资源环境科技发展态势分析平台

Accurate terrain representation is critical to estimating flood risk in urban areas. However, all current global elevation data sets can be regarded as Digital Surface Models in urban areas as they contain building artefacts that cause artificial blocking of flow pathways. By taking surveyed terrain and LIDAR data as ‘truth’, the vertical error in three popular global DEMs (SRTM 1”, MERIT DEM and TDM90) was analysed in six European cities and an Asian city, with RMSE found to be 2.32–5.98 m. To increase the utility of global DEM data for flood modelling, a Random Forest model was developed to correct building artefacts in the MERIT DEM using factors from widely available public datasets, including satellite Night‐Time Lights, global population density and OpenStreetMap buildings. The proposed correction reduced the vertical errors of MERIT by 15%‐67%, despite not using data samples from the target city in training the model. When training data from the target city was included error reduction improved by between 57 and 76 percentage points. The resulting Urban Corrected MERIT DEM improved simulated inundation depth by 18% over original MERIT in a hydrodynamic model of flooding in the UK city of Carlisle, although it did not outperform TDM90 at this site. We conclude that the proposed method has the potential to generate a bare‐earth global DEM in urban areas with improved terrain representation, although in data scarce regions this requires more complete OpenStreetMap building information. In the future the method should be applied to TDM90.