Nonparametric Flood‐Frequency Analysis With Historical Information

Abstract

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 increa...