Articles | Volume 26, issue 6
https://doi.org/10.5194/hess-26-1579-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1579-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
23 Mar 2022
Research article | Highlight paper |
| 23 Mar 2022
| 23 Mar 2022
Towards hybrid modeling of the global hydrological cycle
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Germany
Department of Aerospace and Geodesy, Technical University of Munich, Germany
Martin Jung
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Germany
Marco Körner
Department of Aerospace and Geodesy, Technical University of Munich, Germany
Sujan Koirala
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Germany
Markus Reichstein
Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Germany
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Latest update: 25 Apr 2024
Short summary
We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
We present a physics-aware machine learning model of the global hydrological cycle. As the model...
