Ethiopia’s Omo-Kuraz Watershed Faces Explosive Rise in Heat and Rainfall Extremes by Century’s End

Deep in southwestern Ethiopia, the Omo-Kuraz Watershed feeds the river that supplies more than ninety percent of the water entering Lake Turkana in Kenya. A new study now warns that this vital basin, home to expanding irrigation schemes and hydropower infrastructure, is heading toward a climate future defined not by subtle shifts in average rainfall, but by a dramatic intensification of heat and precipitation extremes. Using a carefully bias-corrected ensemble of the latest CMIP6 climate models, validated against eleven local weather stations, researchers project that days of extreme heat could increase by more than 440 percent by the 2090s under a high-emission scenario, while the heaviest downpours intensify far faster than annual rainfall totals.

The research, published in Environmental Challenges, stands out for the rigor of its methodology. Rather than taking raw climate model output at face value, the team led by Kassa Tesfaye Erenso, Abdella Kemal Mohammed and Tarun Kumar Lohani first evaluated five CMIP6 models against quality-controlled daily observations from eleven ENACTS meteorological stations spanning 1981 to 2022. The stations cover the watershed’s three physiographic zones, from the 2,350-meter highland station at Bonga down to the lowland plains below 1,000 meters. Each model was scored using the Taylor skill score, the modified Kling-Gupta efficiency and root mean square error, producing a composite ranking in which Japan’s MRI-ESM2-0 performed best overall, with temperature efficiency values above 0.81.

A crucial innovation lies in how the ensemble was assembled. Climate models are not statistically independent; shared code, parameterizations and institutional histories mean some simulations carry similar errors. Treating all models equally can therefore give disproportionate weight to closely related model families. The researchers quantified inter-model similarity using pairwise correlations, hierarchical clustering and multidimensional scaling, then combined performance scores with independence factors to produce final ensemble weights ranging from 0.14 to 0.24. Effective ensemble sizes of 3.8 to 4.4 confirmed that the five-model set retains a substantial amount of genuinely independent information. A leave-one-out analysis showed the conclusions did not hinge on any single model.

Bias correction was handled through empirical quantile mapping, a distribution-based technique that aligns the full statistical shape of simulated temperature and precipitation with observed baselines over 1985 to 2004. The improvements were striking. For the watershed-averaged MRI-ESM2-0 simulation, maximum temperature bias fell from minus 2.86 degrees Celsius to essentially zero, and precipitation bias dropped from minus 15.2 percent to minus 2.2 percent. Root mean square error for maximum temperature collapsed from 3.12 to 0.26 degrees Celsius. Critically, an independent validation on the held-out 2005 to 2014 period showed nearly identical skill, with Kling-Gupta efficiency values of 0.78 to 0.94, demonstrating that the correction generalizes beyond its calibration window.

The projections themselves reveal persistent, accelerating warming. By the 2090s, ensemble-mean maximum temperature rises by 2.21 degrees Celsius under the moderate SSP2-4.5 scenario and 3.85 degrees Celsius under the high-emission SSP5-8.5 pathway, while minimum temperatures climb even further, to 2.30 and 4.02 degrees Celsius respectively. The divergence between scenarios grows from a mere 0.17 degrees Celsius in the 2020s to 1.64 degrees Celsius by century’s end, eventually exceeding the inter-model spread itself. Warming is not distributed evenly: the analysis uncovered a pronounced elevation-dependent gradient, with maximum temperature warming increasing by 1.20 degrees Celsius per 1,000 meters of elevation under SSP2-4.5 and by 2.04 degrees Celsius per 1,000 meters under SSP5-8.5, meaning the western highlands emerge as the watershed’s primary warming hotspot.

Precipitation tells a subtler and more troubling story. Annual totals increase only modestly, by roughly 3.5 percent under SSP2-4.5 and 5.8 percent under SSP5-8.5 by the 2090s, concentrated in the September-to-November season and the northeastern headwaters. But the extremes behave very differently. Maximum one-day rainfall rises by 38 percent, precipitation from extremely wet days by 67 percent, and the number of days exceeding 25 millimeters by 125 percent. Meanwhile the September-to-November coefficient of variation surges by about 50 percent under high emissions, signaling far wilder year-to-year swings. The physical explanation follows the Clausius-Clapeyron relationship: a warmer atmosphere holds roughly six to seven percent more moisture per degree of warming, supercharging individual storms even as average rainfall barely moves.

Perhaps the most eye-catching numbers concern how rarely extreme events will remain rare. Fitting generalized extreme value distributions to annual maxima, the team found that the historical 10-year maximum one-day rainfall event of 55 millimeters grows to 78 millimeters and would recur roughly every four years by late century under SSP5-8.5. The historical 100-year rainfall event, at 85 millimeters, intensifies by 59 percent to 135 millimeters and returns approximately every 25 years. The 100-year five-day rainfall total jumps by 61 percent. In contrast, the historical 100-year consecutive dry spell shortens so much that its future equivalent return period stretches to around 200 years, suggesting exceptionally long droughts of past magnitude become far less likely even as rainfall grows more erratic.

The uncertainty analysis adds a strategic dimension. Before 2050, differences among climate models dominate the projection spread, accounting for 52 to 65 percent of total variance, while internal variability contributes a steady 15 to 20 percent. After about 2065, the choice of emissions scenario becomes the largest source of uncertainty at roughly 48 percent. Signal-to-noise analysis shows the anthropogenic warming signal crossing the detection threshold by 2037 under SSP5-8.5 and emerging clearly from natural variability by 2045; under the moderate pathway those milestones slip to 2048 and 2062, a 17-year delay. The comparison between scenarios quantifies the tangible payoff of mitigation: the intermediate pathway limits end-of-century warming by about 1.6 to 1.8 degrees Celsius and buys the basin nearly two additional decades before the climate signal becomes unmistakable.

For water managers, the implications are concrete. The Omo-Kuraz Sugar Development Project and associated irrigation schemes will face higher evaporative demand, more variable reservoir inflows, and design storms that existing infrastructure standards never anticipated. The southeastern lowlands, including the irrigation command area, are flagged as the zone most vulnerable to rainfall variability and associated flood and drought risk, while the northwestern highlands face the steepest warming. The authors recommend climate-adjusted design standards, adaptive reservoir operation, improved flood forecasting and climate-informed irrigation scheduling, tailored to each zone rather than applied uniformly. The projections also align with Ethiopia’s Climate-Resilient Green Economy strategy and support Sustainable Development Goals on clean water and climate action.

The study acknowledges its limits: five models cannot span the full structural uncertainty of the CMIP6 archive, quantile mapping assumes a stationary bias relationship, daily resolution misses sub-daily cloudbursts relevant to flash flooding, and large-scale drivers such as El Nino, the Indian Ocean Dipole and Congo Basin moisture transport were not explicitly diagnosed. Still, by integrating station-validated bias correction, weighted ensembling, extreme-index analysis, return-period statistics and signal-emergence diagnostics into one watershed-scale framework, the work offers a template for climate risk assessment in data-scarce regions. Its central message is stark: in the Omo-Kuraz Watershed, the future will be shaped less by how much rain falls in a year than by how violently it falls, and how often the mercury climbs past thresholds once considered exceptional.

Subject of Research: Bias-corrected CMIP6 projections of temperature and precipitation extremes in the Omo-Kuraz Watershed, Ethiopia

Article Title: Bias-corrected CMIP6 ensemble projections of temperature extremes and precipitation regimes in the Omo-Kuraz Watershed, Ethiopia: Implications for water security

Article References: Erenso, K. T., Mohammed, A. K., & Lohani, T. K. (2026). Bias-corrected CMIP6 ensemble projections of temperature extremes and precipitation regimes in the Omo-Kuraz Watershed, Ethiopia: Implications for water security. Environmental Challenges, 25, Article 101649. https://doi.org/10.1016/j.envc.2026.101649

Image Credits: AI Generated

DOI: 10.1016/j.envc.2026.101649

Keywords: CMIP6, climate extremes, Ethiopia, Omo-Kuraz Watershed, bias correction, water security, elevation-dependent warming, precipitation extremes, SSP scenarios, Lake Turkana, return periods, East Africa