Hydrological behaviour through experimental and modelling approaches: application to the Haute-Mentue catchment

The present work is related to the recent research topics in hydrology devoted to the integration of field knowledge into the hydrological modelling. The study catchment is the Haute-Mentue experimental basin (12.5 km2) located in western Switzerland, in the Plateau region. In order to complete the existing knowledge about the hydrological behaviour of the study catchment, a field experimental approach has been conducted at two scales: catchment (environmental tracing) and local scale (TDR soil moisture measurements). The environmental tracing application has led to the same conclusion as previous researches: hydrological behaviour is strongly influenced by the catchment antecedent conditions and by the rainfall duration and intensity. The geology characteristics (moraine or molasse) explain the main differences in the hydrological behaviour that have been observed so far. As the environmental tracing does not allow easy identification of the mechanisms responsible for the runoff generation, TDR equipments have been installed on two hillslopes with different geological characteristics, which allowed monitoring of the soil moisture at different depths along the hillslope during two intensive campaigns in 2002 and 2003. Association of the environmental tracing and TDR technique has finally allowed precising the conceptual model of two head sub-catchments of the Haute-Mentue catchment. The second part of the research is devoted to the hydrological modelling. A simple conceptual model (TOPMODEL) has been considered as an appropriate representation of the hydrological processes on the Haute-Mentue catchment. In order to estimate TOPMODEL parameters and to take into account uncertainty associated with estimated parameters and model output, a Bayesian approach has been proposed and two Bayesian techniques have been compared: GLUE (Generalized Likelihood Uncertainty Estimation) and MCMC (Monte Carlo Markov Chains). The role of the statistical corrections on the resulting parameters and model output uncertainty has been assessed. In the last part of the present research, the Bayesian methodology has been extended to the case of multi-response calibration. Previous field acquired knowledge (i.e. soil storage saturation deficit, stream water silica and calcium concentrations) has been used to constrain parametrization of the classical and of a modified version of TOPMODEL. In both cases, multi-calibration led to trade-off behaviour of the efficiencies of the simulated responses. The total modelling uncertainty of the new introduced responses was considerably reduced at the expense of an increase in the total modelling uncertainty of the simulated discharges.

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