A note on the applicability of log-Gumbel and log-logistic probability distributions in hydrological analyses: I. Known pdf |
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| Authors: | PAWEL M ROWINSKI WITOLD G STRUPCZEWSKI VIJAY P SINGH |
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| Institution: | 1. Water Resources Department , Institute of Geophysics, Polish Academy of Sciences , ul. Ksiecia Janusza 64, 01-452, Warsaw, Poland E-mail: pawelr@igf.edu.pl;2. Department of Civil and Environmental Engineering , Louisiana State University , Baton Rouge, Louisiana, 70803-6405, USA E-mail: cesing@lsu.edu |
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| Abstract: | Abstract Two probability density functions (pdf), popular in hydrological analyses, namely the log-Gumbel (LG) and log-logistic (LL), are discussed with respect to (a) their applicability to hydrological data and (b) the drawbacks resulting from their mathematical properties. This paper—the first in a two-part series—examines a classical problem in which the considered pdf is assumed to be the true distribution. The most significant drawback is the existence of the statistical moments of LG and LL for a very limited range of parameters. For these parameters, a very rapid increase of the skewness coefficient, as a function of the coefficient of variation, is observed (especially for the log-Gumbel distribution), which is seldom observed in the hydrological data. These probability distributions can be applied with confidence only to extreme situations. For other cases, there is an important disagreement between empirical data and theoretical distributions in their tails, which is very important for the characterization of the distribution asymmetry. The limited range of shape parameters in both distributions makes the analyses (such as the method of moments), that make use of the interpretation of moments, inconvenient. It is also shown that the often-used L-moments are not sufficient for the characterization of the location, scale and shape parameters of pdfs, particularly in the case where attention is paid to the tail part of probability distributions. The maximum likelihood method guarantees an asymptotic convergence of the estimators beyond the domain of the existence of the first two moments (or L-moments), but it is not sensitive enough to the upper tails shape. |
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| Keywords: | probability density functions log-Gumbel pdf log-logistic pdf statistical moments L-moments flood frequency analysis Poland |
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