Bare-earth DEM Generation in Urban Areas for Flood Inundation Simulation using Global Digital Elevation Models

Yinxue Liu*, Paul D Bates, Jeffery C Neal, Dai Yamazaki

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

27 Citations (Scopus)
126 Downloads (Pure)

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 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.
Original languageEnglish
Article numbere2020WR028516
Number of pages25
JournalWater Resources Research
Volume57
Issue number4
Early online date7 Apr 2021
DOIs
Publication statusPublished - 26 Apr 2021

Bibliographical note

Funding Information:
Yinxue Liu was supported by the China-Scholarship-Council (CSC) ? University of Bristol Joint PhD Scholarships Program. Paul Bates was supported by a Royal Society Wolfson Research Merit award. Jeffrey Neal was supported by NERCNE/S006079/1. Dai Yamazaki was supported by JSPS Kakenhi 16H06291. The authors would like to thank the anonymous reviewers and the editors for offering comments to improve this study.

Funding Information:
Yinxue Liu was supported by the China‐Scholarship‐Council (CSC) – University of Bristol Joint PhD Scholarships Program. Paul Bates was supported by a Royal Society Wolfson Research Merit award. Jeffrey Neal was supported by NERCNE/S006079/1. Dai Yamazaki was supported by JSPS Kakenhi 16H06291. The authors would like to thank the anonymous reviewers and the editors for offering comments to improve this study.

Publisher Copyright:
© 2021. The Authors.

Keywords

  • Bare‐earth DEM
  • Urban
  • Flooding
  • MERIT DEM
  • Random Forest

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