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Identification of the parameters of rainfall-runoff models at ungaged locations: can we benefit from the regionalization of flow statistics?

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HAL Id: hal-00583227

https://hal.archives-ouvertes.fr/hal-00583227

Submitted on 5 Apr 2011

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Identification of the parameters of rainfall-runoff models

at ungaged locations: can we benefit from the

regionalization of flow statistics?

G. Boldetti, V. Andréassian, Ludovic Oudin, C. Perrin

To cite this version:

G. Boldetti, V. Andréassian, Ludovic Oudin, C. Perrin. Identification of the parameters of

rainfall-runoff models at ungaged locations: can we benefit from the regionalization of flow statistics?. 7th

EGU General Assembly, May 2010, Vienna, Austria. 1 p. �hal-00583227�

(2)

Geophysical Research Abstracts Vol. 12, EGU2010-11581, 2010 EGU General Assembly 2010 © Author(s) 2010

Identification of the parameters of rainfall-runoff models at ungaged

locations: can we benefit from the regionalization of flow statistics?

Gianluca Boldetti (1), Vazken Andréassian (1), Ludovic Oudin (2), and Charles Perrin (1)

(1) Cemagref, Antony, France (gianluca.boldetti@cemagref.fr), (2) Université Pierre et Marie Curie, Paris, France

This study aims at finding the most likely parameter sets for an ungauged catchments, when an ensemble of parameter sets has been generated for one or more hydrologically similar donor catchments. We find ourselves in a situation where we suspect that these parameters, although equally acceptable for the donor catchment, are not equally valid for the ungauged catchment we are interested in. We need to add an additional constraint to sort the parameter sets.

Here, we want to use as additional constraint the output of previous regionalization studies, i.e. studies which aimed at identifying ’simpler’ caracteristics of the ungaged catchments (long-term annual flow, high-and low-quantiles of the long-term catchment behavior, etc.) The interest of such a procedure comes from our experience with the regionalization of statistical flow values: it is a somewhat easier exercise, compared with the regionaliza-tion of model parameters (physiographic informaregionaliza-tion seem to be a better explicans for statistical flow values than for model parameter values).

We show here that we can find a solution to combine constraints from different regionalization relationships, to reflect their uncertainty and possible complementarity. We illustrate our method on a large set of 900 French catchments. The application on a large dataset allows us to evaluate the strenghts and weaknesses of the proposed method.

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