水文时间序列周期分析的RICp准则  

A RICp criterion for periodicity analysis of hydrological time series

作　　者：吴林倩 谢平[1] 桑燕芳[2,3] 霍竞群 吴子怡 Linqian Wu;Ping Xie;Yanfang Sang;Jingqun Huo;Ziyi Wu(State Key Laboratory of Water Resources and Hydropower Engineering Science,Wuhan University,Wuhan 430072,China;Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Key Laboratory of Compound and Chained Natural Hazards,Ministry of Emergency Management of China,Beijing 100085,China;Monitoring and Research Center,Ecological Environment Administration of Pearl River Basin and South China Sea,Ministry of Ecological Environment,Guangzhou 510610,China)

机构地区：[1]武汉大学水资源与水电工程科学国家重点实验室,武汉430072 [2]中国科学院地理科学与资源研究所,中国科学院陆地水循环及地表过程重点实验室,北京100101 [3]复合链生自然灾害动力学应急管理部重点实验室,北京100085 [4]生态环境部珠江流域南海海域生态环境监督管理局生态环境监测与科学研究中心,广州510610

出　　处：《科学通报》2022年第22期2684-2696,共13页Chinese Science Bulletin

基　　金：国家自然科学基金(91547205)资助。

摘　　要：为准确识别与提取水文时间序列中的显著周期成分,本文借鉴AIC准则的思想,提出了一种以相关系数作为拟合精度指标、以信息熵形式的函数式作为不确定性度量指标的周期显著性评价准则——RIC_(p).以包含多个周期成分的模拟序列进行统计实验,对比不加入RIC_(p)准则的MCCP(moving correlation coefficient-based method for periodicity)方法、仅基于假设检验的HAM(harmonic analysis method)方法与通过熵谱峰值点选取显著周期的MESA(maximum entropy spectral analysis method)方法,从周期规律的完整性以及周期结果的准确性两个方面进行评价.分析结果表明,HAM方法和MCCP方法会给出较多的伪周期成分,MESA方法的识别结果会忽略位于峰值附近的次显著周期,基于RIC_(p)准则的周期分析方法在4种方法中表现最好.基于RIC_(p)准则的周期分析方法的优势在于,可以同时满足显著周期判断、周期显著性分级与周期成分描述的需求.将该方法应用于西南诸河流域6个二级水资源区的地表水资源序列.结果显示,西南诸河流域的径流普遍存在9年的周期活动;进一步的物理成因分析发现,除澜沧江二级区外,其余片区的降水序列均存在与径流相同的周期,说明这些流域的径流周期波动很大程度上受降水因子控制.Identification of periodicity in hydrological time series is important for understanding the complex variability of hydrological processes.Previous studies mainly focused on the comparison,improvement and development of different methods for the identification of periodicity.However,these methods have big subjectivity in the selection of significance levels,which may lead to pseudo-periodic components in the identification results.In this article,a new criterion called RIC_(p)(R for correlation coefficient,IC for information criterion,and subscript p for periodicity)is proposed to accurately identify periodic components in hydrological time series and evaluate their statistical significance.The RIC_(p) is based on the idea of Akaike information criterion,and it is established by consisting of a fitting accuracy index and an uncertainty measurement.The fitting accuracy index is based on the correlation coefficient,and the uncertainty measurement is based on the form of information entropy.Different synthetic time series,which are composed of multiple periodic components and Pearson-Ⅲnoise data,are used to verify the superiority of the RIC_(p) method.The performance of RIC_(p) is compared with the moving correlation coefficient-based method for periodicity identification(MCCP),harmonic analysis method(HAM),and maximum entropy spectral analysis(MESA),by checking the accuracy and reliability of the identification results.The results suggest that in HAM and MCCP methods,more pseudo-periodic components are identified compared to other methods.Differently,the number of periodic components identified by MESA is less than the true value,as some significant periodic components can be missed.By comparison,the RIC_(p) performs the best among these four methods.The advantage of RIC_(p) is that it can describe the periodicity and also evaluate its statistical significance level.The RIC_(p) method is further applied to analyze the surface water resource time series in six secondary water resource regions in Southwest Rivers b

关 键 词：水文过程 周期识别 时间序列分析 相关系数 RICp准则 

分 类 号：P333[天文地球—水文科学]