RESUMO.A distribuição generalizada de valores extremos (GEV) tem tido grande aceitação para discrição dos eventos máximos naturais. Estudos sobre o assunto mostram que a distribuição GEV ajusta valores mais próximos à realidade quando há necessidade de extrapolação dos dados amostrais para grandes períodos de retornos e com o intuito de contribuir para o planejamento de atividades que são influenciadas pela intensidade de água precipitada foi ajustado um modelo de distribuição de probabilidade de chuva intensa por meio da GEV, utilizando momentos LH para estimar seus parâmetros e o teste estatístico proposto por Wang (1998) para verificação da qualidade dos ajustes desenvolvidos no ambiente Matlab. Analisaram-se séries históricas de precipitação máximas para diferentes durações obtidas de pluviográfos no município de Cascavel, Estado do Paraná. Além disso, as referidas séries foram ajustadas pela distribuição Gumbel para efeito de comparação com a GEV. Este trabalho mostra que a distribuição Gumbel subestima a distribuição GEV para grandes períodos de retorno. Palavras-chave: chuvas intensas, distribuição GEV, momentos LH.ABSTRACT. Distribution of frequency and intense rainfall. The generalized extreme value distribution (GEV) has had great acceptance for discretion of the maximum natural events. Previous studies show that GEV distribution fits values closer to reality when there is need for extrapolation of sampling data for longer periods of returns. In order to contribute to the planning of activities that are influenced by the intensity of precipitated water, we adjusted a model of probability distribution of heavy rain through GEV, using LH moments for estimating its parameters and statistical test proposed by Wang (1998) for checking the quality of the adjustments developed in the Matlab. We analyzed historical time series of maximum rainfall for different durations obtained from rain gauges in the city of Cascavel, Paraná State. Moreover, these series were fitted by Gumbel distribution for purposes of comparison with the GEV. This study shows that the Gumbel distribution underestimates the GEV distribution for large return periods.

The objective of this study was to regionalize 7-day 10-year low flows, long-term annual mean, and 90% and 95% permanence flows from Piquiri (PR) river basin. The following regionalization methods were adopted: Traditional, Linear interpolation, Chaves, Modified linear interpolation, and Modified Chaves. The equations obtained by the Traditional method, adding main river length or drainage density as independent variables, significantly improved R 2 equations value. Streamflow forecasting by Linear Interpolation and Chaves methods were as good as those provided by the Traditional Method, thus, these methods could be applied to Piquiri River basin, especially when drainage area is the only available spatial information.

This paper analyzes the variability and the precipitation trend of the State of Paraná, in Brazil. For that, monthly precipitation data belonging to 24 precipitation stations in a 30-year period (1980-2010) were analyzed and they were compared with projections of precipitation for the years 2016-2050. These data were simulated by Eta/Miroc5 for RCP 4.5 (Representative Concentration Pathways) from the Center for Weather Forecasting and Climate Studies CPTEC/INPE and the historical data of precipitation were taken from National Water Agency (ANA). The Mann-Kendall non-parametric test and the Sen’s slope estimator were applied to detect trends and magnitudes, respectively. The Mann-Whitney test was used to compare the median of the historical series (1980-2010) with the simulated series (2016-2050) and the comparison of the means between the two series was performed by Test t. The results draw attention to the great variability and significant changes in the monthly average rainfall that may occur, if the climate change scenarios that were considered become a reality in the near future.

No abstract