Temporal and spatial variability of methane emissions in a northern temperate marsh

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• 作者：Li Sun ; Changchun Song ; Yuqing Miao ; Tianhua Qiao ; Chao Gong
• 关键词：Methane flux variability ; Marsh ; Eddy covariance ; Chamber method
• 刊名：Atmospheric Environment
• 出版年：December, 2013
• 年：2013
• 卷：81
• 期：Complete
• 页码：356-363
• 全文大小：945 K

文摘

Although methane (CH₄) fluxes from northern wetlands in Asia have been described in previous research at different temporal and spatial scales, integrated studies at the ecosystem scale were scarce. In this study, CH₄ fluxes were measured using eddy covariance (EC) technique and the chamber method in a cool temperate marsh in northeast China during the growing season (May-September) of 2011. CH₄ emissions were highly variable, both temporally and spatially during the measurement period. According to the EC observation data, CH₄ fluxes showed a significant diurnal cycle during the mid-growing season with nighttime average flux about 67% of the average daytime values. Daily cumulative CH₄ fluxes varied from 54 to 250聽mg聽CH₄聽m^鈭?聽d^鈭? with an average flux of 136.2聽mg聽CH₄聽m^鈭?聽d^鈭?. The observations of chamber method showed that CH₄ emissions differed markedly among the three main plant communities. Average flux at the Carex lasiocarpa site was about 4 times and 13.5 times of that at the Glyceria spiculosa site and Deyeuxia angustifolia site, respectively. The spatial variability of CH₄ flux was mainly controlled by the varying water table level as well as the spatial distribution of different vascular plants, while the seasonal dynamic of CH₄ emission could be best explained by the change of surface soil temperature and air pressure. A comparison was made between EC measurements and the upscaled chamber based model. The results from the model overestimated CH₄ emission by 28% compared to the EC data. Considering the large variability of methane emission, it is necessary to conduct continuous observations on CH₄ emission from northern wetlands at different temporal and spatial scales to comprehend the variability and also to predict responses to climate change.