Articles | Volume 25, issue 7
https://doi.org/10.5194/nhess-25-2271-2025
https://doi.org/10.5194/nhess-25-2271-2025
Research article
 | 
08 Jul 2025
Research article |  | 08 Jul 2025

Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method

Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi

Data sets

Improving Pluvial Flood Simulations with Multi-source DEM Super-Resolution Yue Zhu et al. https://doi.org/10.5281/zenodo.15212783

Model code and software

Improving Pluvial Flood Simulations with Multi-source DEM Super-Resolution Yue Zhu et al. https://doi.org/10.5281/zenodo.15212783

Download
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.
Share
Altmetrics
Final-revised paper
Preprint