Abstract
No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km2 of land below 10, 5, and 2 m +MSL. The 0.93 million km2 of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available.
Highlights
Coastal flood risk assessments typically require a digital terrain model (DTM) accuracy of at least 0.5 m [1], given that the range of tidal fluctuations and sea-level rise is measured in a few meters only [2]
We show that satellite LiDAR data can yield a global lowland DTM that closely matches three local DTMs, across three continents, within 0.5 m over 83.4% of their area lower than 10 m +MSL, and far exceeds the accuracy of existing radar-based global digital elevation models (GDEMs) below 10 m +MSL that are accurate within 0.5 m for 37.9% (TanDEM-X) at best
This confirms that satellite LiDAR data presents a way forward towards creating accurate global lowland DTMs that will be suitable for assessments of current and future flood risk and land management options
Summary
Coastal flood risk assessments typically require a DTM accuracy of at least 0.5 m [1], given that the range of tidal fluctuations and sea-level rise is measured in a few meters only [2]. While the global vertical accuracy of the recent TanDEM-X GDEM, as determined by comparison with ICESat-1 data, is better at 3.49 m (90% confidence level; RMSE = 2.12 m; [15]), this remains too low for flood risk assessment applications. Both SRTM and TanDEM-X are created from satellite radar data that only partially penetrate vegetation canopy [8,16,17] and, do not represent the actual bare ground surface [18]. Land elevations, as presented in such GDEMs, tend to be too high, as recently demonstrated for the Mekong Delta by Minderhoud et al [19] showing SRTM being 1.8 m higher than a local DTM, inevitably resulting in an underestimation of current and future flood risk [20,21,22,23]
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