Modeling the Distribution of Rainfall Intensity using Hourly Data

Abstract

Problem statement: Design of storm water best management practices to control runoff and water pollution can be achieved if a prior knowledge of the distribution of rainfall characteristics is known. Rainfall intensity, particularly in tropical climate, plays a major role in the design of runoff conveyance and erosion control systems. This study is aimed to explore the statistical distribution of rainfall intensity for Peninsular Malaysia using hourly rainfall data. Approach: Hourly rainfall data were collected from twelve stations spread across the Peninsular. Six hour separation time was used to divide the data into individual rainfall events and four probability distributions namely, Generalized Pareto (GP), Exponential (EXP), Beta (BT) and Gamma (GM) distributions were used to model the distribution of the hourly rainfall intensity. Kolmogorov-Sminov anderson-Darling and Chi-squared goodness-of-fit tests were used to evaluate the best fit. Results: The rainfall frequency, based on 6 h minimum inter-event time, ranges from 115-198 events. The distribution of the rainfall frequency and that of the highest intensity observed, over the recorded period, across the peninsular, is however irregular. The mean rainfall intensity ranges from 2.32-3.88 mm h^-1. Kuala-Lumpur and Penang received the highest, while Segamat and Kedah received the lowest. Conversely, over the period of record, Segamat recorded the highest CV, skewness and kurtosis while Pahang has the least value for these parameters. Goodness-of-fit tests at 5% level of significance indicate that all the models can be used to model the distribution of rainfall intensity in Peninsular Malaysia. However, GP is found to be the most suitable model among the four probability distributions tested. Conclusion: Basic statistics of hourly rain intensity were obtained and probability distributions compared. It was found that GP is the most suitable model. Results can be useful, particularly, to agricultural and storm water management planning.