Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

Knox SH 1 , Bansal S 2 , McNicol G 3 , Schafer K 4 , Sturtevant C 5 , Ueyama M 6 Show all authors , Valach AC 7 , Baldocchi D 7 , Delwiche K 3 , Desai AR 8 , Euskirchen E 9 , Liu J 10 , Lohila A 11 , Malhotra A 3 , Melling L 12 , Riley W 13 , Runkle BRK 14 , Turner J 15 , Vargas R 16 , Zhu Q 13 , Alto T 17 , Fluet-Chouinard E 3 , Goeckede M 18 , Melton JR 19 , Sonnentag O 20 , Vesala T 11 , Ward E 21 , Zhang Z 22 , Feron S 3 , Ouyang Z 3 , Alekseychik P 23 , Aurela M 17 , Bohrer G 24 , Campbell DI 25 , Chen J 26 , Chu H 27 , Dalmagro HJ 28 , Goodrich JP 25 , Gottschalk P 29 , Hirano T 30 , Iwata H 31 , Jurasinski G 32 , Kang M 33 , Koebsch F 32 , Mammarella I 11 , Nilsson MB 34 , Ono K 35 , Peichl M 34 , Peltola O 17 , Ryu Y 36 , Sachs T 29 , Sakabe A 37 , Sparks JP 38 , Tuittila ES 39 , Vourlitis GL 40 , Wong GX 12 , Windham-Myers L 41 , Poulter B 42 , Jackson RB 3

Affiliations 

  • 1 Department of Geography, The University of British Columbia, Vancouver, BC, Canada
  • 2 Northern Prairie Wildlife Research Center, U.S. Geological Survey, Jamestown, ND, USA
  • 3 Department of Earth System Science, Stanford University, Stanford, CA, USA
  • 4 Department of Earth and Environmental Science, Rutgers University Newark, New Brunswick, NJ, USA
  • 5 National Ecological Observatory Network, Battelle, Boulder, CO, USA
  • 6 Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Sakai, Japan
  • 7 Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA
  • 8 Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA
  • 9 Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA
  • 10 Western Geographic Science Center, U.S. Geological Survey, Moffett Field, CA, USA
  • 11 Institute for Atmospheric and Earth System Research/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
  • 12 Sarawak Tropical Peat Research Institute, Sarawak, Malaysia
  • 13 Earth and Environmental Sciences Area, Lawrence Berkeley National Lab, Berkeley, CA, USA
  • 14 Department of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR, USA
  • 15 Freshwater and Marine Science, University of Wisconsin-Madison, Madison, WI, USA
  • 16 Department of Plant and Soil Sciences, University of Delaware, Newark, DE, USA
  • 17 Climate System Research, Finnish Meteorological Institute, Helsinki, Finland
  • 18 Department of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, Germany
  • 19 Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada
  • 20 Département de Géographie, Université de Montréal, Montréal, QC, Canada
  • 21 Wetland and Aquatic Research Center, U.S. Geological Survey, Lafayette, LA, USA
  • 22 Department of Geographical Sciences, University of Maryland, College Park, MD, USA
  • 23 Natural Resources Institute Finland (LUKE), Helsinki, Finland
  • 24 Department of Civil, Environmental & Geodetic Engineering, Ohio State University, Columbus, OH, USA
  • 25 School of Science, University of Waikato, Hamilton, New Zealand
  • 26 Department of Geography, Environment, and Spatial Sciences, & Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA
  • 27 Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, CA, USA
  • 28 Universidade de Cuiaba, Cuiaba, Brazil
  • 29 GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 30 Research Faculty of Agriculture, Hokkaido University, Sapporo, Japan
  • 31 Department of Environmental Science, Faculty of Science, Shinshu University, Matsumoto, Japan
  • 32 University of Rostock, Rostock, Germany
  • 33 National Center for Agro Meteorology, Seoul, South Korea
  • 34 Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
  • 35 Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan
  • 36 Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea
  • 37 Kyoto University, Kyoto, Japan
  • 38 Department of Ecology and Evolutionary Biology, Cornell, Ithaca, NY, USA
  • 39 School of Forest Sciences, University of Eastern Finland, Joesnuu, Finland
  • 40 California State University San Marcos, San Marcos, CA, USA
  • 41 U.S. Geological Survey, Menlo Park, CA, USA
  • 42 Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Glob Chang Biol, 2021 08;27(15):3582-3604.
PMID: 33914985 DOI: 10.1111/gcb.15661

Abstract

While wetlands are the largest natural source of methane (CH4 ) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4 . At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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