Quantification and allocation of uncertainties of climate change impacts on hydropower potential under 1.5 °C and 2.0 °C global warming levels in the headwaters of the Benue River Basin, Cameroon

Rodric M. Nonki, Ernest Amoussou, Christopher J. Lennard, André Lenouo, Raphael M. Tshimanga and Constant Houndenou

Renewable Energy, 2023, vol. 215, issue C

Abstract: Hydropower is the world's largest producer of renewable energy and represents more than 43% of the low-carbon 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.

Keywords: Hydropower; Climate change; Uncertainty quantification; Global warming levels; Analysis of variance (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:215:y:2023:i:c:s0960148123008856

DOI: 10.1016/j.renene.2023.118979

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