Abstract

CAMPOS-ARANDA, Daniel Francisco. Fitting of GEV, GLO and GPA Distributions with Higher-Order L Moments. Ing. invest. y tecnol. [online]. 2016, vol.17, n.1, pp.131-142. ISSN 2594-0732.

The probabilistic analysis of extreme hydrological data aims to estimate reliable predictions associated with high return periods. The LH or higher-order moments can characterize in a more efficient way the right tail of the probability distribution function (PDF), by giving more importance to large data values. The undesirable influence of the sample's small quantities is reduced by using the LH moments in the estimation of predictions. This paper briefly describes the theory of L moments, as a basis for its generalization, which leads to the LH moments, proposed in 1997 by Wang Q.J. By means of the L and LH moments methods, the equations that allow the estimation of the three fitting parameters of the PDF: General Extreme Values (GEV), Generalized Logistics (GLO) and Generalized Pareto (GPA) are cited. A numerical application to the 21 available records annual floods of Hydrological Region No. 10 (Sinaloa) is performed, contrasting its results based on the standard error of fit (SEF). The analysis of results showed that the GPA distribution leads to the best fittings. It can be highlighted that LH moments are a good choice to abate the SEF in the three distributions used.

Keywords : L moments; higher-order L moments; LH moments; GEV, GLO and GPA distributions; standard error of fit; Hydrological Region No. 10 (Sinaloa).

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