Comparison of the MK test and EMD method for trend identification in hydrological time series

Trend identification is an important issue in hydrological time series analysis, but it is also a difficult task due to the diverse performances of methods. This paper mainly investigated the performances between the Mann–Kendall (MK) test and the empirical mode decomposition (EMD) method for trend identification of series. Analyses of both synthetic and observed series indicate the better performance of EMD compared with the other. The results show that pre-whitening cannot really improve trend identification when using the MK test, but cause wrong results sometimes. It can be due to the good correlation of trend, so pre-whitening would weaken trend’s magnitude. If the trend of the analyzed series has small magnitude, it cannot be accurately identified by the MK test, because the trend would be submerged too severely by other components of series to accurately identify trend. When the analyzed series has short length, its trend cannot be accurately identified by the MK test. However, the EMD method can eliminate the influences of trends’ magnitude and series’ length, so it has more effective power for trend identification. As a result, it is suggested that series’ trend can be directly identified by the MK test but need not do pre-whitening; moreover, the influences of trends’ magnitude should be carefully considered for trend identification. Comparatively, the EMD method can adaptively determine the specific shape of the nonlinear and non-stationary trend of series by considering statistical significance, so it can be an effective alternative for trend identification of hydrological time series.