Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 3
Articles | Volume 19, issue 3
https://doi.org/10.5194/gmd-19-1055-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1055-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 3
Development and technical paper
 | 
02 Feb 2026
Development and technical paper |  | 02 Feb 2026

A hybrid physics–AI approach using universal differential equations with state-dependent neural networks for learnable, regionalizable, spatially distributed hydrological modeling

Ngo Nghi Truyen Huynh, Pierre-André Garambois, François Colleoni, and Jérôme Monnier

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Short summary
To better understand hydrological processes and improve flood simulation, combining artificial intelligence (AI) with process-based models is a promising direction. We introduce a hybrid physics–AI approach that seamlessly integrates neural networks into a distributed hydrological model to refine water flow dynamics within an implicit numerical scheme. The hybrid models demonstrate strong performance and interpretable results, leading to reliable streamflow simulations for flood modeling.
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