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Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels, subsidence and changing extreme weather patterns. However, current freely available elevation datasets are not sufficiently accurate to model these risks. We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 1 arcsecond (∼30 m) and a vertical mean absolute error (MAE) of 0.45 m overall. DeltaDTM corrects CopernicusDEM with spaceborne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. Notably, our classification approach produces more accurate results than regression methods recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications. [ABSTRACT FROM AUTHOR]

The latest projections indicate that sea‐level rise (SLR) is certain to exceed 2 m in coming centuries, and a rise by 4 m is considered possible. Radar‐based land elevation models applied to date suggest that the increase of area below mean sea level, that is potentially exposed to permanent flooding, will accelerate as SLR proceeds, being relatively limited initially. However, applying new and more accurate satellite LiDAR elevation data we find the opposite pattern, with the fastest increase in the area of exposed land occurring in the early stages of SLR. In one‐third of countries most of this increase will be caused by the first meter of SLR and in nearly all within the first 2 m. We conclude that in many regions the time available to prepare for increased exposure to flooding may be considerably less than assumed to date, and that better elevation data will support timely preparations. The global LiDAR lowland DTM (GLL_DTM_v2) elevation data set developed for this assessment is available in the public domain. Plain Language Summary: The latest sea‐level rise (SLR) projections indicate that future sea levels are certain to exceed 2 m and a rise by 4 m is considered possible. Land elevation models applied to date suggest that the increase of land area below sea level will be limited at first but will go faster when SLR continues. When we apply a new and more accurate elevation model we find the opposite pattern, with the fastest increase during the early stages of SLR. In one‐third of countries most of this increase will be during the first meter of SLR, and in almost all within the first 2 m. We conclude that in many regions the time available to prepare for increased exposure to flooding may be considerably less than assumed to date, and that better elevation data will support timely preparations. The global LiDAR lowland DTM (GLL_DTM_v2) elevation data set developed for this assessment is available in the public domain. Key Points: Calculations using radar‐based global elevation models available to date have generally underestimated the extent of lowest coastal areas that are most exposed to sea‐level rise (SLR)A recent lowland elevation model (global LiDAR lowland DTM (GLL_DTM_v2)) derived from only satellite LiDAR data is currently most accurate, we recommend such LiDAR data to be used in SLR impact assessmentsApplying this model we find that the greatest increase in coastal area below mean sea level will occur in the early stages of SLR, contrary to earlier assessments [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]