Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 14
Articles | Volume 29, issue 14
https://doi.org/10.5194/hess-29-3145-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3145-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 14
Research article
 | 
24 Jul 2025
Research article |  | 24 Jul 2025

Toward routing river water in land surface models with recurrent neural networks

Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider

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Short summary
Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models for simulating streamflow in river basins, redesigning them with the goal of integrating them in climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine-learning-based river models inside climate models.
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