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Water Resources Management

, Volume 28, Issue 1, pp 227–240 | Cite as

Temporal Change Analysis Based on Data Characteristics and Nonparametric Test

  • Dingfang Li
  • Huantian Xie
  • Lihua Xiong
Article

Abstract

Based on data characteristics and nonparametric test, a new statistical temporal change analysis approach is proposed. The new approach consists of data characteristics analysis, temporal change analysis (including both change point and trend analysis), and result interpretation. Data characteristics are firstly investigated, especially with respect to the assumptions of independence and normality. Then proper nonparametric methods are chosen based on the detected characteristics of the observed data to analyze change points and monotonous linear trend for each of the segments divided by the change points. To avoid shortcoming of the traditional approach of carrying out the trend analysis before change point analysis, it is proposed in this paper that change point detection be performed before trend analysis. At last, statistical analysis results are interpreted according to the physical mechanism of observations. As a study case, the proposed approach has been carried out on three annual discharge series of the Yangtze River at the Yichang hydrological station. The investigations of data characteristics show that the observed data do not meet the assumptions of being independent and identically Gaussian-distributed. So the nonparametric Pettitt’s test was adopted to detect abrupt changes in the mean levels, followed by trend analysis using the nonparametric Mann-Kendall (MK) test. Results indicate the proposed approach is both reliable and reasonable for the temporal change analysis.

Keywords

Data characteristics Nonparametric test Change point analysis Trend analysis Annual discharge series 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (51190094, 61271337 and 11301252). The authors are very grateful to the associate editor and two anonymous reviewers for their helpful suggestions and constructive comments in the improvement of this manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsWuhan UniversityWuhanPeople’s Republic of China
  2. 2.State Key Laboratory of Water Resources and Hydropower Engineering ScienceWuhan UniversityWuhanPeople’s Republic of China
  3. 3.School of ScienceLinyi UniversityLinyiPeople’s Republic of China

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