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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472
EISSN: 1735-2630
Vol. 11, No. 2, 2014, pp. 327-338
Bioline Code: st14034
Full paper language: English
Document type: Research Article
Document available free of charge

International Journal of Environment Science and Technology, Vol. 11, No. 2, 2014, pp. 327-338

 en Long-term periodic structure and seasonal-trend decomposition of water level in Lake Baiyangdian, Northern China
Wang, F.; Wang, X.; Zhao, Y. & Yang, Z. F.


Water level, as an intuitive factor of hydrologic conditions, is of great importance for lake management. In this study, periodic structures of water level and its fluctuations in Lake Baiyangdian are analyzed based on wavelet analysis and seasonal-trend decomposition using local error sum of squares (STL). Data of monthly time series are divided into three types with emphasis on anthropogenic influence from water allocation. It is found that intra-annual characteristics of water level fluctuations are the common periodic structures. Water allocation alters the periodic structures by decreasing and weakening the oscillations of water level, compared with the slight effects of natural hydrologic water supplies and short-term climate changes. An irregular water level decline and short-term oscillation with irregular periodicity are deduced from seasonal-trend decomposition analysis using STL. With seasonality depicted monthly, the influence of water allocation implies irregular oscillations with high-frequency components, especially for monthly changes. The water level fluctuations are influenced by seasonal changes, as demonstrated by three types of time series. The impacts of water allocation on seasonality show the differences with continuous single-peak oscillations representing no influences and continuous double-peak oscillations representing frequent influences. Furthermore, the accumulation of water allocation shows a slight rising trend in average monthly level fluctuations over the last several years. The study helps understand periodic structures and long-term trend changes of water level fluctuations, which will facilitate lake management of Lake Baiyangdian.

Periodic structure; Water level fluctuation; Wavelet analysis; Seasonal-trend decomposition analysis using local error sum of squares; Lake Baiyangdian

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