Pre-whitening approaches have been widely used to remove the influence of serial correlations on the Mann-Kendall trend test (MK_prew). However, previous studies indicate that this procedure may lead to a false reduction of the significance of a trend. An alternative approach (MK_interact) has been proposed to improve the assessment of the significance of a trend in auto-correlated data. Therefore, the present study compared the performance of the MK_prew and MK_interact for detecting trends in auto-correlated series. Sets of Monte Carlo experiments were carried out to evaluate the occurrence of type I and II errors obtained from both approaches. The analyses were also based on 10-day values of the difference between precipitation and potential evapotranspiration (P-EP) obtained from the location of Campinas, State of São Paulo, Brazil. The results found in this study allow us to conclude that the MK_interac outperformed the MK_prew in correctly identifying the significance of trends and that, concerning agricultural interests, the decreasing trend described by the MK_interac during the beginning of the crop growing seasons may reveal an unfavorable temporal distribution of the P-EP values.
Monte Carlo experiments; auto-correlated series; climate change; Pre-whitening
