Fitting statistical distribution of extreme rainfall data for the purpose of simulation

Abstract

In this study, several types of probability distributions were used to fit the daily torrential rainfall data from 15 monitoring stations of Peninsular Malaysia from the period of 1975 to 2007. The study of fitting statistical distribution is important to find the most suitable model that could anticipate extreme events of certain natural phenomena such as flood and tsunamis. The aim of the study is to determine which distribution fits well with the daily torrential Malaysian rainfall data. Generalized Pareto, Lognormal and Gamma distributions were the distributions that had been tested to fit the daily torrential rainfall amount in Peninsular Malaysia. First, the appropriate distribution of the daily torrential rainfall was identified within the selected distributions for rainfall stations. Then, data sets were generated based on probability distributions that mimic a daily torrential rainfall data. Graphical representation and goodness of fit tests were used in finding the best fit model. The Generalized Pareto was found to be the most appropriate distribution in describing the daily torrential rainfall amounts of Peninsular Malaysia. The outputs can be beneficial for the purpose of generating several sets of simulated data matrices that mimic the same characteristics of rainfall data in order to assess the performance of the modification method compared to classical method.