Abstract. L (Linear) moments are used in identifying regional flood frequency distributions for different zones Tunisia wide. 1134 site-years of annual maximum stream flow data from a total of 42 stations with an average record length of 27 years are considered. The country is divided into two homogeneous regions (northern and central/southern Tunisia) using a heterogeneity measure, based on the spread of the sample L-moments among the sites in a given region. Then, selection of the corresponding distribution is achieved through goodness-of-fit comparisons in L-moment diagrams and verified using an L moment based regional test that compares observed to theoretical values of L-skewness and L-kurtosis for various candidate distributions. The distributions used, which represent five of the most frequently used distributions in the analysis of hydrologic extreme variables are: (i) Generalized Extreme Value (GEV), (ii) Pearson Type III (P3), (iii) Generalized Logistic (GLO), (iv) Generalized Normal (GN), and (v) Generalized Pareto (GPA) distributions. Spatial trends, with respect to the best-fit flood frequency distribution, are distinguished: Northern Tunisia was shown to be represented by the GNO distribution while the GNO and GEV distributions give the best fit in central/southern Tunisia.
Best-fit distributions of floods in Tunisia are determined based on L-moment diagram and statistical tests. GEV and GLO distributions provided the best fit to seven and three regions of Tunisia respectively. In each homogeneous region, hierarchical approaches and regression models were developed for gauged and ungauged watersheds. The first two parameters of the distributions (GEV and GLO) were estimated from measured data while the third parameter was represented by the regional average value weighted by the record length of all stations in the region. The obtained parameters were correlated to the catchment size. Quantiles obtained by the proposed models were compared with those obtained using local conventional models. Statistical tests showed that the proposed models provided a much better agreement with observed floods than any of the conventional methods generally used in Tunisia.
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