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A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series

T.A. Cohn·J.F. England·C. E. Berenbrock·R.R. Mason·J.R. Stedinger·J.R. Lamontagne
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Abstract

he Grubbs-Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs-Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less-than” values, and a frequency distribution can be developed using censored-data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right-hand tail of a frequency distribution and provide protection from lack-of-fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.

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T.A. Cohn, J.F. England, C. E. Berenbrock, R.R. Mason, J.R. Stedinger, J.R. Lamontagne. 2013-08-19. A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series. https://doi.org/10.1002/wrcr.20392

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