A document by Michel Wortmann. Click on the document to view its contents.

Flood inundation modelling across large data sparse areas has been increasing in recent years, driven by a desire to provide hazard information for a wider range of locations. The sophistication of these models has steadily advanced over the past decade due to improvements in remote sensing and modelling capability. There are now several global flood models (GFMs) that seek to simulate water surface dynamics across all rivers and floodplains regardless of data scarcity. However, flood models in data sparse areas lack river bathymetry because this cannot be observed remotely, meaning that a variety of methods for approximating river bathymetry have been developed from uniform flow or downstream hydraulic geometry theory. We argue that bathymetry estimation in these models should follow gradually varying flow theory to account for both uniform and nonuniform flows. We demonstrate that existing methods for bathymetry estimation in GFM’s are only accurate for kinematic reaches and are unable to simulate unbiased water surface profiles for reaches with diffusive or shallow water wave properties. The use of gradually varied flow theory to estimate bathymetry in a GFM reduced water surface profile errors by 66% and eliminated bias due to backwater effects. For a large-scale test case in Mozambique this reduced flood extends by 40% and floodplain storage by 79% at the 1 in 5 year return period. The results have significant implications for the role floodplains play in attenuating river discharges because previous GFM’s based on uniform flow theory will overstate the role of the floodplain.

Flood disasters have regularly been reported in the Congo Basin with significant damages to human lives, food production systems and infrastructure. Losses incurred by these damages are huge and represent a major challenge for economic expansion in developing nations. In the Congo River Basin, where availability of in-situ data is a significant challenge, new approaches are needed to investigate flood risks and enable effective management strategies. This study uses recently developed global flood prediction data in order to produce flood risk maps for the Congo River Basin, where flood information currently does not exist. Flood hazard maps that estimate fluvial flooding at a grid cell resolution of 3 arc-seconds (~ 90 m), gridded population density data of 1 arc-second (~ 30 m) spatial resolution, and a spatial layer of infrastructure dataset are used to addresses flood risk at the scale of the Congo Basin. The global flood data provides different return periods of exposure to flooding in the Congo Basin and identifies flood extents. The risk analysis results are presented in terms of the percentage of population and infrastructure at flood risk for six return periods (5, 10, 20, 50, 75 and 100 year). Of the 525 administrative territories, 374 are AGU Books