Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-2921-2025
https://doi.org/10.5194/gmd-18-2921-2025
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

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Cited articles

Acuña Espinoza, E., Loritz, R., Álvarez Chaves, M., Bäuerle, N., and Ehret, U.: To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization, Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, 2024. a
Alain, G. and Bengio, Y.: Understanding intermediate layers using linear classifier probes, arXiv [preprint], https://doi.org/10.48550/arXiv.1610.01644, 2016. a
Baghirov, Z.: zavud/h2mv: v1.0.0 – First release, Zenodo [code], https://doi.org/10.5281/zenodo.12608916, 2024. a
Baghirov, Z., Martin, J., Markus, R., Marco, K., and Basil, K.: Global Physically-Constrained Deep Learning Water Cycle Model with Vegetation: Model Simulations, Zenodo [data set], https://doi.org/10.5281/zenodo.12583615, 2024. a
Beck, H. E., Van Dijk, A. I., Miralles, D. G., De Jeu, R. A., Bruijnzeel, L., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour. Res., 49, 7843–7863, 2013. a
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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.
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