Stochastic generation of hourly rainfall series in the Western Region of Peninsular Malaysis

Abstract

Comprehensive analysis and modeling of rainfall distribution is essential in capturing the characteristics of high intense rainfall. The western region of Peninsular Malaysia which is more urbanized and densely populated is prone to flash flood occurrences due to the high intense rainfall brought by a convective rainfall during the inter-monsoon season. Convective rain is usually short live and intense. Therefore, knowledge pertaining to the distribution of rainfall intensity at short time scale is crucial in planning and decision making prior to, during and after a flood event, thereby minimizing the potentially catastrophic impact of flooding. The selection of appropriate probability distribution to represent rainfall intensity is highly critical to get a better indication of seasonal contribution to the annual rainfall. This study aimed to determine the better distribution of rainfall intensity to represent extreme rainfall events in the western region using Advanced Weather Generator (AWE-GEN). Model development consists of using hourly rainfall data and other meteorological data from three stations located within the studied region. Two probability distributions incorporated in the AWE-GEN model, namely, Weibull and Gamma were fitted to the historical data. Numerical evaluation using Root Mean Square Error goodness-of-fit test was used to compare the performance of the distributions. Results showed that AWE-GEN model is capable of simulating the monthly rainfall series at the west coast region with Weibull being the better distribution representing intensity. It was found that high values in model parameters and contribute to the higher intense rainfall within the studied region. The AWE-GEN model also performs quite well in reproducing the hourly and 24 hour extremes rainfall as well as generating the extreme wet spell; however the model slightly underestimates the extreme dry spell. Results can be beneficial, particularly, for a better rainfall forecasting at watersheds and urban areas.