Logo Logo
Switch language to English
Schipper, Janus Willem (2005): Downscaling of Precipitation in the Upper Danube Catchment Area. Dissertation, LMU München: Fakultät für Physik



This work has been carried out in the framework of the project GLOWA-Danube (GLObal WAter cycle) where a joint effort is made by several groups to model the interaction of the water cycle and society in the Upper Danube catchment area. In particular regional climate models are used to simulate and eventually predict precipitation in this research area, while other groups convert this information into river runoff estimates and groundwater fluxes. It has been agreed in the project that precipitation data and other meteorological data must be handed over to the hydrological groups with a spatial resolution of 1 km. Long term runs with regional climate models are, however, not feasible at 1 km resolution, because they would exceed available computer resources by far. Therefore, a pragmatic downscaling method for precipitation must be implemented which provides data of 1 km resolution on the basis of model results of fairly coarse resolution. This downscaling uses extensively climatological precipitation observations where such downscaling relations can be derived. These observed rates are then adapted to the model results. The data are provided by the German Weather Service (DWD) and the Austrian Weather Service (ZAMG). The years 1991-2000 have been chosen as a reference period for the analysis. The climate simulation is carried out by the mesoscale model MM5 at a resolution of 45 km. The model MM5 offers a wide range of parameterizations with respect to convective processes, the boundary layer, cloud microphysics, and the radiation balance, all directly or indirectly responsible for generating precipitation. Sensitivity studies are performed to find the best configuration for the research area and reference period. A variety of methods is tested to generate observed and simulated climatological time series of precipitation. In particular, a linear average, a running average, a Fourier analysis, and spline interpolation are intercompared. In the end, spline interpolation between monthly values showed the best results for both time series and is used as a basis for the downscaling method. The downscaling method has to correct two major discrepancies between the observed precipitation distribution at the 1 km resolution and the simulated distribution at the 45 km resolution. First, these are small scale details related to topography in the rainfall distribution at the 1 km resolution, which lack in the 45 km resolution. Second, there is an unrealistic southward shift of the rainfall maximum at the northern rim of the Alps in the simulations, which needs to be corrected. A specific correction factor is introduced for each problem. The correlation between the spatial distribution of observed and simulated distributions increases after using the correction factors. Due to the climatological relationships, the results time periods of 10 days and longer are superior to those for periods shorter than 10 days. The precipitation distribution depends, of course, on the wind direction in particular so near the Alps. Wind direction and wind speed are simulated by the MM5 model and combined with the correction factors described above. The correlation between the spatial distribution of observed and simulated precipitation increases more if the wind direction dependent correction factors are introduced. These improved correction factors depend less on climatological relationships and perform therefore better for shorter time periods. Additionally, they will be able to respond better on changes in the weather regime in future climates. Altogether, this investigation provides a new pragmatic method to downscale model simulations on the basis of observations. This method will be used in the project GLOWA-Danube.