Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1539-2024
© 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/hess-28-1539-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Apr 2024
Research article |
| 04 Apr 2024
| 04 Apr 2024
Multi-model approach in a variable spatial framework for streamflow simulation
HYCAR, INRAE, Université Paris-Saclay, Antony, France
Charles Perrin
HYCAR, INRAE, Université Paris-Saclay, Antony, France
Vazken Andréassian
HYCAR, INRAE, Université Paris-Saclay, Antony, France
Guillaume Thirel
HYCAR, INRAE, Université Paris-Saclay, Antony, France
Sébastien Legrand
Compagnie nationale du Rhône, Lyon, France
Olivier Delaigue
HYCAR, INRAE, Université Paris-Saclay, Antony, France
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Cited
17 citations as recorded by crossref.
- The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context M. Staudinger et al. https://doi.org/10.5194/nhess-25-247-2025
- Using century-long reanalysis and a rainfall-runoff model to explore multi-decadal variability in catchment hydrology at the European scale P. Brigode & L. Oudin https://doi.org/10.5194/hess-29-5535-2025
- Why should models talk to each other? A multipronged modelling strategy as an operational framework to guide adaptive watershed management in the Bay of Quinte Basin, Ontario, Canada C. Arnillas et al. https://doi.org/10.1016/j.ecolmodel.2026.111506
- Climate change effects on river droughts in Bavaria using a hydrological large ensemble B. Poschlod et al. https://doi.org/10.5194/hess-30-1165-2026
- A spatially hybrid hydrological modeling approach using subbasin-specific model structures Y. Wang et al. https://doi.org/10.1016/j.envsoft.2026.106944
- Why is there so much variability in crop multi-model studies? D. Wallach et al. https://doi.org/10.1016/j.agrformet.2025.110697
- 140-year daily ensemble streamflow reconstructions over 661 catchments in France A. Devers et al. https://doi.org/10.5194/hess-28-3457-2024
- Partitioning uncertainties of extreme flood estimates using long continuous simulations E. Kritidou et al. https://doi.org/10.1016/j.jhydrol.2025.134804
- What can be expected from a semi-distributed multi-model approach for streamflow forecasting? Tailoring the structure and size of a super-ensemble on the Rhône basin C. Thébault et al. https://doi.org/10.1016/j.jhydrol.2025.133589
- An ensemble learning framework for streamflow reconstruction incorporating aleatoric and epistemic uncertainty D. Ludyawati et al. https://doi.org/10.1016/j.ejrh.2026.103604
- MOHYSE – a simple lumped hydrological model for educational and research purposes J. Martel et al. https://doi.org/10.1080/07011784.2025.2536023
- Spatial runoff response to climate change in the Yarlung Zangbo-Brahmaputra River basin J. Wang & J. Zhao https://doi.org/10.1007/s11431-024-2851-7
- A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting X. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135319
- Streamflow prediction with aleatoric uncertainty quantification in the Yala river basin via Fokker–Planck equation-based frameworks S. Houénafa et al. https://doi.org/10.1016/j.ejrh.2025.102921
- Integrating machine learning ensembles and flood classification for enhanced flood forecasting with dynamic parameter weighting H. Oppel et al. https://doi.org/10.2166/hydro.2025.030
- Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model P. Royer-Gaspard et al. https://doi.org/10.1080/27678490.2024.2374081
- Unveiling the most effective model to predict streamflow capabilities from versatile hydrological models G. Wubu et al. https://doi.org/10.2166/wst.2026.209
17 citations as recorded by crossref.
- The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context M. Staudinger et al. https://doi.org/10.5194/nhess-25-247-2025
- Using century-long reanalysis and a rainfall-runoff model to explore multi-decadal variability in catchment hydrology at the European scale P. Brigode & L. Oudin https://doi.org/10.5194/hess-29-5535-2025
- Why should models talk to each other? A multipronged modelling strategy as an operational framework to guide adaptive watershed management in the Bay of Quinte Basin, Ontario, Canada C. Arnillas et al. https://doi.org/10.1016/j.ecolmodel.2026.111506
- Climate change effects on river droughts in Bavaria using a hydrological large ensemble B. Poschlod et al. https://doi.org/10.5194/hess-30-1165-2026
- A spatially hybrid hydrological modeling approach using subbasin-specific model structures Y. Wang et al. https://doi.org/10.1016/j.envsoft.2026.106944
- Why is there so much variability in crop multi-model studies? D. Wallach et al. https://doi.org/10.1016/j.agrformet.2025.110697
- 140-year daily ensemble streamflow reconstructions over 661 catchments in France A. Devers et al. https://doi.org/10.5194/hess-28-3457-2024
- Partitioning uncertainties of extreme flood estimates using long continuous simulations E. Kritidou et al. https://doi.org/10.1016/j.jhydrol.2025.134804
- What can be expected from a semi-distributed multi-model approach for streamflow forecasting? Tailoring the structure and size of a super-ensemble on the Rhône basin C. Thébault et al. https://doi.org/10.1016/j.jhydrol.2025.133589
- An ensemble learning framework for streamflow reconstruction incorporating aleatoric and epistemic uncertainty D. Ludyawati et al. https://doi.org/10.1016/j.ejrh.2026.103604
- MOHYSE – a simple lumped hydrological model for educational and research purposes J. Martel et al. https://doi.org/10.1080/07011784.2025.2536023
- Spatial runoff response to climate change in the Yarlung Zangbo-Brahmaputra River basin J. Wang & J. Zhao https://doi.org/10.1007/s11431-024-2851-7
- A hybrid method coupling physical process-driven model with generative deep learning for probabilistic flood forecasting X. Yang et al. https://doi.org/10.1016/j.jhydrol.2026.135319
- Streamflow prediction with aleatoric uncertainty quantification in the Yala river basin via Fokker–Planck equation-based frameworks S. Houénafa et al. https://doi.org/10.1016/j.ejrh.2025.102921
- Integrating machine learning ensembles and flood classification for enhanced flood forecasting with dynamic parameter weighting H. Oppel et al. https://doi.org/10.2166/hydro.2025.030
- Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model P. Royer-Gaspard et al. https://doi.org/10.1080/27678490.2024.2374081
- Unveiling the most effective model to predict streamflow capabilities from versatile hydrological models G. Wubu et al. https://doi.org/10.2166/wst.2026.209
Saved (final revised paper)
Latest update: 15 Jun 2026
Short summary
Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Streamflow forecasting is useful for many applications, ranging from population safety (e.g....
