Articles | Volume 17, issue 18
https://doi.org/10.5194/gmd-17-7181-2024
© 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/gmd-17-7181-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
26 Sep 2024
Model evaluation paper |
| 26 Sep 2024
| 26 Sep 2024
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
Dapeng Feng
CORRESPONDING AUTHOR
Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA
Earth System Science, Stanford University, Stanford, CA, USA
Water Security Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Hylke Beck
Climate and Livability Initiative, Physical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Jens de Bruijn
Water Security Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Reetik Kumar Sahu
Water Security Research Group, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Yusuke Satoh
Moon Soul Graduate School of Future Strategy, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
Yoshihide Wada
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Jiangtao Liu
Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Kathryn Lawson
Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA
Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA
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19 citations as recorded by crossref.
- Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani 10.1016/j.advwatres.2025.105063
- Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al. 10.1016/j.jhydrol.2025.134137
- Future Streamflow and Hydrological Drought Under CMIP6 Climate Projections T. Liu et al. 10.3390/atmos16060691
- Physics-informed neural networks enhanced by data augmentation: a novel framework for robust soil moisture estimation using multi-source data fusion J. Hu et al. 10.1016/j.jhydrol.2025.134320
- Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. 10.1038/s41467-025-64367-1
- Quantification of Suspended Sediment Concentration Using Laboratory Experimental Data and Machine Learning Model S. Nookala et al. 10.3390/w17152301
- Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al. 10.5194/hess-29-5453-2025
- Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al. 10.1016/j.rineng.2025.106345
- Development of an MPE-BMA Ensemble Model for Runoff Prediction Under Future Climate Change Scenarios: A Case Study of the Xiangxi River Basin W. Li et al. 10.3390/su17104714
- Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain S. Asadi et al. 10.1016/j.envsoft.2025.106360
- Protocols for Water and Environmental Modeling Using Machine Learning in California M. He et al. 10.3390/hydrology12030059
- Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al. 10.1016/j.jhydrol.2025.133320
- smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework F. Colleoni et al. 10.5194/gmd-18-7003-2025
- Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. 10.1016/j.envsoft.2025.106350
- 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
- Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
- Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
- 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 S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- A comprehensive study of deep learning for soil moisture prediction Y. Wang et al. 10.5194/hess-28-917-2024
15 citations as recorded by crossref.
- Coping with data scarcity in extreme flood forecasting: A deep generative modeling approach A. Sattari & H. Moradkhani 10.1016/j.advwatres.2025.105063
- Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al. 10.1016/j.jhydrol.2025.134137
- Future Streamflow and Hydrological Drought Under CMIP6 Climate Projections T. Liu et al. 10.3390/atmos16060691
- Physics-informed neural networks enhanced by data augmentation: a novel framework for robust soil moisture estimation using multi-source data fusion J. Hu et al. 10.1016/j.jhydrol.2025.134320
- Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. 10.1038/s41467-025-64367-1
- Quantification of Suspended Sediment Concentration Using Laboratory Experimental Data and Machine Learning Model S. Nookala et al. 10.3390/w17152301
- Improving streamflow simulation through machine learning-powered data integration and its potential for forecasting in the Western U.S. Y. Yang et al. 10.5194/hess-29-5453-2025
- Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al. 10.1016/j.rineng.2025.106345
- Development of an MPE-BMA Ensemble Model for Runoff Prediction Under Future Climate Change Scenarios: A Case Study of the Xiangxi River Basin W. Li et al. 10.3390/su17104714
- Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain S. Asadi et al. 10.1016/j.envsoft.2025.106360
- Protocols for Water and Environmental Modeling Using Machine Learning in California M. He et al. 10.3390/hydrology12030059
- Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al. 10.1016/j.jhydrol.2025.133320
- smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework F. Colleoni et al. 10.5194/gmd-18-7003-2025
- Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. 10.1016/j.envsoft.2025.106350
- 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
4 citations as recorded by crossref.
- Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
- Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
- 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 S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- A comprehensive study of deep learning for soil moisture prediction Y. Wang et al. 10.5194/hess-28-917-2024
Latest update: 01 Nov 2025
Short summary
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change....
