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Combination of modified Mann‐Kendall method and Şen innovative trend analysis
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Journal Article
Combination of modified Mann‐Kendall method and Şen innovative trend analysis
2020
Overview
Mann‐Kendall (MK) trend test is frequently employed as the most familiar trend detection method. Its application requires serial independence of available hydrometeorological time series records. As suggested in the literature, the serial correlation effect can be removed from the given time series by using prewhitening, variance correction or overwhitening processes such as in the modified Mann‐Kendall (MMK) procedure. The PW process may cause some of the current trends to be removed along with the serial correlation. In this study, the MMK method is supported by Şen innovative trend analysis instead of Sen slope estimator (SSE). The MMK method is applied to monthly maximum temperatures of Oxford station in England, for which the data length is large and the moving trend slope values are calculated starting from 1854 for all durations between 1873 and 2017. The MMK_SSE and MMK_ITA methods yield significant increasing trends between 0.0037 and 0.0125°C/year annual slopes for January, March, May, July, August, September, October, November, December, but for February, there is not any significant trend. While MMK_SSE does not give any significant trend for April that has maximum positive kurtosis and skew, but MMK_ITA reflects an increasing trend of 0.0059°C per year. The main purpose of this article is to support the classical MK trend identification test by means of the Sen_ITA approach leading to more reliable results. The Sen_ITA method is not affected by serial correlation and this strong feature is tried to be added to MK. In the literature, the SSE method is added to reinforce MK, but the SSE method calculates the trend according to the median value. This reduces the contribution of extreme values to the trend, also the Sen_ITA method is easier to implement than the SSE method. With the combination of the Sen_ITA method, it is expected that the MK method, which is widely used in literature, gives more successful results.
Publisher
John Wiley & Sons, Inc,Wiley
