A STUDY ON COMPARISON OF SIX PROBABILITY DISTRIBUTIONS FOR EXTREME VALUE ANALYSIS OF RAINFALL DATA.

Estimation of rainfall for a given return period is of utmost importance for planning and design of minor and major hydraulic structures. This can be achieved through Extreme Value Analysis (EVA) of rainfall by fitting probability distributions viz., 2-parameters Normal, 2-parameters Log Normal, Pearson Type-3, Log Pearson Type-3, Extreme Value Type-1 (EV1) and Generalized Extreme Value (GEV) to the series of annual 1-day maximum rainfall. Based on the intended applications and the variate under consideration, Method of Moments (MoM), Maximum Likelihood Method (MLM) and L-Moments (LMO) are used for determination of parameters of the distributions. The adequacy of fitting six probability distributions adopted in EVA of rainfall for Afzalpur, Aland and Kalaburagi sites is quantitatively assessed by Goodness-of-Fit (viz., Chi-square and Kolmogorov-Smirnov) and diagnostic test (viz., D-index) tests, and qualitatively assessed by the fitted curves of the estimated rainfall. The outcomes of the study indicates that the GEV (LMO) is better suited amongst six distributions studied in EVA for rainfall estimation for Afzalpur and Kalaburagi whereas EV1 (MLM) for Aland. [ABSTRACT FROM AUTHOR]