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

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Latest update: 20 May 2024
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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.