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  4. Methane degassing in global river reservoirs and its impacts on carbon budgets and sustainable water management
 
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Methane degassing in global river reservoirs and its impacts on carbon budgets and sustainable water management

Journal
Science of The Total Environment
Journal Volume
957
Start Page
177623
ISSN
0048-9697
Date Issued
2024-12-20
Author(s)
Yanlai Zhou
Hanbing Xu
Tianyu Xia
Lihua Xiong
Li-Chiu Chang
Fi-John Chang  
Chong-Yu Xu
DOI
10.1016/j.scitotenv.2024.177623
URI
https://www.scopus.com/record/display.uri?eid=2-s2.0-85210067171&origin=resultslist
https://scholars.lib.ntu.edu.tw/handle/123456789/724421
Abstract
Degassing methane (CH4) through reservoir water compromises hydroelectricity's presumed low-carbon status, which has emerged as a critical hotspot for global carbon dynamics. However, a comprehensive understanding of the involved pathways remains elusive, hindering the accurate estimation of global reservoirs' carbon budget (emission-to-burial ratio). This study presents a holistic upscaling approach to assess methane degassing in global river reservoirs and its impacts on carbon budgets. Firstly, a machine learning model is utilized to characterize the contributions of climate and human factors to annual water residence time. Secondly, the stepwise multiple linear regression method is used to calculate CH4 degassing emissions for each reservoir. Finally, to systematically tackle all sources of uncertainty, separate uncertainty analyses are implemented for the estimates of degassing emissions, areal emissions, and organic carbon burial. Analyzing 30-year data from 6695 reservoirs worldwide, our assessment considers water residence time, temperature, catchment area, and reservoir size. Findings indicate that water releases contribute significantly to global CO2 emissions from reservoirs, elevating the carbon budget by 20 % from 2.02 to 2.18 TgC/year, underscoring the previously underestimated significance of CH4 degassing in shaping the carbon cycle impact of river reservoirs. We propose a redefined threshold for low carbon credits, suggesting that reservoirs with power densities exceeding 6.1 MW/km2, instead of the conventional 4 MW/km2, should qualify. This study underscores the need for sustainable water management and reshaping the carbon dynamics associated with hydroelectricity. Future research can advocate Artificial Intelligence (AI) techniques to enhance water management and mitigate carbon emissions by multi-objectively optimizing reservoir operations.
Subjects
Carbon emission
Hydroelectricity
Methane degassing
River reservoir
SDGs

[SDGs]SDG6

[SDGs]SDG13

Publisher
Elsevier BV
Description
Article number: 177623
Type
journal article

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