Spatial-temporal patterns of methane dynamics in Lake Taihu.
Saved in:
| Title: | Spatial-temporal patterns of methane dynamics in Lake Taihu. |
|---|---|
| Authors: | Yao, Shuchun1, Tao, Yuqiang1, Yan, Renhua1, Li, Lingling1,2, Xue, Bin1,3 |
| Source: | Hydrobiologia. Oct2018, Vol. 822 Issue 1, p143-156. 14p. 1 Chart, 3 Graphs, 1 Map. |
| Subjects: | Atmospheric methane analysis, Lake sediments, Spatiotemporal processes, Organic compounds, Lakes |
| Geographic Terms: | China |
| Abstract: | Lakes are important source of atmospheric methane (CH4). Especially for large and shallow lakes, accurate CH4 emission budgets must consider the spatial and seasonal heterogeneity which characterize these ecosystems. CH4 concentrations in sediment and lake water and CH4 fluxes from sediment were analysed in selected open water areas of Lake Taihu, the third largest freshwater lake in China, to test the hypothesis that CH4 concentrations and fluxes differ both spatially and temporally in relation to biological and environmental features. The results showed that Lake Taihu was a source of CH4 throughout the year. The observed CH4 concentrations and fluxes were higher in the north-western and south-eastern areas of the lake, and lower in the central area. This spatial heterogeneity in methane emissions was mainly related to local organic matter content, plant characteristics, deposition rate and sediment features. Seasonally, CH4 showed stronger emissions in summer. These results suggest that the analysis of CH4 dynamics in shallow lakes should consider the habitat heterogeneity and the different sources of organic matter. Furthermore, the CH4 emission budget should at least include high and low temperature periods to decrease temporal bias. [ABSTRACT FROM AUTHOR] |
| Copyright of Hydrobiologia is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
