Correlation-aided method for identification and gradation of periodicities in hydrologic time series
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 Coeff...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media Deutschland GmbH
2021
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| Online Access: | https://eprints.nottingham.ac.uk/65341/ |
| _version_ | 1848800212777172992 |
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| author | Xie, Ping Wu, Linqian Sang, Yan-Fang Chan, Faith Ka Shun Chen, Jie Wu, Ziyi Li, Yaqing |
| author_facet | Xie, Ping Wu, Linqian Sang, Yan-Fang Chan, Faith Ka Shun Chen, Jie Wu, Ziyi Li, Yaqing |
| author_sort | Xie, Ping |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:47:59Z |
| format | Article |
| id | nottingham-65341 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:47:59Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
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| spelling | nottingham-653412021-06-04T03:11:05Z https://eprints.nottingham.ac.uk/65341/ Correlation-aided method for identification and gradation of periodicities in hydrologic time series Xie, Ping Wu, Linqian Sang, Yan-Fang Chan, Faith Ka Shun Chen, Jie Wu, Ziyi Li, Yaqing 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. Springer Science and Business Media Deutschland GmbH 2021-04-08 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/65341/1/Xie-2021-Correlation-aided-method-for-identi.pdf Xie, Ping, Wu, Linqian, Sang, Yan-Fang, Chan, Faith Ka Shun, Chen, Jie, 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 Periodicity; Correlation analysis; Significance evaluation; Hydrologic time series analysis http://dx.doi.org/10.1186/s40562-021-00183-x doi:10.1186/s40562-021-00183-x doi:10.1186/s40562-021-00183-x |
| spellingShingle | Periodicity; Correlation analysis; Significance evaluation; Hydrologic time series analysis Xie, Ping Wu, Linqian Sang, Yan-Fang Chan, Faith Ka Shun Chen, Jie Wu, Ziyi Li, Yaqing Correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| title | Correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| title_full | Correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| title_fullStr | Correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| title_full_unstemmed | Correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| title_short | Correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| title_sort | correlation-aided method for identification and gradation of periodicities in hydrologic time series |
| topic | Periodicity; Correlation analysis; Significance evaluation; Hydrologic time series analysis |
| url | https://eprints.nottingham.ac.uk/65341/ https://eprints.nottingham.ac.uk/65341/ https://eprints.nottingham.ac.uk/65341/ |