Método para a estimação de quantis de enchentes extremas com o emprego conjunto de análise bayesiana, de informações não sistemáticas e de distribuições limitadas superiormente

• Ano de defesa: 2009
• Autor(a) principal: Wilson dos Santos Fernandes
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: meio ambiente e recursos hídricos; Saneamento; Recursos hídricos Desenvolvimento; Engenharia sanitária; Teoria bayesiana de decisão estatistica; Inundações
• Link de acesso: http://hdl.handle.net/1843/BUDB-8AMJYS

Some recent researches on fluvial processes suggest the idea that some hydrological variables, such as flood flows, are upper-bounded. However, almost all probability distributions that are currently employed in flood frequency analysis are unbounded. The complete predominanceof unbounded distributions in conventional flood frequency analysis is due mainly to the difficulties of estimating upper bounds from short data samples, with zero exceedance probabilities. This work describes an exploratory study on the joint use of an upper-bounded probability distribution and non-systematic flood information, within a bayesian framework. Accordingly, the current local estimate of the Probable Maximum Flood (PMF) appears as a natural estimate of the upper-bound for maximum flows, despite the fact that PMF determination is not unequivocal and depends strongly on the available data. In the bayesian context, the uncertainty on the PMF can be included into the analysis byconsidering an appropriate prior distribution for the maximum flows. In the sequence, systematic flood records, historical floods, and paleofloods can be included into a compound likelihood function which is then used to update the prior information on the upper-bound. Bycombining a prior distribution describing the uncertainties of PMF estimates along with various sources of flood data into a unified bayesian approach, the expectation is to obtain improved estimates of the upper-bound and better describe the uncertainties associated withflood quantiles. The application example was conducted with flood data from the American river basin, near the Folsom reservoir, in California, USA. Other application was conducted with flood data from the Llobregat river at Pont Du Vilomara, located in Cataluña region, Spain. A finalapplication was conducted with flood data from the Pará river at Ponte do Vilela, in Minas Gerais, Brazil. The results show that it is possible to put together concepts that appear to be incompatible: the deterministic estimate of PMF, taken as a theoretical limit for floods, and the frequency analysis of maximum flows, with the inclusion of non-systematic data. Ascompared to conventional analysis, the conciliation of these two concepts within the logical context of bayesian theory advances towards more reliable estimates of extreme floods. On the other hand, upper-bounded probability distributions, besides being physically more plausible, better describe the probabilistic behavior of floods as compared to unboundeddistributions.