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.