地理科学进展 ›› 2013, Vol. 32 ›› Issue (1): 20-30.doi: 10.11820/dlkxjz.2013.01.002
桑燕芳1,2, 王中根1, 刘昌明1
收稿日期:
2012-04-01
修回日期:
2012-09-01
出版日期:
2013-01-25
发布日期:
2013-01-25
作者简介:
桑燕芳(1983-),男,博士,助理研究员,主要从事水文水资源研究。E-mail:sunsangyf@gmail.com
基金资助:
国家自然科学基金项目(41201036,41271048);中国科学院陆面过程与气候变化重点实验室开放基金项目(LPCC201203)。
SANG Yanfang1,2, WANG Zhonggen1, LIU Changming1
Received:
2012-04-01
Revised:
2012-09-01
Online:
2013-01-25
Published:
2013-01-25
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。
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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