Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China
Identifying implicit periodicities in hydrological data is significant for managing river–basin water resources and establishing flood forecasting systems. However, the complexity and randomness of hydrological systems make it difficult to detect hidden oscillatory characteristics. This study discus...
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doaj-33082a7fc24349449862a9aaf797f7e22021-04-30T23:05:04ZengMDPI AGWater2073-44412021-04-01131265126510.3390/w13091265Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, ChinaXingtong Chen0Xiujie Wang1Jijian Lian2State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, ChinaState Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, ChinaIdentifying implicit periodicities in hydrological data is significant for managing river–basin water resources and establishing flood forecasting systems. However, the complexity and randomness of hydrological systems make it difficult to detect hidden oscillatory characteristics. This study discusses the performance and applicability of five period identification methods, namely periodograms, autocorrelation analysis (AA), maximum entropy spectral analysis (MESA), wavelet analysis (WA), and the Hilbert–Huang transform (HHT). The annual and monthly runoff data are sampled from two stations (Huayuankou and Lijin on the Yellow River in China) in the years 1949–2015. The conclusions are as follows: (i) All methods identify the significant periods of 6 months, 12 months, and 18–19 months, which have relatively high energy of peaks; (ii) WA and HHT perform best when dealing with nonstationary time series, but they are ineffective for identifying large-scale periods; (iii) MESA has high resolution and stability but is prone to oscillate at small-scale periods when applied to monthly series; and (iv) periodograms and AA are relatively simple, but their results lack stability and are significantly affected by the data length—the resolution of AA is too low when applied to annual data, and periodograms can easily produce “false peaks”. Generally, it is better to apply multiple methods comprehensively than each method singularly, and this can be effective in reducing subjective influences.https://www.mdpi.com/2073-4441/13/9/1265hydrology period identificationperiodogramautocorrelation analysismaximum entropy spectral analysiswavelet analysisHilbert–Huang transform |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xingtong Chen Xiujie Wang Jijian Lian |
spellingShingle |
Xingtong Chen Xiujie Wang Jijian Lian Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China Water hydrology period identification periodogram autocorrelation analysis maximum entropy spectral analysis wavelet analysis Hilbert–Huang transform |
author_facet |
Xingtong Chen Xiujie Wang Jijian Lian |
author_sort |
Xingtong Chen |
title |
Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China |
title_short |
Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China |
title_full |
Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China |
title_fullStr |
Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China |
title_full_unstemmed |
Applicability Study of Hydrological Period Identification Methods: Application to Huayuankou and Lijin in the Yellow River Basin, China |
title_sort |
applicability study of hydrological period identification methods: application to huayuankou and lijin in the yellow river basin, china |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2021-04-01 |
description |
Identifying implicit periodicities in hydrological data is significant for managing river–basin water resources and establishing flood forecasting systems. However, the complexity and randomness of hydrological systems make it difficult to detect hidden oscillatory characteristics. This study discusses the performance and applicability of five period identification methods, namely periodograms, autocorrelation analysis (AA), maximum entropy spectral analysis (MESA), wavelet analysis (WA), and the Hilbert–Huang transform (HHT). The annual and monthly runoff data are sampled from two stations (Huayuankou and Lijin on the Yellow River in China) in the years 1949–2015. The conclusions are as follows: (i) All methods identify the significant periods of 6 months, 12 months, and 18–19 months, which have relatively high energy of peaks; (ii) WA and HHT perform best when dealing with nonstationary time series, but they are ineffective for identifying large-scale periods; (iii) MESA has high resolution and stability but is prone to oscillate at small-scale periods when applied to monthly series; and (iv) periodograms and AA are relatively simple, but their results lack stability and are significantly affected by the data length—the resolution of AA is too low when applied to annual data, and periodograms can easily produce “false peaks”. Generally, it is better to apply multiple methods comprehensively than each method singularly, and this can be effective in reducing subjective influences. |
topic |
hydrology period identification periodogram autocorrelation analysis maximum entropy spectral analysis wavelet analysis Hilbert–Huang transform |
url |
https://www.mdpi.com/2073-4441/13/9/1265 |
work_keys_str_mv |
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