Hydrology and Earth System Sciences www.hydrol-earth-syst-sci.net 1027-5606 1607-7938 13 9 2009 10.5194/hess-13-1699-2009 http://www.hydrol-earth-syst-sci.net/13/1699/2009/ http://www.hydrol-earth-syst-sci.net/13/1699/2009/hess-13-1699-2009.html http://www.hydrol-earth-syst-sci.net/13/1699/2009/hess-13-1699-2009.pdf 1699 1712 2009-09-29 Effects of intersite dependence of nested catchment structures on probabilistic regional envelope curves B. Guse bguse@gfz-potsdam.de A. Castellarin A. H. Thieken B. Merz Deutsches GeoForschungsZentrum GFZ, Section Hydrology, Potsdam, Germany Center for Disaster Management and Risk Reduction Technology (CEDIM), Karlsruhe, Germany Dipartimento di Ingegneria delle Strutture, dei Trasporti, delle Acque, del Rivelamento, del Territorio (DISTART), Università di Bologna, Bologna, Italy alpS – Centre for Natural Hazards and Risk Management, University of Innsbruck, Innsbruck, Austria This study analyses the intersite dependence of nested catchment structures by modelling cross-correlations for pairs of nested and unnested catchments separately. Probabilistic regional envelope curves are utilised to derive regional flood quantiles for 89 catchments located in Saxony, in the Southeast of Germany. The study area has a nested structure and the intersite correlation is much stronger for nested pairs of catchments than for unnested ones. Pooling groups of sites (regions) are constructed based on several candidate sets of catchment descriptors using the Region of Influence method. Probabilistic regional envelope curves are derived on the basis of flood flows observed within the pooling groups. Their estimated recurrence intervals are based on the number of effective sample years of data (i.e. equivalent number of uncorrelated data). The evaluation of the effective sample years of data requires the modelling of intersite dependence. We perform this globally, using a cross-correlation function for the whole study area as well as by using two different cross-correlation functions, one for nested pairs and another for unnested pairs. In the majority of the cases, these two modelling approaches yield significantly different estimates for the effective sample years of data, and therefore also for the recurrence intervals. 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