Comparisons of the L- and LH-moments in the selection of the best distribution for regional flood frequency analysis in Lake Urmia Basin

Design flood estimation is an important task that is required in the planning and design of many civil engineering projects. Finding the most suitable distribution to flood samples and selecting the appropriate parameter estimation method are of great importance for flood frequency analysis. In this study, L-moments and LH-moments have been used to characterise the upper part of distributions and larger events in flood data. Three extreme value distributions, that is, generalised extreme value (GEV), generalised logistic (GLO) and generalised Pareto (GPA), through different levels of the LH-moments have been applied to describe the annual maximum flood data obtained from 34 sites in Lake Urmia Basin, northwest Iran. The performances of these distributions have been assessed by evaluating the relative root-mean-square error and the minimum L-kurtosis difference criterion. The result shows that the LH-moments are more efficient for obtaining improved values of flood peaks than the L-moments. For the upper part of distributions, the GEV distribution using the L2-moment is the best, followed by the GLO distribution using the L1-moment and the GPA distribution using the L3-moment. The GLO distribution using the L-moments methods is the best among all distributions for a complete data series.