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A framework of change-point detection for multivariate hydrological series

机译：多元水文序列变化点检测框架

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摘要

Under changing environments, not only univariate but also multivariate hydrological series might become nonstationary. Nonstationarity, in forms of change-point or trend, has been widely studied for univariate hydrological series, while it attracts attention only recently for multivariate hydrological series. For multivariate series, two types of change-point need to be distinguished, i.e., change-point in marginal distributions and change-point in the dependence structure among individual variables. In this paper, a three-step framework is proposed to separately detect two types of change-point in multivariate hydrological series, i.e., change-point detection for individual univariate series, estimation of marginal distributions, and change-point detection for dependence structure. The last step is implemented using both the Cramer-von Mises statistic (CvM) method and the copula-based likelihood- ratio test (CLR) method. For CLR, three kinds of copula model (symmetric, asymmetric, and pair-copula) are employed to construct the dependence structure of multivariate series. Monte Carlo experiments indicate that CLR is far more powerful than CvM in detecting the change-point of dependence structure. This framework is applied to the trivariate flood series composed of annual maxima daily discharge (AMDD), annual maxima 3 day flood volume, and annual maxima 15 day flood volume of the Upper Hanjiang River, China. It is found that each individual univariate flood series has a significant change-point; and the trivariate series presents a significant change-point in dependence structure due to the abrupt change in the dependence structure between AMDD and annual maxima 3 day flood volume. All these changes are caused by the construction of the Ankang Reservoir.
机译：在不断变化的环境中，不仅单变量，而且多变量水文序列也可能变得不稳定。对于单变量水文序列，以变化点或趋势形式出现的非平稳性已得到广泛研究，而对于多变量水文序列，非平稳性直到最近才引起关注。对于多变量序列，需要区分两种类型的变化点，即边际分布中的变化点和各个变量之间的依存结构中的变化点。本文提出了一个三步框架来分别检测多元水文序列中的两种类型的变化点，即单个单变量序列的变化点检测，边际分布估计和依存结构的变化点检测。最后一步使用Cramer-von Mises统计（CvM）方法和基于copula的似然比检验（CLR）方法实现。对于CLR，采用三种关联模型（对称，不对称和成对关联）构建多元序列的依存结构。蒙特卡洛实验表明，CLR在检测依赖结构的变化点方面比CvM强大得多。该框架应用于三变量洪水序列，该序列由中国上游汉江上游的年最大日流量（AMDD），年最大3天洪水量和年最大15天洪水量组成。发现每个单独的单变量洪水序列都有一个显着的变化点。由于AMDD与年度最大3天洪水量之间的依存结构突然变化，因此三变量序列呈现出一个显着的依存结构变化点。所有这些变化都是由安康水库的建设引起的。

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  • 来源
    《Water resources research》 |2015年第10期|8198-8217|共20页
  • 作者

    Xiong Lihua; Jiang Cong; Xu Chong-Yu; Yu Kun-xia; Guo Shenglian;

  • 作者单位

    Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China;

    Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China;

    Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China|Univ Oslo, Dept Geosci, Oslo, Norway;

    Xian Univ Technol, State Key Lab Base Ecohydraul Engn Arid Area, Xian, Peoples R China;

    Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China;

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