Statistical analysis of rainfall events and clustering of extreme values are important for risk assessment of floods and decision-making amidst other hydrologic studies. Annual and partial series rainfall data from 1968 to 2015 in Makurdi metropolis was subjected to Frequency analysis in order to determine the best fit probability distribution function (PDF) for the area, to allow for better estimation and prediction of extreme rainfall associated with the area, due to its general low relief that makes it liable to flooding during heavy rain storm. Five commonly used rainfall frequency distributions, namely; Generalized extreme value (GEV); Gumbel (EV1); Generalized Pareto (GPA); Generalized Logistic (GLO) and three parameter Log Normal (LN3) were adopted for the study. The L-Moments method of parameter estimation and a combination of the Chi-squared (χ2), Anderson-Darling (AD) and Kolmogorov-Smirnov (KS) goodness of fit tests were used for the analysis. The result of the goodness of fit tests showed the best fit distribution for annual series is generalized extreme value (GEV) distribution while the best fit for partial series is Generalized Pareto (GPA) distribution. However, water budget analysis is recommended to identify periods of excess and deficit precipitations.
Key words: Rainfall, frequency analysis, probability distribution function, parameter estimation, goodness of fit.
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