Evaluating Extreme Value Rainfall Using Mixed Exponential Distribution with Advanced Weather Generator

Abstract

Changes in rainfall regime is one of the factors that is used to determine the impact of climate change. Climate change substantially impacted human societies and the natural environment with the many occurences of extreme weather events, namely rainfall variation, floods, droughts and rainstorms. In tropical countries such as Malaysia, the occurrence of extreme rainfalls has become more common in recent decades. Advanced Weather Generator (AWE-GEN) which uses meteorological data as input has the potential in predicting extreme events. The aim of this study is to propose and integrate the mixed exponential distribution in representing rainfall intensity using AWE-GEN model and evaluate the ability of the newly developed model in simulating extreme rainfall. Parameters of mixed exponential distribution theoretically represent light and heavy rainfall. The study site is Kemaman, Malaysia which is often affected by flood. Model input uses historical meteorological variables of forty years (1975 - 2015) at hourly scale, specifically precipitation, air temperature, relative humidity and wind speed. Results indicate the AWE-GEN model with mixed exponential distribution is capable of generating rainfall series very well and also able in capturing extreme rainfall events. The estimated parameters obtained revealed that the wet hourly series at study site is mainly dominated by the heavy rainfall at short duration. The outcome of this study could potentially be used in providing invaluable input to authorities requiring detailed rainfall data for planning and forecasts purposes.