Point source emission estimation using eddy covariance: Validation using an artificial source experiment

Dumortier, Pierre; Aubinet, Marc; Lebeau, Frédéric; Naiken, Alwin; Heinesch, Bernard

doi:10.1016/j.agrformet.2018.12.012

Cited by 17 publications

(52 citation statements)

References 24 publications

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“…Spatial uncertainties in bison location interact with uncertainties in flux footprint modelling for methane source attribution (Table 1). Analytical footprint models like the one used here have been found to accurately estimate point sources of trace gas flux (Dumortier et al, 2019), but it is important to note that footprint modelling techniques play a large role in the spatial attribution of observed fluxes of ruminant trace gas flux (Felber et al, 2015). Prajapati and Santos (2018), for instance, found that an analytical model (Kormann and Meixner 2001) predicted flux footprint areas five to six times larger than did an approximation of a Lagrangian dispersion model (Kljun et al, 2003), such that footprint model uncertainty is a major source of uncertainty for measuring methane flux from multiple point sources.…”

Section: Bison Spatial Distributionmentioning

confidence: 99%

“…Spike removal was performed as described by Vickers and Mahrt (1997) and spikes were defined as more than 3.5 standard deviations from the mean mixing ratio for carbon dioxide and more than 8 standard deviations from the mean mixing ratio for methane given the expectation of intermittent methane spikes from the bison herd. The default drop-out, absolute limit, and discontinuity tests were applied using the default settings following recommendations by Dumortier et al (2019), and the Moncrieff et al (1997) and Moncrieff et al (2004) low and high-pass filters were applied. The Webb-Pearman-Leuning correction (Webb et al, 1980) was applied to calculate methane efflux using the open-path LI-7700 sensor.…”

Section: Flux Calculationmentioning

confidence: 99%

“…The eddy covariance flux footprint was calculated using the approach of Hsieh et al (2000) extended to two dimensions following Detto and Katul (2006). Analytical footprint models have been found to give minimally-biased estimates of pointsource fluxes in field settings (Dumortier et al, 2019). We performed the footprint analysis on a grid of 1 m pixels and aggregated values to the 20 × 20 m grid to which the bison locations were estimated ( Figure 1); Figure 3 demonstrates an example of a flux footprint for a single half-hourly period.…”

Section: Footprint Calculationmentioning

confidence: 99%

“…Given that mean methane emissions were not significantly different from zero in the absence of bison -as detailed in Results -we assume that observed methane emissions are due to bison in the flux footprint. The relative contribution of bison to each half-hourly eddy covariance measurement was calculated by expanding the approach of Dumortier et al (2019). From the definition of the footprint function, the measured density of a scalar X, FX, for our study area of 8 × 12 grid cells ( Figure 1) is:…”

Section: Per-bison Methane Flux Estimationmentioning

confidence: 99%

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Methane efflux from an American bison herd

Stoy¹,

Cook²,

Dore³

et al. 2020

Preprint

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Abstract. American bison (Bison bison L.) have recovered from the brink of extinction over the past century. Bison reintroduction creates multiple environmental benefits, but their impacts on greenhouse gas emissions are poorly understood. Bison are thought to have produced some 2&#8201;Tg&#8201;year&#8722;1 of the estimated 9&#8211;15&#8201;Tg&#8201;year&#8722;1 of pre-industrial enteric methane emissions, but few contemporary measurements have been made due to their mobile grazing habits and safety issues associated with direct measurements. Here, we measure methane and carbon dioxide fluxes from a bison herd on an enclosed pasture during daytime periods in winter using eddy covariance. Methane emissions from the study area were negligible in the absence of bison (mean&#8201;&#177;&#8201;standard deviation&#8201;=&#8201;0.0024&#8201;&#177;&#8201;0.042&#8201;&#956;mol&#8201;m&#8722;2&#8201;s&#8722;1) and were significantly greater than zero, 0.048&#8201;&#177;&#8201;0.082&#8201;&#956;mol&#8201;m&#8722;2&#8201;s&#8722;1 with a positively skewed distribution, when bison were present. We coupled an eddy covariance flux footprint analysis with bison location estimates from automated camera images to calculate a mean (median) methane flux of 38&#8201;&#956;mol&#8201;s&#8722;1 (22&#8201;&#956;mol&#8201;s&#8722;1) per animal, or 52&#8201;&#177;&#8201;14&#8201;g&#8201;CH4&#8201;day&#8722;1 (31&#8201;g&#8201;CH4&#8201;day&#8722;1), less than half of measured emission rates for range cattle. Emission estimates are subject to spatial uncertainty in bison location measurements and the flux footprint, but from our measurements there is no evidence that bison methane emissions exceed those from cattle. We caution however that our measurements were made during winter and that evening measurements of bison distributions were not possible using our approach. Annual measurements are ultimately necessary to determine the greenhouse gas burden of bison grazing systems. Eddy covariance is a promising technique for measuring ruminant methane emissions in conventional and alternate grazing systems and can be used to compare them going forward.

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Section: Bison Spatial Distributionmentioning

confidence: 99%

Section: Flux Calculationmentioning

confidence: 99%

Section: Footprint Calculationmentioning

confidence: 99%

Section: Per-bison Methane Flux Estimationmentioning

confidence: 99%

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Methane efflux from an American bison herd

Stoy¹,

Cook²,

Dore³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The use of GPS devices combined with footprint models also provides a more 570 realistic stocking density in the footprint (Felber et al, 2016b(Felber et al, , 2015. This footprint function is 571 however also the subject of several uncertainties (Dumortier et al, 2019). Finally, the GPS tracking 572 method has the advantage of not disturbing the behavior of the cows when compared, for example, to 573 confinement experiments.…”

mentioning

confidence: 99%

Herd position habits can bias net CO2 ecosystem exchange estimates in free range grazed pastures

Motte

Dumortier

Beckers

et al. 2019

Agricultural and Forest Meteorology

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Context matters when rewilding for climate change

Burak,

Ferraro,

Orrick

et al. 2024

People and Nature

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There is a cross‐sectoral push among conservationists to simultaneously mitigate biodiversity loss and climate change, especially as the latter increasingly threatens the former. Growing evidence demonstrates that animals can have substantial impacts on carbon cycling. As such, there are increasing calls to use animal conservation and rewilding to dually overcome biodiversity loss and mitigate climate change. Specifically, trophic rewilding—which involves restoring intact animal communities, functional roles and trophic structure within food webs, and natural ecosystem processes—utilizes a rewilding framework to simultaneously support biodiversity conservation and carbon capture and storage. Trophic rewilding is a complex conservation approach to mitigating climate change, involving accurate estimations of baseline conditions and continuous monitoring of carbon cycling and species impacts within a system. It is also predicated on garnering social support for both the reintroduction and monitoring of a species, and obtaining the animals themselves. We are excited by the growing interest in this potential, but emphasize that a species' net impact on ecosystem carbon dynamics is context‐dependent. Caution is required whenever biodiversity conservation (including rewilding), climate change mitigation, and human welfare do not readily align. Hence—similar to other nature‐based solutions—these burgeoning efforts must avoid sweeping generalizations. To bolster successful trophic rewilding, we highlight a range of social and ecological context dependencies that can vary outcomes in a rewilded carbon cycle and provide ethical considerations for successful implementation. We conclude with an overview of the available technology to predict and monitor progress toward both biodiversity and climate mitigation goals. Read the free Plain Language Summary for this article on the Journal blog.

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Point source emission estimation using eddy covariance: Validation using an artificial source experiment

Cited by 17 publications

References 24 publications

Methane efflux from an American bison herd

Methane efflux from an American bison herd

Herd position habits can bias net CO2 ecosystem exchange estimates in free range grazed pastures

Context matters when rewilding for climate change

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