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

Viewed

Total article views: 3,656 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,834 762 60 3,656 51 60
  • HTML: 2,834
  • PDF: 762
  • XML: 60
  • Total: 3,656
  • BibTeX: 51
  • EndNote: 60
Views and downloads (calculated since 07 Jul 2025)
Cumulative views and downloads (calculated since 07 Jul 2025)

Viewed (geographical distribution)

Total article views: 3,656 (including HTML, PDF, and XML) Thereof 3,529 with geography defined and 127 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Mar 2026
Download
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.
Read more
Share