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dc.creatorBeskow, Samuel-
dc.creatorCaldeira, Tamara Leitzke-
dc.creatorMello, Carlos Rogério de-
dc.creatorFaria, Lessandro Coll-
dc.creatorGuedes, Hugo Alexandre Soares-
dc.date.accessioned2017-09-18T16:33:51Z-
dc.date.available2017-09-18T16:33:51Z-
dc.date.issued2015-09-
dc.identifier.citationBESKOW, S. et al. Multiparameter probability distributions for heavy rainfall modeling in extreme southern Brazil. Journal of Hidrology: regional studies, [S. l.], v. 4, p. 123-133, Sep. 2015. Part B.pt_BR
dc.identifier.urirepositorio.ufla.br/jspui/handle/1/15403-
dc.description.abstracta) Study region: The study was conducted in the Rio Grande do Sul state – Brazil. b) Study focus: Studies about heavy rainfall events are crucial for proper flood management in river basins and for the design of hydraulic infrastructure. In Brazil, the lack of runoff monitoring is evident, therefore, designers commonly use rainfall intensity–duration–frequency (IDF) relationships to derive streamflow-related information. In order to aid the adjustment of IDF relationships, the probabilistic modeling of extreme rainfall is often employed. The objective of this study was to evaluate whether the GEV and Kappa multiparameter probability distributions have more satisfying performance than traditional two-parameter distributions such as Gumbel and Log-Normal in the modeling of extreme rainfall events in southern Brazil. Such distributions were adjusted by the L-moments method and the goodness-of-fit was verified by the Kolmogorov–Smirnov, Chi-Square, Filliben and Anderson–Darling tests. c) New hydrological insights for the region: The Anderson–Darling and Filliben tests were the most restrictive in this study. Based on the Anderson–Darling test, it was found that the Kappa distribution presented the best performance, followed by the GEV. This finding provides evidence that these multiparameter distributions result, for the region of study, in greater accuracy for the generation of intensity–duration–frequency curves and the prediction of peak streamflows and design hydrographs. As a result, this finding can support the design of hydraulic structures and flood management in river basins.pt_BR
dc.languageenen
dc.publisherElsevierpt_BR
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rightsacesso abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceJournal of Hidrology: regional studiespt_BR
dc.subjectHydrology – Statistical methodspt_BR
dc.subjectProbability distributionpt_BR
dc.subjectRainfallpt_BR
dc.subjectHidrologia – Métodos estatísticospt_BR
dc.subjectDistribuição de probabilidadept_BR
dc.subjectPrecipitação pluvialpt_BR
dc.titleMultiparameter probability distributions for heavy rainfall modeling in extreme southern Brazilpt_BR
dc.typeArtigopt_BR
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