Articles | Volume 27, issue 12
https://doi.org/10.5194/hess-27-2357-2023
https://doi.org/10.5194/hess-27-2357-2023
Research article
 | 
30 Jun 2023
Research article |  | 30 Jun 2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen

Viewed

Total article views: 5,828 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,033 1,708 87 5,828 62 74
  • HTML: 4,033
  • PDF: 1,708
  • XML: 87
  • Total: 5,828
  • BibTeX: 62
  • EndNote: 74
Views and downloads (calculated since 08 Aug 2022)
Cumulative views and downloads (calculated since 08 Aug 2022)

Viewed (geographical distribution)

Total article views: 5,828 (including HTML, PDF, and XML) Thereof 5,612 with geography defined and 216 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 03 Oct 2024
Download
Short summary
Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.