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Liu Z, Chen B, Wang S, Xu X, Chen H, Liu X, He JS, Wang J, Wang J, Chen J, Wang X, Zheng C, Zhu K, Wang X. More enhanced non-growing season methane exchanges under warming on the Qinghai-Tibetan Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170438. [PMID: 38286283 DOI: 10.1016/j.scitotenv.2024.170438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/12/2024] [Accepted: 01/23/2024] [Indexed: 01/31/2024]
Abstract
Uncertainty in methane (CH4) exchanges across wetlands and grasslands in the Qinghai-Tibetan Plateau (QTP) is projected to increase due to continuous permafrost degradation and asymmetrical seasonal warming. Temperature plays a vital role in regulating CH4 exchange, yet the seasonal patterns of temperature dependencies for CH4 fluxes over the wetlands and grasslands on the QTP remain poorly understood. Here, we demonstrated a stronger warming response of CH4 exchanges during the non-growing season compared to the growing season on the QTP. Analyzing 9745 daily observations and employing four methods -regression fitting of temperature-CH4 flux, temperature dependence calculations, field-based and model-based control experiments-we found that warming intensified CH4 emissions in wetlands and uptakes in grasslands. Specifically, the average reaction intensity in the non-growing season surpasses that in the growing season by 1.89 and 4.80 times, respectively. This stronger warming response of CH4 exchanges during the non-growing season significantly increases the regional CH4 exchange on the QTP. Our research reveals that CH4 exchanges in the QTP have a higher warming sensitivity in non-growing seasons, which meanwhile are dominated by a larger warming rate than the annual average. The combined effects of these two factors will significantly alter the CH4 source/sink on the QTP. Neglecting these impacts would lead to inaccurate estimations of CH4 source/sink over the QTP under climate warming.
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Affiliation(s)
- Zhenhai Liu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing, 100055, China
| | - Bin Chen
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Coupling Process and Effect of Natural Resources Elements, Beijing, 100055, China.
| | - Shaoqiang Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Hubei Key Laboratory of Regional Ecology and Environmental Change, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China; Technology Innovation Center for Intelligent Monitoring and Spatial Regulation of Land Carbon Sequestrations, Ministry of Natural Resources, Wuhan 430078, China.
| | - Xiyan Xu
- Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
| | - Huai Chen
- Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China; Zoige Peatland and Global Change Research Station, Chinese Academy of Sciences, Hongyuan 624400, China
| | - Xinwei Liu
- Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province, Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China; Zoige Peatland and Global Change Research Station, Chinese Academy of Sciences, Hongyuan 624400, China
| | - Jin-Sheng He
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China; Department of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
| | - Jianbin Wang
- State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
| | - Jinsong Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jinghua Chen
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaobo Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Chen Zheng
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kai Zhu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xueqing Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Carbon Isotopic Evidence for Gas Hydrate Release and Its Significance on Seasonal Wetland Methane Emission in the Muli Permafrost of the Qinghai-Tibet Plateau. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042437. [PMID: 35206625 PMCID: PMC8872400 DOI: 10.3390/ijerph19042437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/12/2022] [Accepted: 02/16/2022] [Indexed: 12/04/2022]
Abstract
In order to determine the significant role of gas hydrate in seasonal wetland methane emission at the drilling-affected permafrost, the carbon isotopic monthly field monitoring of methane (CH4), as well as carbon dioxide (CO2), emitted from near-surface soil and a gas hydrate drilling well (DK-8) was conducted in the Muli permafrost of the Qinghai-Tibet Plateau. The methane source effused from the well DK-8 was calculated as -25.9 ± 1.4‱ and -26.5 ± 0.5‱, respectively, by the Keeling and Miller Tans plots, with the carbon isotope fractionation (εC) between CO2 and CH4 from -25.3‱ to -32.1‱. The carbon isotopic signatures are indicative of thermogenic origin associated with gas hydrate dissociation. The near-surface soil-emitted methane has δ13CCH4 values between -52.0 ± 1.2‱ and -43.2 ± 1.8‱ with the heaviest in December and the lightest in July. Further, the εC values of near-surface soil-emitted gases were between 28.6‱ and 47.9‱, significantly correlated with the δ13CCH4 values. The linear correlation between εC and δ13CCH4 values indicated binary end-member of microbial and thermogenic sources control the seasonal variation of wetland methane emission. The thermogenically derived methane was identified as the dominant methane source in autumn and winter, compared with the increasing contribution of microbially derived methane in spring and summer. The finding provides reliable evidence for gas hydrate release on the seasonal wetland methane emission in the Muli permafrost affected by drilling activities. The combined application of εC and δ13CCH4 to distinguish thermogenic from biogenic methane is well established and powerful in complex environments, which can provide an improved constraint on source apportionment for wetland emitted methane in the permafrost of the Qinghai-Tibet Plateau.
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Microbial Communities in Methane Cycle: Modern Molecular Methods Gain Insights into Their Global Ecology. ENVIRONMENTS 2021. [DOI: 10.3390/environments8020016] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The role of methane as a greenhouse gas in the concept of global climate changes is well known. Methanogens and methanotrophs are two microbial groups which contribute to the biogeochemical methane cycle in soil, so that the total emission of CH4 is the balance between its production and oxidation by microbial communities. Traditional identification techniques, such as selective enrichment and pure-culture isolation, have been used for a long time to study diversity of methanogens and methanotrophs. However, these techniques are characterized by significant limitations, since only a relatively small fraction of the microbial community could be cultured. Modern molecular methods for quantitative analysis of the microbial community such as real-time PCR (Polymerase chain reaction), DNA fingerprints and methods based on high-throughput sequencing together with different “omics” techniques overcome the limitations imposed by culture-dependent approaches and provide new insights into the diversity and ecology of microbial communities in the methane cycle. Here, we review available knowledge concerning the abundances, composition, and activity of methanogenic and methanotrophic communities in a wide range of natural and anthropogenic environments. We suggest that incorporation of microbial data could fill the existing microbiological gaps in methane flux modeling, and significantly increase the predictive power of models for different environments.
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