Interdependence of Extreme Floods

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This research proposal deals with the spatial aspects of large scale floods. Instead of the traditional investigation of events using a selected set of extreme events, a methodology to derive common occurrences from time series properties of observed discharges is suggested. The essential properties can be derived using Fourier and Haar transformations which allow a description of the dependence corresponding to different time scales. Using these techniques critical time scales can be identified and their contribution to floods can be quantified. Complex high dimensional dependence structures cannot be detected using pairwise investigations. New techniques based on spectral properties and topological structures will be used to detect such dependences and to quantify their role. Note that these dependences may act differently, in some cases their consequence is a higher in others a lower large scale flood risk. Observed rainfall, series and output of meteorological models will be compared to detect differences in spatial dependence and spatial structure. This way a meteorologically plausible explanation of simultaneous flood occurrences will be possible. In order to assess probabilities of simultaneously occurring extreme discharges at different locations stochastic discharge and rainfall generators will be developed. Two different approaches – a traditional generator based on pairwise dependences and one reflecting high order dependence will be developed. For the rainfall generator spatial properties of the meteorological model output will also be reflected. The two types of models will be compared with respect to their areal behavior. Outputs of the rainfall generator will be used as input for hydrological models to obtain simulated discharge series. These will then be compared to the observed series and to the series obtained from the discharge generating model and both the plausibility of the models and the role of the meteorological factors and surface properties can be better understood.