Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-479-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-479-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
CORRESPONDING AUTHOR
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
Scott Steinschneider
Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA
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Cited
17 citations as recorded by crossref.
- Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. 10.1016/j.envsoft.2025.106527
- Evaluation of Micrometeorological Models for Estimating Crop Evapotranspiration Using a Smart Field Weighing Lysimeter P. Ratshiedana et al. 10.3390/w17020187
- Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. 10.1016/j.advwatres.2025.104955
- Advancing Infiltration Rate Prediction in Algeria’s Mitidja Plain: A Machine Learning and Empirical Model Comparison A. Mazighi et al. 10.1007/s41748-025-00640-z
- Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models P. Huang & K. Lee 10.1080/19475705.2025.2516725
- Mass Conservative Time-Series GAN for Synthetic Extreme Flood-Event Generation: Impact on Probabilistic Forecasting Models D. Karimanzira 10.3390/stats7030049
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Investigating the streamflow simulation capability of a new mass-conserving long short-term memory (MC-LSTM) model across the contiguous United States Y. Wang et al. 10.1016/j.jhydrol.2025.133161
- Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models L. Zhong et al. 10.1016/j.jhydrol.2024.132165
- Transformer based models with hierarchical graph representations for enhanced climate forecasting T. Ramu et al. 10.1038/s41598-025-07897-4
- An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods E. Taylan 10.3390/su16166905
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. 10.1016/j.catena.2025.108743
- The outcome prediction method of football matches by the quantum neural network based on deep learning Y. Sun & H. Chu 10.1038/s41598-025-91870-8
- Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al. 10.1016/j.jhydrol.2025.133764
- Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. 10.1016/j.ejrh.2025.102426
- Predicting Forest Evapotranspiration Shifts Under Diverse Climate Change Scenarios by Leveraging the SEBAL Model Across Inner Mongolia P. Ji et al. 10.3390/f15122234
- Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
17 citations as recorded by crossref.
- Investigate the rainfall-runoff relationship and hydrological concepts inside LSTM Y. Hu et al. 10.1016/j.envsoft.2025.106527
- Evaluation of Micrometeorological Models for Estimating Crop Evapotranspiration Using a Smart Field Weighing Lysimeter P. Ratshiedana et al. 10.3390/w17020187
- Tackling water table depth modeling via machine learning: From proxy observations to verifiability J. Janssen et al. 10.1016/j.advwatres.2025.104955
- Advancing Infiltration Rate Prediction in Algeria’s Mitidja Plain: A Machine Learning and Empirical Model Comparison A. Mazighi et al. 10.1007/s41748-025-00640-z
- Developing an alternative data-driven model to resemble geomorphologic rainfall-runoff models P. Huang & K. Lee 10.1080/19475705.2025.2516725
- Mass Conservative Time-Series GAN for Synthetic Extreme Flood-Event Generation: Impact on Probabilistic Forecasting Models D. Karimanzira 10.3390/stats7030049
- A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al. 10.5194/hess-28-2871-2024
- Investigating the streamflow simulation capability of a new mass-conserving long short-term memory (MC-LSTM) model across the contiguous United States Y. Wang et al. 10.1016/j.jhydrol.2025.133161
- Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models L. Zhong et al. 10.1016/j.jhydrol.2024.132165
- Transformer based models with hierarchical graph representations for enhanced climate forecasting T. Ramu et al. 10.1038/s41598-025-07897-4
- An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods E. Taylan 10.3390/su16166905
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. 10.1016/j.catena.2025.108743
- The outcome prediction method of football matches by the quantum neural network based on deep learning Y. Sun & H. Chu 10.1038/s41598-025-91870-8
- Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins S. Wi et al. 10.1016/j.jhydrol.2025.133764
- Climate change impact assessment on a German lowland river using long short-term memory and conceptual hydrological models A. Ley et al. 10.1016/j.ejrh.2025.102426
- Predicting Forest Evapotranspiration Shifts Under Diverse Climate Change Scenarios by Leveraging the SEBAL Model Across Inner Mongolia P. Ji et al. 10.3390/f15122234
- Associations between deep learning runoff predictions and hydrogeological conditions in Australia S. Clark & J. Jaffrés 10.1016/j.jhydrol.2024.132569
Latest update: 26 Jul 2025
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.
We investigate whether deep learning (DL) models can produce physically plausible streamflow...