Abstract
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.
Highlights
The nonparametric Mann-Kendall test (MK), known as Kendall’s τau test or the MannKendall trend test (KENDALL; STUART, 1967; MANN, 1945), is widely used to evaluate trends in agro-meteorological and hydrological time series (BLAIN, 2010; BLAIN, 2011a, b and c; BLAIN; PIRES, 2011; BURN; ELNUR, 2002; BURN et al, 2004; MINUZZI et al, 2011; SANSIGOLO, 2008; SANSIGOLO; KAYANO, 2010; STRECK et al, 2011; YUE et al, 2002 and 2003)
The strengths of the MK are usually associated with its simple concept and with the fact that as a nonparametric procedure that does not assume a specific joint distribution of the data, it is minimally affected by departures from normality (YUE; PILON, 2004)
The limitations of this trend test are frequently associated with its own null hypothesis (H0), which assumes that the data are independently and identically distributed
Summary
The nonparametric Mann-Kendall test (MK), known as Kendall’s τau test or the MannKendall trend test (KENDALL; STUART, 1967; MANN, 1945), is widely used to evaluate trends in agro-meteorological and hydrological time series (BLAIN, 2010; BLAIN, 2011a, b and c; BLAIN; PIRES, 2011; BURN; ELNUR, 2002; BURN et al, 2004; MINUZZI et al, 2011; SANSIGOLO, 2008; SANSIGOLO; KAYANO, 2010; STRECK et al, 2011; YUE et al, 2002 and 2003). Agronomy addressed the strengths and the limitations of this statistical test. The strengths of the MK are usually associated with its simple concept and with the fact that as a nonparametric procedure that does not assume a specific joint distribution of the data, it is minimally affected by departures from normality (YUE; PILON, 2004). The limitations of this trend test are frequently associated with its own null hypothesis (H0), which assumes that the data are independently and identically distributed (iid). From a strictly statistical point of view, the non-acceptance of H0 only implies that the dataset under analysis cannot be taken as iid
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