Future projections of extreme hourly rainfall using advanced weather generator (AWE-GEN)

Analysis and Projection of Malaysian Rainfall Distribution is a compilation of research done in the area of statistical hydrology, focusing on rainfall and streamflow data analysis. The authors of articles in this book chapter are actively involved in various research encompassing the many aspects of statistical analysis. Climate change and statistical downscaling is currently widely researched as the impact of climate change on us are becoming more apparent. Various global climate models are used in building models to project future changes in rainfall and temperature. Chapter 1 discusses the use of an Advanced Weather Generator (AWE-GEN) in simulating hourly rainfall data. A summary of several statistical downscaling methodologies and a detailed description of the AWE-GEN is presented. Future projections of extreme rainfall events based on RCP 6.0 scenario for years 2081 to 2100 and their expected behavior at respective return periods are discussed. Chapter 2 presents an investigation into the modelling of daily rainfall occurrence using hidden Markov model. The evaluated hidden states were used to simulate daily rainfall and consequently the wet and dry spells of studied regions. Parameter estimation is another topic which is widely researched and discussed as it is fundamental to probability fitting of any hydrologic series. Hence the third chapter further adds on the existing body of knowledge by looking at L-moment and TL-moment parametric estimation of Lognormal distribution. Analysis of rainfall pattern is another fundamental study frequently carried out prior to in-depth analysis of other hydrologic variables. Principal component analysis (PCA) is one of the statistical tools widely used in determination of homogenous clusters as well as for variable reduction purposes. Chapter 4 of this book uses PCA for clustering homogeneous regions in Peninsular Malaysia which suggest geographical, topographical as well as monsoonal influence in the determination of the clusters. The last chapter of the book is another discussion of parameter estimation. However this chapter uses the partial L-moment for fitting the Generalized Extreme Value distribution. Partial L-moments uses censored data which gives weightage to the extreme tail or the heavier tail. This method will perhaps be more useful for the estimation of large return periods. The use of statistics, and in fact the discovery of new approaches to solving statistical problems in hydrology is an ever expanding field. The authors are grateful for the opportunity to publish and share their work