Tendências e probabilidades de ocorrência de extremos de precipitação e temperaturas do ar no Rio Grande do Sul

• Ano de defesa: 2019
• Autor(a) principal: Kruel, Izabele Brandão
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/001300000bqw2
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Agronomia; UFSM; Programa de Pós-Graduação em Agronomia; Centro de Ciências Rurais
• Palavras-chave em Português: Distribuição geral dos valores extremos; Mudança climática; Testes de aderências; Generalized extreme value; Climate change; Adhesion tests; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA
• Link de acesso: http://repositorio.ufsm.br/handle/1/24087

Climate trends investigations at regional scales, and the magnitude and frequency intensification of extreme weather events, are highly important to adopt measures to reduce the climate change potential impacts. Studies employ statistical models capable of detecting and incorporating temporal changes in the probability of occurrence of potentially damaging meteorological events in crop production. Thus, the present thesis aimed to describe the probabilistic structure of daily series of extreme precipitation values (Epv) and maximum (Tmax) and minimum (Tmin) air temperature, obtained from the meteorological stations of the state of Rio Grande do Sul, using non-stationary models based on the general distribution of extreme values (GEV) with estimated parameters as a function of time covariate. The GEV distribution was employed in its stationary and non-stationary forms with estimated parameters by the maximum likelihood method. To verify GEV’s fit with the study data were used Lilliefors and Anderson-Darling tests, quantile-quantile plots, and Akaike information criteria. Climate trend detection was performed using the nonparametric Mann-Kendall test. If there was a significant trend, the Pettit test was applied to verify the year of its beginning. All statistical methods were conducted considering the 5% significance level. For thirty climatic series among eighteen municipalities studied in Rio Grande do Sul the overall distribution of the extreme values was adjusted. The adoption of non-stationary GEV models resulted in a better adjustment of the probabilistic description of the maximum temperature climatic series for the municipalities of Caxias do Sul, Lagoa Vermelha, Passo Fundo, Rio Grande, Santa Maria, and Santa Vitória do Palmar.