Flux and concentration footprint modelling: State of the art

Vesala, Timo; Kljun, Natascha; Rannik, Üllar; Rinne, Janne; Sogachev, Andrey; Markkanen, Tiina; Sabelfeld, Karl K.; Foken, Thomas; Leclerc, Monique Y.

doi:10.1016/j.envpol.2007.06.070

Cited by 228 publications

(162 citation statements)

References 81 publications

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“…Where the upwind source is extensive and homogeneous, 1 Equation (1) McGinn 388 the location of the EC detectors should be at a height within the fully adjusted IBL (reflective of the surface emission). Where the source is not extensive (finite) and/or not evenly distributed, it is necessary to understand what the influence of the source at upwind distances has on the measured emission at a particular height, the so-called footprint of the source (Vesala et al, 2008). The footprint is dependent on the measurement height, wind speed and direction, atmospheric stability and surface roughness.…”

Section: Ecmentioning

confidence: 99%

“…A simple 'rule-of-thumb' is that the fully adjusted IBL roughly increases in height by 1 m for every 100 m distance from the upwind leading edge of the source area (Vesala et al, 2008). Other more detailed relationships exist in defining this boundary layer, for example, as a function of the surface roughness and distance from the leading edge (Munro and Oke, 1975).…”

Section: General Application Of Micrometeorological Techniquesmentioning

confidence: 99%

“…The footprint is dependent on the measurement height, wind speed and direction, atmospheric stability and surface roughness. Vesala et al (2008) outlines a few approaches to estimating the footprint, using Lagrangian stochastic trajectory, large eddy simulation and closure models. Another approach was used by Baum et al (2008) at a beef cattle feedlot that uses a simplified (parameterized) footprint model, developed by Hsieh et al (2000).…”

Section: Ecmentioning

confidence: 99%

“…Since then, this technique has also been applied to grazing cattle (Laubach et al, 2008). Like other micrometeorological techniques, FG is best suited to operate in the fully adjusted IBL; else a footprint model (Vesala et al, 2008) must be used to gauge the influence of the source configuration on the calculated emission (see the discussion below). …”

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confidence: 99%

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Developments in micrometeorological methods for methane measurements

McGinn

2013

Animal

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Micrometeorological techniques can be applied to estimate methane (CH 4 ) emissions from ruminants and livestock manure using CH 4 concentration measured within the internal surface boundary layer. The main advantage of these techniques is that they are non-intrusive, thereby eliminating the impact of the measurement set-up on the calculated CH 4 emission. This review focuses on four micrometeorological techniques, namely, the integrated horizontal flux (IHF), flux gradient (FG), eddy covariance (EC) and the dispersion modelling using the backward Lagrangian stochastic method (BLS). Each technique has unique advantages and limitations when used for estimating enteric (ruminant) and manure CH 4 emissions. The IHF technique may be theoretically simpler then the FG, EC or BLS techniques, but all require high-resolution instruments to measure concentration. The EC and BLS techniques also require a measurement of the wind statistics. This review discusses the appropriate use of these four micrometeorological techniques for estimating CH 4 emissions in animal agriculture and the recent advances in measurement technology.Keywords: methane, micrometeorology, technique, livestock, manure ImplicationsAccurate measurements of methane (CH 4 ) emissions are essential to understand the importance of livestock's contribution to the greenhouse gas issue, and to develop mitigation strategies to improve the environmental sustainability of animal agriculture. These goals will become even more important as animal production escalates to meet global population demands. A clear understanding of the limitations and accuracy of measurement techniques presented in this review is needed to advance our understanding of CH 4 emissions from animal agriculture. IntroductionAnimal agriculture production is estimated to account for 8% to 10.8% of the global emissions of greenhouse gases (O'Mara, 2011). Methane (CH 4 ) is a large component of these emissions where globally the annual emission of ruminant enteric CH 4 is 91.9 million tonnes, and CH 4 from livestock manure is 11.2 million tonnes (O'Mara, 2011). The enteric CH 4 from ruminants alone is estimated to account for 25% to 40% of the anthropogenic release of CH 4 (Clark, 2013). It follows that accurate measurement of CH 4 emissions from ruminants and livestock manure is necessary to develop best management practices to reduce these CH 4 emissions and to ultimately improve the environmental sustainability of the livestock industry. These goals will become even more relevant with the expected expansion of the livestock industry because of increased demand for meat and milk by an escalating human population estimated to reach nine billion by 2050 (Smith et al., 2007). Fundamental to accurate measurement is the need for techniques that give direct measurements of CH 4 emissions under a variety of management and environmental conditions.A variety of CH 4 emission techniques for intensive livestock systems have been reviewed in the literature, for example, National Research Council (2003) a...

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Section: Ecmentioning

confidence: 99%

Section: General Application Of Micrometeorological Techniquesmentioning

confidence: 99%

Section: Ecmentioning

confidence: 99%

mentioning

confidence: 99%

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Developments in micrometeorological methods for methane measurements

McGinn

2013

Animal

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“…For the three models, the sensitivity agreement of the source area to (a) measurement height, (b) surface roughness, and (c) atmospheric condition can be assessed through comparing the four characteristic dimensions. The FSAM has been widely adopted but only applied during neutral and moderate atmospheric stability and with a limited range of crosswind turbulence intensity (Schmid, 2002;Vesala et al, 2008). Therefore, the FSAM results are used as a reference and compared with the other two models.…”

Section: The H Modelmentioning

confidence: 99%

Flux footprint climatology estimated by three analytical models over a subtropical coniferous plantation in Southeast China

Zhang¹,

Wen

2015

J Meteorol Res

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Spatial heterogeneity poses a major challenge for the appropriate interpretation of eddy covariance data. The quantification of footprint climatology is fundamental to improving our understanding of carbon budgets, assessing the quality of eddy covariance data, and upscaling the representativeness of a tower flux to regional or global scales. In this study, we elucidated the seasonal variation of flux footprint climatologies and the major factors that influence them using the analytical FSAM (Flux Source Area Model), KM (Kormann and Meixner, 2001), and H (Hsieh et al., 2000) models based on eddy covariance measurements at two and three times the canopy height at the Qianyanzhou site of ChinaFLUX in 2003. The differences in footprints among the three models resulted from different underlying theories used to construct the models. A comparison demonstrated that atmospheric stability was the main factor leading to differences among the three models. In neutral and stable conditions, the KM and FSAM values agreed with each other, but they were both lower than the H values. In unstable conditions, the agreement among the three models for rough surfaces was better than that for smooth surfaces, and the models showed greater agreement for a low measurement height than for a high measurement height. The seasonal flux footprint climatologies were asymmetrically distributed around the tower and corresponded well to the prevailing wind direction, which was north-northwest in winter and south-southeast in summer. The average sizes of the 90% flux footprint climatologies were 0.36-0.74 and 1.5-3.2 km 2 at altitudes of two and three times the canopy height, respectively. The average sizes were ranked by season as follows: spring > summer > winter > autumn. The footprint climatology depended more on atmospheric stability on daily scale than on seasonal scale, and it increased with the increasing standard deviation of the lateral wind fluctuations.

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