PeltolaVesalaGaoEtAl2019

Reference

Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A.R., Dolman, A.J., Euskirchen, E.S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R.B., Jocher, G., Joos, F., Klatt, J., Knox, S.H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D.F., Nilsson, M.B., Oechel, W.C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H.P., Sonnentag, O., Wille, C., Zona, D., Aalto, T. (2019) Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations. Earth System Science Data, 11(3):1263-1289. (URL )

Abstract

Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process ("bottom-up") or inversion ("top-down") models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45° N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash-Sutcliffe model efficiency D 0:47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3-41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4-39.9) or 38 (25.9-49.5) Tg(CH4) yr-1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019). © 2019 The Author(s).

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@ARTICLE { PeltolaVesalaGaoEtAl2019,
    AUTHOR = { Peltola, O. and Vesala, T. and Gao, Y. and Räty, O. and Alekseychik, P. and Aurela, M. and Chojnicki, B. and Desai, A.R. and Dolman, A.J. and Euskirchen, E.S. and Friborg, T. and Göckede, M. and Helbig, M. and Humphreys, E. and Jackson, R.B. and Jocher, G. and Joos, F. and Klatt, J. and Knox, S.H. and Kowalska, N. and Kutzbach, L. and Lienert, S. and Lohila, A. and Mammarella, I. and Nadeau, D.F. and Nilsson, M.B. and Oechel, W.C. and Peichl, M. and Pypker, T. and Quinton, W. and Rinne, J. and Sachs, T. and Samson, M. and Schmid, H.P. and Sonnentag, O. and Wille, C. and Zona, D. and Aalto, T. },
    TITLE = { Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations },
    JOURNAL = { Earth System Science Data },
    YEAR = { 2019 },
    VOLUME = { 11 },
    NUMBER = { 3 },
    PAGES = { 1263-1289 },
    NOTE = { cited By 0 },
    ABSTRACT = { Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process ("bottom-up") or inversion ("top-down") models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45° N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash-Sutcliffe model efficiency D 0:47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3-41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4-39.9) or 38 (25.9-49.5) Tg(CH4) yr-1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019). © 2019 The Author(s). },
    AFFILIATION = { Climate Research Programme, Finnish Meteorological Institute, P.O. Box 503, Helsinki, 00101, Finland; Institute for Atmosphere and Earth System Research/Physics, Faculty of Science, University of Helsinki, P.O. Box 68, Helsinki, 00014, Finland; Institute for Atmospheric and Earth System Research/Forest Sciences, Faculty of Agriculture and Forestry, University of Helsinki, P.O. Box 27, Helsinki, 00014, Finland; Meteorological Research, Finnish Meteorological Institute, P.O. Box 503, Helsinki, 00101, Finland; Natural Resources Institute Finland (LUKE), Helsinki, 00790, Finland; Department of Meteorology, Faculty of Environmental Engineering and Spatial Management, Poznań University of Life Sciences, Poznań, 60-649, Poland; Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, 1225 W Dayton St, Madison, WI 53706, United States; Department of Earth Sciences, Faculty of Sciences, Vrije Universiteit Amsterdam, Boelelaan 1085, HV Amsterdam, 1081, Netherlands; University of Alaska Fairbanks, Institute of Arctic Biology, 2140 Koyukuk Dr., Fairbanks, AK 99775, United States; Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark; Max Planck Institute for Biogeochemistry, Hans-Knöll-Strasse 10, Jena, 07745, Germany; School of Geography and Earth Sciences, McMaster University, Hamilton, ON L8S 4K1, Canada; Département de Géographie, Université de Montréal, Montréal, QC H2V-3W8, Canada; Department of Geography and Environmental Studies, Carleton University, Ottawa, ON K1S 5B6, Canada; Department of Earth System Science, Woods Institute for the Environment, Precourt Institute for Energy, Stanford University, Stanford, CA 94305, United States; Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeä, Sweden; Climate and Environmental Physics, Physics Institute, Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland; Institute of Meteorology and Climatology-Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Kreuzeckbahnstrasse 19, Garmisch-Partenkirchen, 82467, Germany; Department of Geography, University of British Columbia, Vancouver, BC V6T 1Z2, Canada; Institute of Soil Science, Center for Earth System Research and Sustainability, Universität Hamburg, Allende-Platz 2, Hamburg, 20146, Germany; Department of Civil and Water Engineering, Université Laval, Québec, QC G1V 0A6, Canada; Global Change Research Group, Dept. Biology, San Diego State University, San Diego, CA 92182, United States; Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, EX4-4RJ, United Kingdom; Department of Natural Resource Sciences, Thompson Rivers University, Kamloops, BC V2C 0C8, Canada; Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada; Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden; GFZ German Research Centre for Geosciences, Telegrafenberg, Potsdam, 14473, Germany; Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, S10-2TN, United Kingdom; Department of Matter and Energy Fluxes, Global Change Research Institute, Czech Academy of Sciences, Běwlidla 986/4a, Brno, 603-00, Czech Republic },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.5194/essd-11-1263-2019 },
    SOURCE = { Scopus },
    URL = { https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85071527458&doi=10.5194%2fessd-11-1263-2019&partnerID=40&md5=255fc1ce0ca71f742acdd2ace3a60dee },
}

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