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Open AccessArticle

Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria

1
Department of Civil Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
2
Department of Environmental Sciences, Faculty of Science, Federal University Dutse, Dutse P.M.B 7156, Nigeria
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3044; https://doi.org/10.3390/w12113044
Received: 17 September 2020 / Revised: 21 October 2020 / Accepted: 28 October 2020 / Published: 30 October 2020
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: power transformation; random forest; representative concentration pathways; multi model ensemble; Nigeria power transformation; random forest; representative concentration pathways; multi model ensemble; Nigeria
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MDPI and ACS Style

Shiru, M.S.; Park, I. Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria. Water 2020, 12, 3044. https://doi.org/10.3390/w12113044

AMA Style

Shiru MS, Park I. Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria. Water. 2020; 12(11):3044. https://doi.org/10.3390/w12113044

Chicago/Turabian Style

Shiru, Mohammed S.; Park, Inhwan. 2020. "Comparison of Ensembles Projections of Rainfall from Four Bias Correction Methods over Nigeria" Water 12, no. 11: 3044. https://doi.org/10.3390/w12113044

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