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Abstract China is undergoing an extensive hydropower development, but the magnitude of greenhouse gas (GHG) emissions risk to reservoirs is not well known. Electricity generation from hydropower is a challenging issue when it comes to sustainable development. In this study, the data on Chinese hydropower reservoirs (n = 12) were collected from the literature and is used as in input to Greenhouse Gas Risk Assessment Tool (GRAT) to predict long-term GHG (CO 2 & CH 4) risk to the hydropower reservoirs and its associated lifecycle GHG emissions (GHG-LCA). The model predicted that till recently (i.e., year 2018) the hydropower reservoirs found under medium risk (i.e., 109 < CO 2 ≤ 628 and 3 < CH 4 ≤ 45 mg·m−2·d−1) which will reduce slowly over a period of 100 years. Out of 12 hydropower reservoirs studied, TGR presently under high risk of CH 4 (i.e., 47 mg·m−2·d−1) and medium CO 2 risk. Therefore, assessment is required to know the magnitude of CH 4 and its effect on regional climate so that mitigation measures could be taken in advance. The carbon emissions as CO 2 eq of the studied reservoirs are found as 134.69 ± 225.23 Gg yr−1, with a large share from CH 4 emission. The associated life cycle GHG emissions are also estimated as 0.06 ± 0.09 gCO 2 eq·kWh−1, which is higher than the global estimates (i.e. 0.015 gCO 2 eq·kWh−1). Graphical abstract Unlabelled Image Highlights • Electricity generation from hydropower is a challenging issue when it comes to sustainable development. • This study predict long-term GHG risk to the Chinese hydropower reservoirs and its associated life cycle emissions. • The carbon emissions as CO 2 eq of the studied reservoirs are found as 134.69 ± 225.23 Gg yr-1, with large share from CH 4 emission. • The associated life cycle GHG emissions are also estimated as 0.06±0.09 gCO 2 eq.kWh−1. [ABSTRACT FROM AUTHOR]

Climate changes are likely to be significantly affected by reservoirs/lakes due to emission of greenhouse gas (GHG), change in the magnitude and seasonality of river runoffs and severe extreme events. In this study, a coupled GHG Risk Assessment Tool (GRAT) and Soil Water Assessment Tool (SWAT) are used to predict the GHG risk of Koteshwar reservoirs located in Uttarakhand, India. Before running the GRAT model, SWAT model was used to simulate the runoffs (one of the major input of GRAT). The model was calibrated (2004–09) and validated (2010–13) at Uttarkashi station using monthly discharge data. The model performance was checked by R2, NSE, RSR, and p-value as 0.785, 0.60, 0.63, and 0.04, respectively, during calibration and 0.790, 0.66, 0.57, and 0.06 during validation and shows satisfactory model performance on monthly time step. Further, GRAT model is also applied and the results show that Koteshwar reservoir is found to be under high risk of CO2(CO2> 645 mg m−2d−1) and medium risk of CH4(CH4< 45 mg m−2d−1) till 2023. Subsequently, the GHG risk is minimized after passage of time over 100 years. These models may be used by the policy-makers to know the potential of GHG and its vulnerability to the reservoirs after the impoundment. [ABSTRACT FROM PUBLISHER]