ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 48 Issue 10 > 2022,48(10):1312-1320. DOI:10.7519/j.issn.1000-0526.2022.080501 Prev Next
Three Trend Analysis Methods in Precipitation Characteristic Analysis of Eastern Pamirs
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Abstract:
Based on monthly precipitation data of three meteorological stations in the eastern pamirs during the past 58 years (1961-2018), this paper uses innovative trend analysis (ITA), Mann-Kendall (M-K) nonparametric test and Sen’s slope estimation method to analyze the variation trend of precipitation in the eastern Pamirs. The results show that the results obtained by the three methods are highly consistent. In recent years, the precipitation in the eastern pamirs has an increasing trend. According to Sen’s slope estimation method, 69.44% shows an increasing trend of precipitation, and 11.11% shows a decreasing trend. The precipitation increases in 75% of the months and decreases in 22.22% of the months by M-K method. According to ITA analysis, precipitation increases in 94.44% of the months and decreases in 5.56% of the months. However, there are also differences among the three methods in the analysis process. For example, the most obvious difference is that the β value of Tashkogan meteorological station in five months by using Sen’s slope estimation method is 0, indicating no trend of change, which is significantly different from the results of the other two methods. ITA and M-K methods can distinguish the trend of time series more accurately, and are almost not affected by outliers. In addition, ITA method has the strongest significance trend, followed by M-K method, and Sen’s slope estimation performance is relatively general. The ITA analysis has passed the 0.10 significance level test in 〖JP2〗63.89% of the months and M-K has passed the 0.10 significance level test in 25% of the months. Compared with the traditional analysis method, ITA has the advantage that it can give the range and trend of increase or decrease of different levels of precipitation in different months. Therefore, ITA can effectively identify the hidden micro trend that cannot be determined by the traditional test, and is more helpful to analyze the change trend of hydrometeorological data series.〖JP〗
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