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Environmental Science & Technology
Methane Emissions from a Small Wind Shielded Lake Determined by
Eddy Covariance, Flux Chambers, Anchored Funnels, and Boundary
Model Calculations: A Comparison
Carsten J. Schubert1,*, Torsten Diem1,b , Werner Eugster2
1
Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dept. of Surface Waters - Research
and Management, CH-6047 Kastanienbaum, Switzerland, Phone: 0041 58 765 2195, Fax: 0041 58 765
2168, email: [email protected]
2
ETH Zurich, Institute of Agricultural Sciences, CH-8092 Zurich, Switzerland
b
Present address: University of St Andrews, School of Geography & Geosciences, St Andrews, KY16
9AL, Scotland, UK
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Abstract
Lakes are large sources of methane, held to be responsible for 18% of the radiative
forcing, to the atmosphere. Periods of lake overturn (during fall/winter) are short and
therefore difficult to capture with field campaigns but potentially one of the most
important periods for methane emissions. We studied methane emissions using four
different methods, including eddy covariance measurements, floating chambers, anchored
funnels, and boundary model calculations. Whereas the first three methods agreed rather
well, boundary model estimates were 5-30 times lower leading to a strong
underestimation of methane fluxes from aquatic systems. These results show the
importance of ebullition as the most important flux pathway and the need for continuous
measurements with a large footprint covering also shallow parts of lakes. Although fluxes
were high, on average 4 mmol m-2 d-1 during the overturn period, water column microbial
methane oxidation removed 75% of the methane and only 25% of potential emissions
were released to the atmosphere. Hence, this study illustrates secondly the importance of
considering methane oxidation when estimating the flux of methane from lakes during
overturn periods.
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Introduction
Atmospheric methane concentrations have increased from 0.800 ppm before
industrialization to a level of around 1.875 ppm today (1) and held responsible for 18% of
the radiative forcing (2). Whereas the biggest natural methane emissions stem from
wetlands, it was only recently suggested that a substantial part– between 6 and 16% of
natural methane emissions – might originate from lakes and other freshwater systems (3).
Conservative estimates assume that small lakes with surface areas smaller than 1 km2
account for only one third of the total emissions from lakes and freshwater systems. This
has now been called into question by more recent estimates of the global number of lakes
(4), which substantially exceeds previous estimates (5), and the findings that small lakes
have higher methane fluxes per unit area than larger lakes (6). Consequently, the amount
of methane emitted from small lakes (< 1 km2) might be much higher than previously
suggested.
We used 4 different methods including continuous eddy-covariance flux measurements
in combination with bi-weekly lake water profile measurements, floating chambers, and
anchored funnels to determine methane emissions during late season overturn. Eddycovariance flux measurements (7, 8) have so far not been used in natural aquatic systems
to estimate methane emissions. They are advantageous since they capture both diffusive
flux (normally estimated from surface-water methane concentrations) and ebullition
(normally determined using bubble capturing funnels). It is furthermore the only method
that allows continuous measurements over longer time periods (here 2.5 months).
In small lakes, methane stored in the hypolimnion, which can be emitted to the
atmosphere during lake turnover, may contribute up to 45% to the methane budget of
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such lakes (9). This suggests a potential for large emissions during late season overturn.
Here we critically evaluate this methane emission potential with measurements from
Rotsee, a small holomictic lake (lakes which mix at least once a year (10)) in Switzerland
that tends to release its immense methane content, accumulated during the biologically
productive season, during mixing (11).
In lakes, the degradation of organic material (OM) is accomplished to a large extent via
methanogenesis (12), a process in which methane is produced by the fermentation of OM
or by carbon dioxide reduction (13). This methane is released from the sediment into the
water column, where it either diffuses or is transported via ebullition towards the water
surface, and is finally emitted to the atmosphere. During its passage through the water
column, methane concentrations are reduced by either aerobic or anaerobic oxidation
(11). However, if a chemocline (or any other physical barrier like a thermocline or
halocline) exists, the methane diffuses only very slowly through this barrier and can
accumulate in the hypolimnion during the productive season. Holomictic lakes such as
Rotsee begin to mix in fall or winter, when decreasing incident radiation and lower air
temperatures lead to a decrease in surface water temperatures and eventually to complete
mixing of the water column due to a density inversion (14).
Late season turnover is an important period for methane and carbon dioxide emissions
from lakes, since methane evasion rates by diffusion may be insignificant throughout
most of the year but very high during turnover periods (15-17).
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Materials and methods
Location
Lake Rotsee is a holomictic lake with a surface area of 0.5 km2 (2.5 km long and 200 m
wide). It is relatively wind shielded which led to wind speeds between 0.04 and 6.9 m s-1
in 1.25 m above the lake surface. During the measurement period the wind speed at 10 m
height was lower than 3.7 m s-1 during 1926 hours (1080 hours below 1 m s-1) and higher
than 3.7 m s-1 only during 69 hours.
Water temperature was between 14.7 and 7.3°C from 20 October to 1 December (see
Figure S1). The footprint area of the eddy covariance method is shown in Figure S2.
Eddy covariance flux measurements
Eddy-covariance flux measurements were conducted using a Gill/Solent 3-d ultrasonic
anemometer-thermometer (model R2A, Lymington, UK) and a Los Gatos Research
FMA-100 fast methane analyser (Mountain View, CA, USA) as described by Eugster &
Plüss (2009) (7). The sonic anemometer was mounted on a moored buoy 70 m from the
lake shore, and air (sucked from 1.25 m above lake surface) was guided to the methane
analyser using a Synflex 1300 tube (75 m) with an outer diameter of 10 mm. Turbulent
flow was maintained by a tri-scroll vacuum pump (BOC Edwards XDS-35i) with a flow
rate of 26.3 l min–1 (7). Data were recorded at 20.8 Hz resolution. Fluxes were computed
as covariances between vertical wind speed and concentration fluctuations after shifting
the methane time-series relative to the wind speed time-series to account for delays
caused by the tube and the methane analyser. The optimum time lag was determined with
a cross-correlation procedure for each 30-minute averaging interval with a search
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window around the expected time delay (3.5–6.0 s). Fluxes were corrected for highfrequency damping losses (18) to compensate for imperfect turbulent flow in the inlet and
in the vacuum cell of the methane analyser. A damping constant of 0.54 s–1 was used
based on least-squares fitting of a theoretical cospectrum to the measured cospectra. On
average, this correction increased measured fluxes by 35% (median 28%). Data quality
screening was done in the same way as done by Eugster et al. (2011) (8) for the data from
the Wohlensee run-of-river reservoir: 30-minute averages during which no clear crosscorrelation between vertical wind speed and CH4 concentration fluctuation was found
were removed and considered a flux that is not significantly different from zero. In
addition to the procedure discussed in detail by Eugster et al. (2011) (8) we used two
additional screening criteria which were specific for the Rotsee site: (a) 30-minute
averages during which the variance of CH4 concentration exceeded 0.4 ppm2, or (b)
during which the inclination angle of the streamlines of the wind field deviated more than
20° from the horizontal direction were removed. Since the sensors were mounted on a
floating buoy the second criterion removes both artefacts from strongly divergent flows
over the lake and conditions where the buoy was pushed downwind by the very rare
stronger winds.
Diffusive flux and ebullition
To estimate diffusive and ebullitive fluxes from the lake, two floating chambers (buoyed
buckets with a volume of 22 liter and a collecting area of 0.086 m2) were used.
The chamber sides were immersed a few centimeters into the water and had an outlet
tube connected to a three-way stopcock, which allowed the sampling of air with glass
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syringes. At the beginning of each measurement a gas sample of 40 ml was taken and
injected into a glass bottle (30 ml) filled with NaCl-saturated water. While pushing the
gas into the bottle, the salt water was allowed to flow out of the bottle via a second
needle. After an hour of free floating, another glass bottle was filled with a second gas
sample as described above. To make sure the gas inside the chamber was mixed we
pumped the volume of the 50ml syringe several time in and out of the chamber. On most
sampling days two chambers were deployed twice for a total of 4 flux measurements,
except on sampling dates after the lake was mixed, when the chambers were only
deployed once, resulting in two flux measurements. The chambers were allowed to drift
over the lake surface for 0.8–2 hours on 19 days, in total for 72 hours (which compares to
6 m2 h).
Reported values represent the mean from one sample campaign. To derive a continuous
curve these mean values were added over time until the next sample campaign when new
mean values were obtained.
Additionally, two custom-made gas traps (funnels) with a collecting area of 0.79 m2 were
used to capture ebullition (19) from 24 October until 15 December. An air-tight cylinder
of known volume with a septum-lined cap was screwed onto the top of the funnel for gas
collection and sampling. The traps were deployed in an area upwind of the sonic
anemometer relative to the average wind direction using a moored buoy system that
allowed the funnel to hang upright approximately ~1 m below the water surface. When
deploying the funnels, care was taken to avoid catching gas bubbles that might be
liberated from the sediments upon anchoring. In total the area and time of the lake
covered by the funnels was 948 m2 h.
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Gas samples taken from the cylinder every 2-3 days were transferred to a N2-filled serum
bottle for methane measurements. The amount of gas collected was determined by
measuring the height of the gas column in the cylinder and calculating the volume. Gas
samples were taken back to the lab and measured for methane concentrations as
described below. Methane emissions were calculated as follows:
F(CH 4 ) =
CH 4 (t 2 )  CH 4 (t1 )
A  t
(1)
where F[mg m–2 d–1] is the flux of methane from the water surface, CH4(tx) is the amount
of methane in the chamber [mg] at the beginning (x=1) and at the end (x=2) of the
measurement, A is the area of the floating chamber or funnel [m2] and t = t2-t1 is the
time [d] elapsed between the measurements.
Methane fluxes were calculated using the boundary layer model of Liss and Slater (1974)
(20):
F = 240 k (Cw - Ceq)
(2)
The model estimates the air-water flux F [mg CH4 m-2 d-1] using the water saturation
concentration Ceq [M] according to Wiesenburg and Guinasso (1979) (21), the measured
water concentration Cw [M] of CH4 at 0.2 m water depth, the transfer velocity k [cm h-1]
and 240 is the unit conversion factor for the given units. For the calculation of the
transfer velocity k600 we used the bi-linear relationship given by Crusius and Wanninkhof
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(2003) (22):for U10 < 3.7 m s-1
k 600 = 0 . 72 U 10 cm h − 1
(3)
for U10
3.7 m s
-1
k 600 = 4 . 33 U 10 − 13 . 3 cm h
−1
(4)
the relationship given by Cole & Caraco (1998) (23)
1. 7
k 600 = 2 . 07+ 0 . 215 U 10
(5)
and the relationship given by MacIntyre et al. (2010) (24)
for a cooling lake
for a heating lake
k 600 = 2 . 04 U
10 +
2. 0
k 600 = 1 . 74 U 10 − 0 . 15
(6)
(7)
where the lake was assumed to be cooling when air temperature was lower than the
surface lake temperature and heating for all other cases. Wind speeds at 10m were
obtained from the ones measured at 1.25m above lake surface with the sonic anemometer
and the correction given in equation (1) in Crusius & Wanninkhof (2003) (22), under the
assumption of neutral, stable boundary layer. The transfer velocity k600 was then
transformed into the transfer velocity k for methane using:
k = k600(Sc/600)c
(8)
where Sc is the Schmidt number of the greenhouse gas (CH4) at water surface
temperature and c is -2/3 for U10 < 3.7 m s-1 and -1/2 for higher wind speeds (25).
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Methane concentrations and isotopic composition
Water samples were taken at 0.2, 2, 4, 6, 8, 10, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5,
15, 15.5 m depth and transferred into 120 ml septum bottles directly after the Niskin
bottles came on board. The bottles were filled inserting a flexible tubing down to the
bottom of the bottle to avoid methane to escape by turbulent mixing, i.e., in the manner
commonly employed for Winkler titrations. The samples were poisoned using mercury
chloride, closed with a butyl stopper and sealed with an aluminium crimp. In the
laboratory a 20% headspace (He) volume was introduced and samples were equilibrated
overnight. Subsequently they were measured at a constant temperature with a gas
chromatograph (Agilent) equipped with a Carboxen 1010 column (30 m, Supelco) and a
flame ionization detector (FID). The oven temperature was 40°C. Methane standards
were made by dilution of pure CH4 (99.9%) and calibrated against commercial ones of
15ppm, 100ppm, 1000ppm and 1% (Scott, Supelco). Water concentrations were
determined according to McAuliffe (1971)(26) using equilibrium solubilities given by
Wiesenburg & Guinasso (1979)(21). Methane concentration in replicate samples varied
by <2%. Subsequently the methane concentrations in 0.2 m depth were used in the
boundary layer model of Liss & Slater (1974)(20) to calculate methane emission rates as
described above.
Methane isotopic composition was determined with a trace gas analyzer connected to a
mass spectrometer (GV Instruments). Notations are in the  notation; i.e.:
13C = [(13C/12C)sample/( 13C/12C)standard] -1
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The precision of the method was ± 0.7 ‰. We used isotopic measurements since changes
in methane isotopic composition towards heavier 13C values together with decreasing
dissolved methane concentrations can clearly distinguish oxidation in the water columns
from mixing of different water bodies.
Methane oxidation
Methane oxidation was estimated similar to the method described by Utsumi et al. (1998)
(27). Oxygen concentrations in the water column were measured with a SBE 19 CTD
(conductivity, temperature, depth) probe (Sea Bird Electronics) equipped with an oxygen
sensor (detection limit 1mol). At several depths (in total 65 samples at 20 days, see
Table S1) four replicate 125 ml bottles were filled with water to determine methane
concentrations as described above. Samples were taken with a flexible tubing reaching
the bottom of the bottle to avoid air intrusion into the sample. The sample depths were
chosen depending on the state of the oxycline, i.e., 1 to 5 samples were taken depending
on how well the oxic and anoxic water column was separated. In one sample biogenic
activity was stopped immediately with Cu(I)Cl, while the others were incubated in the
dark either at 7°C or at 20°C (whichever was closer to the water temperature). Every day
or every second day (for samples from the anoxic zone) one sample was poisoned with
HgCl2. After methane concentrations were measured as described above, methane
oxidation was calculated by the decrease in methane concentration over time. Methane
oxidation rates were then integrated over the whole water column and summed up
assuming rates to be the same from one sampling day to the following sampling day.
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Results and discussion
Four different methods were employed to determine the magnitude of methane
emissions from the surface waters of Rotsee to the atmosphere. The first of these was
based on eddy-covariance flux measurements (7);Eugster, 2011 #19308}. A study carried
out with the same instruments by Tuzson et al. (2010)(28) confirmed that the eddy
covariance method correctly integrates over a footprint area with heterogeneous CH4
sources spaced 3-5 m apart and hence should also be able to correctly quantify ebullition
within the footprint area of the measurements (Figure S2).Methane concentrations from
14 October 2008 to 6 January 2009 varied between minimum values of 1.88±0.02 ppm
(close to the mean global methane concentration in the atmosphere) and maximum values
of 3.26±0.30 ppm. Methane fluxes determined using the eddy-covariance method are
shown in Figure 1b, with positive values denoting methane flux into the atmosphere.
Short-term positive and negative spikes must be considered noise that indicate the current
limits of system performance for direct CH4 flux measurements. Hence, we filtered 30minute data with a 5-point running average low-pass filter (bold line in Figure 1b) to
emphasis the signal that can be seen in our time series. It can clearly be seen that two
main emission events occurred during the three-month period; i.e., between 27 and 31
October and between 20 and 24 November. During these mixing events, responsible for
the main increments in the cumulative flux shown in Figure 1a, deep water containing
high concentrations of dissolved methane was mixed upwards towards the surface layer.
In the second method employed, methane emissions were calculated from surface water
methane concentrations using the boundary layer model of Liss & Slater (1974)(20).
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In the third method, methane that escaped from the lake was collected in floating
chambers. Since these devices drifted freely distances of 50 to 400 m over the water
surface, travelling on most days across the width of the lake upwind of the sonic
anemometer and including both shallow littoral and deeper areas, these values were
considered representative of average methane emission values for the part of the lake
covered by the eddy-covariance measurement. This interpretation includes the experience
that disturbances heavily affect floating chamber measurements and hence fixing
chambers at fixed locations would most likely have failed to provide realistic flux
measurements, although they clearly would allow for an improved spatial representation
at the expense of considerable additional systematic errors in the overall flux
measurements.
Additionally, methane emission by ebullition was measured by locating two anchored
funnels connected to gas tight cylinders (19) above various sites. Sites from the shore
(shallow) towards the centre of the lake (deep), overall between 5 and 13 m deep, lying in
the footprint area of the eddy covariance method were chosen and included bubbling and
non-bubbling areas.
In Figure 2 methane emissions revealed by the different methods are presented. As would
be anticipated based on the variety of fluxes measured (diffusion only, diffusion plus
ebullition, ebullition only), the different methods yielded different results.
Cumulative methane effluxes derived from the eddy-covariance measurements (method
1), the floating chambers (method 3), and the funnels (method 4) are comparable: 4692,
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4130, and 4558 mg m-2 (92.0, 81.0, and 89.4 mg m–2 d–1), respectively, for the period 27
October to 16 December, during which measurements were made using all four methods.
Fluxes derived using the formula of MacIntyre et al. (2010)(24) (830 mg m-2) were nearly
two times higher than those obtained using the formula of Cole & Caraco (1998) (23)
(460 mg m-2), which in turn were almost three times higher than those derived using the
formula of Crusius & Wanninkhof (2003) (22) (160 mg m-2). As our floating chamber
measurements neglected the decrease of diffusive flux due to increasing methane partial
pressure in the headspace we tried to correct for that by calculating diffusive fluxes with
a non-linear flux calculation. For this we used a set of 8 measurements which seemed to
be unaffected by ebullition (we chose a methane increase of less than 10 ppm in the
headspace as criteria) and calculated fluxes using the R (R Core Development Team,
2011)(29) library HMR (Pedersen, 2011)(30), which is based on a model by Hutchinson
& Mosier (1981)(31). This resulted in an average increase of fluxes by a factor of 1.5.
The diffusive flux we calculated with the boundary layer model represent between 3-18%
of the flux measured by eddy covariance. Therefore, assuming we underestimated the
diffusive fluxes measured with the floating chambers by a factor of 2/3 the corrected
emission of the floating chambers amount to 4200 - 4500 mg m-2 d-1.
Heat fluxes were calculated from the temperature measurement in the upper water
column (Figure S1) to reveal which process (convective turbulence or wind induced
turbulence) was the major factor during the experiment. On average heat flux was in a
normal range for lake surface water with 89 W m-2. Resulting convective turbulence had
average values of 1.8*10-8 W kg-1 (or m2 s-3 to compare with (24); values in our study
were between 1.2 and 5.8*10-8 W kg-1 at the lower end). Convective plume transport was
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therefore on average 0.006 m s-1, whereas wind driven transport was between 0.0006 m s1
for wind speeds of 0.5 m s-1 and 0.009 m s-1 for wind speeds of 7 m s-1. This clearly
shows that above 4.3 m s-1 wind speed the turbulence induced by wind was the dominant
factor, whereas below this value the convective turbulence was the relevant emission
factor. This additionally to the discussion below explains why the emission estimates
with the boundary model give relatively low values compared to the other methods, since
at the time of our experiment only during 31 hours wind speeds were higher than 4.3 m s1
compared to 1963 hours were convective turbulence prevailed.
Our results confirm that effluxes derived from the boundary layer model may strongly
underestimate methane fluxes (5-30 fold compared to methods 1, 3, and 4) when
ebullition is present and convective turbulence is dominant in the lake (19). Thus we can
conclude that the eddy-covariance method, floating chambers, and a combination of
ebullition measurements with additional diffusive flux calculation using a turbulent
boundary layer model (e.g. Crusius & Wanninkhof, 2003 or Cole & Caraco, 1998) (22,
23) or k600 values derived from eddy-covariance measurements (e.g. (24)) equally
represent lake emissions. It has to be noted that the uncertainties of the funnel and
floating chamber fluxes are most likely higher than the ones of the eddy covariance
measurements which is clearly the method of choice. This is mainly due to the fact that
eddy covariance would detect diffusive plus ebullition flux and that it could be set up to
measure continuously over a longer time span. Our results confirm that ebullition is the
most important emission pathway (3, 19). This is in agreement with a very recent report
in which it was noted that since ebullition is not generally included into aquatic emission
pathways, the emissions by inland waters are most likely underestimated (32). Hence, we
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strongly recommend either eddy covariance or a combination of ebullition and diffusive
flux measurements for estimating methane effluxes from aquatic systems. Still, a word of
caution needs to address potential problems with spatial representation of a whole lake
with the methods used here: during manual sampling the visual detection of bubbles at
the lake surface helps to detect ebullition hot spots. However, ebullition hot spots that are
active during the absence of researchers on the lake (actually most of the time) may have
been missed by our approach and may need a correction in future. Follow-up studies,
both with respect to the experimental design and the procedure how to extrapolate
existing measurements to the entire lake surface are necessary.
Looking at the eddy-covariance flux measurements (Fig. 1) it is obvious that, despite
these large effluxes during the two main turnover events, the total amount of methane
released from Rotsee was only 5.4 g CH4 m-2 during winter overturn (84 days). On the
one hand this is high, i.e., on the order of 50% of daily emissions that have recently been
reported from tropical reservoirs (33). On the other hand, this is rather low compared to
the potential emissions of 21.3 g CH4 m-2 that could be postulated simply by taking the
total amount of methane dissolved in the hypolimnion into account (i.e., neglecting new
methane production).
What is happening in the water column during these events? Figure S1 (supplement)
shows the oxygen concentration profile on 20 October, with around 11 mg O2 l–1 in the
uppermost 9 m. The lack of oxygen below this depth indicates the existence of anoxic
conditions in the hypolimnion. The oxygen measurements from 20 October to 1
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December clearly show the mixing of the water column with oxygen penetrating more
and more deeply into the hypolimnion. This is especially evident between 14 and 26
November, when we measured strong emissions with the eddy-covariance method, and
the oxycline moved from above 11 m to below 14 m depth. Figure 3 shows the methane
concentrations in the water column before and during this mixing event (20 and 24
November with concentrations in the bottom water of up to 16 mg l-1 (1 x 103 M).
Higher dissolved methane concentrations towards the sediment identify the sediments to
be the source of the methane. On 17 November, methane concentrations in the uppermost
10 m were in the 3.5 - 11.2 g l-1 (0.2 – 0.7 M) range. After a strong mixing event on 21
November, which moved the oxycline about 3 m downwards (Fig. S1 supplement),
methane concentrations in the upper 10 m were 15- to 20-fold higher (73-170 g l-1 or
4.6-10.6 M). After 3 days, however, these high concentrations in the surface waters had
already strongly decreased to 15-23 g l-1 (1-1.5 M), indicating strong oxidation of
methane in the oxic zone, which on 24 November already included the top 14 m of the
lake. Measured methane oxidation rates (Fig. S4) and isotope analysis (Fig. 3) give strong
support for methane oxidation to be largely responsible for this strong decrease and that
diffusion is of less importance (a diffusion-driven profile would show decreasing values
towards the surface). This is a clear indication that oxidation in the mixed layer of the
lake is the primary removal process for methane, and only a small fraction is emitted.
Isotope analysis yields further strong support for this interpretation (Fig. 3). The isotopic
composition of the carbon in the dissolved methane during the mixing event on 21
November (13C = -66 ‰ on average) differed drastically from that of the methane in the
oxic zone before the event ( 13C = -30 ‰ on average), indicating strongly that this
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methane originates from the hypolimnion. However, by 24 November, 3 days after the
mixing event, the 13C of the methane had not just returned to the value it had had on 17
November, but had even become heavier by ~15 ‰. The drastic change from a  13C
value of -66‰ to -15 ‰ (fractionation factor =0.948) clearly indicates that methane was
oxidized by methanotrophs (microorganisms that consume methane), since
microbiologically mediated oxidation of methane is accompanied by a strong isotopic
fractionation, leaving heavier methane behind (11, 34). We measured average methane
oxidation rates of 26 ± 43 M CH4 l-1 d-1 in the anoxic zone/chemocline and 1.0 ± 2.3
M CH4 l-1 d-1 in the oxic zone (Fig. S3 supplement). A peak value of 12 M CH4 l-1 d-1
was measured in the oxic zone during the second strong mixing event on 23 November.
This provides evidence for an existing methanotrophic community (mainly
Methylomonas and Methylobacter (11) in the oxic zone capable of oxidizing the methane
which occurs in very high concentrations in the epilimnion during lake mixing. That
methanotrophic communities react very quickly to increased methane concentration has
recently been shown in the Gulf of Mexico oil spill (35). The generally higher oxidation
rates with a peak value in the anoxic zone/chemocline of 225 M CH4 l-1 d-1 provide
clear evidence that anaerobic oxidation is important. However, the microorganisms that
mediate the anaerobic oxidation of methane in lakes are still unknown.
In Figure 4 cumulative values for both methane oxidation (7 g m-2 in the oxic water layer
and 33 g m-2 in the whole water column) and methane effluxes (5.4 g m-2) are shown. It
becomes obvious that rates of methane oxidation exceed rates of methane effluxes by a
factor of about 6 owing to the very effective microbiological barrier that hinders larger
methane effluxes to the atmosphere. Hence, true methane emissions are only 25% of
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what could potentially be released if all stored methane in the hypolimnion was to be
emitted without microbial oxidation (i.e., 2.5 t of 10 t CH4 are emitted during late season
turnover). These values are in good agreement with other investigations; e.g. from a
Japanese lake, where 74% of the methane was oxidized (27), and from a Finnish lake,
where 83-88% of the methane was oxidized (36).
With 75% oxidation occurring in the water column and a greenhouse gas forcing factor of
25 CO2-equivalents per kg of CH4 (IPCC 2007) (2), this means that each mass unit of
methane produced in the hypolimnion represents only 6.25 CO2-equivalents at the lake
surface; i.e., only 25% of the greenhouse gas forcing that would be erroneously assumed
from the gas production in sediment cores if oxidation in the lake was neglected. Hence,
our findings have strong implications for the recently proposed role that lakes, and to a
smaller extent reservoirs, might play not only in the global methane budget (37) (3) but
also in the continental carbon sink estimations (32). Although all lakes (n=4) and
reservoirs (n=11) Diem et al. (2008) investigated in Switzerland are methane emitters
(38), they were able to show that the amount of methane released over the year by these
lakes, including that released during late season turnover, is much smaller than it would
be if all the methane stored in the hypolimnion were to be released to the atmosphere
without microbial interaction. This reduces the importance of storage in the emission
estimates (e.g. Michmerhuizen et al. (1996)(39) only assumed an oxidation of 1-7% of
the storage methane) and counteracts the possibly higher number of small lakes as
pointed out recently (4) . In return, it implies that the huge anthropogenic methane
emissions from rice fields, cattle, and energy production are even more important as
greenhouse gas sources, and are worth reducing in the future if we wish to limit the
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increase in global surface air temperature due to greenhouse gas production to 2°C.
Finally, we showed that methane emission can yield similar results, when quantified by
different methods and over different time frames. Whether this conclusion is valid across
different lakes and different times of the year, or if different methods are more suitable
for certain conditions needs to be further investigated. Nevertheless, it will be interesting
to see in how much future emission estimates using more sophisticated measurements
including eddy covariance will increase existing greenhouse gas emission estimates from
continental aquatic systems.
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Acknowledgements
We would like to thank Beat Müller, Michael Schurter, and Oliver Scheidegger for their
help during field work and BM for providing the bathymetry data. We also thank Johny
Wüest for his support in calculating heat fluxes as well as convective and wind induced
turbulence. This project was supported by an ETH Scientific Equipment grant, the
Competence Centre for Environment and Sustainability (CCES), and funds from the
Swiss Federal Institute of Aquatic Science and Technology (Eawag).
Supporting Information Available. This information is available free of charge via the
Internet at http://pubs.acs.org/.
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Figures
Figure 1
Cumulative methane flux (a) and methane flux (b) during lake turnover (14 October 2008
to 6 January 2009) estimated from eddy covariance measurements. The thick line in panel
(b) shows 30-minute flux averages, whereas the bold line shows the 2.5 hour low-pass
filtered values. Two major emission events can be noted between 27 October and 31
October and between 20 and 24 November. Cumulative methane fluxes amount to 5.4 g
m-2. DOY (day of the year).
Figure 2
Methane emissions measured during lake turnover (14 October 2008 to 6 January 2009)
by different methods including the eddy covariance method, the boundary layer model
(Liss & Slater 1974) (20) using transfer velocity k600 calculated with the bi-linear
relationship given by Crusius & Wanninkhof (2003)(22),the relationship given by Cole &
Caraco (1998) (23), the relationship by Mac Intyre et al. (2010) (24) chambers floating
freely over the lake surface, and funnels collecting methane bubbles. Several chamber
and funnel measurements over one day were averaged to give a daily mean. Those mean
values were used to interpolate CH4 flux values until the next sampling date to obtain a
continuous curve. Orange and violet squares indicate when floating chamber and funnel
samples were taken, respectively. Whereas methane emissions estimated with the eddy
covariance, the funnels, and floating chambers agree very well,values calculated with the
boundary layer model are much lower.
Figure 3
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Methane concentrations (a) and methane carbon isotopes (b) measured on three different
days during a major emission event between 17 November (DOY 322) and 24 November
(DOY 329). On 17 November methane concentrations are low in the epilimnion, an
increase to high values on a mixing event on 21 November (DOY 326) can be noted,
however, three days later methane concentrations were already much lower again. The
carbon isotope values indicate strongly oxidized methane on 17 and 24 November and
original source methane (not oxidized) from the hypolimnion on 21 November during the
mixing event.
Figure 4
Cumulative methane efflux to the atmosphere and oxidation in the oxic and anoxic water
column of Rotsee. Methane oxidation rates were assumed to be the same from one
sampling date to the other (altogether 20 days, see Table S1). It can be noted that the
combined aerobic and anaerobic oxidation of methane in the water column clearly
exceeds methane emission to the atmosphere by a factor of 7 to 8.
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(36) Kankaala, P.; Eller, G.; Jones, R. I., Could bacterivorous zooplankton affect lake
pelagic methanotrophic activity? Fundamental and Applied Limnology 2007, 169, (3),
203-209.
(37) St Louis, V. L.; Kelly, C. A.; Duchemin, E.; Rudd, J. W. M.; Rosenberg, D. M.,
Reservoir surfaces as sources of greenhouse gases to the atmosphere: A global estimate.
Bioscience 2000, 50, (9), 766-775.
(38) Diem, T.; Koch, S.; Schwarzenbach, S.; Wehrli, B.; Schubert, C. J., Greenhouse
gas emissions (CO2, CH4 and N2O) from perialpine and alpine hydropower reservoirs.
Biogeosciences Discussions 2008, 5, 3699-3736.
(39) Michmerhuizen, C. M.; Striegl, R. G.; McDonald, M. E., Potential methane
emission from north-temperate lakes following ice melt. Limnology and Oceanography
1996, 41, (5), 985-991.
ACS Paragon Plus Environment
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Page 27 of 31
Fig. 1
th
6 Oct
26 Oct
th
15 Nov
th
5 Dec
th
25 Dec
300
320
340
360
th
th
14 Jan
-2
Cumul. CH4 flux (g m )
6
5
4
3
2
1
0
15
-2 -1
CH4 flux (µg m s )
1
2
3
4
5
6
7
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43
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45
Environmental Science & Technology
10
5
0
-5
-10
280
DOY
ACS Paragon Plus Environment
380
Fig. 2
6000
CH4 efflux (mg m-2)
1
2
3
4
5
6
7
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9
10
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Environmental Science & Technology
4000
Page 28 of 31
C&C
C&W
MacIntyre
Floating ch.
Funnels
Eddy Cov.
2000
0
280
300
320
340
DOY
ACS Paragon Plus Environment
360
380
Page 29 of 31
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8
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1000
10000
100000
%&'()*&! *87=! =
!
0
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2
4
6
Depth (m)
1
2
3
4
5
6
7
8
9
10
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Environmental Science & Technology
8
10
12
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-80
-70
-60
-50
-40
-30
-20
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0
"#
δ $%&'()*&! +,! -./! 01234
ACS Paragon Plus Environment
Environmental Science & Technology
Fig. 4
40000
-2
Sum of methane emission/oxidation (mg m )
1
2
3
4
5
6
7
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9
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Oxidation (oxic)
Emission (EC)
Oxidation (anoxic)
30000
20000
10000
0
280
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DOY
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Environmental Science & Technology
MAIOLICA (CCES project) CH4 efflux (mg m-2)
1
2
Methane in aquatic systems 3
4
5
6
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9
10
Eddy Correlation (EC) sensor
11
12
13
14
15
Los Gatos
16
17
CH4 sensor
18
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Fig. 2
23
24
6000
25
26
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4000
30
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CH4 sensor box
32 CH4 sensor
33
34 pump
High-resolution direct2000
35
absorption spectroscopy
36
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38
0
39
ACS Paragon Plus Environment
40
Werner
Eugster
280
41
ETH
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of
Grassland
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42
Met Station
C&C
C&W
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Eddy Cov.
300
320
340
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360
380