Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5519-2020
https://doi.org/10.5194/hess-24-5519-2020
Research article
 | 
23 Nov 2020
Research article |  | 23 Nov 2020

On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment

Maxime Jay-Allemand, Pierre Javelle, Igor Gejadze, Patrick Arnaud, Pierre-Olivier Malaterre, Jean-Alain Fine, and Didier Organde

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Cited articles

Abbaris, A., Dakhlaoui, H., Thiria, S., and Bargaoui, Z.: Variational data assimilation with the YAO platform for hydrological forecasting, P. Int. Ass. Hydrol. Sci., 364, 3–8, https://doi.org/10.5194/piahs-364-3-2014, 2014. a
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Arnaud, P., Lavabre, J., Fouchier, C., Diss, S., and Javelle, P.: Sensitivity of hydrological models to uncertainty in rainfall input, Hydrolog. Sci. J., 56, 397–410, 2011. a
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. a
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Short summary
This study contributes to flash flood prediction using a hydrological model. The model describes the spatial properties of the watersheds with hundreds of unknown parameters. The Gardon d'Anduze watershed is chosen as the study benchmark. A sophisticated numerical algorithm and the downstream discharge measurements make the identification of the model parameters possible. Results provide better model predictions and relevant spatial variability of some parameters inside this watershed.