Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2271-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2271-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2025
Research article |
| 08 Jul 2025
| 08 Jul 2025
Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method
Yue Zhu
CORRESPONDING AUTHOR
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Future Cities Laboratory, Singapore-ETH Centre, Singapore
Paolo Burlando
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Puay Yok Tan
Department of Architecture, National University of Singapore, Singapore
Christian Geiß
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany
Department of Geography, University of Bonn, Bonn, Germany
Simone Fatichi
Department of Civil and Environmental Engineering, National University of Singapore, Singapore
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Short summary
This study addresses the challenge of accurately predicting floods in regions with limited terrain data. By utilising a deep learning model, we developed a method that improves the resolution of digital elevation data by fusing low-resolution elevation data with high-resolution satellite imagery. This approach not only substantially enhances flood prediction accuracy, but also holds potential for broader applications in simulating natural hazards that require terrain information.
This study addresses the challenge of accurately predicting floods in regions with limited...
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