Title: Analyse de Sensibilité et Estimation de Paramètres pour la Modélisation Hydrologique : Potentiel et Limitations des Méthodes Variationnelles - Sensitivity Analysis and Parameter Estimation for Hydrological Modelling: Potential and Limitations of Variational Methods
Authors: CASTAINGS WILLIAM
Publication Year: 2007
JRC Publication N°: JRC43747
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC43747
Type: PhD Theses
Abstract: The rainfall-runoff transformation is characterized by the complexity of the involved processes and by the limited observability of the atmospheric forcing, catchment properties and hydrological response. It is therefore essential to understand, analyse and reduce the uncertainty inherent to hydrological modelling (sensitivity and uncertainty analysis, data assimilation). Variational methods are widely used in other scientific disciplines (ex. Meteorology, oceanography) facing the same challenges. In this work, they were applied to hydrological models characterised by different modelling paradigms (reductionist vs. systemic) and runoff generation mechanisms (infiltration-excess vs. saturation excess). The potential and limitations of variational methods for catchment hydrology are illustrated with MARINE from the Toulouse Fluids Mechanics Institute (IMFT) and two models (event based flood model and continuous water balance model) based on TOPMODEL concepts developed at the Laboratory of Environmental Hydrology (LTHE). Forward and adjoint sensitivity analysis provide a local but extensive insight of the relation between model inputs and prognostic variables. The gradient of a performance measure (characterising the misfit with observations), calculated with the adjoint model, efficiently drives a bound-constrained quasi-newton optimization algorithm for the estimation of model parameters. The results obtained are very encouraging and plead for an extensive use of the variational approach to understand and corroborate the processes described in hydrological models but also estimate the model control variables (calibration of model parameters and state estimation using data assimilation).
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