Statistical Modeling of Monthly Rainfall in Selected Stations in Forest and Savannah Eco-climatic Regions of Nigeria

Objective: Nigerian agriculture is mainly rain-fed and highly dependent on weather especially rainfall. Therefore modeling of monthly rainfall in some selected stations in Nigeria was undertaken in this study. Methodology: Data (rainfall) spanning a period of 30 years (1981-2010) for fourteen stations which were collected from the Nigerian Meteorological Agency (NIMET) were utilized. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were used. The accuracy and trend of time series was analyzed to give the monthly rainfall prediction for the succeeding year. The results showed that the model fitted into the data well and the stochastic seasonal fluctuation was successfully modeled. Rainfall was minimal in January, February, March and December over the selected stations in northern, Nigeria but increased progressively in strength and amount in the months of June, August and September over the stations in South west, and June and September over the stations in South -south, Nigeria. The highest rainfall of 230 mm was recorded in September over Warri and the lowest rainfall of 52 mm was recorded in August over Maiduguri. The rainfall recorded over the selected stations in South-south stations was visibly higher than what was recorded over the stations in the northern and the South-west stations. In northern Nigeria, the peak monthly mean rainfall amount of 91 mm was observed in August and rainfall amount was very low in January (0.0 mm), February (0.0 mm), March (0.0 mm) and December (0.0 mm). Over South-west, the Peak monthly mean rainfall amount of 215 mm was observed in June and September and rainfall amount was very low in January (0.0 mm) and December (0.0 mm). Over the stations in South-south, the Peak monthly mean rainfall amount of 325 mm was experienced on September and rainfall amount is very low in December (0.0 mm). Conclusion: The study concluded that Seasonal Autoregressive Integrated Moving Average (SARIMA) model was a proper method for modeling and predicting the monthly rainfall. The results are useful for forecasting the pattern of rainfall in the study area and provide information that would be helpful for decision makers in formulating policies to mitigate the problems of water resources management, soil erosion, flooding and drought.