Verifying inventory predictions of animal methane emissions with meteorological measurements

VERIFYING INVENTORY PREDICTIONS OF ANIMAL METHANEEMISSIONS WITH METEOROLOGICAL MEASUREMENTS ?

O. T. DENMEAD1, R. LEUNING1, D. W. T. GRIFFITH2, I. M. JAMIE2, M. B. ESLER2,L. A. HARPER3 and J. R. FRENEY4

1F.C. Pye Laboratory, CSIRO Land and Water, Canberra, ACT, Australia;2Department ofChemistry, University of Wollongong, Wollongong, NSW, Australia;3Southern Piedmont

Conservation Research Unit, USDA-Agricultural Research Service, Watkinsville, GA, U.S.A.;4CSIRO Plant Industry, Canberra, ACT, Australia

(Received in final form 24 February 2000)

Abstract. The paper examines the strengths and weaknesses of a range of meteorological flux mea-surement techniques that might be used to verify predictions of greenhouse gas inventories. Recentresearch into emissions of methane (CH4) produced by enteric fermentation in grazing cattle andsheep is used to illustrate various methodologies. Quantifying this important source presents specialdifficulties because the animals constitute moving, heterogeneously distributed, intermittent, pointsources. There are two general approaches: one, from the bottom up, involves direct measurements ofemissions from a known number of animals, and the other, from the top down, infers areal emissionsof CH4 from its atmospheric signature. A mass-balance method proved successful for bottom-upverification. It permits undisturbed grazing, has a simple theoretical basis and is appropriate for fluxmeasurements on small plots and where there are scattered point sources. The top-down methodo-logies include conventional flux-gradient approaches and convective and nocturnal boundary-layer(CBL and NBL) budgeting schemes. Particular attention is given to CBL budget methods in bothdifferential and integral form. All top-down methodologies require ideal weather conditions for theirapplication, and they suffer from the scattered nature of the source, varying wind directions andlow instrument resolution. As for mass-balance, flux-gradient micrometeorological measurementswere in good agreement with inventory predictions of CH4 production by livestock, but the standarderrors associated with both methods were too large to permit detection of changes of a few per centin emission rate, which might be important for inventory, regulatory or research purposes. Fluxescalculated by CBL and NBL methods were of the same order of magnitude as inventory predictions,but more improvement is needed before their use can be endorsed. Opportunities for improving theprecision of both bottom-up and top-down methodologies are discussed.

Keywords: Mass balance, Flux-gradient, Boundary-layer budgeting, Enteric fermentation.

1. Introduction

Although seemingly pedestrian, research in support of national and internationalgreenhouse gas inventories offers many challenges, particularly in the develop-ment of appropriate meteorological techniques for verifying inventory predictions.

? The CSIRO right to retain a non-exclusive royalty-free license in and to any copyright isacknowledged.

Boundary-Layer Meteorology96: 187–209, 2000.© 2000Kluwer Academic Publishers. Printed in the Netherlands.

188 O. T. DENMEAD ET AL.

This paper illustrates the point in describing recent research into emissions ofmethane (CH4) produced as a result of enteric fermentation in ruminant animals.The Intergovernmental Panel on Climate Change (IPCC) estimates that this sourcecontributes 23% of global anthropogenic methane emissions (Prather et al., 1995).

As in most inventory algorithms, the calculation of CH4 emissions from ru-minant animals is based on a bottom-up approach. The production by individualanimals in particular categories (type of animal, age, weight) is calculated from thequality and quantity of the animal’s feed intake. That figure is then multiplied bythe stocking density for that category in particular regions to produce a regionalinventory. There are two possible meteorological approaches to verification: one,like the inventory approach, from the bottom up, involves direct measurements ofCH4 emissions from individual or groups of animals, and the other, from the topdown, infers areal emissions from the atmospheric CH4 signature.

While the methodology of meteorological flux measurement on different scalesis the main thrust of this paper, it is appropriate to consider briefly the biogeo-chemistry of CH4 from animal sources. Enteric fermentation is the process inanimals by which gases, including CH4, are produced as a by-product of microbialfermentation associated with digestion of feed. This metabolic pathway occurs innon-ruminant animals, but is pronounced in ruminant animals, notably cattle andsheep, where microbial activity in the rumen and caecum produces relatively largequantities of CH4. About 90% of the CH4 is produced in the rumen and is expelledthrough the animal’s mouth and nostrils during respiration and eructation (Murrayet al., 1976). Harper et al. (1999) found a cyclical pattern of methane emission fromgrazing cattle with most methane emitted during periods of rumination when theanimals were resting and least when they were actively feeding. Judd et al. (1999)found also that CH4 emission from grazing sheep was about 40% higher by daythan by night. The main sink for atmospheric CH4 is reaction with hydroxyl radic-als in the troposphere, but some oxidation of CH4 also occurs in soils, amountingto about 6% of the tropospheric sink (Prather et al., 1995).

Most inventory estimates of CH4 production by ruminant animals are basedon formulae arising from careful measurements in respiration-chambers conductedin order to assess the energy value of foods. There are questions as to how wellthese estimates can be extrapolated from country to country and from laboratoryto field, e.g., Johnson et al. (1994). Recent attempts at verification in the fieldhave included the use of a sulfur hexaflouride (SF6) tracer technique in which SF6

diffuses at a known rate from a permeable capsule inserted into the animal’s rumen(Johnson et al., 1994). The rate of CH4 emission is calculated from the ratio ofCH4 to SF6 concentrations in samples of the animal’s breath collected over time.Another approach has been to graze animals in a portable wind tunnel (Lockyerand Jarvis, 1995). Methane production is calculated from the changes in the con-centration of CH4 in air entering and leaving the tunnel. Judd et al. (1999) useda non-disturbing micrometeorological flux-gradient technique in which sensibleheat was used as a tracer of turbulent transfer. In this instance, sheep populated

VERIFYING INVENTORY PREDICTIONS 189

the entire upwind fetch and fencing was arranged so as to have a uniform stockingdensity of 20 sheep ha−1, extending hundreds of m upwind and laterally. We haveused micrometeorological techniques on various scales in an integrated approachto verifying inventory predictions for cattle and sheep. We have tested our resultsagainst the methodologies employed by IPCC (Houghton et al., 1996) and theAustralian National Greenhouse Gas Inventory Committee (NGGIC, 1996).

As is evident from the discussion so far, flux measurements in the out-of-doorsare complicated because grazing animals are moving, elevated, point sources ofCH4 with periodic emission patterns, and the soil is a CH4 sink. Measuring gasemissions from the bottom up requires a 2- or 3-dimensional approach. This needhas been met by the development of a mass-balance method suitable for continuousmeasurements from small areas and distributed point sources (Denmead et al.,1998). In extending to larger scales, we have employed both conventional flux-gradient analyses similar to those used by Judd et al. (1999) and boundary-layerbudgeting techniques that integrate fluxes over large areas of the landscape andover relatively long times (Denmead et al., 1996). Most emphasis in this paperis given to the use of convective boundary-layer (CBL) budgeting techniques thathave been used previously for estimating regional fluxes of carbon dioxide andwater vapour (Denmead et al., 1996), but have not been extended so far to the moredifficult task of estimating regional fluxes of trace gases such as CH4.

2. A Bottom-up Flux Measurement Technique Using Mass Balance

As elaborated in Denmead et al. (1998), the mass balance method is non-disturbing,has a simple theoretical basis and is appropriate for flux measurements on smallplots and where there are heterogeneous surface sources. Further, although themethod utilises measurements of the wind profile, no assumptions are needed aboutthe shape of the profile and the method is independent of atmospheric stability. Wehave used it to measure CH4 emissions from grazing and feedlot cattle (Harper etal., 1999) and grazing sheep (Leuning et al., 1999).

The general approach is to calculate gas production from sources within a testspace by measuring the difference between the rates at which the gas is transportedinto and out of the space by the wind. A rough rule of thumb is that the plume froman emitting source extends to a height of about 1/10 of the upwind fetch. In ourexperiments, the animals were grazed in a small, square field, 22 m on a side, andmeasurements of CH4 concentrationρm were made on samples of air drawn fromfour heights,z (0.5, 1, 2 and 3.5 m), along each of the four boundaries. Horizontalsampling tubes, 25 mm in diameter, were installed at each height and extendedalong the complete 22 m. To achieve equal sampling along the length of the tubes,20 mm lengths of capillary tubing 0.3 mm in diameter were inserted and sealedinto the larger tubes at 1 m intervals. Tests confirmed equal flow rates through thecapillaries within 5%.


Figure 1.Schematic of experimental test plot and gas sampling system employed in mass balancestudies. Side lengthX was 22 m and gas intakes were at 0.5, 1, 2 and 3.5 m heights.u denotes totalwind speed,U the wind speed normal to north and south boundaries andV that normal to west andeast boundaries. After Denmead et al. (1998).

Figure 1 from Denmead et al. (1998) is a schematic of the sampling systememployed. Air was pumped from each of the 16 sampling arms surrounding thetest plot along heated airlines to a mobile laboratory where the gas concentrationmeasurements were made. Each sampling line incorporated a buffering vessel of35 L capacity, making the total volume of the line 50 L, so as to guard against rapidchanges in gas concentration during the measurement cycle. For convenience, thesides of the test plot were aligned along north–south (N–S) and west–east (W–E)compass directions. Simultaneous measurements were made in successive 100 speriods of the mean horizontal wind speed at the same heights as the samplingarms using small sensitive cup anemometers, and the mean wind direction using afast-response propeller anemometer. The vector winds for a 30 or 45 min run,U

(N–S) andV (W–E), were calculated from these measurements. Gas concentrationswere multiplied by the appropriate vector winds to obtain the horizontal gas fluxesat each height on each boundary. The difference between these fluxes integratedover downwind and upwind boundaries represents production. For the samplingsystem employed, the flux of gas from the plot,F , is given by

F = X∫ Z

0[U (〈ρg,S,z〉 − 〈ρg,N,z〉)+ V (〈ρg,E,z〉 − 〈ρg,W,z〉)]dz, (1)

whereZ denotes the height of the top of the plume emitted from the test plot,the overbars denote time means and the angular brackets spatial means, i.e., meanconcentrations along each boundary, andX is the length of each boundary.

Figure 2 illustrates the use of the mass balance technique for measuring CH4

production by animals. It shows successive concentration profiles measured by


Figure 2.CH4 concentrations on boundaries of test plot measured in consecutive runs when therewere 4 cattle grazing in the plot and the wind was from the west–northwest.

infrared gas analysis on the upwind and downwind boundaries of the 22 m test fieldduring a study of CH4 production by 4 cattle, described by Harper et al. (1999).Winds during the runs represented in the figure were west–northwesterly at around2 m s−1. As expected, CH4 concentrations on the upwind boundaries were nearlyconstant with height and close to the then clean-air mixing ratio of approximately1700 ppb. There was essentially no enrichment on the downwind boundaries at thetop measuring height of 3.5 m, indicating that the sampling array was high enoughto sample all the emitted CH4. Typical enrichments at lower heights in this studyand the study described by Leuning et al. (1999) were in the range 10–300 ppb.That resolution is attainable with good practice with a number of modern measur-ing systems including infrared gas analysers (IRGA), gas chromatography (GC),Fourier Transform infrared (FTIR) spectroscopy and tunable diode lasers (TDL).Figure 2 also illustrates the intermittent nature of animal CH4 production. Largeenrichments occurred on the downwind sides of the test plot in the runs at 2050and 2232, about two hours apart. These resulted from animal activity. Harper etal. (1999) found that high production occurred when the animals were resting andruminating and low production when they were feeding. Similar observations weremade by Judd et al. (1999). The intermittency makes for a high natural variabilityin flux measurements when only a small number of animals is involved and makesshort-term determinations difficult.

Rates of CH4 production observed by us in mass-balance studies with grazingcattle and sheep are compared with those predicted by the IPCC and NGGIC meth-odologies in Table I. The inventory methodologies proceed in two stages. First they


TABLE I

Comparison of inventory predictions with observations of methane emissions inmass-balance studies.

Methodology Reference

Mass balance IPCC NGGIC

(Mean± SE) (g head−1 d−1)

Grazing cattle 232 ± 76 180 186 Harper et al. (1999)

Grazing sheep 10.8± 1.4 10.1 11.1 Leuning et al. (1999)

predict feed intake; then they predict what proportion of the energy content of thefeed will be converted to CH4. For simplicity, we have assumed the same feedintake as was measured in the field. Comparisons with the more detailed two-stagepredictions are given in Harper et al. (1999) and Leuning et al. (1999). The level ofagreement evident in Table I is very encouraging. Even in the worst case, observedand predicted emissions agreed to within 23%, and we conclude that the currentmethodologies of both IPCC and NGGIC appear to predict CH4 emissions fromgrazing animals satisfactorily. We note, however, that the mass-balance methodgave standard errors (SE) that were roughly 15 to 30% of the estimates of the meananimal CH4 production. On that basis, mass-balance approaches would be unableto verify small differences in CH4 production of say, 10%, that could be significantin inventory and regulatory terms.

As summarised by Denmead et al. (1998), the main disadvantage of the massbalance method is that many gas analyses are required for each flux determination(16 in the present applications). The time required, two minutes per sample inour case, can make the method cumbersome and introduce errors if wind direc-tion changes substantially during a run, while the fact that fluxes are calculatedas residuals, small differences between large numbers, introduces a requirementfor high-precision analytical techniques. Further, the method can be unreliable inlight winds and when wind directions are variable. Despite these difficulties, it hasconsiderable scope for trace gas flux measurement in many situations where con-ventional micrometeorological methods cannot be used: small plots, elevated pointsources, heterogeneous surface sources. We note that the use of a fast responsegas analyser such as a tunable diode laser would alleviate one of the measurementproblems. With that instrument, it is possible to scan all 16 samples in two minutes(G. W. Thurtell, personal communication, 1999).


3. Top-Down Flux Measurement Techniques

The larger scale micrometeorological flux measurements used to illustrate varioustechniques in this paper were made at Wagga Wagga, NSW, in the south-east ofAustralia. Land use in the Wagga Wagga region comprises approximately 30%cropping and 70% pastures grazed by sheep and cattle. Census data indicate av-erage stocking densities for the region of 0.3 cattle and 1.9 sheep ha−1. WaggaWagga was therefore expected to be a modest (but not strong) source region forCH4 and attempts were made to compare the relevant inventory predictions withatmospheric measurements of the inputs of CH4 in the region.

Measurements of atmospheric CH4 concentration were made by FTIR spectro-scopy at seven levels between 0.5 and 22 m above the ground on a tower erectedin a rural area with land use typical of the region. Details of the measurementtechnique are given by Leuning et al. (1997). The data were logged as 30 minaverages at each level. Figure 3 shows a sequence of concentration profiles onthe tower over 24 h. While indicating that there were both sources and sinks ofCH4 in the region, the profiles also suggest some possible micrometeorologicalapproaches to flux measurement. We note first that in almost all cases, a maximumconcentration occurred near the ground, usually at a height of 1 to 2 m. Belowthis height, concentration gradients were usually positive, pointing to the existenceof a soil sink. Above it, gradients were predominantly negative, indicating a re-gional source of CH4 and upward transfer to the atmosphere. Second, a markeddiurnal cycle in CH4 concentration existed at all heights. Minimum concentrationsoccurred in the morning, followed by a build-up through the day and night. Thenight-time build-up was particularly pronounced.

From the analysis of Leclerc and Thurtell (1990), the footprint for measure-ments up to 1-m height at Wagga Wagga was of order 100 to 300 m, which waswithin the boundaries of the field in which the tower was located. There were no an-imals in the field. This suggests that use of conventional micrometeorological fluxmeasurement techniques in the air layer 0–1 m, based on flux-gradient relations oreddy correlation, might give an indication of the rate of uptake of CH4 by the soil.The footprint for the top two measuring heights on the tower, 14 and 22 m, wasof order 0.5 km by day to 5 km by night so that use of conventional micrometeor-ological flux measurement techniques near the top of the mast might be expectedto indicate the strength of the local animal source of CH4. The diurnal changesin ambient CH4 concentrations evident in Figure 3 suggest two other possibleapproaches to flux measurement based on boundary-layer budgeting: convectiveboundary-layer (CBL) budgeting, which utilises the build-up in gas concentrationin the mixed layer by day to infer the average surface flux of the gas in a regionwith a lateral extent of tens of km, and nocturnal boundary-layer (NBL) budget-ing, which infers regional fluxes from changes in gas storage below the nocturnalinversion.


Figure 3.24-hour sequence of profiles of atmospheric CH4 concentration on a 22-m mast located inan extensive grazing area near Wagga Wagga, NSW.

3.1. FLUX -GRADIENT TECHNIQUES

As indicated above, these were employed in an attempt to provide paddock-scaleestimates of CH4 uptake by the soil and local estimates of CH4 emissions fromgrazing animals at Wagga Wagga, NSW. More detailed accounts of their use aregiven in Leuning et al. (1997). In brief, flux densities of CH4, Fm, were calculatedby a tracer technique involving water vapour. Formally, the vertical flux density ofa gasFg can be represented by the flux-gradient relationship

Fg = −Kg(∂ρg/∂z), (2)

whereKg is an eddy diffusivity for gas transport. The flux density of water vapour,E was measured by eddy correlation at 1.5 m and 22 m along with simultaneousmeasurements through FTIR spectroscopy of the vertical concentration gradientsof water vapour,ρv, and CH4, ρm, between 0.5 and 1 m and between 14 m and


22 m. Then, assuming thatKg is the same for all non-reactive gases, it followsfrom Equation (2) that

Fm,z = E(∂ρm/∂z

∂ρm/∂z

). (3)

Allowance was made for the change in gas storage in the air layer below the heightof measurement:

Fm,0 = Fm,z +∫ z

0(∂ρm/∂t) dz, (4)

t denoting time. In applying Equation (3), we recognise that the footprints at thedifferent heights of measurement were quite different. This may not have been ofmuch consequence for water vapour, but could have been important for CH4, par-ticularly for the measurements at the top of the tower. Given the relative sparsenessof livestock in the region and their heterogeneous distribution, the concentrationmeasurements at 14 m and 22 m may have represented quite different stockingdensities. The heterogeneous source distribution may also have created problemsof horizontal advection of CH4. Judd et al. (1999), who employed flux-gradienttechniques, overcame these difficulties by having a high and uniform stockingdensity of 20 sheep ha−1 extending much further upwind than the footprint for thetop measurement level. We note that use of eddy correlation would overcome theproblem of different footprints for different sampling heights, but that techniquedoes not appear to have been tried in the present context.

Although the concentration profiles in Figure 3 suggest the existence of aground level sink, it was not possible to calculate the magnitude of that sink throughEquations (3) and (4) with any certainty. Chamber measurements in the same field,performed simultaneously by Meyer et al. (1997), gave a mean flux of approxim-ately−2±0.2 ng CH4 m−2 s−1. This is so small that the differences in CH4 mixingratio between 0.5 and 1 m expected for such a flux were often<1 ppb, comparedto the instrument precision for FTIR, as employed in these investigations, of about3 ppb.

The relative precision of calculations of the CH4 flux at the top of the WaggaWagga tower using FTIR spectroscopy was higher. By day, more than half themeasured differences in mixing ratio between 14 and 22m were in the range 0 to10 ppb. The mean of this modal class was 3.7 ppb. By night, the range was muchwider (Figure 3 for instance) so that then, differences could be measured with lessrelative error. As well, the more easily measured storage term, the second term onthe right hand side of Equation (4), accounted for a good part of the surface flux.On some nights, it was as much as 80% ofF0. The mean daytime flux (0600–1800) for 12 days of measurement was 0.076± 0.018µg CH4 m−2 s−1 and themean nighttime flux (1800–0600) was 0.109± 0.015µg CH4 m−2 s−1. For thereasons outlined above, more weight should probably be attached to the nighttimemeasurements.


TABLE II

Comparison of inventory predictions with micro-meteorological flux-gradient estimates of CH4 emis-sions over 12 days at Wagga Wagga, NSW.

Methodology Rate of CH4 emission

(g ha−1 d−1 ± SE)

Flux-gradient estimates 79± 16

IPCC default values 85

NGGIC default values 92

Inventory predictions were made for the Wagga Wagga region on the basisof the stocking density in the region and the default emission factors of IPCC(Houghton et al., 1996) and NGGIC (1996). These are compared with the 24-htower measurements in Table II. As for the mass balance measurements, the flux-gradient calculations agreed very well with both inventory predictions, but again,the standard error (SE) for the micrometeorological measurements was too large topermit detection of the small changes in emission rate which might be importantfor inventory and regulatory purposes.

3.2. CONVECTIVE BOUNDARY-LAYER BUDGETING

The technique was employed at Wagga Wagga to estimate regional fluxes of CH4,which arose almost wholly from the grazing of sheep and cattle. The convectiveboundary layer (CBL) is the well-mixed layer of air that develops between heightsof about 100 m and 2 km above the surface during daytime convective conditions.It is often capped by a sharp temperature inversion. As described in Denmead etal. (1996), it acts as a natural mixing chamber, integrating surface gas fluxes alongthe path of the prevailing wind. The height of the mixed layer increases throughthe day because of an input of heat energy at the ground surface, while its gasconcentration changes because of a flux of gas at the surface and the entrainmentof air with a different concentration from above. The average surface flux of the gascan be calculated from the build-up of gas concentration within the CBL and theCBL height, following procedures described by Denmead et al. (1996) and outlinedbriefly below. Typically, that flux represents the surface gas exchange over a regionof order 100 km2.

Following Denmead et al. (1996), we can write the conservation equation fornon-reactive gases in the CBL as

dCmdt= F0

h+(C+ − Cm

h

)(dh

dt−W+

), (5)


whereCm is the mean gas concentration in the CBL,C+ is the gas concentrationjust above the CBL (and so is the concentration of air entrained into the CBL as thelatter grows) andW+ is the mean vertical velocity at the top of the CBL. The firstterm on the right hand side of Equation (5) accounts for the effects of the surfaceflux on the mixed-layer concentration and the second accounts for the effects ofentrainment and subsidence. Rearrangement of Equation (5) leads to

F0 = hdCmdt− (C+ − Cm)

(dh

dt−W+

), (6)

which allowsF0 to be calculated directly from time series of the relevant atmo-spheric parameters. Raupach et al. (1992) call Equation (6) the differential form ofthe CBL (DCBL) budget method.

By rearranging Equation (6) and integrating with respect to time, Raupach et al.(1992) developed a second form of the CBL budget equation, which they called theintegral CBL or ICBL budget method. Here we present an expanded form of thatmethod developed by Denmead et al. (1996). If we denote the integrated surfaceflux between times 0 andt by I (t) so that

I (t) =∫ t

0F0(t) dt, (7)

then

I (t) = γm[h2(t)− h2(0)]2

− {h(t)[C+(t)− Cm(t)] − h(0)[C+(0)− Cm(0)]}

+∫ t

0W+(C+ − Cm) dt, (8)

whereγm is the vertical gradient of gas concentration just above the CBL, i.e.,dC+/dz|h+. The ICBL method allows the average surface flux to be calculated fromfewer observations of the relevant atmospheric parameters than are required in theDCBL approach. Both methods were employed in the Wagga Wagga study.

Making direct measurements in and above the CBL on the time scale requiredby either the DCBL or ICBL budget methods is a very large undertaking, par-ticularly for the DCBL method. We chose instead to work with ground-basedapplications of both methods using developments of Denmead et al. (1996). Theseare outlined below. Some aircraft and balloon observations were made during thestudy and these have been used to verify variousa priori assumptions about gasconcentrations in and above the CBL.

The mixed-layer concentrationCm was estimated from the CH4 concentrationat 22 m on the Wagga Wagga towerCs through an aerodynamic resistancera.Assuming the CH4 flux was constant with height between 22 m, denoted byzs, andthe bottom of the mixed layerzm, which we set at 100 m,

Fm = (Cs − Cm)/ra, (9)


so that

Cm = Cs − raFm. (10)

The resistancera was calculated from micrometeorological measurements at 22 musing conventional similarity theory,

ra = ln[(zm − d)/(zs − d)] − ψ(zm − d, zs − d)ku∗

, (11)

whered is the zero-plane displacement,k (= 0.41) is the von Karman constant,u∗ is the friction velocity andψ is the integrated form of the stability function forunstable conditions given by Paulson (1970),

ψ(zm − d, zs − d) = 2 ln

[1+ [1− 16(zm − d)/L]1/21+ [1− 16(zs − d)/L]1/2

], (12)

L denoting the Obukhov length.Incorporating Equation (10) into Equations (6) and (8) and, for want of bet-

ter knowledge in our ground-based scheme, settingW+ to zero, leads to surfaceversions of the DCBL and ICBL budget methods. For the DCBL method, we have

F0 = hdCs/dt − (C+ − Cs)dh/dt1+ radh/dt , (13)

and for the ICBL method,

I (t) = h(t)[Cs(t)− C+(t)] − h(0)[Cs(0)− C+(0)]1+ [h(t)ra(t)− h(0)ra(0)]/t . (14)

For estimatingh, we adopted the simple encroachment model of Tennekes (1973),

dh

dt= Fθv

hγθv. (15)

Fθv [= (H + 0.07λE)/ρcp] is the surface flux of potential virtual temperatureθv,H being the surface flux of sensible heat,λ the latent heat of evaporation,E thesurface flux of water vapour,ρ the density of air andcp its specific heat at constantpressure, andγθv is the vertical gradient ofθv just above the top of the CBL. Onintegration, Equation (15) gives

h(t) =√[h2(0)+ 2

∫ t

0(Fθv/γθv)dt]. (16)

In the applications at Wagga Wagga, we assumed that the CBL started to growwith the onset of convection at 0800. The parametersψ , u∗, L, H andE were


obtained from eddy correlation measurements of the fluxes of momentum, sensibleheat and water vapour at 23.5 m.

In similar ground-based applications for measuring regional CO2 fluxes, Den-mead et al. (1996) assumed thatC+ for CO2 was equal to the maritime atmosphericCO2 concentration as measured at the baseline monitoring station at Cape Grim,Tasmania and we made a similar assumption about CH4 in the Wagga Wagga study.The baseline value for the mixing ratio of CH4 at the time of the study was 1694ppbv (I. E. Galbally, personal communication, 1995). It was assumed also that atthe top of the CBL, there would be an instantaneous jump in CH4 concentrationfromCm toC+, i.e., thatγm = 0.

Figures 4 and 5 illustrate the changes in gas concentration in the lower atmo-sphere, on which CBL budgeting is predicated. Figure 4 shows the diurnal course ofCH4 mixing ratio at 22 m on October 23, 1995, the day on which the concentrationprofiles in Figure 3 were measured. This day followed two days of heavy rainduring which concentrations at all levels on the Wagga Wagga tower were close tobaseline. With the onset of convective conditions in the morning of October 23, theCH4 mixing ratio at 22 m was rapidly reduced from high overnight values to theclean-air baseline value, and then built up steadily through the day. The build upbetween 1000 and 1500 was 14 ppb. The profiles on the left of Figure 5 show CH4

concentrations within and above the CBL on the same day at a location about 50 kmwest of Wagga Wagga, obtained through aircraft flask sampling. The samples wereanalysed for CH4 subsequently, using FTIR spectroscopy. The profile at 1800 isfor the fully developed CBL whenh (measured by radiosonde ascent) was about1.6 km andCm about 18 ppb above the baseline value. At this case our assumptionsabout CH4 concentrations in and above the CBL seem justified. For both the 0930and 1800 samplings, the CBL appeared to be well mixed, with all mixing ratioswithin a few ppb of each other, and, as evident in the profile at 1800, there was aclear jump at the top of the CBL to a constant value ofC+, which was very closeto the baseline value.

The profiles on the right of Figure 5 result from aircraft flask sampling at thesame location three days later. The fully developed profile at 1530 illustrates thesame features evident on October 23: a nearly constantCm and a jump at the topof the CBL to a smaller constant value very close to the baseline. However, themorning sampling revealed that much of the previous day’s build-up in CH4 withinthe CBL was still present in the airabovethe present day’s developing CBL, i.e.,there was no clear jump to a constantC+ aboveh. We point out that the aircraftsampling program validated the assumptions aboutC+ andγm for fully developedprofiles. Samplings on 17 occasions gave a mean value of 1695± 1 ppb forC+,compared with the assumed value of 1694 ppb, but deviations from the ideal stateat the start of the day undoubtedly caused errors in our calculations. Changingwind directions, weather conditions not conducive to CBL development and thehigh precision required in gas concentration measurements, particularly for DCBLbudgeting, were other limitations. In order to avoid complications from these vari-


Figure 4.Diurnal course of atmospheric CH4 concentration at 22 m at same location and on sameday as profiles in Figure 3 were measured.

ous sources, applications of the DCBL and ICBL budget methods were restrictedto periods between 1000 and 1500 each day, and comparisons with inventory pre-dictions and the flux-gradient estimates ofFm were made separately for morningand afternoon periods. As well, a distinction was made between ‘all’ days and‘preferred’ days, the latter being days with constant wind directions and moderateto strong winds over the measuring periods. Typically, the wind on those days hada strong W component and wind speeds were> 3 m s−1. By contrast, the non-preferred days were characterised by highly variable wind directions, particularlyin the mornings, and wind speeds< 2 m s−1. There were 16 days in the study, ofwhich seven were preferred days.

Results of the DCBL and ICBL calculations for the period 1000 to 1500 forall 16 days in the study are shown in Figure 6 along with the calculations of thegradient flux at 22 m and predictions of CH4 production by livestock in the WaggaWagga region by the NGGIC (1996) algorithm using default emission factors. Res-ults for the preferred days are distinguished by symbols. It is noteworthy that therewas little to choose between the DCBL and ICBL budget methods in their results,particularly on the preferred days. The large negative fluxes calculated by the CBLbudget methods on October 11 and 16 were associated with CH4 mixing ratios (at22 m), which were 50 to 200 ppb above the baseline value and which decreasedsteadily through the day. As well, wind directions were variable and light. The


Figure 5. Profiles of atmospheric CH4 concentration in flask samples of air collected by aircraftabove an extensive grazing area near Wagga Wagga, NSW.

missing days in the sequence are October 19 when there was no convective activityand October 21 and 22 when there was rain.

Figure 7 presents the data as ensemble averages for all 16 days in the study.The DCBL and gradient flux calculations are for each 30-min run between 1000and 1500, and the ICBL fluxes are for morning and afternoon periods of 1000–1230 and 1230–1500. In all cases, results for the afternoon periods should bethe more reliable because of non-stationary conditions in the morning, which willhave affected the gradient calculations, and abnormally large CH4 concentrationsin the developing CBL, as in Figure 5, which will have affected the CBL budgetcalculations. The DCBL budget fluxes were variable throughout the day, exhibitingnegative values at times. There are two probable causes for this: the difficulty ofresolving the small concentration changes that occurred from one run to the nextand the entrainment of air with a different CH4 concentration than assumed in thecalculations.

Table III summarises the data separately for the different calculation periodsfor all 16 days and for the seven preferred days. As shown graphically in Figure7, the morning fluxes were generally higher than those in the afternoon, the oneexception being for the ICBL fluxes on the preferred days, when they were almostidentical. In this ground-based application of CBL budgeting, the afternoon fluxes


Figure 6.Comparison of DCBL and ICBL budget estimates of average regional CH4 emissions atWagga Wagga, NSW on 16 days with tower-based micrometeorological flux-gradient measurementsof the CH4 flux (GradientFm) and inventory predictions by NGGIC (1996). Points are CBL estimateson ‘preferred’ days defined in text.

Figure 7. Ensemble averages of DCBL and ICBL budget estimates of regional CH4 emissions atWagga Wagga, NSW, tower-based micrometeorological flux-gradient measurements of the CH4 flux(GradientFm) and inventory predictions by NGGIC (1996) for the 16 days represented in Figure 6.

should be considered the more reliable. There appears to be little, if anything, to begained by attempting the more demanding DCBL analysis instead of the simplerICBL approach. Both methods gave much the same answers with much the sameSE. For the afternoon budget calculations, it seems fair to assert that they providedestimates of CH4 emissions which were of the same order of magnitude as theinventory predictions, but the level of uncertainty was too high for them to be usefulin inventory verification. It should be noted that the inventory predictions in TableIII are themselves approximate in as much as they are based on default emissionfactors. Animal CH4 production can vary quite widely from the default values,


depending on animal type and size, and feed quality. Further, Judd et al. (1999)found that daytime production by sheep was 40% higher than at night, which wouldhave the effect of increasing the inventory predictions given in Table III by 20%.Nevertheless, the standard errors associated with the budget estimates were toohigh to claim that they were significantly different from inventory estimates.

More elaborate approaches employing aircraft for direct measurements of con-centrations in and above the CBL and perhaps a Lagrangian observation systemcan be expected to yield more precise results.

3.3. NOCTURNAL BOUNDARY-LAYER BUDGETING

At night when convective heating ceases, the CBL is replaced by the nocturnalboundary-layer (NBL), a shallow, weakly turbulent layer that often extends toheights of only a few tens of metres, and is bounded by a low-level radiativeinversion. The inversion inhibits vertical mixing so that there is no flux at the topof the NBL and emissions of gases from the surface are contained in a shallow airlayer whose concentration changes appreciably. The surface flux of CH4 can becalculated from Equation (4), assuming thatFm is zero at the top of the NBL,

F0 =∫ Z

0(∂ρm/∂t)dz, (17)

Z being the height of the NBL.The technique has been used successfully by Choularton et al. (1995) to meas-

ure CH4 emissions from peat bogs and Judd et al. (1999) used a simplified versionto measure CH4 production by sheep. We employed it on one occasion at WaggaWagga to estimate regional CH4 fluxes. A helium filled balloon was used to carryan air sampling line aloft in vertical traverses to a height of 100 m, made everytwo hours. A GC was then used to measure CH4 concentrations in air pumpedfrom heights of 2, 10, 20, 40, 60, and 100 m. Figure 8 shows two concentrationprofiles from that study, measured four hours apart. Both profiles show virtuallyno gradient in CH4 mixing ratio at 100 m, indicating no vertical flux there, andthe 100 m concentration was the same on both occasions indicating that all theCH4 emitted over the four-hour period was contained between the surface and thatheight. The calculated flux of CH4 was 0.216µg m−2 s−1, which is in keeping withthe other budget fluxes, but is somewhat larger than the inventory predictions forthe census stocking rate.

In assessing the utility of NBL budgeting techniques, Denmead et al. (1996)concluded that whereas the growth and height of the CBL can be predicted reason-ably well, this is not so for the NBL. Its shifting height makes it difficult to workwith a fixed sampling array and there will be times when the radiative inversionis impossibly deep or even absent so that the method is then not feasible. When abudget can be made, the requirement to measure concentration profiles rather thanconcentrations at a single point makes it a more complicated procedure than CBL

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TABLE III

Comparison of DCBL and ICBL budget estimates of the average surface flux of CH4 at Wagga Wagga with mi-crometeorological flux-gradient calculations and inventory predictions for all 16 days in the study and for sevenpreferred days. (See text for definition of preferred.) Figures in brackets are standard errors.

Data Period DCBLFm ICBL Fm GradientFm IPCC NGGIC

µg m−2 s−1 µg m−2 s−1 µg m−2 s−1 µg m−2 s−1 µg m−2 s−1

All days 1000–1230 0.359 (0.159) 0.351 (0.211) 0.130 (0.039) 0.085 0.106

1230–1500 0.120 (0.127) 0.078 (0.150) 0.084 (0.035) 0.085 0.106

1000–1500 0.221 (0.109) 0.198 (0.123) 0.106 (0.028) 0.085 0.106

Preferred days 1000–1230 0.272 (0.116) 0.157 (0.117) 0.125 (0.059) 0.085 0.106

1230–1500 0.184 (0.124) 0.166 (0.164) 0.075 (0.053) 0.085 0.106

1000–1500 0.210 (0.091) 0.163 (0.098) 0.086 (0.042) 0.085 0.106


Figure 8. Use of nocturnal boundary-layer budgeting to estimate CH4 production in an extensivegrazing area near Wagga Wagga, NSW. The two concentration profiles were obtained by pumpingair to a gas analyser through an air line carried aloft by a balloon. The area between the profilesrepresents CH4 production over four hours.

budgeting. Another weakness is uncertainty about the extent of the surface thatthe budget represents. On the other hand, few assumptions are required and theconcentration changes usually will be larger and hence more easily detectable thanin CBL budgeting or daytime flux-gradient analyses. A point of relevance here isthat there may be some difference between daytime and nighttime fluxes of CH4

from animal sources. As noted above, Judd et al. (1999) found that daytime CH4

fluxes were 40% higher.

4. Concluding Remarks

The indications from the mass balance measurements are that they can be usedsuccessfully to verify inventory methodologies for methane emissions from smallgroups of grazing animals. Our work and that of Judd et al. (1999) show that con-ventional micrometeorological techniques based on flux-gradient analyses can alsobe successful in verifying inventory predictions on paddock and regional scales.For both types of assessment, discrepancies between prediction and observationwere only about 30% in the worst case. This is close to the low end of the un-certainty range for global methane emissions from enteric fermentation of 20% to80%, given for instance by NGGIC (1996), and so meteorological approaches haveimproved confidence in the inventory predictions of emissions from this important


source. However, the uncertainties attached to both methods are still too large forthem to be used to verify small changes in emission rates of say, 10%, which mightbe important for inventory, regulatory or research purposes. The challenge is toimprove the precision with which atmospheric CH4 fluxes can be measured byalmost an order of magnitude. For mass balance methods, necessary improvementsinclude fast response sampling, such as is possible with TDL, to overcome effectsof varying wind directions and a resolution of better than 10 ppb in measuringatmospheric CH4 mixing ratios.

For conventional micrometeorological approaches, particularly flux-gradientapplications, low stocking rates and scattered sources such as those encounteredat Wagga Wagga make the problem difficult, but the biggest limitation appears tobe instrument resolution. As employed by us, FTIR spectroscopy has a detectionlimit of about 3 ppb (Leuning et al., 1997) for measurements of CH4 mixing ra-tios. Judd et al. (1999) indicate a resolution of about 1 ppb for very careful GCmeasurements and Zahniser et al. (1995) suggest precisions of about 10 ppb forTDL measurements in fast eddy correlation applications and 3 ppb for gradientmeasurements, although other authorities suggest higher precisions for TDL. Non-etheless, present detection limits are of similar size to the concentration differenceslikely to be encountered in daytime flux-gradient or eddy correlation applicationswhen stocking densities are as small as they were in the Wagga Wagga study. Theprospects are better for higher stocking densities, but even with 20 sheep ha−1 andan emission rate about five times that at Wagga Wagga, Judd et al. (1999) still founda coefficient of variation of 68% in their measured fluxes. In principle, eddy cor-relation or eddy accumulation should improve the precision of flux measurementprimarily because they remove the problem of different footprints, which bedevilsthe flux-gradient approach. However, they may introduce other problems such asthe need to make corrections for an apparent loss of flux (Moore, 1986) and inthe case of eddy correlation, the effects of simultaneous fluxes of heat and watervapour (Webb et al., 1980).

Direct applications of CBL budget methods require difficult measurements ofatmospheric parameters and specialised measurement systems involving aircraft,balloons or kites, and DCBL methods require the measurements to be made fre-quently. Ground-based methods such as we employed are a compromise, but theyare just as data intensive, the precision required in concentration measurementsis just as high and they create some difficulties through their assumptions. Ourexperience is that the assumptions about mixing in the CBL and the concentrationsof CH4 above it were met on select days when winds were constant in directionand moderately strong, but not on all days. Estimating CBL height from the groundand subsidence of the air mass require other assumptions which we have not beenable to test. Lack of convective activity is another limitation. In the Wagga Waggastudy, weather conditions approached the ideal on only seven of a total of 20 days.Both DCBL and ICBL budget methods gave estimates of the regional CH4 fluxthat were of the same order of magnitude as inventory predictions, but uncertainty


levels were much higher than for the other methods discussed above. Here, there isa big challenge for improvement.

Although attempted only once by us, NBL budgeting appears to offer goodopportunities for large-scale micrometeorological measurements of CH4 fluxesfrom animal sources. Concentration changes are larger than by day and so canbe determined with less relative error. In the Wagga Wagga study for instance,there were enrichments of as much as 100 ppb in the CH4 mixing ratio up to 100mover a period of four hours. Judd et al. (1999) report an enrichment of 200 ppbover 13 hours. Penalties will be a more elaborate gas sampling system than isusually employed for daytime flux measurements and the possible introductionof uncertainties arising from non-stationarity and advective effects. As well, theremay be a real difference between nighttime and daytime flux values. Judd et al.(1999), for instance, report a difference of 40%.

Finally, we draw attention to some recent innovative attempts to measure CH4

fluxes on field and regional scales. D. W. T. Griffith (personal communication,2000) has employed line-source geometry to measure CH4 emissions from graz-ing cattle. The animals were strip-grazed on pasture behind an electric fence andnitrous oxide (N2O) was released simultaneously at a known rate at several pointsalong the fence. The flux of CH4was calculated from the ratio of the enrichmentsof CH4 and N2O in the well-mixed plume downwind of the fence and the knownrelease rate of N2O. The geometry also permitted a second independent estimateof the CH4 flux from available solutions of the problem of atmospheric dispersionfrom cross-wind line sources. The experimental approach allowed a high stockingdensity to be created and ensured a known source distribution. On a much broaderscale, Fowler (1999) reports the use of a simple mass balance approach based onupwind and downwind sampling of the boundary layer by aircraft to calculateaverage CH4 fluxes from regions hundreds of km in lateral extent. In one study,a CH4 budget was made for the whole of the U.K. The boundary layer was about1 km deep and downwind boundary-layer enhancement in the CH4 mixing ratiowas 20–150 ppb. The data provided sufficient spatial detail to assess the relativemagnitude of sources from different areas of the country and from different sourcecategories. Fowler (1999) notes that the method requires a clearly defined boundarylayer, the absence of deep convection and frontal activity along the wind trajectory,and steady boundary-layer winds. Its use for inventory verification in situationswhere there are multiple sources of CH4 might require some inverse modellingalso.

Acknowledgements

We acknowledge the technical assistance provided in the field studies by CSIROofficers Alan Jackson, Chris Drury and Barry Smith. The studies were part of anNGGIC Inventory Development Project onVerifying Current Estimates of Non-


CO2 Greenhouse Gas Emissions from Animals, Landfills and Pastures with DirectMeasurements, funded by Environment Australia.

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Verifying inventory predictions of animal methane emissions with meteorological measurements

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