HEAVY RAINFALL FORECASTING MODEL FOR GUINEA SAVANNA ZONE, NIGERIA

Abstract

Rainfall which is the main source for freshwater and rainwater is presently one of the main useful resources on earth. Enhanced rain forecast would assist humans in various parts of the world to prepare for adverse effects that accompany heavy rainfall and could in due course save lives and reduce damages to infrastructure. The study aimed at heavy rainfall forecasting model for Guinea Savanna Zone, Nigeria. Secondary data collected on daily basis were used for this research and included wind (direction and speed), relative humidity, rainfall, thunderstorms (TSs) (predictors) and forecasting model outputs for 1st January, 1981 to 31st December, 2015. The data were sourced from the Nigerian Meteorological Agency and European Centre for Medium–Range Weather Forecasts (ECWMF). Both parametric and non-parametric tests were used to determine rainfall characteristics over the study area. Multiple Linear Regression (MLR) model was developed and used to assess the relationship between predictors and the predictant. The Probability of Detection (POD) was used to examine the existing rainfall forecasting numerical models and compare their results with the actual heavy rainfall. “Heavy rainfall forecasting model was developed using the Artificial Neural Networks (ANNs) and used to forecast heavy rainfall (accumulated rainfall of 50 mm and above per day) for 2019. Gumbel extreme probability theory was used to examine the suitability of the model developed. The results show a general upward trend in rainfall and heavy rainfall with inter-annual variability. The long term mean for the study area is 1220.5 mm, highest mean monthly rainfall of 263.5 mm occurs in August, while the lowest of 1.9 mm occurs in December and it has single maximum. 1983 had the lowest mean rainfall of 1020.3 mm and 2014 has the highest mean of 1467.2 mm. The total regional monthly heavy rainfall indicated that November has the lowest total of 01, while August has the highest of 257. Thunderstorms have the strongest relationship with heavy rain. Both ECMWF and UKMet rainfall forecast models under-estimated 24-hour rain accumulation. The developed model is capable of forecasting heavy rain over the study area. It is therefore recommended that local heavy rainfall forecast should be embraced by NiMet with the results disseminated to the grassroots. Thunderstorms forecast should be given more attention. More meteorological stations should be established in tertiary institutions and Local Government Area Headquartres. Short-term rain forecast should be given more attention. Similar study should be carried out in other ecological zones of Nigeria to also develop a heavy rainfall forecasting model for them.