No abstract

The hypothesis of stationarity is a fundamental condition for the application of the statistical theory of extreme values, especially for climate variables. Decadal-scale fluctuations commonly affect maximum and minimum river discharges. Thus, the probability estimates of extreme events need to be considered to enable the selection of most appropriate time series. The current study proposed a methodology to detect the fluctuation of long wet and dry periods. The study was carried out at the gauging station 4C-001 in Pardo River, State of São Paulo, Brazil. The Spearman, Mann–Kendall and Pettitt’s non-parametric tests were also performed to verify the existence of a temporal trend in the maximum annual daily flows. The graph achieved from the Pettitt’s statistical variable allowed for the identification and separation of the longest dry period (1941 to 1975) and the longest wet period (1976 to 2011), decreasing again in 2012. Analysing the series separately, it was observed that both mean and standard deviation were higher than those corresponding to the dry period. The probable maximum flows for the corrected series showed estimates 10% higher than those estimated for the uncorrected historical series. The proposed methodology provided more realistic estimates for the extreme maximum flows.