Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 7
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.
https://doi.org/10.5194/nhess-25-2271-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 7
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

Viewed

Total article views: 1,553 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,183 274 96 1,553 57 76
  • HTML: 1,183
  • PDF: 274
  • XML: 96
  • Total: 1,553
  • BibTeX: 57
  • EndNote: 76
Views and downloads (calculated since 18 Nov 2024)
Cumulative views and downloads (calculated since 18 Nov 2024)

Viewed (geographical distribution)

Total article views: 1,553 (including HTML, PDF, and XML) Thereof 1,463 with geography defined and 90 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Oct 2025
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint