Tendências climáticas no município de Pelotas, estado do Rio Grande do Sul, Brasil

Changes in the frequency of occurrence of extreme weather events have been pointed out as a likely impact of global warming. In this context, this study aimed to detect climate change in series of extreme minimum and maximum air temperature of Pelotas, State of Rio Grande do Sul, (1896 - 2011) and its influence on the probability of occurrence of these variables. We used the general extreme value distribution (GEV) in its stationary and non-stationary forms. In the latter case, GEV parameters are variable over time. On the basis of goodness-of-fit tests and of the maximum likelihood method, the GEV model in which the location parameter increases over time presents the best fit of the daily minimum air temperature series. Such result describes a significant increase in the mean values of this variable, which indicates a potential reduction in the frequency of frosts. The daily maximum air temperature series is also described by a non-stationary model, whose location parameter decreases over time, and the scale parameter related to sample variance rises between the beginning and end of the series. This result indicates a drop in the mean of daily maximum air temperature values and increased dispersion of the sample data.354769777COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçãoAlterações na frequência de ocorrência dos eventos meteorológicos extremos têm sido apontadas como um provável impacto do aquecimento global. Nesse contexto, objetivou-se detectar a presença de alterações climáticas em séries de temperatura do ar mínima e máxima extremas de Pelotas, Rio Grande do Sul (1896-2011), quantificando sua influência na probabilidade de ocorrência dessas variáveis. Utilizou-se da distribuição geral de valores extremos (GEV) empregada em suas formas estacionária e não estacionárias. Nesse último caso, os parâmetros da GEV são variáveis ao longo do tempo. Com base em testes de aderência e no método da razão da máxima verossimilhança, verificou-se que um modelo GEV em que o parâmetro de localização se eleva ao longo do tempo, apresenta o melhor ajuste da série de temperatura mínima diária. Esse resultado descreve significativa elevação na média dos valores dessa variável, indicando potencial redução na frequência do fenômeno geada. A série de temperatura máxima diária é também descrita por um modelo não estacionário, cujo parâmetro de localização decresce ao longo do tempo e o de escala, relacionado à variância amostral, eleva-se entre o início e o fim da série. Esse resultado indica queda na média dos valores de temperatura máxima diária e elevação da dispersão dos dados amostrais

Inadequacy of the gamma distribution to calculate the Standardized Precipitation Index

ABSTRACT The Standardized Precipitation Index was developed as a probability-based index able to monitor rainfall deficit in a standardized or normalized way. Thus, the performance of this drought index is affected by the use of a distribution that does not provide an appropriate fit for the rainfall data. The goal of this study was to evaluate the adjustment of the gamma distribution for the rainfall amounts summed over several time scales (Pelotas, Rio Grande do Sul, Brazil), to assess the goodness-of-fit of alternative distributions to these rainfall series and to evaluate the normality assumption of the Standardized Precipitation Index series calculated from several distributions. Based on the Lilliefors test and on a normality test, it is verified that the gamma distribution is not suitable for calculating this Index in several timescales. The generalized normal distribution presented the best performance among all analysed distributions. It was also concluded that the drought early warning systems and the academic studies should re-evaluate the use of the gamma distribution in the Standardized Precipitation Index calculation algorithm. A computational code that allows calculating this drought index based on the generalized normal distribution has also been provided

Using multi-parameters distributions to assess the probability of occurrence of extreme rainfall data

Soil erosion, soil saturation and floods are frequently associated with extreme rainfall events. Thus, the scientific literature agrees on the need to carry out studies that improve the assessment of the probability of occurrence of extreme rainfall values. The main goal of this study was to compare the performance of the multi-parameters distributions Wakeby, Kappa and Generalized Extreme Value in fitting the annual maximums of daily, 2-day and 3-day rainfall amounts obtained from the weather station of Campinas, located in the State of São Paulo, Brazil (1890-2012). As a secondary aim, the presence of climate trends and serial correlation in these series was also evaluated. The auto-correlation function and the Mann-Kendall tests have shown the presence of no serial correlation and climate trends in the above mentioned series. The results obtained from goodness-of-fit procedures allowed us to conclude that the Kappa and the Generalized Extreme Value distributions present the best performance in describing the probabilistic structure of the series under analysis