The Importance of Digital Elevation Model Selection in Flood Simulation and a Proposed Method to Reduce DEM Errors: A Case Study in Shanghai

• Published in: International journal of disaster risk science 2021-12, Vol.12 (6), p.890-902
• Main Authors: Xu, Kepeng, Fang, Jiayi, Fang, Yongqiang, Sun, Qinke, Wu, Chengbo, Liu, Min
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Case studies; China; Climate Change; Datasets; Digital Elevation Models; Earth and Environmental Science; Earth Sciences; Environment; Errors; Flood control; Flood management; Flood mapping; Flooding; Floods; Hydrology; Landscape/Regional and Urban Planning; Mapping; Methods; Natural Hazards; River basins; Satellite observation; Simulation; Statistical methods; Sustainable Development; Water depth
• ISSN: 2095-0055; 2192-6395
• DOI: 10.1007/s13753-021-00377-z

Digital Elevation Models (DEMs) play a critical role in hydrologic and hydraulic modeling. Flood inundation mapping is highly dependent on the accuracy of DEMs. Various vertical differences exist among open access DEMs as they use various observation satellites and algorithms. The problem is particularly acute in small, flat coastal cities. Thus, it is necessary to assess the differences of the input of DEMs in flood simulation and to reduce anomalous errors of DEMs. In this study, we first conducted urban flood simulation in the Huangpu River Basin in Shanghai by using the LISFLOOD-FP hydrodynamic model and six open-access DEMs (SRTM, MERIT, CoastalDEM, GDEM, NASADEM, and AW3D30), and analyzed the differences in the results of the flood inundation simulations. Then, we processed the DEMs by using two statistically based methods and compared the results with those using the original DEMs. The results show that: (1) the flood inundation mappings using the six original DEMs are significantly different under the same simulation conditions—this indicates that only using a single DEM dataset may lead to bias of flood mapping and is not adequate for high confidence analysis of exposure and flood management; and (2) the accuracy of a DEM corrected by the Dixon criterion for predicting inundation extent is improved, in addition to reducing errors in extreme water depths—this indicates that the corrected datasets have some performance improvement in the accuracy of flood simulation. A freely available, accurate, high-resolution DEM is needed to support robust flood mapping. Flood-related researchers, practitioners, and other stakeholders should pay attention to the uncertainty caused by DEM quality.