Upgrading 1D-2D flood models using satellite laser altimetry and multi-mission satellite surface water extent maps

Abstract. Digital elevation models (DEMs) are essential datasets, particularly for flood inundation mapping in one-dimensional (1D) to two-dimensional (2D) flood models. Given the current uncertainties stemming from changes in weather patterns affecting flooding, reducing inaccuracies in flood models is imperative. This study aims to enhance the performance of 1D-2D flood models using satellite Earth observation (EO) data in the lower Chao Phraya (CPY) basin. It introduces two workflows applied to upgrade the 1D-2D flood model: DEM analysis and flood map analysis. The DEM analysis workflow evaluates 10 DEM products (LDD, JICA, merged LDD-JICA, ASTER GDEM V3, STRMv3, MERIT, GLO30, FABDEMv1-2, TanDEM-X, and TanDEM-EDEM) using satellite laser altimetry data from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) according to standard criteria for DEM selection as input to the flood model. Findings indicate that the merged LDD-JICA and FABDEMv1-2 DEMs exhibit the highest level of accuracy, with root mean square error (RMSE) values of 1.93 and 1.95 m, respectively. The flood map analysis workflow involves comparing flood extent maps derived from multi-mission satellite datasets, and simulated flood maps. This study utilizes surface water extent (SWE) maps from the WorldWater project, obtained from the Sentinel-1 and Sentinel-2 imaging satellites, and flood maps from the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand to verify flood maps produced by the 1D-2D flood model. The results reveal that the flood maps from the 1D-2D flood model tend to overestimate flood extent, with a critical success index (CSI) range of 0.072 – 0.230. Our study demonstrates the potential to enhance the skill of 1D-2D flood models using satellite EO data, thereby improving the reliability of flood inundation predictions.