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应用生态学报 ›› 2018, Vol. 29 ›› Issue (4): 1089-1097.doi: 10.13287/j.1001-9332.201804.015

• 水文变异与非一致性专栏 • 上一篇    下一篇

基于相关系数的水文相依性变异分级方法——以自回归模型为例

赵羽西1, 谢平1,2, 桑燕芳3*, 吴子怡1   

  1. 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国家领土主权与海洋权益协同创新中心, 武汉 430072;
    3中国科学院地理科学与资源研究所陆地水循环与地表过程重点实验室, 北京 100101;
  • 收稿日期:2017-08-27 出版日期:2018-04-18 发布日期:2018-04-18
  • 通讯作者: * E-mail: sangyf@igsnrr.ac.cn
  • 作者简介:赵羽西,女,1993年生,硕士研究生.主要从事变化环境下的水文水资源研究.E-mail: debby19931119@163.com
  • 基金资助:

    本文由国家自然科学基金项目(91547205,91647110,51579181)、湖南省水利科技项目(湘水科计[2015]13-21)和中国科学院地理科学与资源研究所“秉维”优秀青年人才计划项目资助

Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example

ZHAO Yu-xi1, XIE Ping1,2, SANG Yan-fang3*, WU Zi-yi1   

  1. 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;
    2Collaborative Innovation Center for Territorial Sovereignty and Maritime Rights, Wuhan 430072, China;
    3Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
  • Received:2017-08-27 Online:2018-04-18 Published:2018-04-18
  • Contact: * E-mail: sangyf@igsnrr.ac.cn
  • Supported by:

    This work was supported by the National Natural Science Foundation of China (91547205, 91647110, 51579181), the Water Engineering and Science Project of Hunan Province (Xiangshuikeji [2015]13-21) and the ‘Bingwei’ Youth Innovation Promotion Association of Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.

摘要: 水文过程演变存在时间相依性,含有相依成分的水文序列无法满足水文计算中的一致性假设,给水问题研究带来诸多困难.针对水文序列相依性变异这一现象,以自相关模型为例,提出基于相关系数的水文相依性变异分级方法.该方法通过计算相依成分与原序列之间的相关系数,并选取合理的相关系数阈值,将相依性变异程度分为无变异、弱变异、中变异、强变异、巨变异.通过推导相关系数与序列各阶自相关系数之间的公式,说明相关系数主要取决于1阶到p阶自相关系数的大小,从而阐明分级方法的理论基础.以一阶和二阶自回归模型为例,利用统计试验验证了公式的合理性,并说明了相关系数和自相关系数的联系.将所提分级方法应用于3个实测径流序列进行分析,结果显示水文过程常常存在相依性与随机性并存的现象.

Abstract: Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.