Assessing global elevation models for mapping the low elevation coastal zone

Abstract

Elevation data are critical for assessments of coastal hazards, including sea-level rise (SLR), flooding, storm surge, tsunami impacts, and wave run-up. Previous research has demonstrated that the quality of data used in elevation-based hazard assessments must be well documented and applied properly to assess potential impacts. Global digital elevation models (DEMs), at 30- to 90-meter resolution, have been used extensively to map and characterize coastal environments and the at-risk resources (population and built structures) contained therein. The inherent absolute vertical accuracy of global DEMs precludes their usefulness for assessing exposure to fine increments (< 1 meter) of coastal inundation at high confidence levels. However, global DEMs are highly suitable for delineation of the global low elevation coastal zone (LECZ) (elevation < 10 meters). An accuracy evaluation of global DEMs over the United States has been conducted to quantify their performance in correctly mapping the LECZ, namely in terms of vertical uncertainty and corresponding confidence levels for several representations of the coastal zone. The evaluation approach includes comparison of the DEMs with an extensive set of high-accuracy geodetic control points as the independent reference data covering a variety of coastal relief settings. The 1-arc-second (30-meter) global DEMs evaluated include ALOS World 3D, ASTER GDEM, Copernicus, FABDEM, and NASADEM, and the 3-arc-second (90-meter) global DEMs include CoastalDEM, Copernicus, MERIT, and TanDEM-X. Additionally, lower resolution (1-kilometer) global DEMs were also assessed, namely the Global Lidar Lowland DTM (derived from ICESat-2) and the GEDI 1-km DEM. The results of the accuracy characterization show that FABDEM performs the best (minimal vertical bias and lowest vertical root mean square error) for high-confidence mapping of the LECZ. Among 90-m DEMs, CoastalDEM performs best, although the differences across datasets are minimal. The results also demonstrate the importance of rigorously accounting for elevation uncertainty when applying global DEMs for coastal mapping applications.