Comparing TanDEM-X Data with Frequently-Used DEMs for Flood Inundation Modelling

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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 (~90 m) 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 ~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 sets—a 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 0.11–0.21 m, indicating the suitability of TanDEM‐X for flood modeling.
Original languageEnglish
Number of pages18
JournalWater Resources Research
Early online date19 Dec 2018
Publication statusE-pub ahead of print - 19 Dec 2018


  • TanDEM‐X
  • Digital Elevation Models
  • flood models
  • Small Island Developing States
  • Synthetic Aperture Radar
  • vegetation

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