Large scale influence on extreme precipitation

Extreme precipitation and flooding events in middle Europe lead to high death tolls and huge existential and financial losses. Evaluating how the probability of such events changes with respect to climate change can help in preventing casualties and reducing impact consequences. We create Intensity-Duration-Frequency (IDF) curves which describe the major statistical characteristics of extreme precipitation events (return level, return period, time scale). They provide information on the probability of exceedance of certain precipitation intensities for a range of durations and can help to visualize how extreme the event for different durations is. We modeled the underlying distribution of block maxima with the Generalized Extreme Value (GEV) distribution. Maxima from different durations are used and enable a model that can evaluate different time scales. All durations are modeled in one single model in order to prevent quantile-crossing and to assure that estimated quantiles are consistent.

Large-scale information is included by letting the GEV parameters depend on variables like NAO, temperature and blocking. We found an increase in probability of extreme precipitation with year as proxy for climate change and temperature, while the effect of the other variables depends on the season. Since it is easier to project average values than extremes, we use the relations between average large-scale covariates and extreme precipitation to create future IDF-relations based on climate projections. Furthermore, we plan to add a spatial component to the model that enables the usage of data from several neighboring stations in one model and interpolate to ungauged sites. This will be the basis for investigating how gridded data sets can be used to complement the station-based approach. One focus will lie on the challenge of dependence between neighboring grid points.