Articles | Volume 16, issue 1
https://doi.org/10.5194/esd-16-29-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-16-29-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2025
Research article |
| 07 Jan 2025
| 07 Jan 2025
A first attempt to model global hydrology at hyper-resolution
Barry van Jaarsveld
CORRESPONDING AUTHOR
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Niko Wanders
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Edwin H. Sutanudjaja
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Jannis Hoch
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Fathom, Bristol, United Kingdom
Bram Droppers
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Joren Janzing
WSL, Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland
Rens L. P. H. van Beek
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Marc F. P. Bierkens
Department of Physical Geography, Utrecht University, Princeton laan 8a, Utrecht, the Netherlands
Deltares, Unit Subsurface and Groundwater Systems, Utrecht, the Netherlands
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Cited
12 citations as recorded by crossref.
- Source identification of sudden water pollution events in the Dongliao River using a hybrid machine learning framework Y. Wang et al. https://doi.org/10.1038/s41598-026-41724-8
- Identification of key factors influencing monthly runoff simulation through the integration of deep learning and physically-based hydrological models J. Zhang et al. https://doi.org/10.1016/j.envsoft.2026.106927
- Graphical representation of global water models H. Müller Schmied et al. https://doi.org/10.5194/gmd-18-2409-2025
- Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions J. Janzing et al. https://doi.org/10.5194/hess-29-7041-2025
- Meteorological to hydrological drought propagation: The influence of future climate change at grid scale T. Abraham et al. https://doi.org/10.1007/s00704-025-05963-5
- Built-Up Fraction and Residential Expansion Under Hydrologic Constraints: Quantifying Effects of Terrain, Groundwater and Vegetation Root Depth on Urbanization in Kunming, China C. Shen et al. https://doi.org/10.3390/hydrology13020048
- River temperature response to atmospheric heatwaves is modulated by discharge and meltwater A. van Hamel et al. https://doi.org/10.1038/s43247-026-03269-6
- Modelling the impact of climate and land cover changes on hydrological cycle components: a case of the middle Huai river basin K. Disasa et al. https://doi.org/10.1007/s00704-025-05964-4
- Modelling runoff in a glacierized catchment: the role of forcing product and spatial model resolution A. von der Esch et al. https://doi.org/10.5194/hess-29-6761-2025
- Scalability and Computational Performance of an Ecohydrological Model Using Machine Learning-Based Prediction N. Cortés-Torres et al. https://doi.org/10.3390/w18040466
- Wastewater matters: incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08) D. Fridman et al. https://doi.org/10.5194/gmd-18-3735-2025
- Data derived reservoir operations simulated in a global hydrologic model J. Steyaert et al. https://doi.org/10.5194/hess-29-6499-2025
12 citations as recorded by crossref.
- Source identification of sudden water pollution events in the Dongliao River using a hybrid machine learning framework Y. Wang et al. https://doi.org/10.1038/s41598-026-41724-8
- Identification of key factors influencing monthly runoff simulation through the integration of deep learning and physically-based hydrological models J. Zhang et al. https://doi.org/10.1016/j.envsoft.2026.106927
- Graphical representation of global water models H. Müller Schmied et al. https://doi.org/10.5194/gmd-18-2409-2025
- Hyper-resolution large-scale hydrological modelling benefits from improved process representation in mountain regions J. Janzing et al. https://doi.org/10.5194/hess-29-7041-2025
- Meteorological to hydrological drought propagation: The influence of future climate change at grid scale T. Abraham et al. https://doi.org/10.1007/s00704-025-05963-5
- Built-Up Fraction and Residential Expansion Under Hydrologic Constraints: Quantifying Effects of Terrain, Groundwater and Vegetation Root Depth on Urbanization in Kunming, China C. Shen et al. https://doi.org/10.3390/hydrology13020048
- River temperature response to atmospheric heatwaves is modulated by discharge and meltwater A. van Hamel et al. https://doi.org/10.1038/s43247-026-03269-6
- Modelling the impact of climate and land cover changes on hydrological cycle components: a case of the middle Huai river basin K. Disasa et al. https://doi.org/10.1007/s00704-025-05964-4
- Modelling runoff in a glacierized catchment: the role of forcing product and spatial model resolution A. von der Esch et al. https://doi.org/10.5194/hess-29-6761-2025
- Scalability and Computational Performance of an Ecohydrological Model Using Machine Learning-Based Prediction N. Cortés-Torres et al. https://doi.org/10.3390/w18040466
- Wastewater matters: incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08) D. Fridman et al. https://doi.org/10.5194/gmd-18-3735-2025
- Data derived reservoir operations simulated in a global hydrologic model J. Steyaert et al. https://doi.org/10.5194/hess-29-6499-2025
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Policy makers use global hydrological models to develop water management strategies and policies. However, it would be better if these models provided information at higher resolution. We present a first-of-its-kind, truly global hyper-resolution model and show that hyper-resolution brings about better estimates of river discharge, and this is especially true for smaller catchments. Our results also suggest that future hyper-resolution models need to include more detailed land cover information.
Policy makers use global hydrological models to develop water management strategies and...
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