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Rainfall frequency analysis using a mixed GEV distribution: a case study for annual maximum rainfalls in South Korea

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dc.contributor.authorYoon, Philyong-
dc.contributor.authorKim, Tae-Woong-
dc.contributor.authorYoo, Chulsang-
dc.date.accessioned2021-06-23T03:03:32Z-
dc.date.available2021-06-23T03:03:32Z-
dc.date.created2021-01-21-
dc.date.issued2013-07-
dc.identifier.issn1436-3240-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/27553-
dc.description.abstractExtreme rainfalls in South Korea result mainly from convective storms and typhoon storms during the summer. A proper way for dealing with the extreme rainfalls in hydrologic design is to consider the statistical characteristics of the annual maximum rainfall from two different storms when determining design rainfalls. Therefore, this study introduced a mixed generalized extreme value (GEV) distribution to estimate the rainfall quantile for 57 gauge stations across South Korea and compared the rainfall quantiles with those from conventional rainfall frequency analysis using a single GEV distribution. Overall, these results show that the mixed GEV distribution allows probability behavior to be taken into account during rainfall frequency analysis through the process of parameter estimation. The resulting rainfall quantile estimates were found to be significantly smaller than those determined using a single GEV distribution. The difference of rainfall quantiles was found to be closely correlated with the occurrence probability of typhoon and the distribution parameters.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleRainfall frequency analysis using a mixed GEV distribution: a case study for annual maximum rainfalls in South Korea-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Tae-Woong-
dc.identifier.doi10.1007/s00477-012-0650-5-
dc.identifier.scopusid2-s2.0-84878610880-
dc.identifier.wosid000319752500009-
dc.identifier.bibliographicCitationStochastic Environmental Research and Risk Assessment, v.27, no.5, pp.1143 - 1153-
dc.relation.isPartOfStochastic Environmental Research and Risk Assessment-
dc.citation.titleStochastic Environmental Research and Risk Assessment-
dc.citation.volume27-
dc.citation.number5-
dc.citation.startPage1143-
dc.citation.endPage1153-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaEnvironmental Sciences & Ecology-
dc.relation.journalResearchAreaMathematics-
dc.relation.journalResearchAreaWater Resources-
dc.relation.journalWebOfScienceCategoryEngineering, Environmental-
dc.relation.journalWebOfScienceCategoryEngineering, Civil-
dc.relation.journalWebOfScienceCategoryEnvironmental Sciences-
dc.relation.journalWebOfScienceCategoryStatistics & Probability-
dc.relation.journalWebOfScienceCategoryWater Resources-
dc.subject.keywordPlusGAMMA-DISTRIBUTION-
dc.subject.keywordAuthorExtreme rainfall-
dc.subject.keywordAuthorFrequency analysis-
dc.subject.keywordAuthorMixed distribution-
dc.subject.keywordAuthorRainfall quantile-
dc.identifier.urlhttps://link.springer.com/article/10.1007/s00477-012-0650-5-
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ERICA 공학대학 (DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING)
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