Spatio temporal analysis of methane emis 001

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Spatio-temporal analysis of methane emission in a boreal peatland during one
growing season as measured by eddy covariance

Inke Forbrich1*, Lars Kutzbach1,2, Christian Wille1,2, Jiabing Wu1,3, Thomas Becker1,4 and Martin Wilmking1

Introducion and Objectives

Methods


Peatlands are a major natural source of methane (CH4). To quantify their
source strength detailed information about temporal and spatial dynamics
are needed. With the increased application of new CH4 analyzers, eddy
covariance measurements of CH4 have recently become more common.
This method allows quasi-continuous measurements of turbulent fluxes
and it integrates over a large source area (“footprint“).
Generally,during the summer, peak emission rates of CH4 occur.
Production rates are high [1] and aerenchymatuous plants stimulate both
production and transport [2]. However, less information exists about
shortterm CH4 dynamics during this period. With nearly continuous
timeseries of CH4 emissions it is possible to analyze whether oscillations
are due to environmental controls (e.g. air pressure as steering
parameter for ebullition [3]) or changes in the footprint of the flux
measurement (similar to [4]).

spatial analysis:

In general, seasonal CH4 dynamics in boreal peatlands are best
described with peat temperature below the water table [5].

Shortterm changes are not attributed to a certain
environmental control yet. Here, we test whether short term
oscillations in our recorded CH4 fluxes are due to changes of
the flux footprint.

• high resolution aerial pictures (1Pixel: 1m*1m) [8]
• analytical footprint model according to Kormann & Meixner 2001 [9]
Integral footprint contribution of the three microsite types
Ahummock, Alawn, Aflark are computed for each pixel (in upwind
direction of tower).

temporal analysis:
• a simple model of FCH4 as function of peat temperature (adapted from
[1])
FCH4_i = exp(ai+bi*Tpeat) i=lawn, flark, hummock
• The microsite flux is weighted by footprint fraction
FCH4 = ∑(Ai * FCH4_i)

Location: 62°47’N, 30°56’E, Eastern Finland, oligotrophic mire complex
Classified fetch area: Eddy tower (T) is centered (z=2m), radius =

200m, circle area:12.5ha (Fig.1)

Fig. 1: Classified fetch area: lawns

Equipment: Sonic Gill 3D anemometer, (orange), flarks (green), hummocks +
trees (dark green)
Li-7000, Campbell Sci. TGA100A
(data analysis according to [6])
Three microsites: hummocks (dry),
lawns (intermediate), flarks (wet)
boa

Results of spatial analysis

• The point of maximum contribution to the fluxprint is < 15m from the
tower.

Fig. 3: Time series of measured and modelled CH4 fluxes (FCH4 = Flawn). The inset graph shows
the model results for the same time period as Fig. 2.


• The 80% flux footprint area is within the classified fetch area in 97% of
the measurements.

• Spatial weighting increases model performance.

• 1.4% of the measurements are biased by low turbulence (u* 100%.
• The footprint fraction of flarks (the strongest CH4 source) is neglectable
(Fig. 2). Because Flawn >> Fhummock [9], the temporal analysis is conducted
for this microsite: FCH4 = Flawn.

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mean
annual
air

Climate:
temperature +2.1°C (January: 10.6°C, July: +16.0°C), mean annual
precipitation 667mm (years: 19712000) [7]

[1] Saarnio et al. (1997), Oecologia 110:414-422
[2] Saarnio et al. (2002), Plant and Soil 267:343-355
[3] Tokida et al. (2005), Geophys. Res. Let.:32,L13823
[4] Neftel et al. (2008), Env. Pollution: 152:644-652
[5] Rinne et al. (2007), Tellus 59B: 449-457

measurements
simple model
R² = 0.6 RMSE = 0.85
spatially weighted model
R² = 0.7 RMSE = 0.7

• Footprint model and aerial pictures are combined with the help of an
arbitrary two-dimensional Cartesian coordinate system. The tower is
situated in the point of origin and the maximum value for the x- and yaxis is 200m (cf. Fig. 1).


Study Site Salmisuo

Vegetation cover:
P. sylvesteris,
A. polifolia, Sph. fuscum (hummocks),
E. vaginatum, Sph. balticum, Sph.
papillosum (lawns), Sch. palustris,
Sph. balticum (flarks)

Results of temporal analysis

• The use for gap-filling is limited to conditions with well-developed turbulence.

Discussion and Conclusions
In Salmisuo, CH4 flux of microsites decrease in the order: Flarks > Lawns >>
Hummocks [9]. In the footprint, lawns usually cover more area than hummocks,
while the spatial extent of flarks is neglectable. As the eddy covariance
measurements integrate over this area, the lawns contribute by far the largest part
of the measured flux. Short term oscillations can be partially explained by the
changes of the footprint fraction of this microsite. However, these results are

specific for Salmisuo with its characteristic microsite distribution. Furthermore,
they can be specific for the growing season when vegetation cover plays a crucial
role in C cycling. During the cold seasons, microsite contribution could be less
significant while meteorological conditions gain importance.
Methodologically, the footprint model is restrained to atmospheric conditions with
well-developed turbulence which can lead to gaps in the model timeseries.
Furthermore, as the calculated footprint fraction is a result of a model itself, its use
increases the uncertainty of the time series model.

Fig. 2: Random section of time series of footprint contributions of hummocks, lawns
and flarks for ten days. The contribution of flarks can usually be neglected, while the
extent of lawns and hummocks show an opposite trend.
[6] Wille et al. (2008), Glob. Chn. Biol.:14:1-14
[7] FMI (2002)
[8] Becker et al. (2008), Biogeosci. 5:1387-1393
[9] Hormann (2009), diploma thesis

• including additional variables (e.g. water level or air pressure) does not increase
model performance.


• Shortterm oscillations in measured CH4 flux can be attributed to footprint
fraction of spatially dominant microsite with significant CH4 emission rates
• Methodologically, detailed analysis of footprint model performance and
uncertainy analysis of time series model is needed in the future
1
Institute of Botany and Landscape Ecology, Ernst Moritz Arndt University Greifswald, Germany 2
Institute of Soil Science, University Hamburg, Germany
3
Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, P.R.China
4
National Environmental Research Institute, Aarhus University, Roskilde, Denmark
* correspondence: inke.forbrich@uni-greifswald.de