Correlation-aided method for identification and gradation of periodicities in hydrologic time series

Ping Xie, Linqian Wu, Yan Fang Sang, Faith Ka Shun Chan, Jie Chen, Ziyi Wu, Yaqing Li

Research output: Journal PublicationArticlepeer-review

Abstract

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.

Original languageEnglish
Article number14
JournalGeoscience Letters
Volume8
Issue number1
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Correlation analysis
  • Hydrologic time series analysis
  • Periodicity
  • Significance evaluation

ASJC Scopus subject areas

  • Earth and Planetary Sciences (all)

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