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
12 citations as recorded by crossref.
- 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
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- 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
- 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
- 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
- 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
- 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
- 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
10 citations as recorded by crossref.
- 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
- Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins Y. Xu et al. 10.1016/j.jhydrol.2024.131598
- 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
- 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
- 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
- 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
- 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
Latest update: 23 Nov 2024
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...
