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.
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.
the Creative Commons Attribution 4.0 License.
Toward routing river water in land surface models with recurrent neural networks
Mauricio Lima
CORRESPONDING AUTHOR
École Polytechnique, Palaiseau, France
California Institute of Technology, Pasadena, CA, USA
Katherine Deck
California Institute of Technology, Pasadena, CA, USA
Oliver R. A. Dunbar
California Institute of Technology, Pasadena, CA, USA
Tapio Schneider
California Institute of Technology, Pasadena, CA, USA
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ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
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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.
Machine learning is playing an increasingly important role in hydrological modeling. In this...