Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations

Show full item record



Peltola , O , Vesala , T , Gao , Y , Raty , O , Alekseychik , P , Aurela , M , Chojnicki , B , Desai , A R , Dolman , A J , Euskirchen , E S , Friborg , T , Goeckede , 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 , vol. 11 , no. 3 , pp. 1263-1289 .

Title: Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
Author: Peltola, Olli; Vesala, Timo; Gao, Yao; Raty, Olle; Alekseychik, Pavel; Aurela, Mika; Chojnicki, Bogdan; Desai, Ankur R.; Dolman, Albertus J.; Euskirchen, Eugenie S.; Friborg, Thomas; Goeckede, Mathias; Helbig, Manuel; Humphreys, Elyn; Jackson, Robert B.; Jocher, Georg; Joos, Fortunat; Klatt, Janina; Knox, Sara H.; Kowalska, Natalia; Kutzbach, Lars; Lienert, Sebastian; Lohila, Annalea; Mammarella, Ivan; Nadeau, Daniel F.; Nilsson, Mats B.; Oechel, Walter C.; Peichl, Matthias; Pypker, Thomas; Quinton, William; Rinne, Janne; Sachs, Torsten; Samson, Mateusz; Schmid, Hans Peter; Sonnentag, Oliver; Wille, Christian; Zona, Donatella; Aalto, Tuula
Contributor organization: Institute for Atmospheric and Earth System Research (INAR)
Viikki Plant Science Centre (ViPS)
Micrometeorology and biogeochemical cycles
Ecosystem processes (INAR Forest Sciences)
INAR Physics
Date: 2019-08-22
Language: eng
Number of pages: 27
Belongs to series: Earth system science data
ISSN: 1866-3508
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 degrees 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 = 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
1172 Environmental sciences
1171 Geosciences
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

Files in this item

Total number of downloads: Loading...

Files Size Format View
essd_11_1263_2019.pdf 9.001Mb PDF View/Open

This item appears in the following Collection(s)

Show full item record