水文时间序列分析方法研究进展

doi:10.11820/dlkxjz.2013.01.002

摘要/Abstract

摘要： 水文时间序列不仅组成复杂,而且特性也复杂多变。目前认为水文时间序列主要表现出随机性、模糊性、非线性、非平稳性和多时间尺度变化等复杂特性。本文首先简要介绍了用于揭示水文过程复杂变化特性的时间序列分析方法的相关进展,包括序列相关性分析方法、水文频率分析方法、模糊分析方法、混沌理论分析方法、信息熵分析方法和小波分析方法等6种。然后,对各种分析方法存在的主要问题和有待解决的问题进行讨论,指出了各种方法应用于水文时间序列分析时存在的主要缺陷和不足。最后指出,不断改进和完善时间序列分析方法,探讨各种方法的联合和耦合,加强物理成因分析和数理统计分析相结合,是提高水文时间序列分析结果精度和可靠性的有效手段,也是研究和解决环境变化影响下水文水资源问题的有效途径。

关键词: 不确定性, 非线性, 混沌性, 耦合, 水文时间序列分析, 小波分析, 信息熵

Abstract: Hydrological time series are usually composed of various components with complicated characteristics.At present it is generally thought that hydrological time series mainly show stochastic,fuzzy,nonlinear, non-stationary,and multi-temporal scale characteristics.In this paper,research progresses on the time series analysis methods to study the characteristics of hydrological processes and their applications are summarized,including serial correlation analysis methods,hydrological frequency analysis methods,fuzzy analysis methods,chaos theories and methods,information entropy theories,and wavelet analysis methods.The main issues and the problems to be solved with regard to the methods mentioned above are discussed,namely,the disadvantages and limitations in their applications to hydrological time series analysis.Finally,it is pointed out that further improvement and optimization of the methods,combination and coupling between the methods,emphasis on the combination of the analyses of physical mechanisms and mathematical statistics,are the key not only to improving the results of hydrological time series analysis,but also to studying and solving the hydrology and water resources issues caused by environmental change.

Key words: chaos, coupling, hydrological time series analysis, information entropy, nonlinearity, uncertainty, wavelet analysis

桑燕芳, 王中根, 刘昌明. 水文时间序列分析方法研究进展[J]. 地理科学进展, 2013, 32(1): 20-30.

SANG Yanfang, WANG Zhonggen, LIU Changming. Research progress on the time series analysis methods in hydrology[J]. PROGRESS IN GEOGRAPHY, 2013, 32(1): 20-30.

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