Abstract
Within the Clim2Power project, two case studies focus on seasonal variations of the hydropower production in the river basins of the Danube (Germany/Austria) and the Douro (Portugal). To deliver spatially highly resolved climate data as an input for the hydrological models, the forecasts of the German Climate Forecast System (GCFS2.0) need to be downscaled. The statistical-empirical method EPISODES is used in this approach. It is adapted to the seasonal data, which consists of ensemble hindcasts and forecasts. Beside this, the two case study regions need specific configurations of the statistical model, providing appropriate predictors for the meteorological variables. This paper describes the technical details of the adaptation of the EPISODES method for the needs of Clim2Power. We analyse the hindcast skill of the downscaled hindcasts of all four seasons for the two variables near-surface (2 m) temperature and precipitation, and conclude that on the average the skill is conserved compared to the global model. This means that the seasonal information is available at a higher spatial resolution without losing skill. Furthermore, the output of the statistical downscaling is nearly bias-free, which is, beside the higher spatial resolution, an added value for the climate service.
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