Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method
<p>Accurate flood simulation remains a significant challenge in many flood-prone regions, particularly in developing countries and urban areas, where the availability of high-resolution topographic data is especially limited. While publicly available digital elevation model (DEM) datasets are...
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Copernicus Publications
2025-07-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/25/2271/2025/nhess-25-2271-2025.pdf |
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| author | Y. Zhu Y. Zhu P. Burlando P. Y. Tan C. Geiß C. Geiß S. Fatichi |
| author_facet | Y. Zhu Y. Zhu P. Burlando P. Y. Tan C. Geiß C. Geiß S. Fatichi |
| author_sort | Y. Zhu |
| collection | DOAJ |
| description | <p>Accurate flood simulation remains a significant challenge in many flood-prone regions, particularly in developing countries and urban areas, where the availability of high-resolution topographic data is especially limited. While publicly available digital elevation model (DEM) datasets are increasingly accessible, their spatial resolution is often insufficient for reflecting fine-scaled elevation details, which hinders the ability to simulate pluvial floods in built environments. To address this issue, we implemented a deep-learning-based method, which efficiently enhances the spatial resolution of DEM data, and quantified the effect of the improved DEM on flood simulation. The method employs a tailored multi-source input module, enabling it to effectively integrate and learn from diverse data sources. By utilising publicly accessible global datasets, such as low-resolution DEM datasets (i.e. 30 m Shuttle Radar Topography Mission, SRTM) in conjunction with high-resolution multispectral imagery (e.g. Sentinel-2A), our approach allows us to produce a super-resolution DEM, which exhibits superior performance compared to conventional methods in reconstructing 10 m DEM data based on 30 m DEM data and 10 m multispectral satellite images. We evaluated the performance of the super-resolution DEM in flood simulations. Compared to conventional methods (e.g. bicubic interpolation), the simulation results demonstrated that our approach significantly improved the accuracy of flood simulations, with a reduction in the mean absolute error of floodwater depth of about 13.1 % and an increase in the intersection over union (IoU) for inundation area predictions of about 46 %. Accordingly, this study underscores the practical value of machine learning techniques that leverage publicly available global datasets to generate DEMs that allow for the enhancement of flood simulations.</p> |
| format | Article |
| id | doaj-art-3f64be0f66444ccf9b653fa722049a1e |
| institution | DOAJ |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-3f64be0f66444ccf9b653fa722049a1e2025-08-20T03:17:46ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812025-07-01252271228610.5194/nhess-25-2271-2025Improving pluvial flood simulations with a multi-source digital elevation model super-resolution methodY. Zhu0Y. Zhu1P. Burlando2P. Y. Tan3C. Geiß4C. Geiß5S. Fatichi6Institute of Environmental Engineering, ETH Zurich, Zurich, SwitzerlandFuture Cities Laboratory, Singapore-ETH Centre, SingaporeInstitute of Environmental Engineering, ETH Zurich, Zurich, SwitzerlandDepartment of Architecture, National University of Singapore, SingaporeGerman Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, GermanyDepartment of Geography, University of Bonn, Bonn, GermanyDepartment of Civil and Environmental Engineering, National University of Singapore, Singapore<p>Accurate flood simulation remains a significant challenge in many flood-prone regions, particularly in developing countries and urban areas, where the availability of high-resolution topographic data is especially limited. While publicly available digital elevation model (DEM) datasets are increasingly accessible, their spatial resolution is often insufficient for reflecting fine-scaled elevation details, which hinders the ability to simulate pluvial floods in built environments. To address this issue, we implemented a deep-learning-based method, which efficiently enhances the spatial resolution of DEM data, and quantified the effect of the improved DEM on flood simulation. The method employs a tailored multi-source input module, enabling it to effectively integrate and learn from diverse data sources. By utilising publicly accessible global datasets, such as low-resolution DEM datasets (i.e. 30 m Shuttle Radar Topography Mission, SRTM) in conjunction with high-resolution multispectral imagery (e.g. Sentinel-2A), our approach allows us to produce a super-resolution DEM, which exhibits superior performance compared to conventional methods in reconstructing 10 m DEM data based on 30 m DEM data and 10 m multispectral satellite images. We evaluated the performance of the super-resolution DEM in flood simulations. Compared to conventional methods (e.g. bicubic interpolation), the simulation results demonstrated that our approach significantly improved the accuracy of flood simulations, with a reduction in the mean absolute error of floodwater depth of about 13.1 % and an increase in the intersection over union (IoU) for inundation area predictions of about 46 %. Accordingly, this study underscores the practical value of machine learning techniques that leverage publicly available global datasets to generate DEMs that allow for the enhancement of flood simulations.</p>https://nhess.copernicus.org/articles/25/2271/2025/nhess-25-2271-2025.pdf |
| spellingShingle | Y. Zhu Y. Zhu P. Burlando P. Y. Tan C. Geiß C. Geiß S. Fatichi Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method Natural Hazards and Earth System Sciences |
| title | Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method |
| title_full | Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method |
| title_fullStr | Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method |
| title_full_unstemmed | Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method |
| title_short | Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method |
| title_sort | improving pluvial flood simulations with a multi source digital elevation model super resolution method |
| url | https://nhess.copernicus.org/articles/25/2271/2025/nhess-25-2271-2025.pdf |
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