Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 10
Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-2921-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2921-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 10
Model description paper
 | 
19 May 2025
Model description paper |  | 19 May 2025

H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation

Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft

Viewed

Total article views: 3,949 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,247 307 395 3,949 67 122
  • HTML: 3,247
  • PDF: 307
  • XML: 395
  • Total: 3,949
  • BibTeX: 67
  • EndNote: 122
Views and downloads (calculated since 18 Sep 2024)
Cumulative views and downloads (calculated since 18 Sep 2024)

Viewed (geographical distribution)

Total article views: 3,949 (including HTML, PDF, and XML) Thereof 3,850 with geography defined and 99 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Feb 2026
Download
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Read more
Share