International Journal of Environment and Climate Change

9(6): 331-342, 2019; Article no.IJECC.2019.026 ISSN: 2581-8627 (Past name: British Journal of Environment & Climate Change, Past ISSN: 2231–4784)

Flood Vulnerability Assessment of Local Government Area, ,

Endurance Okonufua1*, Olabanji O. Olajire2 and Vincent N. Ojeh3

1Department of Road Research, Nigerian Building and Road Research Institute, Ota, Ogun State, Nigeria. 2African Regional Centre for Space Science and Technology Education in English, OAU and Centre for Space Research and Applications, Federal University of Technology, Akure, Nigeria. 3Department of Geography, Taraba State University, P.M.B. 1167, Jalingo, Nigeria.

Authors’ contributions

This work was carried out in collaboration among all authors. Authors EO, OOO and VNO designed the study, performed the statistical analysis, wrote the protocol and wrote the first draft of the manuscript. Authors OOO and VNO managed the analyses of the study. Author EO managed the literature searches. All authors read and approved the final manuscript.

Article Information

DOI: 10.9734/IJECC/2019/v9i630118 Editor(s): (1) Dr. Anthony R. Lupo, Professor, Department of Soil, Environmental and Atmospheric Science, University of Missouri, Columbia, USA. Reviewers: (1) Ionac Nicoleta, University of Bucharest, Romania. (2) Neha Bansal, Mumbai University, India. (3) Abdul Hamid Mar Iman, Universiti Malaysia Kelantan, Malaysia. Complete Peer review History: http://www.sdiarticle3.com/review-history/48742

Received 26 March 2019 Accepted 11 June 2019 Original Research Article Published 18 June 2019

ABSTRACT

The study was conducted in Afikpo South Local Government covering a total area of 331.5km2. Remote sensing and Geographic Information System (GIS) were integrated with multicriteria analysis to delineate the flood vulnerable areas. Seven criteria were considered; rainfall, runoff, slope, distance to drainage, drainage density, landuse and landcover, and soil. The various criteria were fit into fuzzy membership classes based on their effect in causing flood. The fuzzy members of all criteria were then overlaid to generate the flood vulnerability map. The result of the flood vulnerability map shows that very low vulnerable zones cover 86.7% of the total area, low vulnerable zones cover 1.6% of the total area, moderate vulnerable zones cover 2.17% of the total area, highly vulnerable zones cover 2.3% of the total area while very highly vulnerable zones cover 7.3% of the total area. Built up was used as a measure of the effect of flooding on human lives and

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*Corresponding author: E-mail: [email protected], [email protected]

Okonufua et al.; IJECC, 9(6): 331-342, 2019; Article no.IJECC.2019.026

properties in Afikpo South Local Government. Built up covers a total area of 38.6km2. Over sixty eight (69.8%) of built up lies in very low vulnerable zone, 3% lies in low vulnerable zone, 3.7% lies in moderate vulnerable zone, 0.6% lies in highly vulnerable zone and 17.9% lies in very highly vulnerable zone. The study provides information on target areas that may be affected by flood in Afikpo South Local Government. This information is useful for decision making on flood early warning and preparedness as well as in mitigation preparedness within Afikpo LGA.

Keywords: Flooding; vulnerability assessment; multicriteria analysis; fuzzy overlay.

1. INTRODUCTION to flood every year and more than 800 million are living in flood prone lands [4]. Flooding is caused Vulnerability can be defined as the degree of loss by heavy rainfall or when rivers overflow their resulting from the occurrence of a natural or banks and submerge dry lands or dam failure [5]. manmade phenomenon of a given magnitude to Flood is triggered by torrential rainfall have killed a known environmental constituent or set of many across the world. Increase in flood environmental constituents said to be at risk [1]. magnitude and frequency has been related to It is expressed on a scale from 0 (no damage) to climate change coupled with increasing 1 (total loss). The location of the areas where the population growth and urbanisation [6]. The largest economical losses due to natural or current trend and future scenarios of flood risk manmade disasters could occur is of invaluable requires that accurate spatial and temporal importance to urban planning. Therefore, there is information on potential hazards and risk of a need to complement natural hazard studies floods be carried out [7]. Flood risk analysis with vulnerability and risk assessments studies. provides a rational basis for prioritizing resources Such study could serve the purpose of early and management actions majorly in flood warning or total prevention of locating viable vulnerable areas [8]. Floods have consequences economic activities to the areas where they are on the economy, environment, human health and vulnerable. cultural heritage. Economic assessment of flood risk is simply about calculating the average Increasing numbers of natural disasters of which annual costs of damages or loss. Economic flooding is the greatest and most common is assessment of flood risk management measures being reported on major news media all around is about balancing the reduction of annual the world as causing great damages on existing damage cost against the average annual cost of infrastructures in urban areas. In April 2013 at risk reduction measures over their lifetime [9]. Cook County (Illinois), USA, critical facility that supplied power for a major international airport In Nigeria, recent flooding has left both the was inundated by catastrophic flooding just 4 government and the governed devastated [10]. inches away from the point of causing potential Flood displaces more people and causes disaster. Hurricane Sandy’s impact in 2012 on damages to properties and infrastructures than the East Coast of Illinois proved that there is a any other natural disaster in Nigeria [11]. In need for infrastructural preparedness and critical 2012, it was recorded that Nigeria suffered a loss examination of critical facilities across major U.S. of more than $16.9b in damaged properties, oil cities. According to [2] Iran has suffered from production, agricultural and other losses due to loss of over $ 3.7 billion as a result of flooding flood disaster. Increase in flood occurrence particularly along the southern shore of the coupled with lack of coping capacity and high Caspian Sea and in northern and northeastern levels of vulnerability of the populace have Iran. As reported by [3] in August 2001, flood continued to put many lives and properties at risk killed 210 people and cost $31 million in damage. in Nigeria [12]. While reliable infrastructures is During 2002-11, there were also dangerous and vital to human needs, currrent urbanisation smaller floods at the same places, which led to a trends in the country are defining negative loss of $65 million and the deaths of 28 people regards to flood vulnerability. Of particular [3]. concern is the fact that the impoverished part of the population who are most likely to settle in The increase in the rate of flooding across the flood risk zones are the least able to adopt world in recent times has been alarming. About measures for adaptation during flood disasters 70 million people across the globe are exposed [13].

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In Nigeria, several scientists at different times management planning. There are various ways have studied flood risk and vulnerability of measuring the impact of flooding in terms of assessment. Nkeki et al. [14] using the moderate the social, economic, environmental and resolution imaging Spectroradiometre (MODIS) geographical effects. This study takes into data of NASA Terra satellite reported that consideration the geographical dimension of geospatial methods are powerful techniques in flood impact in the study area. The aim of this mitigating and monitoring the effect of flooding study is to evaluate the spatial extent of flood risk along the Niger-Benue basin. Ojeh and Victor- in Afikpo South Local Government of Ebonyi Orivo [15] reported heavy inundation of State. Olajire et al. [17] posited that one way to farmlands soon after a heavy rainfall in Odah, mitigate the effects of flood is to ensure that all Iwhre-otah and Erorin communities in Oleh, areas that are vulnerable are identified and Isoko area of in the Niger Delta area adequate precautionary measures taken to of Nigeria led to loss of 0.608 kilometers ensure adequate preparedness, effective farmland at Odah, 0.441 kilometers at Iwhreotah, response, quick recovery and effective 0.547 kilometers at Erorin in 2011 and 0.485 prevention. kilometers at Odah, 0.425 kilometers at Iwhreotah and 0.598 kilometers in 2012 2. MATERIALS AND METHODS respectively. Their study revealed that all the crops cultivated in the area (cassava, melon, 2.1 Study Area yam, maize, plantain) were affected by flooding above 50 percent of total yield of each crop Afikpo South Local Government Area (LGA) is cultivated in the area except yam (46.9%). located between longitude 7° 40’ E to 7° 53’ E and latitude 5° 41’ N to 5° 57’ N (Fig. 1). Afikpo Okwu-Delunzu et al. [16] using Google image South is historically known as Edda. It is the and Shuttle Radar Topography Mission (SRTM) homeland of , an Igbo subgroup. Its of 2012 in Anambra East and environs, Nigeria administrative headquarters is at Nguzu Edda. discovered that 71% of the study area was liable Edda is Bordered by Uwana to the east, Akaeze to flooding of which farmlands accounted for to the west, to the south and Amasiri to 41.7% of the landuse. Olajire et al. [17] observed the north. The basic occupation of the people in from their study of the Ala river basin, Akure, the LGA are farming and fishing. Ondo state using geographical information system integrated with fuzzy logic that the Afikpo South LGA covers a total of 330 sq km in causative factors of flood within the basin were area with a population of 157,072 according to slope, distance to drainage, drainage density, the 2006 Nigeria population census. The climate soil type and land use. is characterized by two distinct seasons; the dry and rainy season. The rainy season is In Nigerian newspapers and magazines, Afikpo oppressive and overcast. The dry season is south has always been part of communities muggy and mostly cloudy. According to Koppen mentioned yearly as part of flood prone local and Geiger as presented by the University of government areas of the country. Torrential Vienna’s updated maps at http://koeppen- rainfall is the cause of flooding in this area. The geiger.vu-wien.ac.at/, the climate of Afikpo is flood ravages farmland, human lives and classified as Aw with average temperature of properties. In some places houses are 27.2°C and average annual rainfall of 2,022mm. submerged by water. The flooding in these areas Afikpo South landscape features lush vegetation is usually triggered by overflow of Obubra river in with parklands and short trees added to a litter of Cross River State and Akpoha river in Ebonyi woodlands and forests. State. Little has been done by the state government to mitigate flooding in Afikpo South 2.2 Methodology Local Government, even with the yearly flood early warning from the Nigerian Meteorological Shuttle Radar Topography Mission (SRTM) Agency (NIMET) and National Emergency satellite imagery was acquired from the United Management Agency (NEMA). Given that floods State Geological Survey (USGS) website. The continue to cause yearly significant human, data was used to delineate the stream network material and infrastructural damages in Afikpo and slope of the study area using surface and South Local Government of Ebonyi State, flood hydrology analysis tool in ArcGIS software. The risk mitigation is a key issue and the first step is generated stream network was then used to to identify risk areas for effective flood calculate the drainage density and distance to

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drainage. The distance to stream was calculated causing flood. The fuzzy membership tool in using spatial analysis tool. Drainage density was ArcGIS was used to create a fuzzy membership generated using the line density tool. Soil data class for the criteria (Table 1). Fuzzy Large and were extracted from the Harmonized World Soil Fuzzy MS Small was employed for the analysis. Database (HWSD) by Food Agriculture Fuzzy Large function is used when the higher Organization (FAO). The data was input values have more likelihood to be a georeferenced and digitized. It was converted to member of the set causing flood. Fuzzy MS a raster format for further analysis. The raster Small function is used when very small input soil map was then reclassified. values are likely to be member of the set causing flood. They are defined based on specified mean Gridded rainfall and runoff data was acquired and standard deviation. Fuzzy membership set from ERA-Interim for the period of 30years. The values of criteria that enhance flooding as 1 and data covered a period of 1986 – 2015. The values that do not support flooding as 0. gridded data was interpolated to model rainfall Parameters set to 0 do not support flooding while and runoff within the study area. The gridded those set as 1 enhance flooding. The fuzzy data was then interpolated using the Inverse members of all criteria were then overlaid using Distance Weighting method to generate the fuzzy overlay technique to generate the flood risk spatial distribution of rainfall and runoff over the map. Fuzzy overlay techniques addresses study area. inaccuracies in attribute and geometry of input dataset. It models the inaccuracies of class Landsat 8 Operational Land Imager (OLI) boundary. It addresses situations in which Imagery path 188 and row 056 for 17th of boundary between classes are not clear. Fuzzy January 2015 was acquired from the United Logic overlay define possibilities and not State Geological Survey (USGS) website for the probabilities [17]. All criteria fuzzy members was study area. The imagery was used to generate overlaid using the fuzzy overlay tool in ArcGIS the landuse and landcover map of the study area software. Fuzzy overlay combines all the sets of using supervised classification. 0 and 1 in all input criteria using Logic and generates a final map which was reclassified The various criteria were put into a fuzzy using the classify tool in ArcGIS software into membership class based on their effect in very low, low, moderate, high and very high.

Fig. 1. Map of Afikpo: Study location

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Table 1. Fuzzy membership class of criteria

Criteria Value Fuzzy membership Slope 0 – 5 Fuzzy MS Small 5.1 – 10 10.1 – 15 15.1– 36.4 Rainfall 170.9 – 173.4 Fuzzy Large 173.5 – 176.7 176.8 – 181.0 181.1 – 186.7 186.6 – 194.1 Runoff 3.12 – 3.18 Fuzzy Large 3.19 – 3.30 3.31 – 3.51 Distance to Drainage 0 – 30 Fuzzy MS Small 30.1 – 150 150.1 – 1,490.5 Drainage Density Low Fuzzy Large Medium High Landuse Built Up - 4 Fuzzy large Light Vegetation - 4 Dense Vegetation - 2 Wetland – 2 Soil Dystric Gleysols - 4 Fuzzy Large Dystric Nitosols - 2

3. RESULTS AND DISCUSSION distribution map for Afikpo South Local Government. 3.1 Slope 3.3 Runoff The terrain of an area affects the occurrence of flood in a locality. Fig. 2 shows the elevation map Surface runoff flows from steeper slope areas of the study area. Slope is the major terrain and gathers in low lying areas. Accumulation of factor that contributes to flooding. Slope tend to surface runoff water in low lying areas is what affect the direction of flow of surface runoff. causes flood. Runoff distribution within the study Surface runoff flows from steep slope and area for a period of 30 years on the average gathers in flat areas thereby causing flood in the ranges from 3.12 mm – 3.51 mm. Surface runoff low-lying zones. Fig. 3 shows the slope of Afikpo are more in the southern parts of Afikpo South South Local Government. Slope of 0° – 5° are Local Government and this decreases flat/almost flat areas and they are highly prone to northwards. Fig. 5 shows the surface runoff flood. Slope of 10° – 15° are gentle and are less distribution map for Afikpo South local prone to flood and slope greater than 15o are not Government. prone to flood. 3.4 Distance to Drainage 3.2 Rainfall Rivers tend to overflow their banks after an Intense rainfall is the major cause of flood. intensive rainfall. The overflow of river water Places with high rainfall are more susceptible to enters into dry land thereby affecting all objects flooding. Rainfall average for 30 years with in the on its course which includes lives, properties and study area ranges from 170.9mm – 194.1mm. infrastructures. “Nigeria Town and Regional The southern part of Afikpo South Local Planning Regulation of 1986 states that buildings Government has more rainfall and this reduces must be at least 30m away from major water moving northwards. Fig. 4 shows the rainfall channels [18]. Settlements within 30m distance

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from drainage are highly prone to flood, those are not prone to flood. Fig. 6 shows the distance within distances of 30m – 150m are less prone to to drainage map of Afikpo South Local flood and those at distances greater than 150m Government.

Fig. 2. Elevation map of Afikpo South LGA, Ebonyi State

Fig. 3. Slope map of Afikpo South LGA, Ebonyi State

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Fig. 4. Map of rainfall distribution in Afikpo South, Ebonyi State

Fig. 5. Map of surface runoff in Afikpo South, Ebonyi State

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Fig. 6. Distance to drainage Map of Afikpo South, Ebonyi State

3.5 Drainage Density within the study area; dystric gleysols and dystric nitosols. The soils were classified based on their Flood occurs at points where river tributaries drainage properties. Dystric nitosols are meet. The more drainage tributaries that meets moderately well drained which would cause at a point on a river course, the denser flow of less flood. Dystric gleysols are poorly drained water becomes and the more likely the rivers thereby causing flood. A major part of Afikpo overflow their banks at this point thereby causing South Local Government is covered with dystric flood. Fig. 7 shows the drainage density map of nitosols. Afikpo South Local government. The drainage density was classified into three; low, moderate 3.8 Flood Vulnerability and high. All criteria were overlaid using fuzzy overlay to 3.6 Landuse and Landcover delineate the flood vulnerability in Afikpo South Fig. 8 shows the landuse and landcover map of Local Government. The flood vulnerability was Afikpo South Local Government. Four landuse classified into five; very low, low, moderate, high and landcover types were identified; built up, light and very high. Fig. 10 shows the flood vegetation, dense vegetation and wetland. The vulnerability map for Afikpo South Local built-up areas are highly prone to flood as there Government. Afikpo South Local Government is a direct impact on human lives, properties and covers an area of 331.5 sq km. Very low infrastructures. Light vegetation is also prone to vulnerable zones cover an area of 287.28 sq km, flood as they include farmlands, pastures, low vulnerable zone cover an area of 5.18 sq km, grasslands and derived Savannahs which are moderately vulnerable zones cover an area of also of economic importance. 7.18 sq km, highly vulnerable zones cover an area of 7.73 sq km and very highly vulnerable 3.7 Soil zone covers an area of 24.13 sq km. The Built-up areas affected are a measure of the effect of Fig. 9 shows the soil map of Afikpo South Local flood on human lives and properties. The Built up Government. Two soil types were identified area covers a total of 38.6 sq km. Built up within

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very low vulnerable zones cover an area of 26.96 built up in highly vulnerable zones cover an area sq km, built up in low vulnerable zones cover an of 1.86 sq km and built up in very highly area of 1.17 sq km, built up in moderately vulnerable zones cover an area of 6.91 sq km. vulnerable zones cover an area of 1.43 sq km, Fig. 11 shows the built-up vulnerability map.

Fig. 7. Drainage density map of Afikpo South, Ebonyi State

Fig. 8. Landcover/Landuse map of Afikpo South, Ebonyi State

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Fig. 9. Soil map of Afikpo South, Ebonyi State

Fig. 10. Flood vulnerability map of Afikpo South, Ebonyi State

It can be seen from Fig. 10 that though Afiko is expansion is to the North West in terms of the expanding in all side of the area, however more built up locations.

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Fig. 11. Built up vulnerability map of Afikpo South, Ebonyi State

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© 2019 Okonufua et al.; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Peer-review history: The peer review history for this paper can be accessed here: http://www.sdiarticle3.com/review-history/48742

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