Robust detection of discordant sites in regional frequency analysis

Abstract

The discordancy measure in terms of the sample L‐moment ratios (L‐CV, L‐skewness, L‐kurtosis) of the at‐site data is widely recommended in the screening process of atypical sites in the regional frequency analysis (RFA). The sample mean and the covariance matrix of the L‐moments ratios, on which the discordancy measure is based, are not robust against outliers in the data, and consequently, this measure can be strongly affected by the discordant sites present in the region. We propose to replace the classical mean and covariance matrix estimates by their robust alternatives on the basis of the minimum covariance determinant estimator. The performance of the classical and robust measures for discordant sites identification is assessed in a series of Monte Carlo simulation experiments within the framework of the RFA. The simulation study shows that the robust discordant measure outperforms the classical one and is consistent with the heterogeneity measure H. Thus we recommend its use as a tool for discordant sites detection and formation of homogeneous regions in RFA.