Evaluation of best- fit probability distribution models for prediction of rainfall in Southern Nigeria

Abstract

Suitable and adequate hydrological design data especially for flood design, are not readily available. When data is available, it is often outdated and irrelevant to current events especially in this era of global warming and climate change. This study presents rainfall frequency analysis for some cities in Southern Nigeria using the annual maximum series of daily rainfall data for the stations. The objective of the study was to select the probability distribution model from among six commonly used probability distribution models namely: Generalized Extreme value distribution (GEV), Extreme value type I distribution (EVI), Generalized Pareto distribution (GPA), Pearson Type III (PIII), log Normal (LN) and Log Pearson Type III (LP111) distributions. These distributions were applied to the annual maximum series of daily rainfall at each station using the parameters of the distributions estimated by the method of moments. The best fit probability distribution model at each location was selected based on the results of seven goodness of fit tests values with a scoring and ranking scheme. Our results indicate that the best-fit distribution models at the study locations are PIII for Ibadan and Benin City; GEV for Onitsha, Enugu, Owerri, Calabar, and Port Harcourt; EVI and LN for Uyo; EVI for Akure and LP111 for Ikeja. This implies that GEV performed better by occupying 50% of the studied area, followed by EVI and PIII which performed by occupying 20% each. These best fit probability distribution models are recommended for use for necessary design at each location for flood hazard mitigation.