| Summary: | This thesis deals with the problem of how to estimate values of meteorological parameters that correspond to return periods that are considerably longer than the length of the observational data sets. The problem is approached by considering the output of weather-and climate models as pseudo-observations.These pseudo-observationalrecords, which are one to two orders of magnitude longer than the observational records, open the possibilityto reduce the large statisticaluncertainty in the 104-year estimate from observations, as well as to examine the assumption that all extremes (up to 104-year return periods) are part from the same population. We quantify the statistical uncertainty in the 104-year surge level if es¬timated from hundred-year records (as the observational records are). This is done by dividing the 5336-year long outputs of the climate model EcBilt-Clio into subsets of hundred year.We found thatannualmaxima of hundred¬year surge records can generally, within the uncertainty, be described by a Gumbel distribution. However, the total 5336-year record can clearly not be described by a single Gumbel distribution, but requires a GEV distribu¬tion instead. This implies that uncertainty ranges calculated from Gumbel distributions will produce numbers that are misleadinglylow. A statistical criterion is developed to determine whether all annual ex¬tremes can be described bya single GEV distribution or not.We foundthat for speci?c geographical locations in EcBilt-Clio, the extreme winds can not be described with a single GEV distribution, but requires a Generalized Two-Component Extreme Value (GTCEV) distribution. The meteorology resulting in the second component of the GTCEV distribution (so-called ’su¬perstorms’) has the following characteristics: the extreme winds are related to situations in which two vortices merge into a single one. In addition, the cyclones are embedded in a strong jet stream, and extreme precipitation accompanies the development of the cyclone. It is found that the area for which a second population is detected shifts due to the greenhouse e?ect from the North-Atlantic ocean to the European continent. We explored the suitability of the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecast archive for extreme value analysis of surges. The combined seasonal forecasts of the ECMWF cumu¬late to 1600 years. The high resolution in time and space and the more complete physics make these data highly appropriate to be analyzed with extreme value statistics. The long model record reduces the statistical un¬certaintyin the 104-year surge estimate with a factor four. We demonstrate that the archived ECMWF seasonal forecasts can also be used for extreme value estimates of other variables than wind and surge only. Four examples are presented, i.e., the Rhine discharge at Lobith, the sluicing of Lake IJssel water into the sea, the closure-frequency of the ’Maeslant’-barrier, and the (wave and sea level dependent) load on the Pet¬temer sea wall. The examples illustrate that the ECMWF data set o?ers unforeseen possibilities in modeling (hydrological) extremes. Especially, the simultaneous modeling of multiple extremes opens new perspectives. Preliminary results obtained with the so-called Challenge data are pre¬sented. The ’superstorm’ that we analyzed in the Challenge data has similar characteristics as the events in the EcBilt-Clio model. This result supports the idea that the earlier detected ’superstorms’ are not a model-artifact, but rather seems to be indeed a phenomenon belonging to the real world.
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