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- Title
Bare‐Earth DEM Generation in Urban Areas for Flood Inundation Simulation Using Global Digital Elevation Models.
- Authors
Liu, Yinxue; Bates, Paul D.; Neal, Jeffery C.; Yamazaki, Dai
- Abstract
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 artifacts 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 analyzed 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 modeling, a Random Forest model was developed to correct building artifacts 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. Plain Language Summary: Terrain representation plays a vital role in flood mapping. For wide area flood simulation where the model grid is typically larger than individual buildings, topography data without ground objects, such as buildings and trees, are preferred as this can generate more accurate inundation simulations than when ground objects are included. However, current global topography data all contain building height artifacts to some extent. This is especially a problem in areas where buildings are densely packed. The resulting vertical biases in popular global topography data were found to be 2.32–5.98 m (root mean square error) in seven cities in Europe and Asia. To address this problem, this study describes an approach using publicly available datasets that can reduce this bias by a significant amount (15%–67%). With the bias reduced topography, simulated water levels were improved over the original topography in a case study of flooding in the UK city of Carlisle. The proposed correction model has the potential to generate a bare‐earth topography in urban areas globally, which will be useful for improving flood mapping and risk assessment. Key Points: Popular global DEMs have substantial vertical errors in urban areas, with RMSE found to be 2.32–5.98 m in European and Asian citiesThe proposed method can reduce the vertical errors of the MERIT DEM in urban areas by 15%–67%A flood simulation of the UK city of Carlisle showed that the MERIT‐Urban Corrected DEM reduced the RMSE for predicted inundation by 18%
- Subjects
CARLISLE (Pa.); UNITED Kingdom; DIGITAL elevation models; CITIES &; towns; STANDARD deviations; FLOODS; COMMON misconceptions; ADAPTIVE reuse of buildings; BIOSPHERE
- Publication
Water Resources Research, 2021, Vol 57, Issue 4, p1
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2020WR028516