Implications of Simulating Global Digital Elevation Models for Flood Inundation Studies

The Shuttle Radar Topography Mission (SRTM) has long been used as a source topographic information for flood hazard models, especially in data‐sparse areas. Error corrected versions have been produced, culminating in the latest global error reduced Digital Elevation Model (DEM) ‐ the Multi‐Error‐Removed‐Improved‐Terrain DEM (MERIT). This study investigates the spatial error structure of MERIT and SRTM, before simulating plausible versions of the DEMs using fitted semi‐variograms. By simulating multiple DEMs, we allow modellers to explore the impact of topographic uncertainty on hazard assessment even in data‐sparse locations where typically only one DEM is currently used. We demonstrate this for a flood model in the Mekong Delta and a catchment in Fiji using deterministic DEMs and DEM ensembles simulated using our approach. By running an ensemble of simulated DEMs we avoid the spurious precision of using a single DEM in a deterministic simulation. We conclude that using an ensemble of the MERIT DEM simulated using semi‐variograms by landcover class give inundation estimates closer to a LIDAR based benchmark. This study is the first to analyze the spatial error structure of the MERIT DEM and the first to simulate DEMs and apply these to flood models at this scale. The research work‐flow is available via an R package called demgenerator.