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Principle of correlation coefficient-based classification of hydrological trend and its verification

Under the great influence of climate change and human activities during the recent decades, climatic and hydrological processes in many basins and regions worldwide are changing significantly. Analyzing the variability of hydroclimatic variables, including temperature, evaporation, precipitation and runoff, is an important topic in the hydrology studies, which contributes to understanding the changes in hydrological regime, water resources management, water disasters control and many other issues. Trend is one of those important forms of hydrological variability, and thus has received much attention over the last several decades. Many techniques and methods have been developed for trend detection, such as the least squares linear regression, Sen’s robust slope estimator, Mann-endall non-parametric test, Spearman rank correlation, Student’s t -test and others. Present studies about the issue of trend mainly focused on the improvement, comparison and applicability of trend identification methods. However, there lacks effective approach for the classification of significance degree of trends in hydrological time series. In this article, by employing the index of correlation coefficient, we mainly proposed a method for the trend identification and classification of its significance degree. Its basic idea is to calculate the correlation coefficient between the hydrological time series analyzed and its time order, based on which the significance degree of trend can be classified as five ranks: no, weak, mid, strong and very strong. By deducing the relationship between the correlation coefficient and the trend’s slope, their positive correlation is mathematically formulated. Results of the statistical tests verified the effectiveness of the method, and also clarified the influences of mean values and variance of a time series on the classification practice. Trend variability of water level series over different time scales and measured at the Gaoyao hydrological station was analyzed by the proposed method, and the index of weight was used to investigate the relationship among the significance degrees of trends on different time scales, which were evaluated by the proposed approach. Results indicated that the trends of all water level series presented downward trends, but they showed different significance degrees at multi-time scales. Those trends with bigger weights at certain time scales had bigger influence on the total variability of trends. By considering the physical formation mechanisms of the variability of runoff regimes in the basin, reasonability of the results and effectiveness of the proposed method were verified, which would be helpful for the evaluation of the influence of climate change and human activities on the changes in hydrological process.