B038-0003
Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics

Wednesday, 9 December 2020
Poster
Xue Feng1, Malte Julian Deventer2, G. H. Crystal Ng3, Stephen Sebestyen4, Tyler D Roman4, Timothy J Griffis5, Dylan B Millet6 and Randall K Kolka7, (1)University of Minnesota Twin Cities, Civil, Environmental, Geo-Engineering, Minneapolis, MN, United States, (2)University of California Berkeley, Berkeley, CA, United States, (3)University of Minnesota, Twin Cities, Department of Earth Science, Minneapolis, MN, United States, (4)USDA Forest Service Northern Research Station, Grand Rapids, MN, United States, (5)Univ Minnesota, Saint Paul, MN, United States, (6)University of Minnesota Twin Cities, St Paul, MN, United States, (7)USDA Forest Service, Grand Rapids, United States
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 newly developed eddy covariance long-term dataset spanning 11 years from the Marcell Experimental Forest in northern Minnesota, U.S.A., to analyze the influence of soil temperature and water table elevation on peatland CH4 emissions. We demonstrate the importance of seasonal water availability in controlling the sensitivity of CH4 emission increase to soil temperature – higher water tables dampen the springtime increases in CH4 emissions as well as their subsequent decreases during late summer to fall. Importantly, these results imply that any hydro-climatological 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.