Probability Distributions of Annual Maximum River Discharges in North-Western and Central Europe

ABSTRACT: The goal of this study is to evaluate the goodness of fit of alternate PDFs (probability distribution functions) to sequences of annual maximum streamflows in North-Western and Middle Europe. Though we never know with certainty the true population from which observed streamflows arise, studies such as this may provide some guidence on which PDFs provide a reasonable approximation. L-Moment diagrams wer

Robust detection of discordant sites in regional frequency analysis

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.Hydraulic EngineeringCivil Engineering and Geoscience

Some Developments in Forward Search Clustering

The Forward Search (FS) represents a useful tool for clustering data that include outlying observations, because it provides a robust clustering method in conjunction with graphical tools for outlier identification. In this paper, we show that recasting FS clustering in the framework of normal mixture models can introduce some improvements: the problem of choosing a metric for clustering is avoided; membership degree is assessed by posterior probability; a testing procedure for outlier detection can be devised