Abstract
This study presents a probabilistic model for daily extreme rainfall. The Annual Maximum Series (AMS) data of daily rainfall in Makurdi was fitted to Generalized Extreme Value (GEV) distribution using Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo (Bayesian MCMC) simulations. MLE is a reliable principle to derive an efficient estimator for a model as sample size approaches infinity. Results in this study show that despite the asymptotic requirement of the MLE, its performance can be improved when adopting Bayesian MCMC. The comparison between the performance of MLE and Bayesian MCMC methods using Percent Bias (PBIAS), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) proved Bayesian MCMC is the better method to estimate the distribution parameters of extreme daily rainfall amount in Makurdi. Based on the 36-year record of rainfall (1979-2014) in Makurdi, return levels for the next 10, 100, 500, 1000 and 10000 years were derived. Key words: Extreme daily rainfall, generalized extreme value distribution, parameter estimation, t-year return level, Makurdi.
Highlights
Recent researches and observations of the climate system have shown that the climate system is more complex than concluded by the working group 1 of the Intergovermental Panel on Climate Change report (IPCC, 2007)
The time series plot of annual daily maximum rainfall for Makurdi in Figure 1 shows that the highest magnitude of 149.3 mm occurred in the year 2000
While this high magnitude appears as an outlier, there is historical evidence that it did occur, in a year characterized by rampant flooding in rural and urban regions of Makurdi, which led to loss of lives, disruption of economic activities, destruction of agricultural lands and other properties
Summary
Recent researches and observations of the climate system have shown that the climate system is more complex than concluded by the working group 1 of the Intergovermental Panel on Climate Change report (IPCC, 2007). According to Pielke (2011) these climate systems models do not appear to be capable of providing skillful predictions of regional, local societally and environmentally important impacts in the coming decades. Earlier studies such as Houghton et al (1990) reported that the Earth’s climate is warming and will continue to warm in the future, as a result of changes in atmospheric carbon dioxide (CO2) and other trace gases. This global warming will lead to changes in annual or seasonal.
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