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1 Komi, K.; Neal, J.; Trigg, M. A.; Diekkruger, B. 2017. Modelling of flood hazard extent in data sparse areas: a case study of the Oti River Basin, West Africa. Journal of Hydrology: Regional Studies, 10:122-132. [doi: https://doi.org/10.1016/j.ejrh.2017.03.001]
Weather hazards ; Flooding ; Forecasting ; Hydrology ; Models ; Calibration ; Simulation ; Performance evaluation ; Rainfall-runoff relationships ; Satellite observation ; Remote sensing ; River basins ; Floodplains ; Case studies / West Africa / Oti River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H048094)
http://www.sciencedirect.com/science/article/pii/S2214581817300757/pdfft?md5=b29831cd1e8f2bbfc9b9d2dbdebcdcce&pid=1-s2.0-S2214581817300757-main.pdf
https://vlibrary.iwmi.org/pdf/H048094.pdf
https://vlibrary.iwmi.org/pdf/H048094.pdf
(1.59 MB) (1.59 MB)
Study region: Terrain and hydrological data are scarce in many African countries. The coarse spatial resolution of freely available Shuttle Radar Topographic Mission elevation data and the absence of flow gauges on flood-prone reaches, such as the Oti River studied here, make flood inundation modelling challenging in West Africa.
Study focus: A flood modelling approach is developed here to simulate flood extent in data scarce regions. The methodology is based on a calibrated, distributed hydrological model for the whole basin to simulate the input discharges for a hydraulic model which is used to predict the flood extent for a 140 km reach of the Oti River.
New hydrological insight for the region: Good hydrological model calibration (Nash Sutcliffe coefficient: 0.87) and validation (Nash Sutcliffe coefficient: 0.94) results demonstrate that even with coarse scale (5 km) input data, it is possible to simulate the discharge along this region’s rivers, and importantly with a distributed model, derive model flows at any ungauged location within basin. With a lack of surveyed channel bathymetry, modelling the flood was only possible with a parametrized sub-grid hydraulic model. Flood model fit results relative to the observed 2007 flood extent and extensive sensitivity testing shows that this fit (64%) is likely to be as good as is possible for this region, given the coarseness of the terrain digital elevation model.
Study focus: A flood modelling approach is developed here to simulate flood extent in data scarce regions. The methodology is based on a calibrated, distributed hydrological model for the whole basin to simulate the input discharges for a hydraulic model which is used to predict the flood extent for a 140 km reach of the Oti River.
New hydrological insight for the region: Good hydrological model calibration (Nash Sutcliffe coefficient: 0.87) and validation (Nash Sutcliffe coefficient: 0.94) results demonstrate that even with coarse scale (5 km) input data, it is possible to simulate the discharge along this region’s rivers, and importantly with a distributed model, derive model flows at any ungauged location within basin. With a lack of surveyed channel bathymetry, modelling the flood was only possible with a parametrized sub-grid hydraulic model. Flood model fit results relative to the observed 2007 flood extent and extensive sensitivity testing shows that this fit (64%) is likely to be as good as is possible for this region, given the coarseness of the terrain digital elevation model.
2 Fox, S.; Agyemang, F.; Hawker, L.; Neal, J.. 2024. Integrating social vulnerability into high-resolution global flood risk mapping. Nature Communications, 15:3155. [doi: https://doi.org/10.1038/s41467-024-47394-2]
(Location: IWMI HQ Call no: e-copy only Record No: H052885)
(2.56 MB) (2.56 MB)
High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.
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