资源环境科技发展态势分析平台

Flood risk, particularly in Small Island Developing States, is increasing. Although spaceborne Digital Elevation Models (DEMs) have provided a capacity to model flooding at the global scale, their relatively coarse resolution (similar to 90m) has led to a limited ability to provide fine-scale flood assessments in smaller catchments such as those in Small Island Developing States. Following the release of the TanDEM-X DEM at similar to 12-m resolution, the aim of this research is to determine whether TanDEM-X can improve flood estimates in comparison to Shuttle Radar Topography Mission (SRTM) and Multi-Error-Removed Improved-Terrain (MERIT) DEMs. Suitable methods to process TanDEM-X to a Digital Terrain Model (DTM) are identified through testing of seven DTMs produced through combinations of different vegetation removal approaches. Methods include Progressive Morphological Filtering and Image Classification of two TanDEM-X auxiliary data setsa Height Error Map and Amplitude map. The LISFLOOD-FP hydrodynamic model output flood extent and water surface elevation for the TanDEM-X DTMs, SRTM, and MERIT are compared against the LiDAR model for a catchment in Fiji. The main findings show that the unprocessed TanDEM-X has improved predictive capacity over SRTM, but not MERIT. The TanDEM-X processing method combining Image Classification of the Amplitude map and Progressive Morphological Filtering produces the DTM with the highest flood model skill in comparison to all tested DEMs. This DTM reports a 12-14 percentage point higher flood model skill score than MERIT and a lower water surface elevation root-mean-square error of 0.11-0.21m, indicating the suitability of TanDEM-X for flood modeling.

Plain Language Summary Flood risk is increasing almost everywhere, making it vital to identify at-risk areas. Highly accurate elevation data are essential for flood risk estimation, which in high-income countries is usually provided by LiDAR. However, countries such as Small Island Developing States are often reliant on spaceborne elevation data sets due to the high cost of LiDAR, despite experiencing some of the greatest levels of flood risk. These spaceborne data sets have greater errors than LiDAR and often measure vegetation canopy height instead of ground height, reducing the accuracy of flood estimates used by policy makers to assess risk. This paper aims to identify whether newly released spaceborne data set TanDEM-X could improve flood estimates in these areas by comparing flood simulations from a hydrodynamic model using TanDEM-X data with simulations based on other spaceborne data sets and LiDAR for the Ba catchment in Fiji. The results showed that TanDEM-X performs closest to the LiDAR model but only after vegetation removal processing. Further studies should be conducted in other locations, but these results indicate a possible method for improving inundation estimates in data-sparse areas. This should provide useful information for flood modeling and disaster management communitiesessential given predictions of more extreme rainfall and greater exposure on floodplains.