Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 15
Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3589-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3589-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 15
Research article
 | 
07 Aug 2025
Research article |  | 07 Aug 2025

A distributed hybrid physics–AI framework for learning corrections of internal hydrological fluxes and enhancing high-resolution regionalized flood modeling

Ngo Nghi Truyen Huynh, Pierre-André Garambois, Benjamin Renard, François Colleoni, Jérôme Monnier, and Hélène Roux

Viewed

Total article views: 2,440 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,087 325 28 2,440 43 72
  • HTML: 2,087
  • PDF: 325
  • XML: 28
  • Total: 2,440
  • BibTeX: 43
  • EndNote: 72
Views and downloads (calculated since 23 Jan 2025)
Cumulative views and downloads (calculated since 23 Jan 2025)

Viewed (geographical distribution)

Total article views: 2,440 (including HTML, PDF, and XML) Thereof 2,424 with geography defined and 16 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Oct 2025
Download
Short summary
Understanding and modeling flash-flood-prone areas remains challenging due to limited data and scale-relevant hydrological theory. While machine learning shows promise, its integration with process-based models is difficult. We present an approach incorporating machine learning into a high-resolution hydrological model to correct internal fluxes and transfer parameters between watersheds. Results show improved accuracy, advancing the development of learnable and interpretable process-based models.
Read more
Share