Abstract
The Pre-Whitening (PW), the Trend-Free Pre-Whitening (TFPW) and the Modified Trend-Free Pre-Whitening (MTFPW) were developed to remove the influence of serial correlations on the Mann-Kendall trend test. The main purpose of this study was to compare the performance of these algorithms for evaluating trends in auto-correlated series. The PW, TFPW and MTFPW were also applied to the monthly values of the rainfall (Pre), minimum (Tmin) and maximum (Tmax) air temperature data obtained from the weather station of Ribeirão Preto, State of São Paulo, Brazil. Sets of Monte Carlo simulations were carried out to evaluate the occurrence of the type I and the type II errors obtained from these three algorithms. The TFPW has the highest power. However, it also presented the highest occurrence of type I errors. The PW clearly limits the influence of serial correlation on the occurrence of type I errors. Nevertheless, this feature is accomplished at a cost of a great reduction of its ability to detect trends. The MTFPW leads to a better balance between the probabilities of both statistical errors. It was also concluded that the hypothesis of the presence of no climate change in the location of Ribeirão Pareto cannot be accepted.
Highlights
The current concern and uncertainties associated with the global warming have motivated several authors to investigate the presence of climate trends, at regional scale, in several parts of the world
According to Blain (2013) the influence of serial correlations on trend analyses is frequently neglected in Brazilian agrometeorological studies
All these statements, associated with the assumption that ‘chasing greenhouse signature involves searching for traces of changes in a time series of variables of concern’ (Radziejewski and Kundzewicz, 2004), reinforce the need to evaluate the occurrence oftype I and II errors obtained from the PW, Trend-Free Pre-Whitening (TFPW) and Modified Trend-Free PreWhitening (MTFPW) algorithms
Summary
The current concern and uncertainties associated with the global warming have motivated several authors to investigate the presence of climate trends, at regional scale, in several parts of the world. This modified version is referred to as MTFPW Despite all these efforts to ensure that the null hypothesis of no trend will be correctly rejected/accepted, authors such as Khaliq et al (2009) and Sansigolo and Kayano (2010) indicate that the majority of the studies that investigates trends in time series assume that the data are serially independent. According to Blain (2013) the influence of serial correlations on trend analyses is frequently neglected in Brazilian agrometeorological studies All these statements, associated with the assumption that ‘chasing greenhouse signature involves searching for traces of changes in a time series of variables of concern’ (Radziejewski and Kundzewicz, 2004), reinforce the need to evaluate the occurrence oftype I and II errors obtained from the PW, TFPW and MTFPW algorithms. We expect that this study should provide evidences for accepting/rejecting the hypothesis of the presence of climate change signals in one of the most important agricultural region of the State of São Paulo, Brazil (RibeirãoPreto)
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