Uncovering the Underestimated Greenhouse Gas Emissions of Hydropower Reservoirs: A Global Synthesis

Abstract Hydropower reservoirs, long considered as a clean energy solution, are increasingly recognized as major greenhouse gas (GHG) emissions hotspots. However, over 97% of these hydropower reservoirs remain unmonitored, creating major uncertainties in global GHG budgets. Here, machine learning models were employed to predict GHG fluxes from hydropower reservoirs lacking reported direct GHG measurements using 67 widely accessible environmental predictors covering climate, land use, soil properties, geology, and topography. The predicted GHG fluxes revealed marked spatiotemporal heterogeneity, characterized by distinct monthly variability and emission hotspots predominantly concentrated in tropical and arid zones, particularly across Africa. Moreover, scaling the predictions to 5,293 unmonitored hydropower reservoirs yielded total emissions of 304 ± 37 Tg CO 2 eq year −1 , enabling a more geographically representative assessment of global reservoir GHG emissions. Methane notably dominates emissions (55.3%), followed by carbon dioxide (41.3%) and nitrous oxide (3.4%), with the latter often overlooked but gaining relevance during increasing eutrophication. In addition, our gas‐specific models identified temperature‐related variables as the primary drivers of GHG dynamics, emphasizing the central role of thermal regimes, especially in African reservoirs. Our findings call for a re‐evaluation of the climate costs of hydropower and suggest that future warming may further amplify reservoir‐based GHG emissions. The predictive capacity of our models provides a scalable tool for refining global GHG inventories and guiding low‐carbon hydropower development under a changing climate.