Publication
Water resources management and hydrological risks are based, to a large extent, on our competence to generate relevant hydrological scenarios under present and future climatic situations. In alpine areas, characterized by highly complex topography, the availability of discharge data is generally very limited and the hydrological regime is frequently affected by hydraulic infrastructures (e.g. dams for hydro-power production). Flood scenario estimation is thus a complicated task; classical methods are badly adapted to such a context. The present work proposes a global concept based on the simulation of meteorological weather variables transformed subsequently in flood scenarios using a rainfall-runoff model. The meteorological scenarios are obtained from simulation using a stochastic weather generator conditioned by both atmospheric circulation indices and meteorological variables. Atmospheric circulation indices are derived from reanalysis data set provided by the National Centre for Environmental Prediction (NCEP) for the period 1982-2001. Appropriate circulation indices and meteorological series were selected by means of sensitivity analysis. The stochastic generator developed within the framework of this research combines two approaches widely used in recent prediction purposes. The first uses a set of generalized linear models – that extend the classical linear models – to explain regional and daily meteorological variables using the appropriate set of predictors. The second uses nearest neighbor method, based on the hypothesis that similar synoptic situations will produce similar meteorological situations, to disaggregate daily regional meteorological variables into hourly and locally distributed variables. The generation process was applied to the Upper Rhone River basin, an alpine catchment located in the Valais canton – Switzerland (upstream to Lake Leman) to generate a series of hourly meteorological scenarios (precipitation, temperature and lapse rate temperature) at multiple sites (48 raingauges and 11 thermal stations). The ability of the stochastic generator to reproduce statistics of key hydrological variables and extreme weather events was evaluated at multiple spatial (ranging from 100 to 5500 km2) and temporal (ranging from 3 h to 3 days) scales. These statistics are classical mean, standard deviation and lag-1 correlation coefficient for total and liquid precipitation, as well as temperature. Other associated variables, such as the snow/rain line altitude have been studied. In general, statistics related to meteorological scenarios show moderate to very good agreement between simulations and observations. For extreme events, distributions of annual maxima precipitations and of seasonal maximum and minimum temperatures are also well reproduced, even if results obtained at both hourly and locally scales show less good – but still satisfactory – performance than those obtained at both daily and regional scales. This is definitely n
Michael Lehning, Dylan Stewart Reynolds, Tobias Jonas