Identification of a probability distribution for extreme rainfall series in East Malaysia

• Main Author: Ibrahim, Ismail
• Format: Thesis
• Language: English; English; English
• Published: 2004
• Subjects: GB Physical geography; GB651 - 2998 Hydrology. Water
• Online Access: http://eprints.uthm.edu.my/7132/1/24p%20ISMAIL%20IBRAHIM.pdf; http://eprints.uthm.edu.my/7132/2/ISMAIL%20IBRAHIM%20COPYRIGHT%20DECLARATION.pdf; http://eprints.uthm.edu.my/7132/3/ISMAIL%20IBRAHIM%20WATERMARK.pdf; http://eprints.uthm.edu.my/7132/
• Institution: Universiti Tun Hussein Onn Malaysia

The goal of this study was to evaluate the goodness-of-fit of the alternate probability distributions to sequences of the annual maximum stream flows in the East Malaysian states of Sabah and Sarawak. We will never know with certainty, the actual amount of rainfall that will occur in the future. So a statistical analysis of this nature can provide guidance on which probability distributions can give reasonable approximation. Basically, this study is a statistical analysis on extreme annual rainfall series in East Malaysia. It will discuss the comparative assessment of eight candidate distributions in providing accurate and reliable maximum rainfall estimates for East Malaysia. The models considered were the Exponential (EXP), Gamma (GAM), Generalized Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Gumbel (GUM), Pearson Type III (PE3) and Wakeby (WAK). Annual maximum rainfall series for one-hour resolution from a network of ten Principal Gauging Stations located five each in Sabah and Sarawak were selected for this study. On top of that, data for the fifteen-minutes were also taken for analysis to act as a check to the result. The length of rainfall records varies from seventeen to twenty-one years. Model parameters were estimated using the L�moment method. The quantitative assessment of the descriptive ability of each model was based on using the Probability Plot Correlation Coefficient (PPCC) test combined with Relative Root Mean Squared Error (RRMSE), Root Mean Squared Error (RMSE) and Maximum Absolute Error (MAE). Ranking of PPCC in descending order and the other three criteria on ascending orders were taken and the top three distributions from the ranking for each station were chosen. The GEV distribution came out on top that occurs frequently on most of the stations is selected as the best fitting distribution to describe the extreme rainfall series for East Malaysia.