Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler

Keyword: Flood Vulnerability, Flood Modeler, Remote Sensing, Sentinel-2, Surface Runoff.

Abstract: This study aimed at flood modeling and vulnerability analysis of Abia State using Remote Sensing and Flood modeler. The methodology involved acquisition of Sentinel-2 imagery covering Abia State, Rainfall data and ALOS PALSAR. Image subsetting was done to extract the area of study from the acquired dataset, this was followed by analysis of DEM accuracy using root mean square error, image classification to extract the landuse/ landcover of the study area, surface runoff modelling to determine surface runoff potential in the study area and flood modelling. The flood frequency return as modeled by Flood Modeler indicated a 25.04km2 inundation extent for 2-year return period, 28.10km2 inundation extent for a 5-year period and 26.04km2 inundation extent for a 10-year return period. Increasing to its peak extent by 3.67% by the 5-year return period, and then decreased by 2.24% by the 10-year return period. The surface runoff potential revealed that 35.99% of the study area with an area coverage of 1630.19 km2 had low infiltration potential, 32.51% with an area of 1472.56 km2 had moderate infiltration while 31.50% with an area of 1426.82 km2 had high infiltration. This indicated that the study area had a high extent of low surface infiltration which will lead to flooding during heavy or frequent rainfalls. This study recommends flood modeler as it is reliable for flood modeling, having been proven with correlation results of 0.8196that it fits to the ground flood points gotten during field validation.

Article Info: Received: 09 Jan 2021; Received in revised form: 19 Mar 2021; Accepted: 11 Apr 2021; Available online: 28 Apr 2021

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Page No: 159-163

Cite this Article:

C. N. Baywood, R. E. Njoku, U. A. Emmanuel, E. C. Igbokwe et al."Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler". International Journal of Environment Agriculture and Biotechnology(ISSN: 2456-1878),vol 6, no. 2, 2021, pp.159-163 AI Publications

C. N. Baywood, R. E. Njoku, U. A. Emmanuel, E. C. Igbokwe, P.(2021).Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler. International Journal of Environment Agriculture and Biotechnology(ISSN: 2456-1878).6(2), 159-163

C. N. Baywood, R. E. Njoku, U. A. Emmanuel, E. C. Igbokwe, P.(2021).Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler. International Journal of Environment Agriculture and Biotechnology(ISSN: 2456-1878).6(2), pp.159-163.

C. N. Baywood, R. E. Njoku, U. A. Emmanuel, E. C. Igbokwe. 2021."Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler". International Journal of Environment Agriculture and Biotechnology(ISSN: 2456-1878).6(2):159-163.

C. N. Baywood, R. E. Njoku, U. A. Emmanuel, E. C. Igbokwe."Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler", International Journal of Environment Agriculture and Biotechnology,vol.6,no. 2, pp.159-163,2021.

@article { c.n.baywood2021flood,
title={Flood Modeling and Vulnerability Analysis of Abia State using Remote Sensing and Flood Modeler},
author={C. N. Baywood, R. E. Njoku, U. A. Emmanuel, E. C. Igbokwe , R},
journal={International Journal of Environment Agriculture and Biotechnology},
volume={6},
year= {2021} ,
}