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Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology
Version 1
: Received: 8 July 2020 / Approved: 10 July 2020 / Online: 10 July 2020 (08:13:07 CEST)
A peer-reviewed article of this Preprint also exists.
Ekeu-wei, I.T.; Blackburn, G.A. Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology. ISPRS Int. J. Geo-Inf. 2020, 9, 512. Ekeu-wei, I.T.; Blackburn, G.A. Catchment-Scale Flood Modelling in Data-Sparse Regions Using Open-Access Geospatial Technology. ISPRS Int. J. Geo-Inf. 2020, 9, 512.
Abstract
Consistent data is seldom available for whole-catchment flood modelling in many developing regions, thus this study demonstrates how the complementary strengths of open and readily available geospatial datasets and tools can be leverage to map flood risk within acceptable levels of uncertainty for flood risk management. Available fragmented remotely-sensed and in situ datasets (including hydrological data, altimetry, digital elevation model, bathymetry, aerial photos, optical and radar imageries) are systematically integrated using 2-dimensional CAESAR-LISFLOOD model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Experimental modelling, calibration and validation is undertaken for the whole Niger-South hydrological catchment area of Nigeria, then segmented into sub-domains for re-validation to understand how data variability and uncertainties impact on the accuracy of model outcomes. Furthermore, aerial photos are applied for the first time in the study area for flood model validation and to understand how different physio-environmental properties influence synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria.
Keywords
Open-access; geospatial; remote sensing; hydrodynamic model; CAESAR-LISFLOOD; data-sparse; flood risk management
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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