Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-479-2024
https://doi.org/10.5194/hess-28-479-2024
Research article
 | 
07 Feb 2024
Research article |  | 07 Feb 2024

On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration

Sungwook Wi and Scott Steinschneider

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Latest update: 22 Jul 2024
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Short summary
We investigate whether deep learning (DL) models can produce physically plausible streamflow projections under climate change. We address this question by focusing on modeled responses to increases in temperature and potential evapotranspiration and by employing three DL and three process-based hydrological models. The results suggest that physical constraints regarding model architecture and input are necessary to promote the physical realism of DL hydrological projections under climate change.