Implications of Simulating Global Digital Elevation Models for Flood Inundation Studies

Abstract

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.

Perspectives on Digital Elevation Model (DEM) Simulation for Flood Modeling in the Absence of a High-Accuracy Open Access Global DEM

“…Here we demonstrate the benefits of DEM simulation in flood inundation modeling based on the recently published work of Hawker et al (2018).…”

“…This idea is relatively well known in the field of geostatistics (Goovaerts, 1997;Hunter and Goodchild, 1997;Deutsch and Journel, 1998;Kydriakidis et al, 1999;Holmes et al, 2000). Using an ensemble of simulated DEMs has been shown to greatly affect the characterization of surface derivatives (Fisher, 1991;Veregin, 1997;Holmes et al, 2000;Endreny and Wood, 2001;Raaflaub and Collins, 2006), landslide hazard (Davis and Keller, 1997;Murillo and Hunter, 1997;Darnell et al, 2008) and flood inundation estimation (Wilson and Atkinson, 2005;Hawker et al, 2018). Research in this area, especially in the flood community, has been largely stagnant for the past decade which seems a shame given the improvement in computational resources and the number of DEMs now available.…”

“…In Hawker et al (2018), DEM simulation is carried out by first quantifying the spatial error structure of a global DEM, and then using the fitted error covariance function to simulate plausible versions of the DEM. The fitted error covariance function was calculated for SRTM and MERIT DEMs by fitting a semivariogram to difference maps (i.e., SRTM/MERIT -reference LiDAR DEM) of 20 floodplain locations.…”

Simple-Yet-Effective SRTM DEM Improvement Scheme for Dense Urban Cities Using ANN and Remote Sensing Data: Application to Flood Modeling

“…The most improvement occurred in the impervious area of Nice with a 44.7% RMSE reduction, while building areas in Singapore showed the most RMSE reduction, with a 42% improvement. It would be interesting in future work to consider other error patterns, such as that in [40]. It is an interesting finding that the SRTM DEM of a place, where no good-quality surveyed DEM is available, can still be significantly improved with ANN trained in a faraway dense urban area where high-quality ground truth data are available.…”

Flooding in the Mekong Delta: the impact of dyke systems on downstream hydrodynamics

“…2. It has resulted from extensive artificial canal development that began in 1819 (Hung, 2011). Regarding land resources, the VMD area comprises about 4 × 10 6 ha, and three-quarters of this region is used for agricultural production (Kakonen, 2008).…”