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Implications of Simulating Global Digital Elevation Models for Flood Inundation Studies
Author(s) -
Hawker Laurence,
Rougier Jonathan,
Neal Jeffrey,
Bates Paul,
Archer Leanne,
Yamazaki Dai
Publication year - 2018
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2018wr023279
Subject(s) - digital elevation model , shuttle radar topography mission , terrain , flood myth , remote sensing , lidar , land cover , spurious relationship , elevation (ballistics) , meteorology , geology , environmental science , computer science , cartography , geography , land use , mathematics , civil engineering , archaeology , machine learning , engineering , geometry
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.