一种基于ITA改进的水文气象序列趋势分析法

基于ITA(innovative trend analysis)法,提出了改进其趋势显著性水平的参数型趋势检测方法,给出一种刻画时间序列趋势的数量指标,并采用自举法检验趋势的显著性水平。经过蒙特卡洛数值模拟,将改进的趋势检测法对人工数据序列的检测结果分别与经典的Mann-Kendall秩次检验法和ITA法进行比较,验证了其可行性。将改进的趋势检测法应用于4种不同长度、不同地区、不同水文气象要素的时间序列数据进行趋势分析,结果表明,在5%的显著性水平上,黑河上游的年径流量、日本福冈每年发生风暴的天数和琼海的年平均气温都呈现显著的增加趋势,而北京的最大日降水量在10%的显著性水平上为显著下降趋势。

An improved trend analysis method for hydro-meteorological time series based on ITA

Based on the innovative trend analysis(ITA)method, this paper improved the parametric test method of trend significance, proposed a quantitative index to describe the average trend of time series, and used the bootstrap method to test the significance of the trend. After the Monte-Carlo numerical simulation, the detection results of the new method on the artificial data sequences were compared with the classic Mann-Kendall(MK)method and ITA method respectively, from which its feasibility was verified. Finally, the new method was applied to the trend analysis of four kinds of time series with different lengths, in different regions, and about different hydro-meteorological elements. The results showed that at the 5% significance level, there was a significant increasing trend in series of the annual runoff in the upstream of Heihe River, the number of storm days per year in Fukuoka, Japan, and the annual average temperature in Qionghai. Meanwhile, there was a significant decreasing trend at a significant level of 10% in the maximum daily precipitation in Beijing.