Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 6
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.
https://doi.org/10.5194/hess-26-1579-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 6
Research article
 | Highlight paper
 | 
23 Mar 2022
Research article | Highlight paper |  | 23 Mar 2022

Towards hybrid modeling of the global hydrological cycle

Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein

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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.
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