How IDF Relations Changed in the Past and How They Will Change in the Future

We investigate intensity-duration-frequency (IDF) relations. They describe the major statistical characteristics of extreme precipitation events (return level, return period, time scale) and provide information on the probability of exceedance of certain precipitation intensities. IDF relations help to visualize either how extreme (in terms of probability/frequency/return period) a specific event is or which intensity is expected for a given probability. We model the distribution of extreme precipitation in an extreme-value statistics setting. To increase model efficiency, we include the duration and model a duration-dependent GEV. The durations range from minutes to days and are modeled in one single model in order to prevent quantile-crossing and to assure that estimated quantiles are consistent. This way, we are capable of considering large-scale influences by using covariates for the GEV parameters.

The influence of climate change is included by letting the GEV parameters (covariates) depend on the covariates NAO, temperature, humidity, blocking and year (as a proxy for climate change). We found an increase in probability of extreme precipitation with year and temperature, while the effect of the other variables depends on the season. We present a downscaling approach under the perfect-prognosis assumption as a proof-of-concept, where we use future values of large-scale covariates from climate projections to derive future GEV distributions. This poses some challenges because the polynomial dependencies of the past might not hold for an extrapolation into the future. Right now, our model is based on measurement stations, but we will give an outlook how we plan to include gridded datasets of precipitation observations or reanalyses.