A review of hydropower reservoir GHG estimation methods

Accurate greenhouse gas (GHG) emissions estimations from hydropower reservoirs are critical for ensuring that this renewable energy source effectively contributes to climate mitigation. In this paper, we critically review and compare a range of methodologies, including direct field measurements (e.g. floating chambers and eddy covariance), empirical/statistical models (like G-res and HydroCalculator), process-based simulations, satellite remote sensing, machine learning (ML) techniques, and hybrid modelling frameworks that integrate these components. Our analysis evaluates each approach against key criteria: accuracy and uncertainty, scalability and transferability, and data requirements and transparency. Direct measurements remain the gold standard for site-specific validation; however, they are limited by spatial and temporal coverage and demand substantial resources. Empirical models offer simplicity but struggle to capture dynamic environmental drivers, often leading to under- or overestimation of emissions. Process-based models provide critical mechanistic insights but require extensive input data and computational resources. While satellite observations and ML enhance spatial and temporal coverage and predictive capability, explainable AI can overcome the “black-box” nature of ML Hybrid approaches that combine in-situ data, remote sensing, ML, and process-based elements show the most significant promise for an accurate and scalable emissions estimation. As the European Union and other regions work to meet stringent climate targets, robust reservoir GHG accounting is essential for guiding investments and driving mitigation actions in genuinely low-carbon hydropower. Our findings highlight the necessity of integrated monitoring networks, open-access data, and interdisciplinary collaboration to develop next-generation tools that bridge precise measurement with large-scale modelling for informed climate and energy policy. • Critical review of GHG estimation methods for hydropower reservoirs conducted. • Traditional and emerging approaches are systematically analysed and compared. • Evaluation focuses on accuracy, scalability, uncertainty and data needs. • Hybrid models with ML and remote sensing show highest potential. • Research gaps and future directions identified for policymakers and stakeholders.