Journal article
Rainfall can significantly reduce pond methane emissions by depressing ebullition
Water research, Vol.285, pp.1-8
01/10/2025
PMID: 40638962
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Source: InCites
Abstract
Small freshwater bodies like ponds are recognised as hotspots of methane (CH4) emissions. However, the impact of rainfall on pond CH4 emissions remains largely unknown. Here we studied the impact of different rainfall events on CH4 fluxes from an urban pond through continuous and high-frequency measurements of CH4 ebullition and diffusion across an entire rainy season, which included 15 rainfall events with precipitation ranging from 3.5 to 66.9 mm. The mean total CH4 emission flux (ebullition and diffusion) decreased from 7.1 ± 3.5 mg m-2 h-1 before rainfall to 3.9 ± 2.7 mg m-2 h-1 after rainfall, and recovered gradually 12 h after the end of rainfall. This significant decline was likely caused by the air temperature drop accompanied by rainfall and the dilution effect of rainwater. Cumulatively, excluding the impact of rainfall resulted in a 35.3 % overestimation of total CH4 emissions, with CH4 ebullition contributing 90.4 % of this overestimation. Given the expected increase in rainfall and significant CH4 emissions in the rainy season worldwide, high-frequency and long-term measurements of CH4 diffusion and ebullition during and shortly after rainfall should help improve the global estimates of small water CH4 emissions.
Details
- Title
- Rainfall can significantly reduce pond methane emissions by depressing ebullition
- Creators
- Xueqi Niu - Tianjin UniversityZhifeng Yan - Tianjin UniversityWenxin Wu - University of WaterlooZizhang Hua - Tianjin UniversityLiping Hao - Tianjin UniversityJudith A Rosentreter - Southern Cross University
- Publication Details
- Water research, Vol.285, pp.1-8
- Publisher
- Elsevier Ltd; OXFORD
- Grant note
- National Key Research and Development Program of China [2022YFF1301002].
- Identifiers
- 991013296651002368
- Copyright
- © 2025 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Journal article