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Chinese Journal of Applied Ecology ›› 2018, Vol. 29 ›› Issue (4): 1089-1097.doi: 10.13287/j.1001-9332.201804.015

• Special Features of Hydrological Variability and Inconsistency • Previous Articles     Next Articles

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.

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.