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Scholarly Research Journal for Humanity Science & English Language, Online ISSN 2348-3083, SJ IMPACT FACTOR 2017: 5.068, www.srjis.com PEER REVIEWED JOURNAL, DEC-JAN 2019, VOL- 7/31

SPATIAL ASSESSMENTOF FLOOD RISK FACTORS AND VULNERABILITY MAPPING IN SULEJA DISTRICT OF NORTHERN NIGERIA Ado Kibon Usman1, Nura Khalil Umar2, Bako Anslem3 & J. A. Opara4 1

Department of Geography and Environmental Management Ahmadu Bello University,

Zaria, Nigeria 2

Ministry of Education Science & Technology, Jigawa State, Nigeria

Department of Geography, Federal University Gasua, Yobe State, Nigeria

School of Engineering and Applied Sciences, Kampala International University, Kampala-

Uganda Abstract This study aim at assessing flood risk factors and mapping areas vulnerable to flood in the study area, using Geo-spatial techniques. The method follows a multi-parametric approach and integrates some of the flood causative factors such as: rainfall distribution, elevation and slope, drainage network and density, land-use/land-cover and soil type. The Spatial Multi-Criteria Analysis (MCA) was used to rank and display potential locations, while the Analytical Hierarchy Process (AHP) method was employed using pair-wise comparison to compute the priority weights of each factor. The various layers were integrated in weighted overlay tool in ArcGIS to generate the final vulnerability map, representing three levels of estimated flood vulnerability zones (high, moderate and low). The normalized criterion weights were obtained for each factor, and the results shows that, rainfall (34) and slope (31) have the highest influence on flood in the study area.The Consistency Ratio (CR) with an acceptable level of 0.05 was obtained which further validated the strength of the judgement. With ArcGIS, the factor weights from the AHP were incorporated to produce a Geo-hazard map and it showed that areas that are high vulnerable to flood in Suleja constitute about 37%, while moderate and low vulnerable areas constitute about 45% and 18% respectively. Elements at high risk of flood are those found at the extreme northeast, where elevation is very low, southwest where rainfall distribution is high (along the fringes of Tafa LGA and Abuja FCT respectively) and on low lying areas along the depressions. Therefore using the Geo-hazard map as a guide, local councils and other stakeholders can act to prepare for potential floods when the rains come or, better yet, proactively promote an appropriate land-use policy that will minimize threat to lives due to flood. Keywords: Flood, Geo-hazard, Mapping, Risk-factors, Vulnerability

Scholarly Research Journal's is licensed Based on a work at www.srjis.com 1. Introduction The socio-economic condition in the whole of Sub-Saharan Africa and most of Nigerian societies is characterized by persistent high poverty level and low food security. This is further compounded by environmental susceptibility to climatic variability as well as Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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impoverished and malnourished households to diseases and prolonged violent conflict (Odunola and Balogun, 2015). Dwindling human socio-economic development has also contributed to creating vulnerability and thus, weakening the ability of people to cope with hazards and their effects (Ojo, 2013). Floods are among the most recurring and devastating natural hazards, which result from a number of basic causes of which the most frequent are climate logical in nature, but very often induced by humanâ€&#x;s abuse of the environment (Few et al., 2004). Flood occurs when there is an inundation of any area which is not normally covered with water, through a temporary rise in the level of a river, lake or sea, and when excess precipitation exceed natural infiltration, evaporation, and possible transmission (United Nations International Strategy for Disaster Reduction-UNISDR, 2015). A variety of climatic (such as: intense and/or long-lasting precipitation, snow melt etc.) and non-climatic processes (such as elevation, soil character, slope orientation, urbanization etc.) influence flood processes, resulting in river, flash, urban, glacial lake outburst and coastal floods (Wisner et al., 2012). However, flood risk results from the combination of hazards, conditions of vulnerability and insufficient capacity or measures to reduce the potential negative consequences (Moses, Ijeoma and Bitrus, 2015). The high vulnerable potential of floods in particular may be related to their rapid occurrence and to the spatial dispersion of the areas which may be impacted by these floods. Both characteristics limit the ability to issue timely flood warnings. In flood prone areas, runoff rates often far exceed those of other water flow types due to the rapid response of the catchments to intense rainfall, modulated by soil moisture and soil hydraulic properties. The small spatio-temporal scales of floods, relative to the sampling characteristics of conventional rain and discharge measurement networks, make also these events particularly difficult to observe and predict (Borgaet al., 2011). Small streams in urban areas can also rise quickly after heavy rain due to higher generated runoff and less concentration (Ozcan and Musaoglu, 2010). Changes in the urban area and in storm intensity produce higher flows that exceed the capacity of small culverts under roads designed for nonurbanized areas. Although such structures can be adequate when designed, their capacity may turn out to be inadequate and thereby cause overflows onto the roads creating new water paths and flood the built up areas. In developing countries, inadequate maintenance of the drainage channels, and debris and solid waste disposed into such drainage systems may accentuate the situation (Associated Programme on Flood Management-APFM, 2012). The Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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rainfall runoff process, however, is highly complex, non-linear and spatio-temporally varying because of the variability of the terrain and climate attributes (Chang and Guo, 2006). In Nigerian context, flood events are influenced by a range of factors including: the overflow of the numerous rivers that transverse the country, unprecedented rainfall amounts and intensity, dam breakage and levee failures, insufficient drainage systems, urbanization and the underutilization of dams in some parts of the country (Emmanuel, Olawumi and Durojaye, 2012). Suleja among other locations lies on the valley of “River Gorges” and characterized by many first order streams which drains directly into River Niger. Communities along the floodplain and surrounding land are affected by floods, which in some years cause considerable damage to their crops and houses. Erosion of river bank is also a significant problem to these communities (Aminu, et al., 2013). The flood event of 2005, 2012 and 2016 led to the death of hundreds of people. It also brought about the collapse of roads that links the north to the southern FCT axis (Niger State Emergency Management Agency as cited in Vanguard, 2018). The current trend and future scenarios of flood risks therefore demand for accurate spatial information on the potential hazards and risks of floods. Geo-hazard mapsneed to be created, as they provide a basis for the development of flood risk management plans. The maps should be effectively communicated to various target groups (including decision makers, emergency response units and the public) as a measure to reduce flood risk by integrating different interests, potential and conflicts over space in a city (Yahaya and Abdalla, 2010). The aim of this study therefore is to assess flood risk factors and map areas that are vulnerable to flood in Suleja Niger State, Nigeria, using Geo-spatial techniques. 2. Study Area and Methodology The study area lies between Latitude 9º12′1.17″ N and Longitude 7º10′20.25″ E of WGS84. It shares boundary with Gurara to the North-West, Tafa to the East in Niger State and Gwagwalada, Zuba to the south, in Federal Capital Territory (Fig. 1). It covers a land area of 136.33 km2 and had a population of 216,578 in 2006 (NPC, 2006). It encompasses ten (10) wards namely: Bagama „A‟, .Bagama „B‟, Magajiya, Iku South I, Iku South II, Hashimi „A‟, Hashimi „B‟, Maje, KurminSarki and Wambai. The area has gentle rock and the soils are derived from geological parent materials developed on sand stone formations. The soils are usually deep, red and enriched with clay sub-soil (Niger State Bureau of Statistics, 2011; Aminu, et al., 2013). The area experience tropical climate and summers are much rainier Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies

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than the winters in Suleja. The average annual temperature is 26.3 °C and the total rainfall is 1328 mm. The driest month is December. The highest precipitation occurs in September; with an average of 272 mm. March is the warmest month of the year with average temperature of 29.0 °C. The lowest average temperature is in August at 24.5°C (Aminu, et al., 2013).

Fig. 1: The Study Area Source: Topographic/Administrative Maps 3. Methodology 3.1 Types and Sources of Data The data used for this study were largely acquired from secondary source (Table 1). Table 1: Types of Data, Sources and Date of Acquisition Data Type SPOT Imagery Topographic map Soil Map Rainfall data ASTER data

Resolution/Scale 7 1m

Source SPOT 7

Date 2017

1:100,000

State Ministry of Lands and Survey, Suleja

2014

1:650.000

Department of Geography and 2017 Environmental management, ABU, Zaria (1997- River Basin Development Authority, Niger 2017

20 years 2017) 30m

United States Geological Survey (USGS)

2018

Source: Author’s Computation, 2019 3.2 Data Processing The SPOT 7 and ASTER data were imported in to ArcGIS and geo-referenced using the UTM Zone 32 North and datum WGS 1984 in ArcGIS. This was followed by sub-setting, to Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies

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obtain the Area of Interest (AOI). Unsupervised classification was performed on the Spot Imagery in order to have a general view of the area which was followed by object based supervised classification method in ArcGIS for the final LULC map. The image was classified based on „Classification Scheme‟ of Anderson, Hardy, Roach and Witner (2001). The ASTER data was used to generate the Digital Elevation Model (DEM) of Suleja with the help of the Spatial Analyst tool. Slope amount was obtained from the sub-mapped ASTER data of the study area. The ASTER data was further subjected to spatial analysis using 3D spatial analyst tool to generate the Slope map of the area. The topographic map of Suleja and the Soil map of Nigeria were subsequently scanned and imported into ArcGIS and georeferenced based on the map layer projection system. The study area was clipped from the map and its drainages digitized. Verification was also done using the Spot imagery and the ASTER layers. Thereafter, the Kernel density tool was employed to drive the drainage density map. Different soil types of the study area were digitized into polygons from the clipped Soil map. Further more, the mean annual rainfall for the 20 years was used to create rainfall map of the study area by interpolating the figures using “Kriging”. All data layers derived were converted to raster data sets having the same pixel size. 3.3 Method of Data Analysis The relationship between the six thematic layers and their various attributes were derived under the AHP which usually determine the relative importance of the criteria in a specified Multi-Criteria decision-making problem (Yalcin and Akyurek, 2004). The procedure by which the weights are produced followed the logic developed by Saaty (1980). Pair-wise comparison of the flood risk factors was performed and results was put into a comparison matrix based on Saaty‟s fundamental scale (Table 2). The matrix was populated with values from 1-9 and fractions from 1/9 to 1/3 representing importance of one factor against another in the pair. The values in the matrix need to be consistent, which means that if x is compared to y, it receives a score of 9 (highly significance), y to x should score 1/9 (little significance). An object compared to itself gets the score of 1 (equal importance). The weights calculated from each column was summed up and every element in the matrix was divided by the sum of the respective column. The consistency ratio (CR) was also calculated in order to ensure that the comparison of criteria made by decision makers is consistent. The rule is that a CR less than or equal to 0.10 signifies an acceptable reciprocal matrix, whereas greater than 0.10 is not acceptable (Equation 1). Reclassification of the layers was done into appropriate Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies

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classes using AHP into the inverse ranking system on a scale of 1 to n th values, where 1 represent very low and nth very high for rainfall, drainage, elevation, slope and LULC. For soil, 1 denotes poor drain and nth well drain soils (Table 2). All the weighted data sets were integrated in ArcGIS 10.3 to produce the flood vulnerability map by weighted overlay where each class individual‟s weight was multiplied by the map scores and the results added (Equation 2).

---------------------------------------------------------------- (Equation 1) Where: CI- represents Consistency Index which reflects the consistency of one‟s judgment

λ is calculated by averaging the value of the consistency vector (factor weight) RI- denotes Random Inconsistency index that is dependent on the sample size (Table 3) S = ∑ wi Xi ------------------------------------------------------- (Equation 2) Where: S = Vulnerability wi = Weight for each map Xi = Individual map Table 2: Fundamental Scale of Pair-wise Comparison& Random Inconsistency Indices Intensity Definition 1 Equal importance 3 Moderate importance 5 Strong importance

Explanation Two elements contribute equally to the objective Experience and judgment slightly favor one element over another Experience and judgment strongly favor one element over another Very Strong One element is favored very strongly over another; importance its dominance is demonstrated in practice Extreme importance The evidence favoring one element over another is of the highest possible order of affirmation 2,4,6, and 8 These are the intermediate values

7 9

Random Inconsistency Indices (RI) for n to 10 n value RI

0.58

0.89

1.12

1.24

1.32

1.41

1.45

1.49

Source: Adapted from Saaty (1980)

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4. Results and Discussion 4.1 Characteristics of Flood Risk Factors in Suleja Fig. 2 represents the characteristics of flood risk factors in the study area. From Fig. 2, it can be seen that, Rainfall (1) ranges from 1324-1486mm per annum, averaging about 1405mm. The areas bordering the FCT are those with high rainfall (1420-1486mm) as compare to the other parts. The drainage density (2) ranges from 0-322km2. High density (227-322km2) occurs in linear fusion along Tafa and Gaurara axis. Furthermore, Sandy (3) soil lies predominantly to the north, and spread towards northeast and northwest, while the south and the central parts and some portion of the northwestern margin is covered with loamy soil. The west and the southeast are mainly covered with clay soil. Further, the elevation of Suleja (4) ranges from 279-556m above sea level. Thus, to the north, the study area is predominantly a highland which descends towards the south and western parts. Also the area is dominated by steep gradient descent towards the extreme west and southwestern sides of the region, with the slope (5) ranges between 0 - 80%. However, from the LULC (6) features of the area most of the development is on the eastern part of the city along the river course and the northwest route in a linear synthesis. There is also concentration in the south and some dispersal across the study area. Settlements like: Fadaman-abuchi, Bamburu, Zariyawa, Gajiri, Numba, Chaza and Gwazunu are heavily farming oriented (Aminuet al., 2013), hence, the abundant farmland (Fig. 2).

Rainfall Distribution (1)

Drainage Density (2)

Soil Classification (3)

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Elevation (4)

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LULC Structure (6)

Slope (5)

Fig 2: Characteristics of Flood Risk Factors Source: Source: Authorâ€™s GIS Analysis, 2019 4.2 Pair-wise Comparison for Flood Risk Factors in Suleja Table 3 shows the Pair-wise comparison for the six flood causative factors, while Figure 3 represents the reclassified maps produced based on the weights generated using the AHP. Table 3: Pair-wise Comparison for Flood Causative Factors Rainfall 13241362 13631419 14201486 Drainage 227-322 172-226 106-171 36-105 0-35 Soil Clay Loam Sandy Elevation 276-382 383-428 429-459 460-491 492-556

Low 1

Moderate High 5/7 5/9

Nil

Class Low

7/5

7/9

Moderate

9/7

9/5

High

Very high High 1 7/5 5/7 1 3/7 3/5 3/7 3/5 1/7 1/5 Clay Loam 1 5 1/5 1 9/1 9/5 Very high High 1 3 1/3 1 1/5 1/3 1/7 1/5 1/9 1/7

Moderate 7/3 5/3 1 3/3 1/3 Sandy 9 5 1 Moderate 5 3 1 1/3 1/7

Low 3 5/3 3/3 1 1/3 Nil

Low 7 5 3 1 1/3

Very low 7 5 3 3 1 Nil

Class Very High High Moderate Low Very Low Class Poorly Drained Moderately Drained Well Drained Very low Class 9 Very High 7 High 7 Moderate 3 Low 1 Very Low

Ado Kibon Usman, Nura Khalil Umar, Bako Anslem & J. A. Opara (Pg. 8583-8596) Slope

Very Low Low

Moderate

High

Very High 0-16 1 9/7 9/5 9/3 9 17-26 7/9 1 7/5 7/3 7 27-38 5/9 5/7 1 5/3 5 39-54 1/9 3/7 3/5 1 3 55-80 1/9 1/7 1/5 1/3 1 LULC Built-up Farmland Bare Surf. Vegetation River Built-up 1 9/7 9/5 3 9 Farmland 7/9 1 7/5 7/3 7 Bare Land 5/9 5/7 1 5/3 5 Vegetation 1/3 3/7 3/5 1 3 River 1/9 1/7 1/5 1/3 1

8591

Class Very low low Moderate high Very high Vulnerability Very High High Moderate Low Very Low

Source: Authorâ€™s AHP and GIS Analysis 2019 The pair-wise comparison done for areas with high rainfall against those with low had the ratio of 9/7 indicating that high rainfall rated 9, have extreme influence on flood over those low and moderate (5). This procedure is repeated for all the classes against each other. It was also revealed that, areas with very high drainage densities rated 7, have very strong influence over the rest of the classes (Table 3). Further, clay soils are designated as being poorly drained while, loamy and sandy are considered moderately and well drained respectively (UNCHS-Habitat, 2001). Comparison done that, sandy rated 9 have extreme influence over clay and loamy in that order. Likewise, areas with very high elevation (9) contribute more to flood in the area, as it facilitate surface water run-off (Table 3). Similarly, very low slope (9/7), was found to have the greatest influence against those with low, moderate, high and very high slopes respectively. Similarly, built-up areas (9) also contribute more over the rest of the LULC feature classes (Table 3). Thus, built-up land and cemented surfaces generate more surface runoff since they do not allow water infiltration, while others like vegetation permit interception, thereby reducing the runoff and consequently the flood magnitude (Lindsay-Walters, 2015). Further analysis from Fig. 3 corroborated that, high rainfall, very high drainage densities, poorly drained soil, very low elevation, slope and built up land weighed 38, 32, 74, 51, 47 and 51 respectively, contributes to flood in the study area.

Ado Kibon Usman, Nura Khalil Umar, Bako Anslem & J. A. Opara (Pg. 8583-8596)

Re-classified Rainfall (1)

Re-classified drainage (2)

Re-classified soil (3)

Re-classified elevation (4)

Re-classified slope (5)

Re-classified LULC (6)

8592

Fig 3: Reclassified Flood Risk Factors Based on the Weights Generated using the AHP Source: Source: Authorâ€™s GIS Analysis, 2019 4.3 Flood vulnerability Zone in Suleja This section presents the result of overlay operation based on the pair-wise comparison carried out for the flood risk factors (Table 4), and the flood vulnerability map (Fig. 4).

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Table 4: Weight for Flood Risk Factors Factors

Rainfall

Slope

Elevation

Rainfall Slope Elevation Drainage density LULC Soil

1 1/3 1/3 1/7 1/9 1

3 1 1/3 1/5 1/7 1/9

3 3 1 1/3 1/5 1/7

Drainage Density 7 5 3 1 1/3 1/5

LULC

Soil

Weight

9 7 5 3 1 1/3

1 9 7 5 3 1

34 31 18 9 5 4

Consistency Ratio= 0.05 Source: Author’s GIS Analysis, 2019

Fig. 4: Flood Vulnerability Map of Suleja Source: Author’s GIS Analysis, 2019 From Table 4 it can be seen that, the weights generated reveal that rainfall and slope, weighted 34 and 31, have the greatest influence on flood occurrences in the study area. Elevation, drainage density, land used and soil on the other hand contribute to the flood incidence in that order. This implies that, flood in Suleja occur at the event of rainfall. Its intensity, duration and amount are generally believed to be the principal factors in most flood Copyright © 2017, Scholarly Research Journal for Interdisciplinary Studies

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events in the tropics which are partly or wholly climatological in nature (Muhammad, 2015). The significant findings showed a CR value of 0.05, which fell much below the threshold value of 0.1 and it indicated a high level of consistency. Hence, it was concluded that rainfall and slope have the highest influence on flood within the study area. This agrees with Umar and Muazu (2017), where rainfall and topographic configuration of Hayin-gada was found to substantially influence flood in Katsina. In terms of nature of slope Ismail and Sanyol (2013) in a study conducted in Kaduna further buttress this fact as they observed that areas that lie beside a river may not be liable to flood if it is at a great height while areas that lie far away may experience floods if the intervening land is flat, gentle sloping or if the area lies in a depression. The flood vulnerability map reveals that areas of high vulnerability constitute 37% of the study area while moderate and low vulnerable areas constitute 45% and 18% respectively. Elements at high risk of flood in the study area are those found at the extreme northeast, where elevation is very low, southwest where rainfall distribution is high and on low lying areas along the rivers or depressions. Most of the properties situated within these areas are built-up areas consisting of residential areas, commercial structures, roads and farmlands. Settlements that are more vulnerable include: Diko, Numewa, Kuchiko, Maji, Gauraka and Kwonkashe. Moreover, a critical look at the built-up land shown in Figure 3 with respect to each vulnerability zone in Figure 4 shows that, those in the high risk area outnumber those in the moderate and low risk zones put together. This may be attributed to the fact that the former is on lower elevation consisting of nearly leveled land to gently undulating plates and therefore tend to attract population due to ease of accessibility and presence of fertile soil amongst others. 5. Conclusion and Recommendations The study assessed flood vulnerability in the study area. The findings show that areas that lie along the rivers and on low elevations are more prone to flood at rainfall events than those on higher elevations. It therefore means cultural features such as residential, commercial, educational and health facilities amongst others as well as the populace found within these areas are at great risk of flood. The risk of contracting water-borne diseases such as cholera as a result of the devastation cause by the flood to their sanitation and immediate surroundings might be a side effect. However, the deficiencies particularly of institutional capacities to implement risk reduction measures through public early warning system, may shed light on the risk context of the area and how vulnerability is complicated by increasing Copyright ÂŠ 2017, Scholarly Research Journal for Interdisciplinary Studies

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exposure to risks. If menace flooding of Suleja is continually ignored, the risk of exposure to flood will be on the increase and more properties, farmlands and crops, public infrastructures and lives may continue to be lost in due course. There is need therefore to improve weather forecasts in languages widely spoken in areas that were likely to experience flood, thunderstorms and other extreme weather conditions. Also, resettlement of communities along the rivers to safer areas should be taken in to consideration and ensure that the populace adhere to planning regulations so as to minimize the rate at which local population are exposed to environmental hazards particularly flood. References Aminu, Z., Yakubu, M., Mohammed, A.A. and Niranjan, K. (2013): Impact of Land Use on Soil Quality in Suleja, Niger State. Indian Journal of Science, Vol. 2(2):1-7. Anderson, J.R., Hardy, E.E., Roach, J.T. and Witner, R.E. (2001): Landuse and Landcover Classification System for use with Remote Sensor Data. Geological Survey Professional Paper 964, Presented in United State Geological Survey Circular 671. Washington: United State Government Printing Office. APFM, (2012): Urban Flood Risk Management: A Tool for Integrated Flood Management. Associated Programme on Flood Management. World Meteorological Organization 2012, Vol. 29, Pp. 1â€“43. Emmanuel, A.O., Olawumi, O.R. and Durojaye, E. (2012): An assessment of flood hazard in Nigeria: The case of mile 12, Lagos. Mediterranean Journal of Social Sciences. Vol. 3(2): Pp. 367-375 May 2012 Few, R., Ahern, M., Matthies, F. and Kovats, S. (2004): Floods, health and climate change: a strategicreview. Tyndall Centre for Climate Change Research, 2004, (63). Ismail, M. and Saanyol, I.O. (2013): Application of Remote Sensing (RS) and Geographic Information Systems (GIS) in flood vulnerability mapping: Case study of River Kaduna. International Journal of Geomatics and Geosciences 3 (3): 618-627 Lindsay-Walters, A. (2015): The chances of a river flooding after a period of rain are determined by natural factors. Downloaded from www.markedbyteachers.com on 18th January, 2015 Moses, Z.W., Ijeoma, G.U. and Bitrus, G.A. (2015): Needs for Disaster Risks Reduction Education in Nigeria.Journal of Environmental Science, Toxicology and Food Technology. Volume 9, Pp. 43-47 Muhammad, N. (2015): National Tragedy: Flood Kills 53, displace 100,420 people across Nigeria. Premium-Times Newspaper Published September 20, 2015 Niger State Bureau of Statistics (2011): Facts and Figures on Niger State. Niger State Planning Commission. Statistical Year Book. 2011 Edition Niger State Emergency Management Agency, (2018): Flood in Niger State: Death toll rises to 40, 100 communities now submerged. In Vanguard dated September 15, 2018 NPC, (2006). Population and housing census of the federal republic of Nigeria: National and state population and housing priority tables. Population Commission Office, Niger state. Odunola, O.O. and Balogun, F.A. (2015): Analyzing Household Preparedness on Flood Management in Riverside: A Focus on Apete Community in Ibadan, Nigeria. IOSR Journal of Humanities and Social Science (IOSR-JHSS) Volume 20, Pp. 07-32 e-ISSN: 2279-0837, p-ISSN: 2279-0845

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