Reservoir methane emission response to varying organic carbon loads under multiple socioeconomic development scenarios

Guni Xiang, Zhihao Xu, Kaiheng Chen, Pan Yang, Jingchuan Xue, Sibo Zhang and Weilun Gao

Ecological Modelling, 2025, vol. 510, issue C

Abstract: Reservoirs are riverine organic carbon (OC) retention and methane (CH4) emission hotspots. However, the response of reservoir CH4 cycling processes and emissions to varying OC loads under different socioeconomic development scenarios remains unclear. Accordingly, this study evaluates the impact of OC loads on reservoir CH4 cycling processes and predicts future emissions under multiple socioeconomic development scenarios. A coupled physical-biogeochemical reservoir model was used to simulate CH4 production, oxidation, diffusion, and ebullition dynamics. Three socioeconomic development scenarios (i.e., low, steady, and high-speed development) were adopted to project riverine OC loads and reservoir CH4 emission. Using the second largest reservoir in China (i.e., Danjiangkou Reservoir) as a case study, the results demonstrate that increased OC loads led to a reduction in sediment CH4 oxidation efficiency. As OC loads increased, the dominant sediment CH4 release pathway shifted from diffusion to ebullition. A linear relationship was also observed between CH4 ebullition and OC loads, while CH4 diffusion exhibited a nonlinear growth trend. The findings highlight the importance of OC loads in regulating reservoir CH4 cycling. Furthermore, CH4 emissions may significantly increase (34 %–125 %) by 2050, raising emission control concerns. This study provides a modelling tool and fresh implications for future reservoir emission estimations and watershed management in the pursuance of carbon (C) neutrality goals.

Keywords: Carbon emission modelling; Biogeochemical process; Socioeconomic development; Organic carbon load; Emission prediction (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:510:y:2025:i:c:s0304380025003266

DOI: 10.1016/j.ecolmodel.2025.111340

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