Articles | Volume 27, issue 12
https://doi.org/10.5194/hess-27-2283-2023
© Author(s) 2023. 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-27-2283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jun 2023
Research article |
| 21 Jun 2023
| 21 Jun 2023
Snow data assimilation for seasonal streamflow supply prediction in mountainous basins
Sammy Metref
CORRESPONDING AUTHOR
Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, Institut des Géosciences de l'Environnement, Université Grenoble Alpes, Grenoble, France
Datlas, Grenoble, France
Emmanuel Cosme
Centre National de la Recherche Scientifique, Institut de Recherche pour le Développement, Institut des Géosciences de l'Environnement, Université Grenoble Alpes, Grenoble, France
Matthieu Le Lay
Électricité de France – Division Technique Générale, Saint-Martin-le-Vinoux, France
Joël Gailhard
Électricité de France – Division Technique Générale, Saint-Martin-le-Vinoux, France
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Short summary
Predicting the seasonal streamflow supply of water in a mountainous basin is critical to anticipating the operation of hydroelectric dams and avoiding hydrology-related hazard. This quantity partly depends on the snowpack accumulated during winter. The study addresses this prediction problem using information from streamflow data and both direct and indirect snow measurements. In this study, the prediction is improved by integrating the data information into a basin-scale hydrological model.
Predicting the seasonal streamflow supply of water in a mountainous basin is critical to...
