基于相关系数的水文周期变异分级方法及验证

Correlation coefficient-based method for grading significance level of periodicity in hydrologic series and its verification

周期成分是非一致性水文时间序列中的一种变异成分,对周期成分大小的定量描述与量化分级具有重要的现实意义。基于相关系数提出了一种周期变异程度分级方法,适用于可识别出简单周期成分的水文序列。首先通过计算水文序列和其含有的周期成分之间的相关系数,然后选定分级阈值,将周期变异程度划分为无、弱、中、强及巨变异5种等级。通过推导相关系数与周期成分半振幅之间的表达式,说明以相关系数作为分级指标的合理性,并分析各变量对相关系数求解结果的影响。将所提方法分别应用于人工生成序列和实测水文时间序列,结果显示该分级方法合理可靠,有助于理解和定量评估流域环境变化及其对水文过程的影响。

A periodic component is one of the time-varying components in an inconsistent hydrologic series, and quantifying and grading its significance level is practically significant. This paper presents a new method that uses the correlation coefficient to grade the significance level of periodicity in a hydrologic series containing only one simple periodic component. It calculates the correlation coefficient between the series and its periodic component, then uses four selected thresholds of this index to grade the corresponding periodicity significance into five levels: no, weak, mid, strong, and very strong. We derive a relationship of correlation coefficient versus half amplitude of periodicity demonstrating the reasonability of the index used in this method, and examine the influence of each variable on the correlation coefficient. Application to synthetic series and observed hydrologic series shows that our method is reliable and effective, thus helping quantify environmental change and its impact on hydrologic variability.