Perhaps the most eye-catching paper with relevance to flood frequency analysis in recent time is that published by Hosking [1990]. In this work, L-moments were introduced. They have become popular tools for solving various problems related to parameter estimation, distribution identification, and regionalization. It can be shown that L-moments are linear function of probability weighted moments (PWM's) and hence for certain applications, such as the estimation of distribution parameters, serve identical purposes [ Hosking, 1986]. In other situations, however, L-moments have significant advantages over PWM's, notably their ability to summarize a statistical distribution in a more meaningful way. Since L-moment estimators are linear functions of the sample values, they are virtually unbiased and have relatively small sampling variance. L-moment ratio estimators also have small bias and variance, especially in comparison with the classical coefficients of skewness and kurtosis. Moreover, estimators of L-moments are relatively insensitive to outliers. These often-heard arguments in favor of estimation of distribution parameters by L-moments (or PWM's) should, nevertheless, not be accepted blindly. In flood frequency analysis, the interest is the estimation of a given quantile, not in the L-moments themselves. Although the latter may have desirable sampling properties, the same does not necessarily apply to a function of them, such as a quantile estimator. In fact, several simulation studies have demonstrated that for some distributions, other estimation methods may be superior in terms of mean square errors of quantile estimators [ Hosking and Wallis, 1987; Rosbjerg et al., 1992]. As compared with for example the classical method of moments, the robustness vis-à-vis sample outliers is clearly a characteristic of L-moment estimators. However, estimators can be ``too robust'' in the sense that large (or small) sample values reflecting important information on the tail of the parent distribution are given too little weight in the estimation [ Bernier, 1993, INRS-Eau, University of Quebec, unpublished paper]. Hosking [1990] assessed that L-moments weight each element of a sample according to its relative importance, but more research is needed to validate that statement.