Open Access Publications
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Open access publications by faculty, postdocs, and graduate students in the Department of Plant and Soil Sciences.
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Browsing Open Access Publications by Author "Bansal, Sheel"
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Item Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions(Nature Communications, 2021-04-15) Chang, Kuang-Yu; Riley, William J.; Knox, Sara H.; Jackson, Robert B.; McNicol, Gavin; Poulter, Benjamin; Aurela, Mika; Baldocchi, Dennis; Bansal, Sheel; Bohrer, Gil; Campbell, David I.; Cescatti, Alessandro; Chu, Housen; Delwiche, Kyle B.; Desai, Ankur R.; Euskirchen, Eugenie; Friborg, Thomas; Goeckede, Mathias; Helbig, Manuel; Hemes, Kyle S.; Hirano, Takashi; Iwata, Hiroki; Kang, Minseok; Keenan, Trevor; Krauss, Ken W.; Lohila, Annalea; Mammarella, Ivan; Mitra, Bhaskar; Miyata, Akira; Nilsson, Mats B.; Noormets, Asko; Oechel, Walter C.; Papale, Dario; Peichl, Matthias; Reba, Michele L.; Rinne, Janne; Runkle, Benjamin R. K.; Ryu, Youngryel; Sachs, Torsten; Schäfer, Karina V. R.; Schmid, Hans Peter; Shurpali, Narasinha; Sonnentag, Oliver; Tang, Angela C. I.; Torn, Margaret S.; Trotta, Carlo; Tuittila, Eeva-Stiina; Ueyama, Masahito; Vargas, Rodrigo; Vesala, Timo; Windham-Myers, Lisamarie; Zhang, Zhen; Zona, DonatellaWetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.