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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- smash v1.0: a differentiable and regionalizable high-resolution hydrological modeling and data assimilation framework F. Colleoni et al. https://doi.org/10.5194/gmd-18-7003-2025
- A novel hybrid deep learning model with dynamic parameterization for accurate flood simulation B. Sun et al. https://doi.org/10.1016/j.jhydrol.2026.135182
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- Differentiable parameter learning of reservoir operation modules Z. Chen & T. Zhao https://doi.org/10.1016/j.jhydrol.2026.134915
- A machine learning framework for runoff simulation in small catchments: Integrating atmosphere–ocean–land large-scale indices and catchment variables C. Ma et al. https://doi.org/10.1016/j.ejrh.2026.103181
- From data modification to preservation: evolution of DEM depression processing algorithms in hydrological modelling L. Ma et al. https://doi.org/10.1007/s12665-026-12971-9
- Trends and Transitions in Hydrological Modelling: A Systematic Review of Model Approaches and Applications (2015–2025) T. Peerbhai et al. https://doi.org/10.1007/s11269-026-04815-9
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- Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models A. Jamaat et al. https://doi.org/10.1016/j.jhydrol.2025.134137
- Future Streamflow and Hydrological Drought Under CMIP6 Climate Projections T. Liu et al. https://doi.org/10.3390/atmos16060691
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- When physics gets in the way: an entropy-based evaluation of conceptual constraints in hybrid hydrological models M. Álvarez Chaves et al. https://doi.org/10.5194/hess-30-629-2026
- From descriptive digital twins to closed-loop intelligent agents: adaptive biogeochemical management in river-reservoir systems H. Guo et al. https://doi.org/10.1016/j.watres.2026.126501
- 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. https://doi.org/10.1016/j.envsoft.2025.106360
- Improving differentiable hydrologic modeling with interpretable forcing fusion K. Sawadekar et al. https://doi.org/10.1016/j.jhydrol.2025.133320
- Enhancing sea surface salinity short-term prediction using physically informed deep learning Q. Liu et al. https://doi.org/10.1016/j.apor.2025.104832
- Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106350
- A python framework for differentiable hydrological modeling and research workflow automation W. Ouyang et al. https://doi.org/10.1016/j.envsoft.2026.106895
Saved (final revised paper)
Latest update: 23 Jul 2026
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....
