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>Extension du potentiel de la modélisation hydrologique. : inversions heuristiques de modèles pluie-débit pour l'identification des paramètres simultanément aux pluies ou à la courbe de tarage.
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Extension du potentiel de la modélisation hydrologique. : inversions heuristiques de modèles pluie-débit pour l'identification des paramètres simultanément aux pluies ou à la courbe de tarage.
Rainfall-runoff models are used for flood forecasting by warning authorities. However their implementation on a particular territory is still a challenge. Indeed, they generally need to be calibrated by using observed rainfall and discharge time series. These data may be subject to errors and uncertainties. They are not always available. Then, the model calibration relevancy may be affected and the forecasts may also be subject to significant uncertainties. This research would like to address such issues related to the rainfall-runoff models calibration, by proposing original methods which may set up a model by using less data than the ``classical'' calibration. The unused data might be either subject to uncertainties or not available. Moreover, these unused data may be estimated by the methods. Two model independant approach were suggested. Both are an heuristic inversion algorithm of rainfall-runoff models. The first method estimates simultaneously hourly rainfall time series and models parameters, by using only observed hourly discharge time series and total areal rainfall of flood events. A specific application of this method to set up models (with fixed parameters), generalises to models which are not invertible analyticaly, the ``hydrology backward'' approach proposed by Kirchner (2009).The second method estimates simultaneously models parameters and a rating-curve, by using only observed hourly rainfall and stage time series. Original analysis may be performed on the rainfall time series and the rating-curve estimated by the methods. Also, they extend the applicability rainfall-runoff models to hydrological context with restricted available data and offer promising operational applications. Yet, this research lead us to build a conceptual framework, denoted knowledge space. This framework unifies not only the original approaches which were proposed, but also some more ``classical'' approaches to hydrology as the calibration and the simulation.
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