Variance Correction Prewhitening Method for Trend Detection in Autocorrelated Data
Publication: Journal of Hydrologic Engineering
Volume 20, Issue 12
Abstract
Detecting trends in hydrometerological data through the commonly used Mann-Kendall test is misleading in the presence of data autocorrelation. Autocorrelation seriously interferes with type I errors and power of trend detection. To mitigate this effect, the authors introduce a variance correction prewhitening method. It addresses two important issues that lacked appropriate attention in the past application of trend-free prewhitening method: inflationary variance of slope estimator and deflationary serial variance. After serial and slope variances correction, the new method keeps a better balance between maintaining a low type I error and a relatively strong power of trend detection. In comparison, other methods for the same purpose only address one of these two characteristics. The new method bears some resemblance to the block-bootstrap method; however, it is superior in its simplicity for implementation. Case studies reveal that uncertainties arising from autocorrelation are substantial. Applying more than one test is helpful to interpret results with uncertainties information. The new method provides a robust choice to this strategy.
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Acknowledgments
This research was financially supported by the National Natural Science Foundation of China (41301017), the State Scholarship Fund from China Scholarship Council (201306710008), and the Graduate Students Research Innovative Program of Jiangsu Province (CXZZ130247). The authors are grateful for receiving the data from Water Survey of Canada and National Climate Centre of the China Meteorological Administration. The authors also gratefully acknowledge the editors and three anonymous reviewers for their insightful comments.
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Published In
Journal of Hydrologic Engineering
Volume 20 • Issue 12 • December 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Nov 3, 2014
Accepted: Mar 19, 2015
Published online: May 7, 2015
Discussion open until: Oct 7, 2015
Published in print: Dec 1, 2015
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