应用生态学报2018，Vol.29Issue(4)：1089-1097,9.DOI:10.13287/j.1001-9332.201804.015
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
Correlation coefficient-based classification method of hydrological dependence variability:With auto-regression model as example
摘要
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 correlation coefficient-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.关键词
水文变异/相依性/相关系数/自回归模型/随机过程Key words
hydrological variability/dependence/correlation coefficient/auto-regression model/stochastic process引用本文复制引用
赵羽西,谢平,桑燕芳,吴子怡..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[J].应用生态学报,2018,29(4):1089-1097,9.基金项目
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 （91547205,91647110,51579181）
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. （91547205,91647110,51579181）
