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Flooding has become a household phenomenon, particularly for communities in close proximity or situated in floodplain areas, although only on extreme cases that serious alarms are given. The brunt of this study assessed the flood vulnerability levels of settlements located in the Niger-Benue Trough of Central Nigeria by considering their livelihood assets. Data were sourced via a random administration of questionnaire in 36 communities in the study area earmarked; water level and discharge data obtained; communities were mapped; and remotely sensed data (Spot 5 and the Shuttle Radar Topographic Mission (SRTM) data were retrieved and analyzed using ArcGIS 10.5 and the Statistical Package for Social Sciences (SPSS 25) software. The simulated worst-case scenario of flooding revealed 22 settlements were inundated in the 2019 flood between July – September, covering larger areas before the confluence with more than 50% of the area under the high and moderately high-risk zones. Natural and physical livelihood assets were vulnerable and seriously damaged with indices greater than 3.0, while human, financial and social assets were all below 3.0. Generally, vulnerability index computed for all communities was 2.82, indicating moderate vulnerability of the communities to the flood event of 2019. Also, the Pearson correlation test revealed a strong, positive relationship (r = .769, α = .036) between the level of communities’ flood vulnerability and the livelihood assets in the study. It was therefore recommended that yearly flood events are worth simulating to aid prioritization of decisions and development of a comprehensive flood management plan for the area.
Kawasaki A, Rhyner J. Investing in disaster risk reduction for resilence: roles of science, technology, and education. J. Disaster Res. 2018;13(7):1181-1186.
Wadsworth G. Flood Damage Statistics. Napa, CA: Public Works Department; 1999.
Mmom P, Aifesehi PE. Impact of the 2012 Flood on Water Quality and Rural Livelihood in the Orashi Province of the Niger Delta, Nigeria. Journal of Geography and Geology. 2013;216-225. DOI: 10.5539/jgg.v5n3p216
Nwilo PC, Olayinka ND, Adzandar AE. Flood modelling and vulnerability assessment of settlments in the floodplains using GIS and Cellular Framework approach. Global Journal of Human Social Sciences. 2012;1(3):11-20.
European Commission. Update on Floods in Nigeria: JRC Emergency Report #022. UK: European Commission; 2018. Available:https://reliefweb.int/sites/relefweb.int/files/resources/ 2018-09-24_floods_nigeria_jrc_report.pdf
Li C, Cheng X, Li N, Du X, Yu Q, Kan G. A Framework for Flood Risk Analysis and Benefit Assessment of Flood Control Measures in Urban Areas. Int J Environ Res Public Health. 2016;13(8):787.
Shivaprasad SS, Parth SR, Chakravarthi V, Srinivasa RG. Flood risk assessment using multi-criteria analysis: a case study from Kopili River Basin, Assam, India. Geomatics, Natural Hazards and Risk. 2017;9(1):79-93. DOI: 10.1080/19475705.2017.1408705
Bates PD, De Roo AP. A simple raster-based model for flood inundation simulation. Journal of Hydrology. 2000;236(1):54-77.
Drogue G, Pfister L, Leviandier T, El Idrissi A, Iffly JF, Matgen P, Hoffmann L. Simulating the spatio-temporal variability of streamflow response to climate change scenerios in a meso scale basin. Journal of Hydrology. 2004;29(1):255-269.
Musa S, Onwuka SU, Eneche PS. Geospatial analysis of land use/cover dynamics in Awka Metropolis, Nigeria: A sub-pixel approach. Journal of Geography, Environment and Earth Science International. 2017;11(4):1-19. DOI: 10.9734/JGEESI/2017/35209
Miller SN, Guertin DP. Teaching spatial analysis for hydrology and watershed management. Proceedings of the 1999 ESRI Users Conference. San Diego, CA; 1999.
National Research Council. New strategies for America’s watersheds. Washington DC: National Academy Press; 1999.
Miller R, Guertin DP, Heilman P. An internet-based spatial decision support system for rangeland watershed management. First Interagency Conference on Research in the Watersheds; 2003. Available:https://www.google.com/url?q=http://www.tucson.ars. ag.gov/ unit/publications/PDFfiles/1523.pdf
Qui Z, Prato T. Behavioral and environmental modelling for Integrated Watershed Management. 4th International Conference on Integrating GIS and Environmental Modelling (GIS/EM4): Problems, Prospects and Research Needs; 2000. Available:http://www.srcosmos. gr/srcosmos/showpub.aspx?aa=5790
Zhang D, Chen X, Yao H. Development of a Prototype Web-Based Decision Support System for Watershed Management. Water. 2015;7(1):780-793.
Saldajeno PB, Florece LM, Lasco RD, Velasco TH. Vulnerability assessment of upland communities in Sibalom Natural Park, Antique, using capital-based approach. Journal of Environemntal Science and Management. 2012;1-12.
Nasiri H, Shahmohammadi-Kalalagh. Flood vulnerability index as a knowledge base for flood risk assessment in urban area. Journal of Novel Applied Sciences. 2013;269-272.
Nur I, Shrestha KK. An integrative perspective on community vulnerability to flooding in cities of developing countries. Procedia Engineering. 2017;198(2017):958-967. DOI: 10.1016/j.proeng.2017.07.141
Onuigbo IC, Ibrahim PO, Agada DU, Nwose IA, Abimbola II. Flood vulnerability mapping of Lokoja Metropolis using geographical information system techniques. Journal of Geosciences and Geomatics. 2017;5(5):229-242. DOI: 10.12691/jgg-5-5-2
Winsemius HC, Jongman B, Veldkamp TI, Hallgatte S, Bangalore M, Ward PJ. Disaster risk, climate change and poverty: assessing the global exposure of poor people to floods and droughts. Environ. Dev. Econ. 2018;23(3):328-348.
De Silva MM, Kawasaki A. Socioeconomic vulnerability to disaster risk: A case study of flood and drought impact in a rural Sri Lankan community. Ecological Economics. 2018;152:131-140. DOI: 10.1016/j.ecolecon.2018.05.010
De Silva M, Kawasaki A. A local-scale analysis to understand differences in socioeconomic factors affecting economic loss due to floods among different communities. International Journal of Disaster Risk Reduction. 2020;47(1): 1-12. DOI: 10.1016/j.ijdrr.2020.101526
Balica SF, Douben N, Wright NG. Flood vulnerability indices at varying spatial scales. Water Science & Technology – WST. 2009;2571-2580.
National Bureau of Statistics. Annual Abstract of Statistics, 2011. Abuja: National Bureau of Statistics; 2013.
Retrieved December 3, 2019 Available:https://nigerianstat.gov.ng/download/253
Gigovi´c L, Pamuˇcar D, Baji´c Z, Drobnjak S. Application of GIS-Interval rough AHP methodology for flood hazard mapping in urban areas. Water. 2017;9(360):1-26.
Ogato GS, Bantider A, Abebe K, Geneletti D. Geographic Information System (GIS)-based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West Shoa zone, Oromia Regional State, Ethiopia. Journal of Hydrology: Regional Studies. 200;27(18). DOI: 10.1016/j.ejrh.2019.100659
Krejcie RV, Morgan DW. Determining sample size for research activities. Educational and Psychological Measurement. 1970;607-610.
Mayomi I, Dami A, Maryah UM. GIS based assessment of flood risk vulnerability of communities in the Benue floodplains, Adamawa State, Nigeria. Journal of Geography and Geology. 2013;148-160. DOI: 10.5539/jgg.v5n4p148
Ifatimehin OO, Essoka P.A, Ahmed A. Analysis of land use changes and its hydrological Implication on River Niger, Lokoja, Nigeria. Confluence Journal of Environmental Studies. 2012;7(1):112-119.
Tokula AE, Eneche PS. The role of urban agriculture in the reduction of poverty in Ajaokuta, Kogi State, Nigeria. Ethopian Journal of Environmental Studies & Management. 2018;11(1):100-110. DOI: 10.13140/RG.2.2.13441.76640
Dulal HB, Brodnig G, Onoriose CG, Thakur HK. Capitalizing on assets: Vulnerability and adaptation to climate change in Nepal. Washington, DC 20433: Social Development Department of the World Bank; 2010.
Albano R, Sole A, Adamowski J, Mancust L. A GIS-based model to estimate flood consequences and the degree of accessibility and operability of strategic emergency response structures in urban areas. Nat. Hazards Earth Syst. Sci. Discuss. 2014;14:2847-2865.