Abstract
This study compares multi model ensemble (MME) projections of rainfall using general quantile mapping, gamma quantile mapping, Power Transformation and Linear Scaling bias correction (BC) methods for representative concentration pathways (RCPs) 4.5 and 8.5 of the Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). Using the Global Precipitation Climatology Centre historical period (1961–2005) rainfall data as the reference, projection was conducted over 323 grid points of Nigeria for the periods 2010–2039, 2040–2069 and 2070–2099. The performances of the different BC methods in removing biases from the GCMs were assessed using different statistical indices. The computation of the MME of the projected rainfall was conducted by aggregation of 20 GCMs using random forest regression method. The percentage differences in the future rainfall relative to the historical period were estimated for all BC methods. Spatial projection of the percentage changes in rainfall for Linear scaling, which was the best performing BC method, showed increases in rainfall of 5.5–6.9% under RCPs 4.5 and 8.5, respectively, while the decrease range was −3.2–−4.2% respectively during the wet season. The range of annual increases in precipitation was 5.7–7.3% for RCP 4.5 and 8.5, respectively, while the decrease range was −1.0–−4.3%. This study also revealed monthly rainfall within the country will decrease during the wet season between June and September, which is a significant period where most crops need the water for growth. Findings from this study can be of importance to policy makers in the management of changes in hydrological processes due to climate change and management of related disasters such as floods and droughts.
Highlights
Rainfall is an important component of the climatic and hydrological system as it is the primary source that replenishes different water sources around the globe
The results of the performances of the downscaling models for the four bias correction (BC) methods used in this study are presented in Figure 2 for some of the global climate models (GCMs) used in this study (Table 1)
The comparisons show that there are variations in the performances of the downscaling models depending on the GCM
Summary
Rainfall is an important component of the climatic and hydrological system as it is the primary source that replenishes different water sources around the globe. The many decades of greenhouse gases emissions (GHG) have aggravated the dynamism of the climate of the earth, which has in recent times resulted in more erratic seasonal and annual rainfall in many parts of the globe [1,2,3,4]. This has subsequently affected the intensity, frequency and risk of disasters which have been projected to increase in the future [5,6,7,8,9,10]. These warming conditions are expected to affect two thirds of
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