Generation of spatially correlated synthetic rainfall time series in high temporal resolution : a data driven approach: Fid-Bau Portal

Generation of spatially correlated synthetic rainfall time series in high temporal resolution : a data driven approach

Beck, Ferdinand

State of the art in sewage system design is the dimensioning of tubes and channels based on hydraulic simulations. For a correct estimation of flood and backwater risks not only single precipitation events but long rainfall time series of several decades are used in these simulations. Due to the fast response time in sewage systems, a temporal resolution of 1h or shorter is required. Temporally high resolution precipitation data show pronounced spatial variability. The area for which one 1h precipitation time series is representative is limited. Even a dense measurement network as it is available in Southwest Germany, with about one station in 120 square kilometres, cannot provide adequate measurements for every target location. Besides, the observed time series are rarely long enough for risk assessment. In this work a synthetic time series generator was developed to close the gap between the demand and the offer of high resolution precipitation data in Baden-Württemberg. It could be shown that the dependence structure between monthly rainfall sum, 1h rainfall probability, the average and standard deviation of hourly precipitation is approximately constant over the study region and that it can be described by a Gaussian copula. The copula is exploited to draw random values of the latter three parameters that are conditioned on the monthly precipitation sum. The generation is a two step process. Based on the monthly parameters, an initial time series if filled up with Weibull distributed hourly rainfall amounts. Then the temporal order of the time series is optimized by a simulated annealing scheme. It consecutively exchanges pairs of values in the time series and evaluates the modified time series by statistical target values, e. g. the autocorrelation at different aggregation levels. The optimization stops when the target values are attained as close as possible. The statistical target values are derived from all available rain gauges of Baden-Württemberg and regionalized on a regular grid of 1km times 1km. In a next step the simulation scheme is extended to the generation of several simultaneous, spatially dependent precipitation time series. Spatial information is incorporated at different levels. During the set up of the initial time series, the monthwise spatial dependence of the generation parameters at the different target locations is considered. During the optimization, the spatial correlations of 17 different atmospheric circulation patterns are used as target values in the optimization. The last chapter of this work deals with the detection of climate change signals in the precipitation regime of Baden-Württemberg. Several trend signals could be derived from observed precipitation time series and were extrapolated into the future by means of the global circulation model ECHAM5. It could be shown that there has been an increase in the frequency of extreme hourly precipitation sums during the last decades. It can be expected that this trend will continue in the future. It is discussed in this work how the discovered trend signals can be incorporated in the developed rainfall generation algorithms.