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GEOLOGY, ECOLOGY, AND LANDSCAPES 2020, VOL. 4, NO. 2, 131–139 INWASCON https://doi.org/10.1080/24749508.2019.1600912 RESEARCH ARTICLE Flood risk zone mapping using rational model in a highly weathered Nitisols of Abakaliki Local Government Area, South-eastern Nigeria a,b a b Daniel Aja , Eyasu Elias and Ota Henry Obiahu College of Natural and Computational Sciences, Center for Environmental Science, Addis Ababa University, Addis Ababa, Ethiopia; Department of Soil Science and Environmental Management, Ebonyi State University, Abakaliki, Nigeria ABSTRACT ARTICLE HISTORY Received 5 November 2018 The lack of spatially explicit ﬂood hazard mapping has hampered the development of Accepted 26 March 2019 appropriate ﬂood interventions at community levels in Nigeria. This paper reports on a work conducted to develop a local government level ﬂood hazard map to delineation KEYWORDS ﬂood vulnerable areas. Flood vulnerability mapping in this work was addressed from Hydrologic models; ﬂood a perspective of administrative unit as the unit of investigation. The study is anchored on covariates; mitigation the Modeling Flow based on Relational Rule for ﬂood assessment using ArcGIS in combina- measures; Nitisols; Nigeria tion with modiﬁed rational model. The output of the modiﬁed rational model was integrated into the Geographic Information Systems environment by the arithmetic overlay operation methods. The results show that the delineated areas/sub-catchments however experienced the same rainfall intensity of 414.2 mm/h but the ﬂood extents in the areas are diﬀerent. For instance, the very high ﬂood risk zone covers about 22.8% of the study area while the low risk zone covers about 44.3% and the possible areas likely to experience seasonal ﬂoods with a given rainfall input are mostly below 40 m elevation. The results of this study will be helpful to prioritize development eﬀorts at grassroot in the study location and to formulate ﬂood adaptation strategies. 1. Introduction infrastructure, outbreak of disease epidemics and the loss of human lives (Braman, Pablo, & Maarten, Flood risk zone mapping is an important ﬁrst step in 2010). In 2012, ﬂooding along the river Niger, the the proper management of future ﬂooding events and principal river in West Africa, resulted in the death of to develop adequate mitigation measures (Elmira, 81 and 137 people in Niger and Nigeria, respectively, 2016). In particular, development of ﬂood vulnerabil- while displacing more than 600,000 people (Integrated ity maps at the local government level can achieve Regional Information Network [IRIN], 2013)). The fre- a better result than the conventional national maps quency of occurrence of extreme events is expected to because it can identify rural dwellers and small holder increase as result of projected increase in extreme rain- farmers that are at risk (Asare-Kyei, Forkuor, & fall that may “have dire consequences for the sub-region Venus, 2015). Flood vulnerability maps are useful ’s agricultural sector and food security in West Africa” tools in identiﬁcation of populations and elements (Intergovernmental Panel on Climate Change [IPCC], at risk and to guide early warning system and pre- 2014). ventive measures. They are needed in spatial planning Abakaliki Local Government Area (ALGA) is to prevent development in ﬂood prone areas and for popularly known for rice (Olivia sativa) farming in implementation of a ﬂood insurance scheme (De Nigeria because of the availability of large expanse of Moel, Van Alphen, & Aerts, 2009). swampy areas adequate for rice cultivation. The West Africa has witnessed frequent ﬂoods due to Nigerian Hydrological Services Agency (NIHSA) in high variability in rainfall patterns, geographic location 2014 listed ALGA of Ebonyi State among the moder- and general low elevations. In the last three decades, the ate ﬂood risk areas in the country. Every year, farm- sub-region has witnessed a dramatic increase in ﬂood ers lose signiﬁcant quantities of their farm produce events, with severe impacts on livelihoods, food security due to inundation of crop ﬁelds by seasonal ﬂoods. and damaging properties worth millions of dollar Due to the lack of locally relevant and functional (Armah, Yawson, Yengoh, Odoi, & Ernest, 2010). In ﬂood hazard maps for mitigation and adequate 2007, for example, a series of anomalously high rainfall response to ﬂood hazards in this area, farmers do events caused severe ﬂoods which aﬀected more than not have required knowledge of the extent of ﬂood 1.5 million inhabitants in West Africa. This has resulted coverage in that part of the country. in the destruction of farm lands, destruction of CONTACT Daniel Aja firstname.lastname@example.org College of Natural and Computational Sciences, Center for Environmental Science, Addis Ababa University, Addis Ababa, Ethiopia © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 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. 132 D. AJA ET AL. Despite the major impact of ﬂoods on the liveli- 584 km . The area is characterized by high relative hoods of the people living in the low lying regions of humidity of about 71–75% and surface temperature West Africa, few attempts have been made to deline- of 26°C to 31ºC with mean temperature of 30.4°C. ate the boundaries of ﬂood intensity to indicate areas There is a bimodal rainfall pattern from April to July that are vulnerable to ﬂooding (Asare-Kyei et al., and September to November with a short spell in 2015). The limited researches that were conducted August (Figure 2) and a long-term average rainfall on ﬂood mapping in Nigeria have used remote sen- of 1,296 mm. Hydrologically, the area is located sing data aided by Geographic Information Systems within the derived savannah zone of South-East (GIS). However, they lack certain basic principles in Nigeria, lying within the plains of Ebonyi River, hydrological modeling and prediction which can be Iyiudele and Iyiokwu Rivers that are also tributaries added into ﬂood simulation and mapping in the of Cross River (Figure 1). The inhabitants are pre- country for better outcome (Komolafe, Suleiman, & dominantly agrarians raising livestock and crops at Francis, 2015). both subsistence and export levels. Major crops for This paper reports on a recent study conducted to national and international markets are rice, cassava explore the application of GIS and some hydrologic and yam (Ogbodo, 2013). models in ﬂood extent mapping especially for data scarce environment at community level in ALGA in 2.2. Data sources and methods South-eastern Nigeria. The overall objective of the study was to develop detailed ﬂood hazard map at 2.2.1. Data sources a ﬁne spatial resolution with aim of providing infor- We made use of digital elevation model from ASTER mation for early warning, risk preparedness and to which is a joint product of the Japanese Ministry of put in place adequate response mechanism. Economy, Trade and Industry and the United States National Aeronautics and Space Administration (NASA). The data have a vertical accuracy of 17 m 2. Material and method at 95% conﬁdence level, and a horizontal resolution on the order of 75 m. The land cover data were 2.1. Description of study area obtained from Landsat8 imagery which was down- The study was conducted in ALGA of Ebonyi State, loaded from USGS website. South-eastern Nigeria (Figure 1). The geographical We used soil map from the Harmonized World Soil coordinate lies within 06°04’0ʺN Latitude and 08° Database (HWSD) version 1.2 produced in 2012 by 65’0ʺ E Longitude. ALGA occupies the eastern axis the International Institute for Applied System Analysis of Ebonyi state, covering a land area of about (IIASA) for soil type and texture classiﬁcation. The soil Figure 1. Map of the study area. GEOLOGY, ECOLOGY, AND LANDSCAPES 133 Average Monthly and Annual Rainfall (1997- 2016) Monthly Average Rainfall Annual Average Rainfall 300 140 0 0 Jan Feb Mar AprMay Jun Jul Aug Sep Oct Nov Dec Figure 2. Seasonal distribution and long term annual average rainfall in ALGA. Source: authors’ analysis using raw data from 1997–2016. map has 1 km resolution. We obtained Rainfall data operation. First, the study area was delineated into from the Nigerian Meteorological Agency (NIMET), sub-catchments using ArcGIS10.3 software. Secondly, Abakaliki area synoptic station. Topographic map of a modiﬁed version of the rational model was used to Ebonyi State covering the study area and the shape estimate the run-oﬀ of the respective sub-catchments ﬁles of the administrative boundary and settlements based on rainfall intensity, and a run-oﬀ coeﬃcient. for the study area were gotten from the ministry of Finally, the arithmetic overlay operation was applied lands and survey, Abakaliki. in a GIS environment to integrate the output of the hydrological model with other ﬂood causal factors such as elevation to determine a ﬂood intensity map 2.2.2. Run-oﬀ estimation models for the sub-catchments. Flood prone zones were The methodological approach that was employed in eventually deﬁned through a reclassiﬁcation of the this research work is diagrammatically summarized ﬂood intensity map to derive the Flood Prone Index in Figure 3 as described by Asare-Kyei et al. (2015). (FPI) which determines the ﬂood prone zones of the Hydrological modeling (ﬂood risk zone mapping) area. This approach involves retrieving data values was achieved by using combined application of the from all ﬂood covariates and then calculating peak rational hydrological model and arithmetic overlay Figure 3. Modiﬁed modeling ﬂow diagram for relational-rule-based ﬂood assessment. Monthly Average Rainfall (mm) Annual Average Rainfall (mm) 134 D. AJA ET AL. run-oﬀ rates using the rational model. The covariates 2.2.4. Sub-catchments delineation for ﬂood are land use/land cover (LULC), soil type Digital Elevation Model was used for sub-catchment and soil texture, slope, elevation, rainfall and drainage delineation and slope analysis. The study area was area (Morjani, Zine and Ali., 2014). delineated into 17 sub-catchments by clicking on the spatial analyst tools in ArcGIS environment after all the sinks had been ﬁlled to make it more 2.2.3. Determination of peak run-oﬀ using the perfect. The ﬁlled elevation data layer was maintained rational model and used later for the integration of peak run-oﬀ and The rational model belongs to the group of lumped elevation to determine run-oﬀ concentration at dif- hydrological models which treats the unit of analysis ferent elevations. The Hydrology tool was expanded as a single unit whose hydrological parameters (e.g., to perform various hydrological analyses such as ﬂow rainfall) are considered as average values. The model direction, ﬂow accumulation, stream order, stream to is given by the equation feature and subsequently sub-catchment determina- Qp ¼ 0:0028 C I A (1) tion. The sub-catchments which were generated in a raster format were immediately converted to poly- where, Qp = Peak run-oﬀ rate (m /s) C = run-oﬀ gon by clicking on the conversion tool under spatial coeﬃcient (−), I = rainfall intensity (mm/h), analyst tools. The conversion of the raster format into A = drainage area (ha). A constant (0.0028) is polygon was necessary in order to calculate the areas required to convert the original units in North of the sub-catchments and also to build the attribute American system (where the model was ﬁrst devel- table in the ArcGIS environment. oped) to an international system such as cubic meters per second (m /s). The model operates on a number 2.2.5. Peak run-oﬀ map development of assumptions including: (1) the entire unit of ana- Within each sub-catchment, more than one LULC lysis is considered as a single unit; (2) rainfall is types and slope exist. In order to ﬁnd a run-oﬀ coeﬃ- uniformly distributed over the drainage area; (3) esti- cient that will represent a given sub-catchment, average mated peak run-oﬀ has the same chances of reoccur- values were taken based on the diﬀerent LULC types. rence (return period) as the used rainfall intensity (I) The DEM was also converted to percent slope in and (4) the run-oﬀ coeﬃcient (C) is constant during ArcGIS and was reclassiﬁed into three classes; slope the rain storm. less than 2%; slope between 2% and 6%; and slope The strength of this model lies in its simplicity for greater 6%. Based on Table 1, which speciﬁes a run-oﬀ application and its suitability for a homogeneous coeﬃcient for a particular LULC type and slope, the area. As a result, this model has a wide application average values of run-oﬀ coeﬃcient for each sub- in the calculation/estimation of peak run-oﬀ rate for catchment were computed based on the number of the design of diﬀerent drainage structures and ﬂood LULC that occur in each sub-catchment. Knowing the hazard map production (Nyarko, 2002).The model run-oﬀ coeﬃcients (C), rainfall Intensity (I) and areas converts rainfall in the catchment into run-oﬀ by (A) of each of the sub-catchment within the study area, calculating the product of the rainfall intensity in the discharges (Q ) for each sub-catchment likely to the catchment and its area, reduced by a run-oﬀ cause ﬂooding is obtained. coeﬃcient (C, with a value between 0 and 1) which depends on the soil type, land cover and slope in the study catchment. The run-oﬀ coeﬃcient provides an 2.2.6. Flood covariates and acquisition methods estimation of how much rainfall is lost through inﬁl- 184.108.40.206. LULC analysis. LULC maps of the catch- tration, interception and evapotranspiration. This ment were generated by classifying moderate spatial means that the run-oﬀ coeﬃcient of a catchment resolution (30 m) multitemporal Landsat images can be seen as the fraction of rainfall that actually which were processed prior to analysis. The LULC becomes run-oﬀ. Therefore, accurate estimation of data were generated for three periods namely, 1986, the run-oﬀ coeﬃcient is vital to the successful imple- 1996 and 2016 exploring changes in the LULC type mentation of this method. overtime. Table 1. Rational method run-oﬀ coeﬃcients by soil type and slope. Runoﬀ Coeﬃcient, C Soil Group “A” Soil Group “B” Soil Group “C” Soil Group “D” Slope gradient (%) <2 2–6>6 <2 2–6>6 <2 2–6>6 <2 2–6>6 Forest 0.08 0.11 0.14 0.10 0.14 0.18 0.12 0.16 0.20 0.15 0.20 0.25 Farmland 0.14 0.18 0.22 0.16 0.21 0.28 0.20 0.25 0.34 0.24 0.29 0.41 Bare Land 0.65 0.67 0.69 0.66 0.68 0.70 0.68 0.70 0.72 0.69 0.72 0.75 Residential 0.33 0.37 0.40 0.35 0.39 0.44 0.38 0.42 0.49 0.41 0.45 0.54 Source: (Bengtson, n.d.). GEOLOGY, ECOLOGY, AND LANDSCAPES 135 The diﬀerent bands of the Landsat imagery were X þ Y ¼ Z (3) FRA combined in ArcGIS environment to form composite, and the composites were further processed into where X (m) is the Digital Elevation Model; Y (m /s) mosaic raster prior to analysis. Supervised classiﬁca- represents total discharge; Z (m /s/m) is the run-oﬀ ct tion was conducted on the Landsat imagery to reveal concentration at various elevations; Z is the value FRA four broad LULC classes after training samples and for ﬂood risk areas. signatures were created (using training sample man- In ordertoexplicatethemap foreasyunder- ager), saved and imported in ArcGIS environment. standing, a reclassiﬁcation was done to redeﬁne ﬁve These land use classes identiﬁed were (1) agricultural ﬂood hazard intensity categories, viz. very high, land; (2) forestland; (3) bare land and (4) settlements high, moderate, very low and low risk zones. The (i.e., built up areas). Training and validation data for natural breaks reclassiﬁcation method in ESRI’s these classes were obtained from ﬁeld campaigns ArcGIS was used for this purpose (Kazakis, conducted between December 2017 and Ioannis, & Thomas, 2015; Xiao, Shanzhen and March 2018. Training and validation samples for Zhongqian, 2017). the classiﬁcation were generated by overlaying the training and validation data (polygons) on the satel- lite image and extracting the corresponding values. 3. Results 220.127.116.11 Soil type and texture. The Harmonized 3.1. LULC changes from 1986 to 2016 World Soil Database (HWSD) which was used for soil The LULC changes for the study area between 1986 classiﬁcation is an image ﬁle linked to a comprehensive and 2016 are as presented in Figure 4. The results attribute database where information on soil mapping show that there have been changes in the various units, soil texture for top and sub soils and several other LULC (forest, agricultural lands, bare lands and set- soil properties are stored (Food and Agricultural tlements) from 1986 to 2016. However, only settle- Organization [FAO], 2009). Based on these information, ments (built-up areas) show signiﬁcant change from the extracted soil map of the area was reclassiﬁed into the what it used to be over the years (1986–2016). This four main soil hydrological groups (A–D) deﬁned by the indicates that there is correlation between LULC and United States Soil Conservation Service (USDA, 2009). ﬂooding in the area. In 1986, forestland covered 4,107 ha (7.7%) of the 2.2.7. Integration of GIS model catchment, agricultural land accounted for 38,919 ha The GIS Model (GISM) as presented in Figure 3,was (72.6%), bare land accounted for 7,860 ha (14.7%) of adopted and modiﬁed for analysis (Asare-Kyei et al., the catchment while 2,730 ha (5.1%) were covered 2015). The model uses four main stages for ﬂood risk by settlements (built-up areas). In 1996, 3,530 ha zoning including (1) the generation of the diﬀerent (6.5%) of the study area were forest, 41,130 ha maps of the study area using satellite data, elevation (76.7%) of the study area were agricultural land, map and ﬁeld survey; (2) the inclusion of these data 5,190 ha (9.7%) of the study area were bare land, into the GISM and building of attribute tables; (3) the while 3,770 ha (7%) of the study area were covered use of arithmetic overlay operation to combine the by settlements. By 2016, the following observations hydrological model with the geographic information were made: forest covered 3,720 ha (6.9%), agricul- system model and (4) The creation of ﬂood vulnerabil- tural land covered 38,090 ha (71%), bare land cov- ity map forthe area understudy. ered 5,300 ha (9.9%) while settlements covered Finally, the elevation layer and the peak run-oﬀ 6,510 ha (12.1%) of the study area. layer were combined using arithmetic overlay method in ArcGIS to generate the ﬂood hazard intensity map at diﬀerent elevations. The model combines DEM 3.2 Soil textural class and elevation of the and discharge map within GIS environment to deter- sub-catchments mine ﬂood risk areas. The arithmetic overlay method involves two main stages: The result of the soil classiﬁcation revealed that the study area is predominantly Nitisols (NT) (Figure 5) (1) Determination of run-oﬀ concentrations (Figure representing the hydrological soil group “C” which is 7(a)) within various segments over the landscape. characterized as shown in Table 2 below. High elevation values are concentrated in the upper Ebonyi River (60 masl); upper Iyiokwu River and Ezza Abia sub- X þ Υ ¼ Zct (2) catchments while lower Ebonyi River, Iyiokwu River (2) Estimation of values that can be used to infer and Obiagu Ibom records very low elevation. The low- potential areas likely to be in ﬂood with any est elevation (15 masl) was observed in the southern- storm event (Figure 7(b)). most part of Ebonyi River. 136 D. AJA ET AL. Figure 4. Maps of LULC classiﬁcation (1986–2016) for study area. Figure 5. Map showing soil types in Ebonyi state. Table 2. Hydrological soil groups. Table 3). The Ebonyi river sub-catchment gener- Soil Inﬁltration Relative Run-oﬀ ates the highest amount of run-oﬀ in excess of Groups Rate (in/h) Description Potential 9782 m /s while the Igbegu sub-catchment gener- A >30 Sand, Loamy Sand Low B 0.15–30 Sandy loam, Loam Moderate ated the lowest (0.03 m /s). C 0.05–0.15 Silt Loam, Sandy Clay Loam High D 0.0–0.05 Clay loam, Silt clay loam, Very high Sandy clay & Clay Source: (National Engineering Handbook (Chapter 7), 2009). 3.4 Flood hazard intensity map 3.3 Peak run-oﬀ analysis This map was produced by overlaying the peak run-oﬀ layer with the elevation layer through arith- Themap of thepeakrun-oﬀ rates (m /s) shows metic overlay method as discussed in Section 2.2.7 the distribution of run-oﬀ within the sub- (Figure 7(a,b)). catchments in the area studied (Figure 6 and GEOLOGY, ECOLOGY, AND LANDSCAPES 137 Figure 6. Map showing peak run-oﬀ discharges of sub-catchment. Figure 7. Maps of run-oﬀ concentration (a) and ﬂood vulnerable areas (b). A reclassiﬁcation was done on the ﬂood vulnerable Table 3. Sub-catchment discharges based on August 2016 rainfall. areas map to produce ﬁve classes which represent the Area Run-oﬀ coef- Rainfall inten- Discharge Hazard Index. The index ranges from 1 (very low 2 3 Sub-catchments (km ) ﬁcient (C) sity (mm/h) (m /s) ﬂood hazard intensity) in some part of upper Igbegu 0.07 0.42 414.2 0.03 Ebonyi River sub-catchment to 5 (very high ﬂood Ajaa 582 0.25 414.2 169 Ndiegu 1085 0.21 414.2 264 hazard intensity) in the lower part of Ebonyi River Upper Iyiokwu 883 0.56 414.2 573 sub-catchment. The ﬁnal ﬂood hazard map is repre- Ohachikwe 539 0.38 414.2 238 sented in a graduated color (Figure 8). The map Obiegu Ibom 1099 0.21 414.2 267 Iyiokwu River 7365 0.38 414.2 3245 shows that about 33% of the catchment falls within Upper Ebonyi 4770 0.38 414.2 2102 very high ﬂood hazard areas that cover sub- Okpuituma 1025 0.21 414.2 250 Idda 503 0.25 414.2 146 catchment such as Ebonyi River, Iyiokwu River and Ezza Abia 0.10 0.52 414.2 0.06 Igbegu. On the other hand, the very low ﬂood hazard Agbaje 1632 0.34 414.2 644 Opamana 654 0.34 414.2 258 areas account for 44% of the study area covering sub- Agalegu 747 0.61 414.2 528 catchments such as upper Iyiokwu, upper Ebonyi, Enyigba 538 0.33 414.2 206 Ebonyi River 22195 0.38 414.2 9782 Ndiegu, Okpuituma and Ezza Abia. The very high Amachara 1267 0.38 414.2 558 ﬂood hazard intensity zone is concentrated in Ebonyi 138 D. AJA ET AL. Figure 8. Flood hazard map. river sub-catchment that is characterized by the high- elevations fall within low ﬂood intensity zones in est run-oﬀs and the lowest elevation of 9782 m /s and the Flood hazard Index (FHI). This indicates that 15 m, respectively, resulting in the greater percentage elevation plays a major role in ﬂooding. of the sub-catchment to fall within the very high High run-oﬀ has a positive correlation with ﬂood hazard zone (Figure 8). increased susceptibility of ﬂood hazards. This is con- sistent with the key informant interviews with experts which revealed that communities within Ebonyi River 4. Discussion and Iyiokwu river sub-catchments experience more frequent ﬂood events and more people suﬀer from From the result of LULC change detection, it can be seen ﬂood impacts when compared to other sub- that between 1986 and 1996, forest and bare land areas catchments. As reported in Islam and Sado (2000), decreased by 1.2% and 5%, respectively, while agricul- the high ﬂood risk in Ebonyi River and Iyiokwu river tural land and settlement (built-up area) increased by sub-catchments is related to hydrological parameters. 4.1% and 1.9%, respectively. This shows that forest and Our ﬁndings through focus group discussions and bare land areas have been converted to either agricultural key informant interviews with community members landor settlementsduringthisperiod. also revealed that ﬂooding has been an issue in the Again, between 1996 and 2016, forest area catchment. The state government in collaboration increased by 0.4% probably due to government inter- with the federal government has channelized the vention via aﬀorestation. During this period, agricul- two major Rivers (Iyiokwu and Iyiudele) which are tural land decreased by 5.7% while settlements areas responsible for major ﬂoods within Abakaliki metro- and bare land increased by 5.1% and 0.2%, respec- polis in a bid to control the impact of ﬂooding in the tively. This increase in areas covered by settlement area. The channelization was done between 2013 and and bare land between 1996 and 2016 could be attrib- 2015 through the ecological fund and it has greatly uted to high inﬂux of people to the state capital. It is reduced the frequency and severity of ﬂooding within important to mention that part of the study area the metropolis. However, other areas which are not became a state capital in 1996 which led to rapid within the metropolis continue to witness diﬀerent increase in population. intensities of ﬂooding. Soil classiﬁcation based on the soil attributes in the harmonized world soil database shows that the soil properties in the study area inﬂuence high run-oﬀ 5. Conclusion generation which can ultimately lead to ﬂooding. Areas with low elevations fall in the category of Our study elaborated an approach to synthesize the high ﬂood intensity zone while areas with high relevant database in a spatial framework to produce GEOLOGY, ECOLOGY, AND LANDSCAPES 139 a ﬂood vulnerability map of ALGA through the applica- Ota Henry Obiahu http://orcid.org/0000-0001-8267- tion of simple hydrologic models and arithmetic overlay operations in ArcGIS environment. Coupling of these hydrological modeling with GIS and remote sensing References techniques in this study has shown the potential for accurate ﬂood risk zone mapping. With this method, Armah, F. A., Yawson, D. O., Yengoh, G. T., Odoi, J. O., & ﬂood risk of various land uses can be determined with Ernest, K. A. 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Food and study area by taking cognizance of the spatial extent of AgricultureOrganization 43, http://www.fao.org/ﬁleadmin/ templates/nr/documents/HWSD/HWSD_Documentation. ﬂooding in the area. This study provides important infor- pdf%0Ahttp://www.fao.org/nr/Water/docs/Harm-World- mation that can be useful for decision-makers to prior- Soil-DBv7cv.pdf. itize developmental eﬀorts at local government levels. Integrated Regional Information Network [IRIN]. (2013). West We urge agricultural extension workers in the state to Africa Flood Round-Up. http://www.irinnews.org/news/. step up their game in educating farmers on the use of Intergovernmental Panel on Climate Change [IPCC]. early maturing species and the importance of upland rice (2014). The IPCC’s Fifth Assessment Report (AR5) (Synthesis Report). (September 2013): 1–4. farming to reduce crop inundations by seasonal ﬂooding. Islam, M. M., & Kimiteru, S. (2000). ‘Development of ﬂood Sustainable ﬂood awareness campaign/program is hazard maps of bangladesh using NOAA-AVHRR encouraged even in periods without ﬂooding to continu- images with GIS‘. Hydrologîcal Sciences, 3, 45. ously inculcate the culture of resilience on the Kazakis, N., Ioannis, K., & Thomas, P. (2015). ‘Assessment communities. of ﬂood hazard areas at a regional scale using an index- based approach and analytical hierarchy process‘: A major limitation of this work, however, is that the Application in rhodope-evros region, Greece. Science of hydrological model used does not consider some impor- the Total Environment, 538, 555–563. tant factors that determine the magnitude of ﬂood such as Komolafe, A. A., Suleiman, A. A. A., & Francis, O. A. (2015). antecedent moisture conditions. We recommend an A review of ﬂood risk analysis in Nigeria. American assessment of ﬂood depth in further research on the Journal of Environmental Sciences, 11(3), 157–166. study area to take the above limitations into account. Morjani, E., Zine, E., & Abidine, A., (2014). Methodology Document for the WHO E-Atlas of Disaster Risk . Flood Hazard Modelling Dr Zine El Abidine El Morjani Taroudant Poly-Disciplinary Faculty. Disclosure statement Nigeria Hydrological Services Agency [NIHSA]. (2014). 2014 Flood Outlook for Nigeria. No potential conﬂict of interest was reported by the authors. Nyarko, B. K. (2002). ‘Application of A rational model in gis for ﬂood risk assessment in accra‘, Ghana. Journal of Spatial Hydrology, 2,1–14. Funding Ogbodo,E. N.(2013). ‘Assessment and management strategies for the receding watersheds of ebonyi state, southeast Thispaper is part of the author's MSc thesis work which Nigeria‘. Journal of Environment and Earth Science, 3,3. was funded by the European Union through Intra-ACP USDA Natural Resources Conservation Service, (2009). AFIMEGQ Scholarship . “Part 630 Hydrology National Engineering Handbook Chapter 7 Hydrologic Soil Groups” Yangfan, X., Shanzhen, Y., & Tang, Z. (2017). ‘Integrated ﬂood hazard assessment based on spatial ordered ORCID weighted averaging method considering spatial hetero- Daniel Aja http://orcid.org/0000-0002-8849-381X geneity of risk preference‘. Science of the Total Eyasu Elias http://orcid.org/0000-0003-4008-6470 Environment, 599–600, 1034–1046.
Geology Ecology and Landscapes – Taylor & Francis
Published: Apr 2, 2020
Keywords: Hydrologic models; flood covariates; mitigation measures; Nitisols; Nigeria
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