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Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics.


ABSTRACT: Peatlands are among the largest natural sources of atmospheric methane (CH4) worldwide. Peatland emissions are projected to increase under climate change, as rising temperatures and shifting precipitation accelerate microbial metabolic pathways favorable for CH4 production. However, how these changing environmental factors will impact peatland emissions over the long term remains unknown. Here, we investigate a novel data set spanning an exceptionally long 11 years to analyze the influence of soil temperature and water table elevation on peatland CH4 emissions. We show that higher water tables dampen the springtime increases in CH4 emissions as well as their subsequent decreases during late summer to fall. These results imply that any hydroclimatological changes in northern peatlands that shift seasonal water availability from winter to summer will increase annual CH4 emissions, even if temperature remains unchanged. Therefore, advancing hydrological understanding in peatland watersheds will be crucial for improving predictions of CH4 emissions.

SUBMITTER: Feng X 

PROVIDER: S-EPMC7894081 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics.

Feng Xue X   Deventer M Julian MJ   Lonchar Rachel R   Ng G H Crystal GHC   Sebestyen Stephen D SD   Roman D Tyler DT   Griffis Timothy J TJ   Millet Dylan B DB   Kolka Randall K RK  

Geophysical research letters 20200821 17


Peatlands are among the largest natural sources of atmospheric methane (CH<sub>4</sub>) worldwide. Peatland emissions are projected to increase under climate change, as rising temperatures and shifting precipitation accelerate microbial metabolic pathways favorable for CH<sub>4</sub> production. However, how these changing environmental factors will impact peatland emissions over the long term remains unknown. Here, we investigate a novel data set spanning an exceptionally long 11 years to analy  ...[more]

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