Accurate information on land elevation is key – Towards proper coastal flood risk assessment in data-sparse river deltas and coastal lowlands

Many river deltas and coastal lowlands in the world are densely populated and located in the Global South. Due to relative sea-level rise (rSLR), they face an increasing risk of drowning and flooding and thus require reliable impact and risk assessments of rSLR and flooding. As both sea-level rise (SLR) impact and flood inundation are closely related to land elevation, the quality of these assessments largely relies on vertical accuracy and proper datum referencing of the elevation data used. However, high-quality digital elevation models (DEMs) representing elevation at spatial resolutions and vertical accuracies at centimetre to decimetre scale are still not available or accessible for major parts of the Earth’s coasts, including densely populated Asian and African coastal lowlands or Small Island Development States. In these regions, global DEMs are often used even though they suffer from large vertical errors and artefacts, thereby impacting the quality of flood exposure assessments. While the accuracy of those global DEMs is extensively addressed both in their dataset documentation and literature, the relevance and proper vertical datum conversion from global geoid and ellipsoid models to local sea level is often still omitted in many applied studies; in part because the process is complicated in data-poor regions, where tide gauge records are often insufficient or outdated. Sea surface data based on satellite altimetry may serve as a substitute but the referencing of the land elevation and sea surface data to a common vertical datum includes several steps of datum conversion beforehand, which – if not performed properly – can introduce local errors of sea level to the land elevation up to several metres.

In this study, we test and present a workflow of globally consistent vertical datum conversion of elevation data to continuous local mean sea level by integrating globally available data on coastal elevation and sea surface. We apply our approach to recently published global DEMs and validate them for several key coastal lowlands such as the Ayeyarwady and Mekong Deltas, and show the improvement of the performance of global DEMs for impact assessments in data-poor regions. This proves the potential to improve impact assessments of SLR and flood exposure in coastal lowlands around the world where high-quality elevation information is not accessible.