Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4407-2022
© Author(s) 2022. 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-26-4407-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2022
Research article |
| 29 Aug 2022
| 29 Aug 2022
Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model
Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Rolf W. Hut
Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Nick C. van de Giesen
Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Niels Drost
The Netherlands eScience Center, Science Park 140, 1098 XG Amsterdam, the Netherlands
Willem J. van Verseveld
Catchment and Urban Hydrology, Department of Inland Water Systems, Deltares, P.O. Box 177, 2600MH Delft, the Netherlands
Albrecht H. Weerts
Operational Water Management, Department of Inland Water Systems, Deltares, P.O. Box 177, 2600MH Delft, the Netherlands
Hydrology and Quantitative Water Management Group, Wageningen University and Research, P.O. Box 47, 6700AA Wageningen, the Netherlands
Pieter Hazenberg
Applied Research Center, Florida International University, FL 33174 Miami, United States of America
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Cited
21 citations as recorded by crossref.
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- Multi-model hydrological reference dataset over continental Europe and an African basin B. Droppers et al. 10.1038/s41597-024-03825-9
- Digital twins of the Earth with and for humans W. Hazeleger et al. 10.1038/s43247-024-01626-x
- A new methodology for probabilistic flood displacement risk assessment: the case of Fiji and Vanuatu L. Rossi et al. 10.3389/fclim.2024.1345258
- Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications W. van Verseveld et al. 10.5194/gmd-17-3199-2024
- Understanding the influence of hydrologic parameter uncertainty on Community Water Model predictions: a diagnostic assessment through extensive ensemble simulations J. Kim & K. Ahn 10.1088/1748-9326/add27e
- Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure E. van der Laan et al. 10.1016/j.scitotenv.2024.172678
- A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al. 10.5194/hess-28-5331-2024
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
- A first attempt to model global hydrology at hyper-resolution B. van Jaarsveld et al. 10.5194/esd-16-29-2025
- Prediction of future runoff in the Lancang River Basin based on CMIP6 under climate change K. Lu et al. 10.1016/j.ejrh.2025.102413
- Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent J. Hoch et al. 10.5194/hess-27-1383-2023
- Compound effects of river routing and local rainfall-runoff for inflow simulation of a large reservoir S. Zang et al. 10.1080/02626667.2025.2496280
- Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study X. Zheng et al. 10.5194/hess-29-627-2025
- Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
- Unveiling the Potential of Hybrid Deep Learning Algorithm in Streamflow Projection R. Vogeti et al. 10.1088/1755-1315/1409/1/012001
- A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
- Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods S. Seyedian et al. 10.1007/s11269-024-03883-z
- HERA: a high-resolution pan-European hydrological reanalysis (1951–2020) A. Tilloy et al. 10.5194/essd-17-293-2025
- The eWaterCycle platform for open and FAIR hydrological collaboration R. Hut et al. 10.5194/gmd-15-5371-2022
- HydroMT: Automated and reproducible model building and analysis D. Eilander et al. 10.21105/joss.04897
19 citations as recorded by crossref.
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- Multi-model hydrological reference dataset over continental Europe and an African basin B. Droppers et al. 10.1038/s41597-024-03825-9
- Digital twins of the Earth with and for humans W. Hazeleger et al. 10.1038/s43247-024-01626-x
- A new methodology for probabilistic flood displacement risk assessment: the case of Fiji and Vanuatu L. Rossi et al. 10.3389/fclim.2024.1345258
- Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications W. van Verseveld et al. 10.5194/gmd-17-3199-2024
- Understanding the influence of hydrologic parameter uncertainty on Community Water Model predictions: a diagnostic assessment through extensive ensemble simulations J. Kim & K. Ahn 10.1088/1748-9326/add27e
- Simulation of long-term storage dynamics of headwater reservoirs across the globe using public cloud computing infrastructure E. van der Laan et al. 10.1016/j.scitotenv.2024.172678
- A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al. 10.5194/hess-28-5331-2024
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
- A first attempt to model global hydrology at hyper-resolution B. van Jaarsveld et al. 10.5194/esd-16-29-2025
- Prediction of future runoff in the Lancang River Basin based on CMIP6 under climate change K. Lu et al. 10.1016/j.ejrh.2025.102413
- Hyper-resolution PCR-GLOBWB: opportunities and challenges from refining model spatial resolution to 1 km over the European continent J. Hoch et al. 10.5194/hess-27-1383-2023
- Compound effects of river routing and local rainfall-runoff for inflow simulation of a large reservoir S. Zang et al. 10.1080/02626667.2025.2496280
- Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study X. Zheng et al. 10.5194/hess-29-627-2025
- Reconstruction of missing streamflow series in human-regulated catchments using a data integration LSTM model A. Tursun et al. 10.1016/j.ejrh.2024.101744
- Unveiling the Potential of Hybrid Deep Learning Algorithm in Streamflow Projection R. Vogeti et al. 10.1088/1755-1315/1409/1/012001
- A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
- Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods S. Seyedian et al. 10.1007/s11269-024-03883-z
- HERA: a high-resolution pan-European hydrological reanalysis (1951–2020) A. Tilloy et al. 10.5194/essd-17-293-2025
Latest update: 24 May 2025
Short summary
In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
In recent years gridded hydrological modelling moved into the realm of hyper-resolution...
