Rodric M. Nonki a,b,c,*, Ernest Amoussou c,d, Christopher J. Lennard e, Andr´e Lenouo f, Raphael
M. Tshimanga g, Constant Houndenou c

a Laboratory for Environmental Modeling and Atmospheric Physics (LEMAP), Department of Physics, Faculty of Sciences, University of Yaound´e 1, P.O. Box: 812,
Yaound´e, Cameroon
b West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), WASCAL-Climate Change and Water Resources, University of Abomey –
Calavi, 03, PO Box 526, Cotonou, Benin
c Laboratory Pierre PAGNEY, Climate, Water, Ecosystem and Development (LACEEDE), University of Abomey – Calavi, P.O. Box: 1122, Cotonou, 03, Benin
d Department of Geography and Land Management, University of Parakou, P.O. Box: 123, Parakou, Benin
e Climate System Analysis Group (CSAG), University of Cape Town, Cape Town, South Africa
f Department of Physics, Faculty of Science, University of Douala, P.O. Box: 24157, Douala, Cameroon
g Congo Basin Water Resources Research Center (CRREBaC) and Department of Natural Resources Management, University of Kinshasa, Kinshasa, Congo

A B S T R A C T

Hydropower is the world’s largest producer of renewable energy and represents more than 43% of the lowcarbon
energy. However, it is sensitive to climate variability and change. This study evaluates the climate change impacts on hydropower potential in the headwaters of the Benue River Basin (HBRB) under 1.5 ◦C and 2.0 ◦C global warming levels (GWLs) and quantifies the main sources of uncertainty in the modeling chain.
Precipitation and temperature from 17 members of the Coordinated Regional Downscaling Experiment over the Africa domain (CORDEX-Africa) under two representative concentration pathways (RCPs 4.5 and 8.5) were used to run two calibrated Lumped-conceptual hydrological models (HMs) (Hydrologiska Byrans Vattenavdelning (HBV-Light) and HYdrological MODel (HYMOD)). An analysis of variance (ANOVA) decomposition was used to quantify the uncertainties related to each impact modeling chain step in the hydropower potential calculation process. Results reveal a high uncertainty in both climatic and hydrologic parameters. The change in precipitation associated with an increase in potential evapotranspiration (PET) causes a significant decrease in hydropower generation associated with a large uncertainty range. The ANOVA sensitivity test reveals that the dominant contributing source to hydropower projections uncertainty varies with GWL. Given the likely breach of GWL 1.5 by the early 2030s, these findings contribute information for consideration in water and energy planning in the region over the next decade, and stresses that these considerations are urgent for the socioeconomic well-being of the region.

https://doi.org/10.1016/j.renene.2023.118979
Received 1 April 2023; Received in revised form

Available online 30 June 2023
0960-1481/© 2023 Elsevier Ltd. All rights reserved.