ALBERT

Description: Widespread flooding events are among the major natural hazards in central Europe. Such events are usually related to intensive, long-lasting precipitation over larger areas. Despite some prominent floods during the last three decades (e.g., 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, decadal hindcasts, and also predictions for the upcoming decade combined to a new large ensemble. Global reanalyses for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (Consortium for Small-scale Modeling – CLimate Mode; COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. Evaluation focuses on intensive widespread precipitation events and related temporal variabilities and trends. The presented ensemble data are within the range of observations for both statistical distributions and time series. The temporal evolution during the past 60 years is captured. The results reveal some long-term variability with phases of increased and decreased precipitation rates. The overall trend varies between the investigation areas but is mostly significant. The predictions for the upcoming decade show ongoing tendencies with increased areal precipitation. The presented regional climate model (RCM) ensemble not only allows for more robust statistics in general, it is also suitable for a better estimation of extreme values.

Description: Widespread flooding events are among the major natural hazards in Central Europe. Such events are usually related to intensive, long-lasting precipitation. Despite some prominent floods during the last three decades (e.g. 1997, 1999, 2002, and 2013), extreme floods are rare and associated with estimated long return periods of more than 100 years. To assess the associated risks of such extreme events, reliable statistics of precipitation and discharge are required. Comprehensive observations, however, are mainly available for the last 50–60 years or less. This shortcoming can be reduced using stochastic data sets. One possibility towards this aim is to consider climate model data or extended reanalyses. This study presents and discusses a validation of different century-long data sets, a large ensemble of decadal hindcasts, and also projections for the upcoming decade. Global reanalysis for the 20th century with a horizontal resolution of more than 100 km have been dynamically downscaled with a regional climate model (COSMO-CLM) towards a higher resolution of 25 km. The new data sets are first filtered using a dry-day adjustment. The simulations show a good agreement with observations for both statistical distributions and time series. Differences mainly appear in areas with sparse observation data. The temporal evolution during the past 60 years is well captured. The results reveal some long-term variability with phases of increased and decreased heavy precipitation. The overall trend varies between the investigation areas but is significant. The projections for the upcoming decade show ongoing tendencies with increased precipitation for upper percentiles. The presented RCM ensemble not only allows for more robust statistics in general, in particular it is suitable for a better estimation of extreme values.

Description: Heavy precipitation leading to widespread river floods are one of the main natural hazards affecting Central Europe. Since extreme precipitation events associated with devastating floods have long return periods, long-term datasets are needed to adequately quantify the frequency and intensity of these events. As long-term observations of precipitation across Europe are rare and not homogeneous in space nor time, they are generally not suitable to run hydrological models. In the present study, a combined approach is presented on how to generate a consistent precipitation dataset based on dynamical downscaling and post-processing statistics. Focus is given to five river catchments in Central Europe: Upper Danube, Elbe, Oder, Rhine, and Vistula. Reanalysis data are dynamically downscaled with a regional climate model and bias corrected towards observations. Empirical quantile mapping was identified as one of the most suitable methods to correct the bias in model precipitation. For most of the top ten precipitation events of large European river catchments, bias correction led to clear improvements towards the raw model data. However, results for Western European rivers (e.g., Rhine) are typically better than for Eastern European rivers (e.g., Vistula), which may also be associated with observational gaps for the latter. Two examples of severe river floods are presented in more detail: the Rhine river flood in winter 1995 and the flood in the Upper Danube and Vistula in June 2009. While the former was already well presented without bias correction, for the latter, bias correction improved underestimated precipitation amounts in the Upper Danube but not in the Vistula catchment. In conclusion, this method can be applied to other extensive datasets towards the development of a Pan-European stochastic precipitation dataset.