyol, 2013; Zelenakova et al., 2018; Hassan et al., 2019; Youssef et al., 2019).
Conversely, most of these studies do not provide a comprehensive assessment of flood vulnerability considering the uni-dimensional approach adopted in the assessment. Although the use of a multi-dimensional approach to flood vulnerability is gaining attention across the globe (Rana and Routray, 2018;
Rehman et al., 2019), the rate of adoption for studies in Nigeria and Niger state is very low. Therefore, this study attempts to provide an insight into the drivers of flood vulnerability in the downstream communities in Mokwa LGA using the multi-dimensional approach.
2. LITERATURE REVIEW
2.1. Concept of FloodVulnerability
Vulnerability is one of those words that defies definition, having a variety of connotations depending on the study orientation and perspective. In the scientific literature, there is no clarity on the specific definition of the term vulnerability, and it appears to be subject to interpretation. Within the past two or three decades, a large variety of vulnerability definitions have evolved (Cannon, 1994; Naess, 2006; Borden
et al., 2007; Balica and Wright, 2010).
Vulnerability has been seen from a variety of perspectives, dimensions, and scales, and there is no single theory or model that best explains the idea (Jamshed et al., 2017). Not only does the term “vulnerability” have varied connotations across fields, but it also has different implications in terms of size and scale. Over time, the concept of vulnerability has evolved from a single- dimensional concept of one fundamental risk factor to a multi-dimensional concept (Birkmann & Wisner, 2006).
In the above-mentioned vulnerability definitions, the hazard exposed on societies differ from definition to definition. Some of them give a definition of vulnerability to certain hazards like climate change (IPCC, 1992, 1996 and 2001) or environmental hazards (Blaikkie et al., 1994); but more important for this research is the definition of flood vulnerability. Buckle et al. (2000) defined flood vulnerability as the degree of loss to a given element, or a set of such elements, at risk resulting from a flood of given magnitude and expressed on a scale from 0 (no damage) to 1 (total damage). This definition is considered very important in the study of flood vulnerability. Since the quantification of vulnerability can help in decision making processes, flood magnitude and degree of loss should be useful for flood hazard management of a given area.
In recent years the impacts of floods have gained importance because of the increasing number of people, economic activities and ecosystems that are impacted by its adverse effects.
Societies have developed close to water access, forcing its people to search for innovative ways to control and prosper with the more limited resources as the population grows, adding pressure on the water resources. The societies of developing countries are vulnerable to floods because of: first, socio-economic conditions in terms of poverty and lack of development; second, most of the dams in developing countries are not multipurpose ; third, during floods, planning, design and implementation
by flooding, which may likely increase the vulnerability of certain areas (Balica, 2012). Balica (2012) also suggested that agriculture, urbanisation, deforestation, and enhanced environmental degradation, among others, have been proven to create higher vulnerability to floods; these may likely create more environmental damages. Some of the primary indicators used in environmental vulnerability assessment include degraded area, forest change rate, the percentage of the urbanised area, groundwater level, and percentage of land used for economic activity or natural reserve. Environmental vulnerability no doubt constitutes a natural threat to survival of many communities because they are induced by natural phenomenon such as rainfall, topography and soil characteristics among others. It is almost impossible to alter the occurrence or structure of natural phenomenon, rather we must learn how to live with them.
2.2.4. PHYSICAL VULNERABILITY The physical vulnerability has been defined separately from the physical hazard. Physical vulnerability incorporates only those indicators of physical and structural sensitivity.
The physical vulnerability relates to the physical condition, either natural or human-made, and it can increase a particular region’s vulnerability to floods (Fu¨ssel, 2009). The physical components describe the predisposition of infrastructures to be damaged by flooding events. Balica (2012) identified components of physical vulnerability are topography, proximity to the river, floodwater depth, building condition and material, length of coastline, among others. Physically vulnerable regions will experience higher flood damage and more prolonged, slower recovery time. The physical component comprises geo-morphological and climatic characteristics of the system, and different infrastructures, like buildings, dams, and levees, have shaped its physical conditions. Physical vulnerability in this study is measured with the properties of buildings and its condition.
2.2.2. ECONOMIC FLOOD VULNERABILITY
The concept of economic vulnerability was first developed by Pratt et al. (2004) for South Pacific Applied Geosciences Commission (SOPAC). Economic vulnerability index as developed by Pratt et al. (2004) was based on 50 indicators; this indicator represents the environmental degradation or integrity, resilience, or risk. Economic vulnerability relates to the economic stability of a region, household or individual endangered by a decrease in income due to a decline in income or livelihood means (Kumpulaien, 2006). Economic vulnerability to flooding includes indicators that are associated with monetary flood losses. Gallopin (2006) argued that economic flood vulnerability is related to income or issues which are inherent to economics that are predisposed to be affected by floods.
The economic activities that are likely to be adversely affected by flooding are agriculture, fisheries, navigation, and power production. There is no doubt that a breakdown of the aforementioned economic activities poses a serious threat to household or community economic development and prosperity. This study intends to consider livelihood means, availability of alternative livelihood means, and the number of household members with a livelihood means as indicators for economic vulnerability.
Livelihood means is at the very heart of the rural dwellers welfare, any disruption to the livelihood may result in serious socioeconomic crisis of the household.
Hence, the household may find it difficult to bounce back.
2.2.3. ENVIRONMENTAL FLOOD VULNERABILITY
The environmental vulnerability component of flooding relates to the interrelation between the sector and the environment, and the vulnerability associated with this interaction (Villagran de Leon, 2004). The environmental components of flooding include indicators that describe the damages to the environment caused of the measures are inadequate and
ineffective (Vaz, 2000); fourth, rural areas depend heavily on agriculture and are generally more affected than urban areas; fifth, lack of education; sixth, lack of non-structural measures; and lastly, there is a lack of adequate human and material resources to tackle the massive disaster-like floods that occurred in the past (Mirza, 2003).
2.2. Dimensions of Flood Vulnerability
2.2.1. SOCIAL FLOOD VULNERABILITY
Social vulnerability is usually described as the information pertaining to the losses incurred due to the characteristics of the population which include age, health, gender, poverty and employment (Cutter et al., 2003). Social vulnerability analysis in terms of estimated indexes considering a number of variables has been widely practiced since the late 1980s. For example, Blaikie and Brookfield (1987), Chambers (1989), King and MacGregor (2000), and Cutter et al. (2003) provided strong background and motivation for the development and implication of social vulnerability index.
However, from 2005, construction and mapping of a social vulnerability index have been the main focus (example, de Oliviera Mendes, 2009; Yoon, 2012;
Garbutt et al., 2015; Hou et al., 2016).
In Nigeria, limited attention is paid to the social vulnerability of flooding and other natural disasters experienced in the country. This social component of flooding relates to the presence of human beings and encompasses issues related to deficiencies in mobility of human beings associated with gender, age, or disabilities (van Beek 2006);
Floods can produce destruction of houses, disruption in communication ways, or even kill people. Included in this component are the administrative arrangements of the society, consisting of institutions, organisations, and authorities at their respective level.
2003; IPCC, 2012). Understanding the physical vulnerability of buildings and facilities is an important step towards disaster risk mitigation in major cities for risk assessment (Armas, ̧2012; Thouret et al., 2014). Extant literature review of studies in this direction has shown that there is multiplicity of approach to environmental vulnerability assessment.
It is therefore imperative that these methods are harmonized to enhance the assessment of environmental vulnerability and ensure comparability of results (Lo ́pez-Martı ́nez et al., 2017).
For effective disaster risk mitigation, a thorough review of each vulnerability dimension is required (Fuchs et al., 2012; Lo ́pez-Martı ́nez et al., 2017).
Assessing vulnerability based on indices has emerged as a commonly used quantitative measure (Tate, 2012). To consider different dynamic vulnerability characteristics, a composite index would be ideal since an index summarises complex data in a simpler way for any non-technical person to understand.
(Birkmann, 2006). The process of developing a composite index must include developing the following;
conceptual framework, indicators identification, data normalisation for analysis, weighing and aggregating indicators, and conducting uncertainty measures to gauge the robustness of indicators (Adger et al., 2004). This study develops a comprehensive multi- dimensional model for vulnerability assessment using a composite index method through a participatory approach.
vulnerability assessment in Europe (Birkmann et al., 2013). All these models have confirmed the multifaceted nature of vulnerability and its assessment.
2.4. Flood Vulnerability Assessment
Assessment of vulnerability is an important part of climate change adaptation and disaster risk science studies (Wisner et al., 2004; Adger, 2006; Birkmann, 2006; IPCC, 2012;
Birkmann et al., 2013). Researchers have measured vulnerability from many perspectives, such as social vulnerability (Cutter et al., 2003; Wisner et al., 2004;
Yoon, 2012), physical vulnerability (Thouret et al., 2014; Papathoma-Ko
̈hle et al., 2017), economic vulnerability (Briguglio, 1995; Willroth et al., 2011), institutional vulnerability (Lo ́pez- Martı ́nez et al., 2017), and livelihood vulnerability (Hahn et al., 2009). Under the Intergovernmental Panel on Climate Change (IPCC) approach, exposure, sensitivity, and capacity are also used by researchers to measure vulnerability (Balica et al., 2009; Hahn et al., 2009;
Birkmann et al., 2013; Zhou et al., 2015;
Phung et al., 2016). However, the main challenge of vulnerability assessment lies in integrating components, dimensions, and methodologies within different disciplines (Schro ̈ter et al., 2005; Polsky et al., 2007; Fuchs et al., 2012).
No universally acceptable and standardized methodology for measuring multi-dimensional flood vulnerability exist (Mazumdar and Paul, 2016). Hence, this study tries to adapt various vulnerability indicators and methods to propose a multi-dimensional vulnerability assessment model in Mokwa LGA, Niger State, Nigeria. While the disaster risk literature has identified and explored various dimensions of vulnerability, an integrated framework of multi-dimensional vulnerability has been lacking, especially in the Nigerian context. The most important dimensions are the social and economic vulnerabilities, which is supported by a plethora of literature (Cutter et al., 2.3. Flood Vulnerability Models
Vulnerability models or frameworks are commonly used to address urban flooding problems in climate change adaptation and disaster risk studies. The twin structure of vulnerability proposed by Bohle (2001) attempted to explain both internal and exterior components of vulnerability. The external side of vulnerability relates to the exposure to risks and shocks, while the internal side is explained by coping capacities, that is, the ability to anticipate, resist, cope and recover from the impact of a hazard.
The Pressure and Release (PAR) model, also known as the pressure and release model, views disaster as the meeting of two fundamental forces: the natural hazard event on the one hand, and the other processes that generate vulnerability on the other (Wisner et al., 2004). Bogardi and Birkmann (2004) established the Onion framework, which establishes the relationship between risk and susceptibility to the level of potential losses and damages in the three spheres. The framework emphasises that vulnerability is concerned with a variety of loss types, including both economic and social losses. The vulnerability framework proposed by Turner et al.
(2003) stated that global environmental change can be viewed through the lenses of sensitivity, exposure, and resilience. Furthermore, vulnerability is seen as part of a combined or coupled human-environmental system (Turner et al., 2003). Vulnerabilities, according to the Bogardi, Birkmann, and Cardona Framework (BBC), must be viewed as dynamic phenomena evaluated in the environmental, social, and economic worlds. (Bogardi and Birkmann, 2004;
Birkmann, 2006). Birkmann (2006) broadened the vulnerability theory by proposing five fields, including multi- dimensional features such as physical, social, economic, institutional, and environmental. A vulnerability scoping diagram was also proposed (Polsky et al., 2007). The MOVE (Methods for the Improvement of Vulnerability Assessment in Europe) framework was recently proposed as a way to improve
3. METHODOLOGY
3.1. Study AreaNine communities in Mokwa LGA that have reported annual flood incidence within the last ten years was selected as the study area. These communities are located in the downstream area of the Kainji dam. Mokwa is a Local Government Area in Niger State, in North central Nigeria. Its headquarter is located in the town of Mokwa in the west of the area. The Niger River forms the long southern border of the LGA which stretches from Lake Jebba in the west beyond the confluence of the Kaduna River in the east.
Kwara State and Kogi State are across the Niger from the LGA.
Figure 1: Mokwa LGA in the Context of Niger State and Nigeria
3.2. Data Type, Sampling Procedure and Method of Analysis
The descriptive-cross sectional research design approach was adopted for the study while using georeferenced point data for both spatial events of interest and subjects exposed to the events. Nine communities located in the downstream area of the Kainji dam were considered for sampling. The chosen communities were predicated on annual flood incidence reported in the communities within the last ten years.
Population data for the communities was not available; hence, determining the household population becomes difficult.
Therefore, a purposive sample was adopted to identify households within the communities consistently affected by flooding within the year 2008-2018 under study. The index-based approach to flood vulnerability assessment was adopted; four dimensions (social, economic, physical, and environmental) were assessed. For each dimension
of vulnerability, vulnerability indicators were chosen through a comprehensive literature review. These metrics were selected from empirical research in the field of disaster risk science and climate change. The peculiar nature of the study was considered; hence, metrics were scrutinized and modified accordingly.
Six indicators were assessed for environmental vulnerability (elevation, slope, drainage density, distance to water, land use and soil). The elevation data and slope were derived from Shuttle Radar Topographic Mission (SRTM) data of 30 metres resolution downloaded from United States Geological Survey site (www.usgs.gov) in ArcGIS 10.5 environment. River/Drainage data (shapefiles) were downloaded from www.ecowrex.org, while the distance between the river and communities were determined on ArcGIS 10.5 environment.
The land use map of the area was extracted from global land cover map, while the soil characteristics of the area were derived from Harmonized World
Soil Database v 1.2 (HWSD) developed by Food and Agriculture Organization (FAO). Five indicators were assessed under social vulnerability (age group of household members, years spent in school by household, gender of household head, household size, and number of members with disability).
The four physical vulnerability indicators used are wall, roof, floor and toilet, while three economic vulnerability indicators (income, availability of alternative livelihood means, and number of household member with livelihood means) were assessed. The data for social, economic and physical indicators were gathered through survey using direct observation and questionnaire administration. The four dimensions were given equal importance. However, the indicators for each dimension were weighted unequally using the Analytical Hierarchical Process (AHP).
The weighting of flood risk factors was established by stakeholders in communities with flood experience
within Mokwa LGA. The stakeholders include the village heads, household heads (minimum of ten years stay in the community) youth representative, women representative and ward councillors. The six factors were weighted according to the contribution of the factor to flood vulnerability of the people in the community based on their knowledge. The median weight of each factor was used for the pairwise comparison of the flood risk factors. The pairwise matrix was computed and the normalised pairwise matrix and the criterion weight (CW) was derived. A model equation for each dimension was evolved and was applied to the study.
The datasets were normalised using respective weights for the computation of the composite index. This study also uses an objective weighting technique (AHP) to allocate values to classes of phenomena for each indicator and formulates indices. The indices from each dimension were aggregated and averaged to determine the multi-dimensional vulnerability index based on equation 5.
MVI = (EvVI+EcVI+PVI+SVI)
4 (1)
Where: MVI= Multi-dimensional Vulnerability index; EVI= Environmental Vulnerability Index;
EcVI= Economic Vulnerability Index; SVI= Social Vulnerability Index; PVI= Physical Vulnerability Index
The indicators were normalized using the linear scaling technique. The mathematical expression for the linear scaling normalization technique is presented in equation 6 and 7 for position and negative functional relationship respectively.
Vp = X - Min
Max - Min (2)
Where: V = normalised index, p = positive functional relationship, n = negative functional relationship, x= observed score, min = minimum score, max = maximum score
Each variable was further divided into classes according to its characteristics. With the help of literature, these groups were framed to show, as much as possible, the degree of difference in that particular variable. The Jenks natural breaks method of classification was adopted to arrange the values into different classes. The list of indicators used for the different dimensions is presented in Table 1.
Table 1: Flood Vulnerability Indicators by Dimension
S/No Indicators Source
Environmental Vulnerability Indicators
1 Distance to the water channel Balica (2007)
2 Elevation Youssef et al. (2019), Rimba et al. (2017)
3 Drainage Density Rimba et al. (2017)
4 Slope/Gradient Zelenakova et al. (2018); Youssef et al. (2019)
5 Land use Balica (2012); Fekete (2010); Bowen and Riley (2003), Zelenakova et al.
(2018)
6 Soil Zelenakova et al (2018)
Physical Vulnerability Indicators
1 Wall Schneiderbauer (2007); Clark et al. (1998);
2 Roof Cutter et al. (2003); Muller et al. (2011);
3 Floor Thouret et al. (2014), Gain et al. (2015), Mazumdar and Paul (2016),
4 Toilet Papathoma-Ko ̈hle et al. (2017)
Social Vulnerability Indicators
1 Gender of household head McLanahan (1983); Snyder et al. (2006);
2 Disability status Hahn et al. (2009), Balica et al. (2012), Yoon (2012)
3 Literacy level Fekete (2010); Schneiderbauer (2007); Haki et al. (2004); Steinführer and Kuhlicke 2007
4 Household Size Haki et al. (2004); Cutter et al. (2003); Muller et al. (2011); Martens and Ramm (2007)
5 Age Group Wisner et al. (2004); Haki et al. (2004); Cutter et al. (2003); Muller et al.
(2011) Economic Vulnerability Indicators
1 Income of household head Hahn et al. (2009); Scheuer et al. (2011); Rashetania et al. (2016) 2 Alternative livelihood means Hahn et al. (2009); Scheuer et al. (2011); Rashetania et al. (2016) 3 Number of household member with
livelihood means Hahn et al. (2009); Scheuer et al. (2011); Rashetania et al. (2016)
4. RESULTS AND DISCUSSION
4.1. Weighting of Flood Vulnerability indicators
The criteria weighting of the environmental vulnerability attribute is presented in Table 2. The result shows that distance or proximity to the water channel had a maximum weight of 0.50, which shows that the people posited that 50% of their vulnerability to flooding in the area is due to their proximity to the water or river. Drainage density had a consensus weight of 0.16, elevation (relief) 0.14, and land use 0.11. Soil and slope (gradient) had a consensus weight of 0.02 and 0.07, respectively. The outcome of the weighting of environmental factors indicates that human environmental susceptibility to flooding is highly induced by proximity to the river or river canal, drainage density, relief, and land use attribute. The mathematical expression of the criteria weight for environmental indicators is presented in equation 4.1. This result is at variance with the finding of Ouma and Tateishi (2014). Soil (28%) and slope (26%) were the primary drivers of environmental flood vulnerability in Eldoret Municipality of Kenya (Ouma and Tateishi, 2014).
Table 2: Normalized Pairwise Matrix for Environmental Indicators
Attributes Elevation Slope Drainage Distance Land use Soil Sum CW
Elevation 0.09 0.24 0.07 0.08 0.15 0.18 0.81 0.14
Slope 0.02 0.05 0.05 0.08 0.01 0.18 0.39 0.07
Drainage 0.18 0.14 0.14 0.12 0.22 0.18 0.99 0.16
Distance to Water 0.65 0.33 0.68 0.58 0.52 0.24 3.00 0.50
Landuse 0.05 0.24 0.05 0.08 0.07 0.18 0.67 0.11
Soil 0.01 0.01 0.02 0.06 0.01 0.03 0.14 0.02
Total 1.00
CW= Criteria Weight; Consistency Index (CI)=0.060; Consistency Ratio (CR)=0.067
EvVI= 0.14*Elev + 0.07*Slope + 0.16*DrainD + 0.50*DistW + 0.11*Lnduse + 0.02*Soil (4)
Wher:e EvVI = Environmental Vulnerability Index; Elev=Average elevation of the area; Slope= gradient of the area;
DrainD= Drainage density of the area; Lnduse= Landuse, and DistW= Distance of the community to Waterbody
The criteria weight for the physical vulnerability indicator is depicted in Table 3. The result shows that the availability and condition of toilet recorded a maximum weight of 0.53. Wall material and condition recorded a criteria weight of 0.27. This shows that the wall and availability of toilets accounted for 80% of the physical vulnerability. On the other hand, roof recorded a criteria weight of 0.07, while floor recorded a criteria weight of 0.13. The criteria weighting had a consistency index (CI) of 0.065 and a consistency ratio (CR) of 0.072. Giving that the CR less than 0.1. The criteria weighting is accepted. The equation for the physical vulnerability attribute is presented in equation 5.
Table 3: Normalized Pairwise Matrix for Physical Vulnerability Attributes
Physical Indicators Wall Roof Floor Toilet Sum CW
Wall 0.22 0.36 0.32 0.19 1.09 0.27
Roof 0.04 0.07 0.04 0.12 0.27 0.07
Floor 0.07 0.21 0.11 0.12 0.51 0.13
Toilet 0.66 0.36 0.54 0.58 2.13 0.53
1 1 1 1 1.00
CW= Criteria Weight; Consistency Index (CI)=0.065; Consistency Ratio (CR)=0.072
PVI= 0.27*Wall + 0.07*Roof + 0.13*Floor + 0.53*Toilet (5) Where: PVI= Physical vulnerability index.
The criteria weighting for social vulnerability indicators is presented in Table 4. The result shows that the gender of the household head (female-headed household) and the number of disabled persons in a household are the major indicators of social flood vulnerability, having recorded a criteria weight of 0.38 each. This implies that the gender of the household head and the number of
disabled persons accounted for 38% of the total vulnerability, respectively. The number of years spent at school by the household head reported a criteria weight of 0.14, which is equivalent to 14% of households’ social vulnerability. Age group and household size had minimal effect on the social flood vulnerability of the people. Both age group and household size accounted for 5% of the social flood vulnerability, respectively. The social flood vulnerability index equation is evolved and presented in equation 6. The analysis recorded a CI value of 0.10 and a CR value of 0.09.
Table 4: Normalized Pairwise Matrix for Social Vulnerability Attributes Attribute Age-group H/size Years in
School Female
Household Disabled Scores CW
Age group 0.05 0.05 0.02 0.06 0.06 0.23 0.05
Household size 0.05 0.05 0.02 0.06 0.06 0.23 0.05
Years in School 0.24 0.24 0.09 0.08 0.08 0.72 0.14
Female-headed
Household 0.33 0.33 0.44 0.40 0.40 1.91 0.38
Disabled persons 0.33 0.33 0.44 0.40 0.40 1.91 0.38
1.00 CW= Criteria Weight, Consistency Index (CI)=0.10; Consistency Ratio (CR)=0.09
SVI= 0.05*AG + 0.05*HH + 0.14*YS + 0.38* FHH + 0.38*DP (6)
Where: SVI = Social vulnerability index; AG = Age group; HH = Household Size; YS = Years spent in School;
FHH = Female Headed household and DP= Disabled persons
The criteria weighting for economic flood vulnerability was also determined, and the result is presented in Table 5. The result shows that income levels and alternative livelihood sources are major contributors to economic flood vulnerability; with a criteria weight of 0.45 respectively, both factors accounted for 90% of the economic flood vulnerabilities experienced by flood victims in the study area. The result also indicates that the number of household members that are gainfully employed or engaged in livelihood activities had a minimal contribution of 10% with a criteria weight of 0.1.
Table 5: Normalized Pairwise Matrix for Economic Vulnerability Attribute
Income Alternative
Income Household
employed Scores CW
Income 0.45 0.45 0.45 1.36 0.45
alternative income 0.45 0.45 0.45 1.36 0.45
Household employed 0.09 0.09 0.09 0.27 0.10
1.00 1.00 1.00 1.0
CW= Criteria Weight
EcVI= 0.45*INC + 0.45*AC + 0.10*IND (7)
Where: EcVI= Economic vulnerability index; INC=Income level; AC=alternative livelihood means;
IND= Proportion of independent household member
4.2. Spatial Distribution of Flood Vulnerability in Mokwa 4.2.1. ENVIRONMENTAL VULNERABILITY
The surveyed communities are located on an elevation area of 100 meters above sea level, except Gbajibo, which is about 104 metres above sea level (Fig 2A). The slope pattern ranges from 0 degrees to 7.7 degrees. Five of the nine communities are located within the lowest slope cluster of 0 – 0.36 degrees. These communities are Gbajibo, Jebba, Ndakogitsu, Ndachi, and Sunti community. On the other hand, Raba, Batati, Guzan, and Egbagi communities are located within a slope category of 0.37 – 0.97 (Fig 2B). This implies that all the communities are located on a low plain of less than 1 degree, which increases vulnerability. The drainage density of the communities is between 0.0 -786 sq/km. All the communities are in a low drainage density area, except Sunti community, reducing vulnerability (Fig 2C). The distance of communities to the riverbank is between 111m-218m; Ndachi (111 metres) and Jebba (118 metres) are the closest community to the river, while Gbajibo (218) and Batati are the farthest from the riverbank (Fig 2D). The major land use in the communities is agriculture, accounting for 60%, while the built-up area is barely 1.9% of the area (Fig 2E). The communities are located on four dominant soil types: Ferric Luvisols, Fluvisols, Nitosols, and District Nitosols. Batati, Guzan, and Egbagi communities are the most vulnerable to flooding due to the high clay content of 49%
in the District Nitosols (Fig 2F).
Figure 2: Spatial Distribution Pattern of Flood Vulnerability Indicators
Fig 2A: Elevation Distribution Pattern Fig 2B: Slope Distribution Pattern
Fig 2C: Drainage Density Distribution Pattern Fig 2D: Euclidean Distance to Water
Fig 2E: Landuse Distribution Pattern Fig 2F: Soil Distribution Pattern
The environmental vulnerability index of the communities varied from 0.15 to 0.75. Raba Kede had the highest EvVI of 0.75, while Gbajibo had the least EvVI of 0.15. Guzan, Jebba, Ndachi, Ndakogitsu, Raba kede and Sunti had high vulnerability, while Batati and Egbagi had moderate EvVI. Slope had the highest average index of 0.77, elevation (0.66), soil (0.64), and distance to water (0.62). Drainage density had an index of 0.30, while land use had an index of 0.52. Therefore, high vulnerability in the communities can be attributed to the nature of the slope, elevation level, soil, and distance of the communities to water. In terms of overall environmental vulnerability, a significant difference (t= 9.8; df=8; p-value = 0.001) was observed among the nine communities (Table 6). Hence, it implies a significant variation in the distribution pattern of environmental vulnerability among the communities.
The spatial distribution pattern of environmental flood vulnerability of communities in Mokwa is presented in Figure 3.
Table 6: Environmental Vulnerability Index of the Communities
Community Elevation Slope Drainage
Density DistW Landuse Soil EvVI Remark
Batati 0.76 1.00 0.47 0.21 0.33 1 0.41 Moderate
Egbagi 1.00 0.65 0.15 0.44 0.33 1 0.49 Moderate
Gbajibo 0.00 1.00 0.00 0.00 0.67 0.5 0.15 Low
Guzan 0.95 0.65 0.47 0.80 0.33 1 0.71 High
Jebba 0.27 0.00 0.47 0.93 0.67 0.25 0.66 High
Ndachi Dagwaji 0.61 1.00 0.00 1.00 0.33 0.5 0.70 High
Ndakogitsu 0.71 1.00 0.00 0.80 0.00 0.5 0.58 High
Raba kede 0.78 0.65 0.15 0.91 1.00 0.5 0.75 High
Sunti 0.85 1.00 1.00 0.48 1.00 0.5 0.71 High
Average 0.66 0.77 0.30 0.62 0.52 0.64 0.57 High
Note: DistW= Distance to water 4.2.2. SOCIAL VULNERABILITY
The surveyed households in the communities had 53% of her population between the age bracket of 0-17 years, 29% between 18-60, and 18% above 60 years. The minimum household size is 3, and the maximum is 19. The average household size is nine, which is higher than the national average household size of six in the country, thereby increasing vulnerability. The years spent in school by the household head is between 0 to 14 years, while the average years spent in school is five years. This shows that most household heads are illiterate, with less than primary school experience of education. This also compares to the finding of Fetemi et al. (2020) which reported that 43% of the household head had only primary school education. Being able to read and write afford the people the capacity to understand warning signs and message on flooding which leads to better preparation. About 15% of the household heads were female, against 85% male. Households with at least one disabled member accounted for 9%
in the communities, increasing vulnerability.
The social vulnerability index of the households varies from 0.40 to 0.82. Two communities had a very high social vulnerability index, Egbagi (0.82) and Jebba (0.80). Other communities had a high social vulnerability, Batati (0.56), Guzan (0.67), Ndachi (0.56), Raba (0.67), and Sunti (0.62), except for Ndakogitsu (0.40) and Gbajibo (0.47). The average social vulnerability index of the communities is 0.62. It is important to note that the number of persons with a disability in a household (0.68), female-headed household (0.63), household size (0.59), and years spent in school (0.53) had a high contribution to the social vulnerability of the communities (Table 3). Age-group had a moderate contribution to social vulnerability with an index of 0.35. A significant difference (t= 14.9; df=8; p-value = 0.001) was observed among the nine communities (Table 7). Hence, it implies a significant variation in the distribution pattern of social vulnerability among the communities. The spatial distribution pattern of social flood vulnerability of the communities is presented in Figure 4.
Table 7: Social Vulnerability Index of the Communities
Community Age-group Household
Size Yrs in Sch Gender of
HH Disability SVI Remark
Batati 0.09 0.67 0.00 0.78 0.60 0.56 High
Egbagi 0.38 0.67 0.50 0.89 0.94 0.82 Very High
Gbajibo 0.03 1.00 0.25 0.33 0.67 0.47 Moderate
Guzan 0.23 0.67 1.00 0.56 0.72 0.67 High
Jebba 1.00 1.00 0.50 0.67 1.00 0.80 Very High
Ndachi Dagwaji 0.75 0.67 0.75 1.00 0.00 0.56 High
Ndakogitsu 0.17 0.33 1.00 0.00 0.61 0.40 Moderate
Raba kede 0.00 0.00 0.75 0.67 0.82 0.67 High
Sunti 0.50 0.33 0.00 0.78 0.74 0.62 High
Average 0.35 0.59 0.53 0.63 0.68 0.62 High
Yrs in School= Years spent in school by Household head; HH= Household Head 4.2.3. PHYSICAL VULNERABILITY
The study reveals that 89% of the surveyed houses were constructed with cement and mud, which are permanent building materials, against 11% constructed with zinc, planks, and other temporary materials, thereby reducing vulnerability. However, 52%
of the houses are in bad condition, increasing vulnerability. Houses roofed with Zinc and Aluminum sheets accounted for 83%, while 31% of the roofs are in a bad state (blown off, falling, or leaking). Only 35% of the houses surveyed have cement floors, while 61% of the floors are in a bad state, thereby increasing vulnerability. The household survey shows that only 29% had a toilet facility (Pit toilet), however, about 72% of the toilets are in poor condition.
Batati had a PVI of 0.82, Egbagi 0.93, and Raba 0.83. This shows that these three communities are highly predisposed to flooding based on the communities’ physical attributes. Four other communities were also adjudged to have a high physical vulnerability, and these communities are Gbajibo (0.60), Guzan (0.63), Jebba (0.75), and Ndakogitsu (0.66). On the other hand, Ndachi and Sunti communities had a moderate physical vulnerability level with an index of 0.33 and 0.40, respectively. The primary factors that influence the high level of physical flood vulnerability index recorded in the communities are floor (material/condition), toilet (availability/condition), and the wall (material/condition). These three factors had a high average index of 0.69, 0.68, and 0.67, respectively. A significant difference (t= 6.5; df=8; p-value = 0.001) was observed among the nine communities (Table 8). Hence, it implies a significant variation in the distribution pattern of social vulnerability among the communities. The spatial distribution pattern of the physical flood vulnerability of the communities is presented in Figure 5.
Table 8: Physical Flood Vulnerability of the Communities
Community Wmc Rmc Fmc Tac PVI Remark
Batati 0.93 0.62 1.00 0.74 0.82 Very High
Egbagi 1.00 0.67 0.81 0.95 0.93 Very High
Gbajibo 0.27 0.45 0.75 0.75 0.60 High
Guzan 1.00 0.97 0.91 0.33 0.63 High
Jebba 0.58 0.14 0.66 0.95 0.75 High
Ndachi Dagwaji 0.00 0.00 0.00 0.63 0.33 Moderate
Ndakogitsu 0.73 0.19 0.47 0.73 0.66 High
Raba kede 0.66 0.12 0.88 1.00 0.83 Very High
Sunti 0.90 1.00 0.69 0.00 0.40 Moderate
Average 0.67 0.46 0.69 0.68 0.66 High
Note: Wmc=Wall material/condition; Rmc=Roof material/condition; Fmc=Floor material/condition; Tac=Toilet availability/condition 4.2.4. ECONOMIC VULNERABILITY
Household head’s average monthly income in each community is between ₦9,330 to ₦14,275, while the LGA average is ₦10,933.
This indicates that all the household heads in the communities earn below the national minimum wage of ₦30,000. The minimum income reported is ₦5,000 in Batati and Raba-Kede, respectively, while the maximum income recorded is ₦45,000 in Jebba. The low economic capability of the households makes the households highly vulnerable. This result is in tandem with the submission of Salami et al. (2017) which reported that about 74% of the households in the flood prone area earn less than N20, 000 monthly.
Fetemi et al. (2020) also reported low monthly income for households sampled in Bangladesh, majority of the households (64%) earn between 5000-13200 Bangladesh Taka (BDT). This further strengthen the argument that many of the households that reside in the flood prone areas do so as a result of their poor economic status. Majority of the households may be willing to relocate to safer grounds if their economic status improves. Hence, addressing the economic challenges of the people living in this area will go a long way to reduce the flood vulnerability and causalities experienced during flooding.
The economic flood vulnerability index of the communities is presented in Table 9. The result shows that Ndachi had an index of 0.86, Raba 0.81, Egbagi 0.80, and Sunti 0.80. These four communities had a very high level of economic flood vulnerability.
The other communities had a high level of economic flood vulnerability, and these communities are; Batati (0.77), Gbajibo (0.69), Guzan (0.68), Jebba (0.68), and Ndakogitsu (0.74). The major contributor to the high level of economic flood vulnerability recorded by the communities is income. Income had an average index of 0.92. The number of household members with a means of livelihood had an index of 0.64, while alternative livelihood means had an index of 0.59. The three economic attributes have a high contribution to the household and the community’s economic flood vulnerability. The average economic flood vulnerability of the communities is 0.76. This shows that the communities’ economic attributes are an essential component of vulnerability in the communities studied in Mokwa LGA. A significant difference (t= 39.1; df=8; p-value = 0.001) was observed among the nine communities. The spatial distribution pattern of economic flood vulnerability of the communities is presented in Figure 6.
Table 9: Economic Flood Vulnerability of the Communities
Community Income HML alt-livelihood EcVI Remark
Batati 0.98 0.5 1.00 0.77 High
Egbagi 0.96 0.75 0.29 0.80 Very High
Gbajibo 0.91 0.5 0.57 0.69 High
Guzan 1.00 0.5 0.10 0.68 High
Jebba 0.76 0.75 0.00 0.68 High
Ndachi Dagwaji 0.98 0.75 0.86 0.86 Very High
Ndakogitsu 0.94 0.5 0.95 0.74 High
Raba kede 0.89 0.75 0.71 0.81 Very High
Sunti 0.84 0.75 0.86 0.80 Very High
Average 0.92 0.64 0.59 0.76 High
Figure 3: Spatial Distribution Pattern of Environmental Vulnerability
Figure 4: Spatial Distribution Pattern of Social Vulnerability
Figure 5: Spatial Distribution Pattern of Physical Vulnerability
Figure 6: Spatial Distribution Pattern of Economic Vulnerability
4.3.
4.4. Multidimensional Flood Vulnerability Index of the Communities
The communities’ environmental flood vulnerability index is 0.57, while the social flood vulnerability index is 0.62 (Table 10). The community’s physical flood vulnerability index is 0.66, and the economic flood vulnerability index is 0.76. The analysis indicates that all four vulnerability dimensions contributed positively to the MVI of the communities. Raba-kede and Egbagi communities had the highest multi-dimensional vulnerability index of 0.77 and 0.76, respectively. Gbajibo recorded the least MVI of 0.48. The average MVI of the communities is 0.65. The spatial distribution pattern of the multi-dimensional flood vulnerability index of the communities is presented in Figure 6.
Table 10: Flood Vulnerability Level of the Communities
Community EnVI PVI SVI EcVI FVI Remark
Batati 0.41 0.82 0.56 0.77 0.64 High
Egbagi 0.49 0.93 0.82 0.80 0.76 High
Gbajibo 0.15 0.60 0.47 0.69 0.48 Moderate
Guzan 0.71 0.63 0.67 0.68 0.67 High
Jebba 0.66 0.75 0.80 0.68 0.72 High
Ndachi Dagwaji 0.70 0.33 0.56 0.86 0.61 High
Ndakogitsu 0.58 0.66 0.40 0.74 0.59 High
Raba kede 0.75 0.83 0.67 0.81 0.77 High
Sunti 0.71 0.40 0.62 0.80 0.63 High
Average 0.57 0.66 0.62 0.76 0.65 High
Figure 7: Multi-dimensional Flood Vulnerability Index of the Communities
5. CONCLUSION AND RECOMMENDATIONS
This study was able to show that vulnerability to flooding is a multifaceted problem that must be viewed as a bundle of issues emanating from environmental, economic, physical, and social factors.
Hence, the study developed a multi- dimensional model for measuring flood vulnerability in Niger state while taking cognizance of the area’s peculiarity.
The study demonstrates the use of the participatory approach in the weighting of indicators for holistic measurement of flooding by dimension and the aggregate degree of vulnerability. The study also provides disaggregated and localized result of flood vulnerability in Mokwa.
The study was able to show the multidimensional view of flood vulnerability in Mokwa. Hence, it was easy to identify vulnerable populations and factors responsible for flood vulnerability by dimension, indicators, and location. The indices generated for each dimension will serve as a pointer to the primary factors or drivers of flood vulnerability in the spatial units. This will make it easy for stakeholders and expert involved in risk management to be able to develop the right course of action, policies, and guidelines for disaster management in Mokwa.
The output of this study will provide requisite information that will enrich and enhance the development of appropriate strategies by local institutions for disaster risk reduction as it relates to the various dimensions of vulnerability.
This methodology can be adopted and applied at different levels and can also be applied to other natural hazard by relating it to specific disaster indicators.
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