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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-331
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-331
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 16 Oct 2019

Submitted as: research article | 16 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

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

Maxime Jay-Allemand1,2, Pierre Javelle1, Igor Gejadze2, Patrick Arnaud1, Pierre-Olivier Malaterre2, Jean-Alain Fine3, and Didier Organde3 Maxime Jay-Allemand et al.
  • 1IRSTEA, 3275 Route de Cézanne, 13182 Aix-en-Provence, France
  • 2IRSTEA, 361 rue Jean-François Breton, 34196 Montpellier, France
  • 3Hydris-Hydrologie, Parc Scientifique Agropolis II, 2196 Boulevard de la Lironde, 34980 Montferrier sur Lez, France

Abstract. Flash flood alerts in metropolitan France are provided by SCHAPI (Service Central Hydrométéorologique et d’Appui à la Prévision des Inondations) through the Vigicrues Flash service, which is designed to work in ungauged catchments. The AIGA method implemented in Vigicrues Flash is designed for flood forecasting on small- and medium-scale watersheds. It is based on a distributed hydrological model accounting for spatial variability of the rainfall and the catchment properties, based on the radar rainfall observation inputs. Calibration of distributed parameters describing these properties with high resolution is difficult, both technically (in terms of the estimation method), and because of the identifiability issues. Indeed, the number of parameters to be calibrated is much greater than the number of spatial locations where the discharge observations are usually available. However, the flood propagation is a dynamic process, so observations have also a temporal dimension. This must be larger enough to comprise a representative set of events. In order to fully benefit from using the AIGA method, we consider its hydrological model (GRD) in combination with the variational estimation (data assimilation) method. In this method, the optimal set of parameters is found by minimizing the objective function which includes the misfit between the observed and predicted values and some additional constraints. The minimization process requires the gradient of the cost function with respect to all control parameters, which is efficiently computed using the adjoint model. The variational estimation method is scalable, fast converging, and offers a convenient framework for introducing additional constraints relevant to hydrology. It can be used both for calibrating the parameters and estimating the initial state of the hydrological system for short range forecasting (in a manner used in weather forecasting). The study area is the Gardon d’Anduze watershed where four gauging stations are available. In numerical experiments, the benefits of using the distributed against the uniform calibration are analysed in terms of the model predictive performance. Distributed calibration shows encouraging results with better model prediction at gauged and ungauged locations.

Maxime Jay-Allemand et al.
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Short summary
Our research aims to improve flash flood forecasts and targets small watersheds. We use a model which describes spatially the watersheds properties with many parameters. Our challenge is to identify the spatial variability of these model parameters according few river flow measurements. We test a sophisticated numerical algorithm to perform this identification. Our study area is a Mediterranean watershed subject to flash-floods. Results show improved model predictions inside this watershed.
Our research aims to improve flash flood forecasts and targets small watersheds. We use a model...
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