Articles | Volume 28, issue 3
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

Data sets

The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL) J. Mai et al.

Model code and software

MC-LSTM-PET Wi Sungwook

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.