Articles | Volume 23, issue 3
https://doi.org/10.5194/hess-23-1779-2019
© Author(s) 2019. 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-23-1779-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Apr 2019
Research article |
| 02 Apr 2019
| 02 Apr 2019
The benefits of spatial resolution increase in global simulations of the hydrological cycle evaluated for the Rhine and Mississippi basins
Meteorology and Air Quality Group, Wageningen University, Droevendaalsesteeg 4, 6708 BP Wageningen, the Netherlands
Chiel C. van Heerwaarden
Meteorology and Air Quality Group, Wageningen University, Droevendaalsesteeg 4, 6708 BP Wageningen, the Netherlands
Albrecht H. Weerts
Hydrology and Quantitative Water Management Group, Wageningen University, Droevendaalsesteeg 4, 6708 BP Wageningen, the Netherlands
Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
Wilco Hazeleger
Meteorology and Air Quality Group, Wageningen University, Droevendaalsesteeg 4, 6708 BP Wageningen, the Netherlands
Netherlands eScience Center (NLeSC), Science Park 140, 1098 XG Amsterdam, the Netherlands
Viewed
Total article views: 5,374 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Aug 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,937 | 1,329 | 108 | 5,374 | 186 | 177 |
- HTML: 3,937
- PDF: 1,329
- XML: 108
- Total: 5,374
- BibTeX: 186
- EndNote: 177
Total article views: 4,396 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Apr 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,359 | 939 | 98 | 4,396 | 161 | 157 |
- HTML: 3,359
- PDF: 939
- XML: 98
- Total: 4,396
- BibTeX: 161
- EndNote: 157
Total article views: 978 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Aug 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 578 | 390 | 10 | 978 | 25 | 20 |
- HTML: 578
- PDF: 390
- XML: 10
- Total: 978
- BibTeX: 25
- EndNote: 20
Viewed (geographical distribution)
Total article views: 5,374 (including HTML, PDF, and XML)
Thereof 5,067 with geography defined
and 307 with unknown origin.
Total article views: 4,396 (including HTML, PDF, and XML)
Thereof 4,168 with geography defined
and 228 with unknown origin.
Total article views: 978 (including HTML, PDF, and XML)
Thereof 899 with geography defined
and 79 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
20 citations as recorded by crossref.
- Decline in Terrestrial Moisture Sources of the Mississippi River Basin in a Future Climate I. Benedict et al. https://doi.org/10.1175/JHM-D-19-0094.1
- Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions B. Harrop et al. https://doi.org/10.1029/2022MS003490
- Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate V. Levizzani & E. Cattani https://doi.org/10.3390/rs11192301
- Sensors prioritisation for hydrological forecasting based on interpretable machine learning A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.134015
- Global Bias-Corrected CORDEX Datasets at Half Degree Resolution F. Yakubu et al. https://doi.org/10.1038/s41597-025-06200-4
- Digital twins of the Earth with and for humans W. Hazeleger et al. https://doi.org/10.1038/s43247-024-01626-x
- Evaluating groundwater infiltration risk in sewer networks under climate change N. Zeydalinejad et al. https://doi.org/10.1007/s00477-026-03171-9
- The role of spatial and temporal model resolution in a flood event storyline approach in western Norway N. Schaller et al. https://doi.org/10.1016/j.wace.2020.100259
- Review of approaches for selection and ensembling of GCMs K. Raju & D. Kumar https://doi.org/10.2166/wcc.2020.128
- Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method Y. Liu et al. https://doi.org/10.1016/j.atmosres.2020.105241
- A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model N. Gasset et al. https://doi.org/10.5194/hess-25-4917-2021
- Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan S. Peng et al. https://doi.org/10.1038/s41598-022-27357-7
- Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale L. Guillaumot et al. https://doi.org/10.5194/gmd-15-7099-2022
- Integrating climate models to confront the illusion of certainty in water planning: evidence from Morocco M. Zemzami et al. https://doi.org/10.1007/s10584-026-04108-5
- Regional ensemble of CMIP6 global climate models for Sakha (Yakutia) Republic, Northern Eurasia N. Tananaev https://doi.org/10.1016/j.polar.2024.101066
- Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China A. Lv et al. https://doi.org/10.1016/j.scitotenv.2021.149831
- Exploring Afro-Asian water fluxes in CMIP5 and CMIP6 models: Present-day evaluation and future projections T. Adeliyi et al. https://doi.org/10.1016/j.scitotenv.2025.179374
- Unveiling the Climate Change Impact and Suitability Assessment of CMIP5 and CMIP6 Emission Scenarios for the Mahanadi Reservoir Project Complex, Chhattisgarh Y. Chakravaishya et al. https://doi.org/10.14796/JWMM.C563
- A first attempt to model global hydrology at hyper-resolution B. van Jaarsveld et al. https://doi.org/10.5194/esd-16-29-2025
- Spatial and temporal analysis and trends of extreme precipitation over the Mississippi River Basin, USA during 1988–2017 A. Dommo et al. https://doi.org/10.1016/j.ejrh.2024.101954
20 citations as recorded by crossref.
- Decline in Terrestrial Moisture Sources of the Mississippi River Basin in a Future Climate I. Benedict et al. https://doi.org/10.1175/JHM-D-19-0094.1
- Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions B. Harrop et al. https://doi.org/10.1029/2022MS003490
- Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate V. Levizzani & E. Cattani https://doi.org/10.3390/rs11192301
- Sensors prioritisation for hydrological forecasting based on interpretable machine learning A. Menapace et al. https://doi.org/10.1016/j.jhydrol.2025.134015
- Global Bias-Corrected CORDEX Datasets at Half Degree Resolution F. Yakubu et al. https://doi.org/10.1038/s41597-025-06200-4
- Digital twins of the Earth with and for humans W. Hazeleger et al. https://doi.org/10.1038/s43247-024-01626-x
- Evaluating groundwater infiltration risk in sewer networks under climate change N. Zeydalinejad et al. https://doi.org/10.1007/s00477-026-03171-9
- The role of spatial and temporal model resolution in a flood event storyline approach in western Norway N. Schaller et al. https://doi.org/10.1016/j.wace.2020.100259
- Review of approaches for selection and ensembling of GCMs K. Raju & D. Kumar https://doi.org/10.2166/wcc.2020.128
- Quantifying uncertainties in temperature projections: A factorial-analysis-based multi-ensemble downscaling (FAMED) method Y. Liu et al. https://doi.org/10.1016/j.atmosres.2020.105241
- A 10 km North American precipitation and land-surface reanalysis based on the GEM atmospheric model N. Gasset et al. https://doi.org/10.5194/hess-25-4917-2021
- Climate change multi-model projections in CMIP6 scenarios in Central Hokkaido, Japan S. Peng et al. https://doi.org/10.1038/s41598-022-27357-7
- Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale L. Guillaumot et al. https://doi.org/10.5194/gmd-15-7099-2022
- Integrating climate models to confront the illusion of certainty in water planning: evidence from Morocco M. Zemzami et al. https://doi.org/10.1007/s10584-026-04108-5
- Regional ensemble of CMIP6 global climate models for Sakha (Yakutia) Republic, Northern Eurasia N. Tananaev https://doi.org/10.1016/j.polar.2024.101066
- Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China A. Lv et al. https://doi.org/10.1016/j.scitotenv.2021.149831
- Exploring Afro-Asian water fluxes in CMIP5 and CMIP6 models: Present-day evaluation and future projections T. Adeliyi et al. https://doi.org/10.1016/j.scitotenv.2025.179374
- Unveiling the Climate Change Impact and Suitability Assessment of CMIP5 and CMIP6 Emission Scenarios for the Mahanadi Reservoir Project Complex, Chhattisgarh Y. Chakravaishya et al. https://doi.org/10.14796/JWMM.C563
- A first attempt to model global hydrology at hyper-resolution B. van Jaarsveld et al. https://doi.org/10.5194/esd-16-29-2025
- Spatial and temporal analysis and trends of extreme precipitation over the Mississippi River Basin, USA during 1988–2017 A. Dommo et al. https://doi.org/10.1016/j.ejrh.2024.101954
Saved (final revised paper)
Latest update: 03 Jun 2026
Short summary
The spatial resolution of global climate models (GCMs) and global hydrological models (GHMs) is increasing. This model study examines the benefits of a very high-resolution GCM and GHM in representing the hydrological cycle in the Rhine and Mississippi basins. We find that a higher-resolution GCM results in an improved precipitation budget, and therefore an improved hydrological cycle for the Rhine. For the Mississippi, no substantial improvements are found with increased resolution.
The spatial resolution of global climate models (GCMs) and global hydrological models (GHMs) is...
