Multi-model ensemble projections of climate change trends in the Wabi Shebelle River Basin, Ethiopia, using CMIP6 scenarios

The Wabi Shebelle River Basin is considered quite sensitive to climate change, which could threaten community well-being, agriculture, and water security. This work used a multi-model ensemble of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to predict temperature and precipitation changes under SSP2-4.5 and SSP5-8.5 scenarios, which will assist influence risk reduction methods. With 1985–2014 as the historical baseline, forecasts were evaluated for the near-term (2015–2040), mid-term (2041–2070), and long-term (2071–2100) time periods. The study examines annual and seasonal patterns during the Kiremt (JJAS), Belg (FMAM), and Bega (ONDJ) seasons through comparing model performance to data from 36 meteorological stations (1991–2020) using correlation coefficient, RMSE, and bias metrics. The top-performing precipitation models (ACCESS-CM2, EC-Earth3-CC, INM-CM5-0, MPI-ESM1-2-LR, and UKESM1-0-LL) have low correlations (0.01–0.21), RMSE (3.34–3.71), and biases (0.62–1.15). The best maximum temperature performers (ACCESS-CM2, CMCC-ESM2, INM-CM4-8, and INM-CM5-0) had biases of 1.02–1.07, correlation coefficient of 0.21–0.37, and RMSE of 2.27–2.71. The minimum temperature best performing models (CNRM-CM6-1, ACCESS-CM2, CMCC-CM2-SR5, and INM-CM4-8) had the highest correlations (0.55–0.66), with biases ranging from 1.25 to 1.47 and RMSE of 3.58 to 6.46. These models were chosen, bias-corrected, and blended to reduce individual model errors. Ensemble estimations suggest likely seasonal variations and increases in annual rainfall ranging from + 10.99% to + 32.51% over the long term. Bega rainfall is expected to increase by up to 96.64%, but Kiremt and Belg rains might varying before rebounding. These changes may complemented by a projected trend of increased annual temperatures, with maximums increasing by + 0.80 °C to + 3.5 °C and minimums by + 0.90 °C to + 3.7 °C annually. The most significant changes are estimated under the SSP5-8.5 scenario. Projections of extreme warming and higher inter-seasonal variability would lead to increased agricultural stress and water security concerns, which might threaten regional sustainable development targets. To support progress toward SDGs 2 (Zero Hunger), 6 (Clean Water), 13 (Climate Action), and 15 (Life on Land), the study emphasizes the significance of adaptive measures such as modern irrigation, integrated water management, and climate-resilient agriculture, as well as strengthening early-warning climate services, in managing these projected risks.

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Journal
Discover Sustainability
Published
2026-09-04
DOI
https://doi.org/10.1007/s43621-026-04560-4
Primary Topic
Climate variability and models
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article

Multi-model ensemble projections of climate change trends in the Wabi Shebelle River Basin, Ethiopia, using CMIP6 scenarios

Tadesse Tujuba Kenea, Yoseph Arba Orke, Asaminew Teshome, sintayehu Abera Wondimu
Discover Sustainability
Climate variability and models
article

Multi-model ensemble projections of climate change trends in the Wabi Shebelle River Basin, Ethiopia, using CMIP6 scenarios

Tadesse Tujuba Kenea, Yoseph Arba Orke, Asaminew Teshome, sintayehu Abera Wondimu
article en

Abstract

The Wabi Shebelle River Basin is considered quite sensitive to climate change, which could threaten community well-being, agriculture, and water security. This work used a multi-model ensemble of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to predict temperature and precipitation changes under SSP2-4.5 and SSP5-8.5 scenarios, which will assist influence risk reduction methods. With 1985–2014 as the historical baseline, forecasts were evaluated for the near-term (2015–2040), mid-term (2041–2070), and long-term (2071–2100) time periods. The study examines annual and seasonal patterns during the Kiremt (JJAS), Belg (FMAM), and Bega (ONDJ) seasons through comparing model performance to data from 36 meteorological stations (1991–2020) using correlation coefficient, RMSE, and bias metrics. The top-performing precipitation models (ACCESS-CM2, EC-Earth3-CC, INM-CM5-0, MPI-ESM1-2-LR, and UKESM1-0-LL) have low correlations (0.01–0.21), RMSE (3.34–3.71), and biases (0.62–1.15). The best maximum temperature performers (ACCESS-CM2, CMCC-ESM2, INM-CM4-8, and INM-CM5-0) had biases of 1.02–1.07, correlation coefficient of 0.21–0.37, and RMSE of 2.27–2.71. The minimum temperature best performing models (CNRM-CM6-1, ACCESS-CM2, CMCC-CM2-SR5, and INM-CM4-8) had the highest correlations (0.55–0.66), with biases ranging from 1.25 to 1.47 and RMSE of 3.58 to 6.46. These models were chosen, bias-corrected, and blended to reduce individual model errors. Ensemble estimations suggest likely seasonal variations and increases in annual rainfall ranging from + 10.99% to + 32.51% over the long term. Bega rainfall is expected to increase by up to 96.64%, but Kiremt and Belg rains might varying before rebounding. These changes may complemented by a projected trend of increased annual temperatures, with maximums increasing by + 0.80 °C to + 3.5 °C and minimums by + 0.90 °C to + 3.7 °C annually. The most significant changes are estimated under the SSP5-8.5 scenario. Projections of extreme warming and higher inter-seasonal variability would lead to increased agricultural stress and water security concerns, which might threaten regional sustainable development targets. To support progress toward SDGs 2 (Zero Hunger), 6 (Clean Water), 13 (Climate Action), and 15 (Life on Land), the study emphasizes the significance of adaptive measures such as modern irrigation, integrated water management, and climate-resilient agriculture, as well as strengthening early-warning climate services, in managing these projected risks.

Discover Sustainability
National Institute of Meteorology (TN), Arba Minch University (ET), Adama Science and Technology University (ET)
Climate action
Openalex Percentile: Top 13%
Climate variability and models
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