Identification of periodicities in hydrological time series and evaluation of their statistical significance are not only important for water-related studies, but also challenging issues due to the complex variability of hydrological processes. In this article, we develop a “Moving Correlation Coefficient Analysis” (MCCA) method for identifying periodicities of a time series. In the method, the correlation between the original time series and the periodic fluctuation is used as a criterion, aiming to seek out the periodic fluctuation that fits the original time series best, and to evaluate its statistical significance. Consequently, we take periodic components consisting of simple sinusoidal variation as an example, and do statistical experiments to verify the applicability and reliability of the developed method by considering various parameters changing. Three other methods commonly used, harmonic analysis method (HAM), power spectrum method (PSM) and maximum entropy method (MEM) are also applied for comparison. The results indicate that the efficiency of each method is positively connected to the length and amplitude of samples, but negatively correlated with the mean value, variation coefficient and length of periodicity, without relationship with the initial phase of periodicity. For those time series with higher noise component, the developed MCCA method performs best among the four methods. Results from the hydrological case studies in the Yangtze River basin further verify the better performances of the MCCA method compared to other three methods for the identification of periodicities in hydrologic time series.

中文翻译：

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水文时间序列周期的识别和分级的相关辅助方法

确定水文时间序列中的周期性并评估其统计意义不仅对于与水有关的研究很重要，而且由于水文过程的复杂可变性，也具有挑战性。在本文中，我们开发了一种“移动相关系数分析”（MCCA）方法来识别时间序列的周期性。该方法以原始时间序列与周期性波动之间的相关性为判据，旨在找出最适合原始时间序列的周期性波动，并对其统计意义进行评估。因此，我们以简单正弦变化组成的周期分量为例，并进行统计实验，以考虑各种参数变化来验证所开发方法的适用性和可靠性。比较中还使用了其他三种常用方法，即谐波分析方法（HAM），功率谱方法（PSM）和最大熵方法（MEM）。结果表明，每种方法的效率与样本的长度和幅度呈正相关，但与平均值，变异系数和周期性的长度呈负相关，与周期性的初始相位无关。对于那些具有较高噪声分量的时间序列，已开发的MCCA方法在四种方法中表现最佳。