Articles | Volume 28, issue 7
https://doi.org/10.5194/hess-28-1539-2024
https://doi.org/10.5194/hess-28-1539-2024
Research article
 | 
04 Apr 2024
Research article |  | 04 Apr 2024

Multi-model approach in a variable spatial framework for streamflow simulation

Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue

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Technical note: How many models do we need to simulate hydrologic processes across large geographical domains?
Wouter J. M. Knoben, Ashwin Raman, Gaby J. Gründemann, Mukesh Kumar, Alain Pietroniro, Chaopeng Shen, Yalan Song, Cyril Thébault, Katie van Werkhoven, Andrew W. Wood, and Martyn P. Clark
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-279,https://doi.org/10.5194/hess-2024-279, 2024
Revised manuscript under review for HESS
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Cited articles

Ajami, N. K., Duan, Q., Gao, X., and Sorooshian, S.: Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results, J. Hydrometeorol., 7, 755–768, https://doi.org/10.1175/JHM519.1, 2006. 
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007. 
Andréassian, V., Hall, A., Chahinian, N., and Schaake, J.: Introduction and synthesis: Why should hydrologists work on a large number of basin data sets?, in: Large sample basin experiments for hydrological parametrization: results of the models parameter experiment-MOPEX, IAHS Red Books Series no. 307, AISH, 1–5, https://iahs.info/uploads/dms/13599.02-1-6-INTRODUCTION.pdf (last access: 23 March 2023), 2006. 
Arsenault, R., Gatien, P., Renaud, B., Brissette, F., and Martel, J.-L.: A comparative analysis of 9 multi-model averaging approaches in hydrological continuous streamflow simulation, J. Hydrol., 529, 754–767, https://doi.org/10.1016/j.jhydrol.2015.09.001, 2015. 
Artigue, G., Johannet, A., Borrell, V., and Pistre, S.: Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France), Nat. Hazards Earth Syst. Sci., 12, 3307–3324, https://doi.org/10.5194/nhess-12-3307-2012, 2012. 
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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.
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