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

Xie, Ping and Wu, Linqian and Sang, Yan-Fang and Chan, Faith Ka Shun and Chen, Jie and Wu, Ziyi and Li, Yaqing (2021) Correlation-aided method for identification and gradation of periodicities in hydrologic time series. Geoscience Letters, 8 (1). ISSN 2196-4092

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (4MB) | Preview

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.

Item Type: Article
Keywords: Periodicity; Correlation analysis; Significance evaluation; Hydrologic time series analysis
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Geographical Sciences
Identification Number: https://doi.org/10.1186/s40562-021-00183-x
Depositing User: Yu, Tiffany
Date Deposited: 04 Jun 2021 03:11
Last Modified: 04 Jun 2021 03:11
URI: http://eprints.nottingham.ac.uk/id/eprint/65341

Actions (Archive Staff Only)

Edit View Edit View