Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F19%3A00511511" target="_blank" >RIV/86652079:_____/19:00511511 - isvavai.cz</a>
Result on the web
<a href="https://www.earth-syst-sci-data.net/11/1263/2019/" target="_blank" >https://www.earth-syst-sci-data.net/11/1263/2019/</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5194/essd-11-1263-2019" target="_blank" >10.5194/essd-11-1263-2019</a>
Alternative languages
Result language
angličtina
Original language name
Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
Original language description
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).
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10510 - Climatic research
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Earth System Science Data
ISSN
1866-3508
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
3
Country of publishing house
DE - GERMANY
Number of pages
50
Pages from-to
1263-1289
UT code for WoS article
000482519900001
EID of the result in the Scopus database
2-s2.0-85071527458