Extension of EMA to address regional skew and low outliers

The recently developed expected moments algorithm [EMA] (Cohn et al. 1997) does as well as MLEs at estimating LP3 flood quantiles using systematic and historical information. Needed extensions include use of a regional skewness estimator and its precision to be consistent with Bulletin 17B and to make use of such hydrologic information. Another issue addressed by Bulletin 17B is the treatment of low outliers. A Monte Carlo study illustrates the performance of an extended EMA estimator compared to estimators that employ the complete data set with and without use of regional skew, conditional probability adjustment from Bulletin 17B, and an estimator that uses probability plot regression to compute substitute values for low outliers. Estimators that use a regional skew all do better than estimators that fail to use an informative regional skewness estimator. For LP3 data, the low outlier rejection procedure results in no loss of overall accuracy, and the differences between the MSEs of the estimators that used an informative regional skew were generally negligible in the skew range of real interest.