Page - 387 - in Book of Full Papers - Symposium Hydro Engineering

Bias correction using quantile mapping (qmap) was performed according to the method developed by Gudmundsson et. al. [3]. This method entails an empirical adjustment of the distribution of the forecasted values to fit the distribution of the observed values. The results of using both bias correction methods individually and of using quantile mapping followed by a correction using monthly factor were evaluated using goodness of fit indicators such as the Pearson coefficient, Nash-Sutcliffe efficiency coefficient, bias error, mean absolute error and mean square deviation. 3.2. INDICES Hydro-meteorological indices play a crucial role in the project. Indices have to be interpreted from the viewpoint of reservoir operation. As indices have differing inertia and apply to different periods, they can be used for predictive operation. The appropriateness of these indices, the way they should be interpreted and their usefulness regarding early detection of hydrological stress, is tested by conducting hindcast experiments. Indices providing the best skill in hindcast experiments are selected for conducting forecasts. As long as the forecast quality is adequate, this will lead to an enhancement of the current early detection methods. For a start, the Standardized Precipitation Index (SPI) was used. The SPI is recommended by the World Meteorological Organization for meteorological drought monitoring [2]. The SPI can be calculated for different aggregation periods, e.g. only one month or even up to 60 months. In order to address uncertainty contained in the forecasts, the SPI is calculated for time periods that extend both into the past as well as into the future, thus consisting of different amounts of observed and forecasted values. The performance of indices calculated with different observed and forecasted aggregation periods was compared with results that used only observed values for computing SPIs. In doing so, it is possible to determine how reliable the SPI computed using forecast data is for different forecast lengths. In a next step, the Standardized Precipitation Evaporation Index (SPEI) and the Water Supply Index will be tested in terms of their suitability. 4. RESULTS 4.3. BIAS CORRECTION OF FORECAST DATA Examples for a monitoring station in eastern Germany are shown. The accuracy of the bias-corrected forecast time series was evaluated by means of 387