NONPARAMETRIC FLOOD-FREQUENCY ANALYSIS WITH HISTORICAL INFORMATION

Inclusion of historical information in flood-frequency analysis increases the accuracy of flood estimates; however, some of the major factors affecting this accuracy are the a-priori specification of a particular probability distribution function and the method of estimating its parameters. In this study, a new nonparametric procedure is proposed that altogether eliminates the specification of a distribution and greatly simplifies parameter-estimation problems. The nonparametric method, however, is not particularly efficient in extrapolating distribution function beyond an available record length. Thus, to overcome such a problem, a new kernal is introduced in the form of an extreme-value distribution. Also, the smoothing parameter is estimated by a cross-validation procedure, and a new mixture- distribution model is proposed for inclusion of historical data into analysis. A simulation study employing a two-parameter log-normal distribution shows that the accuracy of flood estimates does not greatly increase with the addition of data length beyond 10 years. The present paper shows that inclusion of historical information into nonparametric analysis improves extrapolation. © ASCE.