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: 2,981 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,448 165 368 2,981 47 85
  • HTML: 2,448
  • PDF: 165
  • XML: 368
  • Total: 2,981
  • BibTeX: 47
  • EndNote: 85
Views and downloads (calculated since 18 Sep 2024)
Cumulative views and downloads (calculated since 18 Sep 2024)

Viewed (geographical distribution)

Total article views: 2,981 (including HTML, PDF, and XML) Thereof 2,908 with geography defined and 73 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 09 Oct 2025
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