Modelando precipitação extrema no Brasil pela teoria dos valores extremos

Pereira, Paulo Vitor da Costa

Use este identificador para citar ou linkar para este item: http://repositorio.uem.br:8080/jspui/handle/1/4363

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Campo DC	Valor	Idioma
dc.contributor.advisor	Isolde Terezinha Santos Previdelli	pt_BR
dc.contributor.author	Pereira, Paulo Vitor da Costa	pt_BR
dc.date.accessioned	2018-04-18T20:15:55Z	-
dc.date.available	2018-04-18T20:15:55Z	-
dc.date.issued	2016	pt_BR
dc.identifier.uri	http://repositorio.uem.br:8080/jspui/handle/1/4363	-
dc.language	por	pt_BR
dc.publisher	Universidade Estadual de Maringá	pt_BR
dc.rights	openAccess	pt_BR
dc.subject	Teoria estatística bayesiana	pt_BR
dc.subject	Decisões estatísticas	pt_BR
dc.subject	Modelo hierárquico bayesiana	pt_BR
dc.subject	Precipitação pluviométrica	pt_BR
dc.subject	Análise	pt_BR
dc.subject	Análise estatística	pt_BR
dc.subject	Brasil.	pt_BR
dc.subject	Bayesian hierarchical model	en
dc.subject	Penultimate bias	en
dc.subject	Precipitation field	en
dc.subject	Return level map	en
dc.subject	Small sample bias	en
dc.subject	Brazil.	en
dc.title	Modelando precipitação extrema no Brasil pela teoria dos valores extremos	pt_BR
dc.title.alternative	Modeling daily rainfall in Brazil with extreme value theory	en
dc.type	doctoralThesis	pt_BR
dc.contributor.referee1	Silvia Lopes de Paula Ferrari - USP	-
dc.contributor.referee2	Eniuce Menezes de Souza - UEM	-
dc.description.resumo	: The accurate modeling of extreme events is growing in relevance, particularly in the environmental sciences in which such events can be seen as a result of climate change. In particular, measuring rainfall risk is also important for the design of hydraulic structures (dams, levees, drainage systems, bridges, etc.) and for flood mapping and zoning. The Brazilian regulatory agency, Agência Nacional de Águas (ANA), makes available rainfall series for 11,368 rain stations throughout Brazil, some of them dating from the 19th century. One of our goals was to produce, using the framework of extreme value theory, maps with reliable estimates of the 25-year return level of a extreme rainfall for each locality covered by ANA. Such dataset present many complex challenges: first, evaluating its quality; then, modeling spatial extremes over large random fields; modeling temporal nonstationarity of the extreme rainfall process due to natural climate seasonality and due to a possible trend owing to climate change; correcting biases resulting from misspecification of the model or from a small sample. In this study, we tackle all these issues. We perform a detailed quality control, and we make a deep discussion of biases resulting either from misspecification of the model or from a small sample, while providing important information regarding the modeling of rainfall extremes, and complementing recent previous studies. In particular, the shape parameter of the extreme-value model seems to have a mean asymptotic value of 0.06.	pt_BR
dc.publisher.country	Brasil	pt_BR
dc.publisher.department	Departamento de Estatística	-
dc.publisher.program	Programa de Pós-Graduação em Bioestatística	pt_BR
dc.publisher.initials	UEM	pt_BR
dc.subject.cnpq1	Ciências Exatas e da Terra	pt_BR
dc.publisher.local	Maringá, PR	pt_BR
dc.description.physical	69 f	pt_BR
dc.subject.cnpq2	Estatística	pt_BR
dc.publisher.center	Centro de Ciências Exatas	pt_BR
Aparece nas coleções:	2.5 Dissertação - Ciências Exatas (CCE)

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