Estimating River Channel Bathymetry in Large Scale Flood Inundation Models. Issue 5 (20th May 2021) - Neal, Jeffrey; Hawker, Laurence; Savage, James; Durand, Michael; Bates, Paul; Sampson, Christopher
Abstract: Flood inundation modeling 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 modeling 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 GFMs are only accurate for kinematic water surface profiles 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 model error compared to a target water surface profile by 66% and eliminated bias due to backwater effects. For a large‐scale test case in Mozambique this reduced flood extents by 40% and floodplain storage by 79% at the 5 years return period. The wet bias associated with uniform flow derived channels could have significant implications for modeling the role floodplains play in attenuating river discharges, potentially overstating their role. Key Points: Flood models in data sparse areas must estimate river bathymetry Existing methods are prone to over‐prediction bias Channel estimation based on gradually varied flow theory is substantially more accurate