Abstract. Natural wetlands constitute the largest and most uncertain sourceof 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 datafrom 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 athttps://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).
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Methane efflux from an American bison herd
Abstract. American bison (Bison bison L.) have recovered from the brink ofextinction over the past century. Bison reintroduction creates multipleenvironmental benefits, but impacts on greenhouse gas emissions are poorlyunderstood. Bison are thought to have produced some 2 Tg yr−1 of theestimated 9–15 Tg yr−1 of pre-industrial enteric methane emissions,but few measurements have been made due to their mobile grazing habits andsafety issues associated with measuring non-domesticated animals. Here, wemeasure methane and carbon dioxide fluxes from a bison herd on an enclosedpasture during daytime periods in winter using eddy covariance. Methaneemissions from the study area were negligible in the absence of bison(mean ± standard deviation = −0.0009 ± 0.008 µmol m−2 s−1) and were significantly greater than zero,0.048 ± 0.082 µmol m−2 s−1, with a positively skeweddistribution, when bison were present. We coupled bison location estimatesfrom automated camera images with two independent flux footprint models tocalculate a mean per-animal methane efflux of 58.5 µmol s−1 per bison, similar to eddy covariance measurements ofmethane efflux from a cattle feedlot during winter. When we sum theobservations over time with conservative uncertainty estimates we arrive at81 g CH4 per bison d−1 with 95 % confidence intervalsbetween 54 and 109 g CH4 per bison d−1. Uncertainty wasdominated by bison location estimates (46 % of the total uncertainty),then the flux footprint model (33 %) and the eddy covariance measurements(21 %), suggesting that making higher-resolution animal location estimatesis a logical starting point for decreasing total uncertainty. Annualmeasurements are ultimately necessary to determine the full greenhouse gasburden of bison grazing systems. Our observations highlight the need tocompare greenhouse gas emissions from different ruminant grazing systems anddemonstrate the potential for using eddy covariance to measure methaneefflux from non-domesticated animals.
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- NSF-PAR ID:
- 10229940
- Date Published:
- Journal Name:
- Biogeosciences
- Volume:
- 18
- Issue:
- 3
- ISSN:
- 1726-4189
- Page Range / eLocation ID:
- 961 to 975
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/bg-18-961-2021
