Abstract
Open-access global Digital Elevation Models (DEM) have been crucial in enabling flood studies in data-sparse areas. Poor resolution (>30m), significant vertical errors and the fact that these DEMs are over a decade old continue to hamper our ability to accurately estimate flood hazard. The limited availability of high-accuracy DEMs dictate that dated open-access global DEMs are still used extensively in flood models, particularly in data-sparse areas. Nevertheless, high-accuracy DEMs have been found to give better flood estimations, and thus can be considered a ‘must-have’ for any flood model. A high-accuracy open-access global DEM is not imminent, meaning that editing or stochastic simulation of existing DEM data will remain the primary means of improving flood simulation. This article provides an overview of errors in some of the most widely used DEM data sets, along with the current advances in reducing them via the creation of new DEMs, editing DEMs and stochastic simulation of DEMs. We focus on a geostatistical approach to stochastically simulate floodplain DEMs from several open-access global DEMs based on the spatial error structure. This DEM simulation approach enables an ensemble of plausible DEMs to be created, thus avoiding the spurious precision of using a single DEM and enabling the generation of probabilistic flood maps. Despite this encouraging step, an imprecise and outdated global DEM is still being used to simulate elevation. To fundamentally improve flood estimations, particularly in rapidly-changing developing regions, a high-accuracy open-access global DEM is urgently needed, which in turn can be used in DEM simulation.
Highlights
Digital Elevation Models (DEM) are a gridded digital representation of terrain, with each pixel value corresponding to a height above a datum
Such bare-earth DEMs are essential for applications, such as flood modeling, that rely on the accurate derivation of surface characteristics
Flood predictions are compared to four models that use a single DEM – Light Detection And Ranging (LiDAR) at 30 m and 90 m resolution and MERIT and Shuttle Radar Topography Mission (SRTM) at 90 m resolution
Summary
Digital Elevation Models (DEM) are a gridded digital representation of terrain, with each pixel value corresponding to a height above a datum. Holmes et al (2000) observe that “ global (average) error is small, local error values can be large, and spatially correlated.” The spatial variation of DEM error is most frequently estimated by calculating accuracy statistics of areas disaggregated by slope and/or landcover class, and more rarely spatial structure of error.
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