Kyung-Duk Kim

Comparative study of flood quantiles estimation by nonparametric models

There are two basic approaches for estimating flood quantiles: a parametric and a nonparametric method. In this study, the comparisons of parametric and nonparametric models for annual maximum flood data of Goan gauging station in Korea were performed based on Monte Carlo simulation. In order to consider uncertainties that can arise from model and data errors, kernel density estimation for fitting the sampling distributions was chosen to determine safety factors (SFs) that depend on the probability model used to fit the real data. The relative biases of Sheater and Jones plug-in (SJ) are the smallest in most cases among seven bandwidth selectors applied. The relative root mean square errors (RRMSEs) of the Gumbel (GUM) are smaller than those of any other models regardless of parent models considered. When the Weibull-2 is assumed as a parent model, the RRMSEs of kernel density estimation are relatively small, while those of kernel density estimation are much bigger than those of parametric methods for other parent models. However, the RRMSEs of kernel density estimation within interpolation range are much smaller than those for extrapolation range in comparison with those of parametric methods. Among the applied distributions, the GUM model has the smallest SFs for all parent models, and the general extreme value model has the largest values for all parent models considered.

A Simulation Study for Effects of Various Factors Based on Generalized Logistic Distribution

The performance of the index flood method for 4 regions whose specifications are representatives of annual maximum rainfall data in South Korea, is investigated. The Monte Carlo simulation is performed to investigate the effects of at-site and regional frequency analysis, sample sizes, intersite dependence, heterogeneity, and applied frequency distributions on the accuracy of quantile estimates. Simulation experiments show that the regional frequency analysis is more accurate than the atsite frequency analysis even in the regions with moderate amounts of heterogeneity, intersite dependence, and misspecfication of the frequency distributions. Heterogeneity increases the relative bias of at-site estimates. Intersite dependence increases the variability of estimates but has little effect on the relative bias. As the nonexceedance probability increases, the performance of the regional frequency analysis over the at-site frequency analysis improves. It is also found that the misspecification of the frequency distribution is more important than heterogeneity as a source of error for large return periods.