Modeling hydrometeorological extremes in Alpine catchments

Abstract: Uncertainties with a modeling framework consisting of a weather generator, two precipitation disaggregation models and the hydrological HBV model was assessed with respect to hydrometeorological extremes in Tyrol, Austria. Extreme precipitation events are expected to increase in intensity and frequency in the Alps during a warmer climate. The Alpine regions may be particularly vulnerable to such changes in climate where many floods in Europe occurred during recent years and caused major damage and loss of life. Weather generators typically provide time series at daily resolution. Different disaggregation methods have therefore been proposed and successfully tested to increase temporal resolution in precipitation. This is essential since flood peaks may be maintained for as little as minutes. Here, the non-parametric method of fragments was tested and compared with the multiplicative microcanonical cascade model with uniform splitting on the reproduction of precipitation extremes. It is also demonstrated that the method of fragments model can be transformed to disaggregate temperature with slight changes in the model structure. Preliminary test results show that the simulation of discharge peaks can be improved by disaggregating temperature in comparison with using daily averages as input in the HBV model.  Test results show that precipitation extremes were simulated within confidence bounds for Kelchsauer and Gurglbach when using historical observations as input. These two catchments had longer records of data available in comparison with Ruetz where the majority of simulated precipitation extremes were found outside confidence ranges. This indicates that the model is data driven. Synthetic data series were constructed with the weather generator from historical data and disaggregated with the two disaggregation models. The differences between the models were bigger for Ruetz where less observed data was available. The method of fragments simulates extremes with the closest resemblance to extremes. This is also true for the reproduction of wet spells and simulated variance. To account for parameter uncertainty in the HBV model, it is highly motivated to simulate discharge with different but suitable parameter sets to account for equifinality. However, the large amount of data produced when disaggregating the weather generated time series transcended the data capacity of the HBV model and made it crash. Other uncertainties related to the framework are the use of theoretical probability distributions in the weather generator and the dependence of high-resolution data for the disaggregation model. Despite these uncertainties, the framework is closer to a physical understanding of the causes of floods than the uncertain frequency analysis method. The framework is also applicable to land-use and climate change studies.