Abstract
Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010–2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.
Highlights
Like many other African countries, incidence of drought is increasing in Nigeria
Meteorological droughts during growing seasons were projected for Nigeria using the standardized precipitation evapotranspiration index (SPEI) and an ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs) for three representative concentration pathway (RCP) 2.6, 4.5 and 8.5
The results of this study revealed that temperature is the dominant variable that may lead to a decrease in the SPEI in Nigeria due to climate change
Summary
Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Investigation of historical changes in climate variables has shown large variabilities over the past decades[1,2,3,4] These variabilities have manifested in the frequencies, intensities, and risks of climatic hazards[5,6,7,8], caused loss of lives[9], destruction of properties[10], devastation of ecosystem[11,12], and damage to economies[13] in many parts of the world. The continuation of the present trend in droughts can have serious social, environmental, and economic consequences for the country It would be highly devastating, if it continues to occur during growing seasons as economy and livelihood of majority of the population heavily rely on rain fed agriculture. Mean projection for minimizing uncertainties is recommended[15,49]
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