Implications of Simulating Global Digital Elevation Models for Flood Inundation Studies

The Shuttle Radar Topography Mission 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 (MERIT) DEM. This study investigates the spatial error structure of MERIT and Shuttle Radar Topography Mission, before simulating plausible versions of the DEMs using fitted semivariograms. By simulating multiple DEMs, we allow modelers 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 semivariograms by land cover class gives inundation estimates closer to a light detection and ranging-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 workflow is available via an R package called DEMsimulation. Plain Language Summary A lack of accurate digital elevation models (DEMs) for flood inundation modeling in data-sparse regions means that predictions of flood inundation are subject to substantial errors. These errors have rarely been assessed due to a lack of information on the spatial structure of DEM errors. In this study, we analyze the vertical DEM error and how this error varies spatially for both the widely used Shuttle Radar Topography Mission (SRTM) DEM and an error reduced variant of SRTM called Multi-Error-Removed-Improved-Terrain (MERIT) DEM for 20 lowland locations. We then use the spatial error characteristics to simulate plausible versions of topography. By simulating many statistically plausible topographies, flood models can assess the effects of uncertain topography on predicted flood extents. We demonstrate this by using a collection of simulated DEMs in flood models for two locations. We conclude that using an ensemble of MERIT DEMs simulated using the spatial error disaggregated by land cover class gives flood estimates closest to that of a benchmark flood model. This study is of interest to others as our calculated spatial error relationships can be used to simulate floodplain topography in the MERIT/SRTM data sets through our open-source code, allowing for probabilistic flood maps to be produced.