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水力发电学报 ›› 2019, Vol. 38 ›› Issue (7): 77-86.doi: 10.11660/slfdxb.20190708

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基于相关系数的水文序列分段趋势识别方法

  

  • 出版日期:2019-07-25 发布日期:2019-07-25

Detection method of piecewise trends in hydrological series based on correlation coefficients


  • Online:2019-07-25 Published:2019-07-25

摘要: 分段趋势变异是非一致性水文序列的一种重要变异形式。本文提出一种基于相关系数的水文序列分段趋势变异识别方法(简称“R识别法”),并对分段趋势变异的显著性进行评价。统计实验结果表明:R识别法对不含有分段趋势序列的误检率非常低,对含有不同类型分段趋势序列及折点前后子序列趋势变异程度不同的序列,正检率较高,说明R识别法对分段趋势折点具有针对性且识别精度高。运用R识别法对洞庭湖三口五站年最大洪峰流量序列进行分段趋势识别与检验,结果表明沙道观站、管家铺站和康家岗站序列均存在不同时间点不同形式的分段趋势变异,总体表现为洪峰流量减少,其主要与三峡水库的径流调节和下荆江裁弯有关。

关键词: 分段趋势, 相关系数, 水文变异, 洞庭湖, 年最大洪峰流量

Abstract: Piecewise trend is an important form of variations in nonstationary hydrological series. This paper describes a new method (namely, R detection method) for detecting piecewise trends in hydrological series and evaluating the significance of corresponding variations based on correlation coefficients. The results of statistical experiments show that this method features a very low rate of false detection of the series with no piecewise trend and a high rate of positive detection of the series with different types of piecewise trends, indicating its high accuracy in the detection. We use the method to detect the piecewise trends in the annual peak flood series from five gauge stations in Dongting Lake. The results show that piecewise trends occur at different time points of the series at the stations of Shadaoguan, Guanjiapu and Kangjiagang, and are featured with different forms, all showing a certain decrease in the peak flood flow as a response to the variations in the runoff regulated by the Three Gorges reservoir and caused by the cutting of the lower Jingjiang.

Key words: piecewise trend, correlation coefficient, hydrological variability, Dongting Lake, annual peak flood

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