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Open AccessArticle

Improving Hydro-Climatic Projections with Bias-Correction in Sahelian Niger Basin, West Africa

1
Graduate Research Program (GRP) Climate Change and Water Resources, West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), University of Abomey-Calavi, Abomey-Calavi BP 2008, Benin
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Ecole Nationale Supérieure des Sciences et Techniques Agronomiques de Djougou, University of Parakou, Parakou BP 123, Benin
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Laboratory of Applied Hydrology, National Water Institute, University of Abomey-Calavi, Abomey-Calavi BP526, Benin
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Department of Soil Science and Land Management, Federal University of Technology, P.M.B 65, Gidan-Kwanu, 920212 Minna, Nigeria
5
Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Nir Y. Krakauer, Tarendra Lakhankar, Ajay K. Jha and Vishnu Pandey
Climate 2017, 5(1), 8; https://doi.org/10.3390/cli5010008
Received: 6 December 2016 / Revised: 4 February 2017 / Accepted: 10 February 2017 / Published: 15 February 2017
(This article belongs to the Special Issue Climate Impacts and Resilience in the Developing World)
Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrological impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction with quantile mapping significantly improved the accuracy of rainfall and temperature simulations compared to observations. The mean of six efficiency coefficients used for monthly rainfall comparisons of 8 GCMs to the observed ranged from 0.69 to 0.91 and 0.84 to 0.96 before and after bias correction, respectively. The range of the standard deviations of the efficiency coefficients among the 8 GCMs rainfall data were significantly reduced from 0.05–0.14 (before bias correction) to 0.01–0.03 (after bias correction). Increasing annual rainfall, temperature, PET and river discharge were projected for most of the GCMs used in this study under the RCP4.5 and RCP8.5 scenarios. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies. View Full-Text
Keywords: climate change; evapotranspiration; IHACRES model; rainfall; runoff; quantile mapping climate change; evapotranspiration; IHACRES model; rainfall; runoff; quantile mapping
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Oyerinde, G.T.; Hountondji, F.C.C.; Lawin, A.E.; Odofin, A.J.; Afouda, A.; Diekkrüger, B. Improving Hydro-Climatic Projections with Bias-Correction in Sahelian Niger Basin, West Africa. Climate 2017, 5, 8.

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