A stochastic rainfall model, NSRPM (Neyman-Scott Rectangular Pulse Model), is able to reflect the cluster characteristics of rainfall events which is unable in the RPM (Rectangular Pulse Model). Therefore NSRPM has advantage in the hydrological applications. The NSRPM consists of five model parameters and the parameters are estimated using optimization techniques such as DFP (Davidon-Fletcher-Powell) method and genetic algorithm. However the DFP method is very sensitive in initial values and is easily converge to local minimum. Also genetic algorithm has disadvantage of long computation time. Nelder-Mead method has several advantages of short computation time and no need of a proper initial value. In this study, the applicability of parameter estimation methods was evaluated using rainfall data of 59 national rainfall networks from 1973-2011. Overall results demonstrated that accuracy in parameter estimation is in the order of Nelder-Mead method, genetic algorithm, and DFP method.

The regional impact of accelerated climate change causes changes of the structure of regional rainfall events. A stochastic rainfall generation model is used to analyze the detail structural variability of rainfall events since traditional approaches of measuring rainfall variability have limitations in analyzing detail change of rainfall structure. In this study, the NSRPM (Neyman-Scott Rectangular Pulse Model) which has 5 model parameters such as mean arrival rate of a storm, mean number of cells in a storm, mean displacement between cells, mean cell life span, mean intensity of a rain cell, is used to overcome the limitations of traditional approaches using rainfall observations. Changes of model parameters were evaluated using hourly rainfall data from 1973 to 2014 in the Han River Basin. The changes of model parameter estimates were characterized and compared with results of traditional analysis.