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

Abstract

The 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.