Journal cover Journal topic
Earth System Science Data The data publishing journal
Journal topic

Journal metrics

Journal metrics

  • IF value: 10.951 IF 10.951
  • IF 5-year value: 9.899 IF 5-year
    9.899
  • CiteScore value: 9.74 CiteScore
    9.74
  • SNIP value: 3.111 SNIP 3.111
  • IPP value: 8.99 IPP 8.99
  • SJR value: 5.229 SJR 5.229
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 38 Scimago H
    index 38
  • h5-index value: 33 h5-index 33
Discussion papers
https://doi.org/10.5194/essd-2019-28
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-28
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 20 Feb 2019

Review article | 20 Feb 2019

Review status
This discussion paper is a preprint. A revision of the manuscript was accepted for the journal Earth System Science Data (ESSD).

Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations

Olli Peltola1, Timo Vesala2,3, Yao Gao1, Olle Räty4, Pavel Alekseychik5, Mika Aurela1, Bogdan Chojnicki6, Ankur R. Desai7, Albertus J. Dolman8, Eugenie S. Euskirchen9, Thomas Friborg10, Mathias Göckede11, Manuel Helbig12, Elyn Humphreys13, Robert B. Jackson14, Georg Jocher15,a, Fortunat Joos16, Janina Klatt17, Sara H. Knox18, Lars Kutzbach19, Sebastian Lienert16, Annalea Lohila1,2, Ivan Mammarella2, Daniel F. Nadeau20, Mats B. Nilsson15, Walter C. Oechel21,22, Matthias Peichl15, Thomas Pypker23, William Quinton24, Janne Rinne25, Torsten Sachs26, Mateusz Samson6, Hans Peter Schmid17, Oliver Sonnentag27, Christian Wille26, Donatella Zona21,28, and Tuula Aalto1 Olli Peltola et al.
  • 1Climate Research Programme, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
  • 2Institute for Atmosphere and Earth System Research/Physics, PO Box 68, Faculty of Science, FI-00014, University of Helsinki, Finland
  • 3Institute for Atmospheric and Earth System Research/Forest Sciences, PO Box 27, Faculty of Agriculture and Forestry, FI- 00014, University of Helsinki, Finland
  • 4Meteorological Research, Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
  • 5Natural Resources Institute Finland (LUKE), FI-00790 Helsinki, Finland
  • 6Department of Meteorology, Faculty of Environmental Engineering and Spatial Management, Poznan University of Life Sciences, 60-649 Poznan, Poland
  • 7Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, 1225 W Dayton St, Madison, 20 Wisconsin 53706 USA, 608-265-9201
  • 8Department of Earth Sciences, Faculty of Sciences, Vrije Universiteit Amsterdam, Boelelaan 1085, 1081 HV Amsterdam, the Netherlands
  • 9University of Alaska Fairbanks, Institute of Arctic Biology, 2140 Koyukuk Dr., Fairbanks, AK 99775
  • 10Department of Geosciences and Natural Resource Management, University of Copenhagen , Denmark
  • 11Max Planck Institute for Biogeochemistry, Jena, Germany
  • 12School of Geography and Earth Sciences, McMaster University, Hamilton, ON, Canada
  • 13Department of Geography & Environmental Studies, Carleton University, Ottawa, ON, Canada
  • 14Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA
  • 15Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
  • 16Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 17Institute of Meteorology and Climatology – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), Kreuzeckbahnstrasse 19, 82467 Garmisch-Partenkirchen, Germany
  • 18Department of Geography, The University of British Columbia, Vancouver, Canada
  • 19Institute of Soil Science, Center for Earth System Research and Sustainability, Universität Hamburg, Hamburg 20146, Germany
  • 20Department of Civil and Water Engineering, Université Laval, Quebec City, Canada
  • 21Global Change Research Group, Dept. Biology, San Diego State University, San Diego, CA 92182, USA
  • 22Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, EX4 4RJ, UK
  • 23Department of Natural Resource Sciences, Thompson Rivers University, Kamloops, BC, Canada
  • 24Cold Regions Research Centre, Wilfrid Laurier University, Waterloo, ON, Canada
  • 25Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 26GFZ German Research Centre for Geosciences, Potsdam, Germany
  • 27Département de géographie, Université de Montréal, Montréal, QC H2V 3W8, Canada
  • 28Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, S10 2TN, United Kingdom
  • anow at: Department of Matter and Energy Fluxes, Global Change Research Institute, Czech Academy of Sciences, Bělidla 986/4a, 603 00 Brno, the Czech Republic

Abstract. Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are in the northern latitudes. These emissions are typically estimated using process (bottom-up) or inversion (top-down) models, yet the two are not independent of each other since the top-down estimates rely on the a priori estimation of these emissions coming from the process models. Hence, independent validation data of the large-scale emissions would be needed.

Here we utilize 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) during years 2013 and 2014. The predictive performance of the RF model is evaluated using the leave-one-site-out cross-validation scheme and the performance (Nash-Sutcliffe model efficiency = 0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide or studies where process models are compared against site-level CH4 emission data. Three wetland maps are utilized in the upscaling and the annual emissions for the northern wetlands yield 31.7 (22.3–41.2, 95 % confidence interval), 30.6 (21.4–39.9) or 37.6 (25.9–49.5) Tg(CH4) yr−1, depending on the map used. To evaluate the uncertainties of the upscaled product it is also compared against two process models (LPX-Bern and WetCHARTs) and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data product is available for further usage at: https://doi.org/10.5281/zenodo.2560164.

Olli Peltola et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topical Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Olli Peltola et al.
Data sets

Dataset for "Monthly Gridded Data Product of Northern Wetland Methane Emissions Based on Upscaling Eddy Covariance Observations" O. Peltola, T. Vesala, Y. Gao, O. Räty, P. Alekseychik, M. Aurela, B. Chojnicki, A. R. Desai, A. J. Dolman, E. S. Euskirchen, T. Friborg, M. Göckede, M. Helbig, E. Humphreys, R. B. Jackson, G. Jocher, F. Joos, J. Klatt, S. H. Knox, L. Kutzbach, S. Lienert, A. Lohila, I. Mammarella, D. F. Nadeau, M. B. Nilsson, W. C. Oechel, M. Peichl, T. Pypker, W. Quinton, J. Rinne, T. Sachs, M. Samson, H. P. Schmid, O. Sonnentag, C. Wille, D. Zona, and T. Aalto https://doi.org/10.5281/zenodo.2560164

Olli Peltola et al.
Viewed  
Total article views: 597 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
403 182 12 597 5 15
  • HTML: 403
  • PDF: 182
  • XML: 12
  • Total: 597
  • BibTeX: 5
  • EndNote: 15
Views and downloads (calculated since 20 Feb 2019)
Cumulative views and downloads (calculated since 20 Feb 2019)
Viewed (geographical distribution)  
Total article views: 430 (including HTML, PDF, and XML) Thereof 428 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 17 Jul 2019
Download
Short summary
Here we develop a monthly gridded dataset of northern (> 45 N) wetland methane (CH4) emissions. The data product is derived using random forest machine learning technique and eddy covariance CH4 fluxes from 25 wetland sites. Annual CH4 emissions from these wetlands calculated from the derived data product are comparable to prior studies focusing on these areas. This product is an independent estimate of northern wetland CH4 emissions and hence could be used e.g. for process model evaluation.
Here we develop a monthly gridded dataset of northern ( 45 N) wetland methane (CH4) emissions....
Citation