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Keywords:

  • long-term data series;
  • Mann–Kendall test;
  • wavelet analysis;
  • precipitation;
  • Beijing area

Abstract

Non-parametric methods including Mann–Kendall (M–K) test, continuous wavelet transform (CWT) and discrete wavelet transform analysis are applied in this paper to detect the trend and periodic trait of precipitation data series in Beijing area where the data set spans nearly 300 years from 1724 to 2009. First, the trend of precipitation variables is elaborated by the M–K test (Sequential M–K test). The results show that there is an increasing trend (the value of this trend is 1.98) at the 5%-significance level and there are not turning points in the whole data series. Then, CWT and wavelet variance are used to check for significant periodic characteristics of data series. In the plots of wavelet transform coefficients and figure of wavelet variance, some periodic events affect the trend of the annual total precipitation series in Beijing area. 85-year, 35-year and 21-year periodic events are found to be the main periodic series of long-term precipitation data, and they are all statistically significant. Moreover, the results of non-parametric M–K test are exhibited on seven different combinations of discrete wavelet components. D5 (32-year periodicity) periodic component is the effective and significant component on data. It is coincident with the result (35-year periodic event as one part of main periodicity) by using CWT analysis. Moreover, approximation mode shows potential trend of the whole data set because it is the residuals as all periodicities are removed from data series. Thus, the mode A + D5 is responsible for producing a real basic structure of the trend founded on the data. Copyright © 2012 John Wiley & Sons, Ltd.