Modeling the distribution of rainfall intensity using hourly data

Abstract

Problem statement: Design of storm water best management practices to control runoffand water pollution can be achieved if a prior knowledge of the distribution of rainfall characteristics isknown. Rainfall intensity, particularly in tropical climate, plays a major role in the design of runoffconveyance and erosion control systems. This study is aimed to explore the statistical distribution ofrainfall intensity for Peninsular Malaysia using hourly rainfall data. Approach: Hourly rainfall datawere collected from twelve stations spread across the Peninsular. Six hour separation time was used todivide the data into individual rainfall events and four probability distributions namely, GeneralizedPareto (GP), Exponential (EXP), Beta (BT) and Gamma (GM) distributions were used to model thedistribution of the hourly rainfall intensity. Kolmogorov-Sminov anderson-Darling and Chi-squaredgoodness-of-fit tests were used to evaluate the best fit. Results: The rainfall frequency, based on 6 hminimum inter-event time, ranges from 115-198 events. The distribution of the rainfall frequency andthat of the highest intensity observed, over the recorded period, across the peninsular, is howeverirregular. 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 ofrecord, 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 beused to model the distribution of rainfall intensity in Peninsular Malaysia. However, GP is found to bethe 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