Introduction
Freshwaters (rivers, streams, reservoirs and lakes) are found to be a net
source of carbon to the atmosphere due to supersaturation of
especially carbon dioxide (CO2) but also methane (CH4). Global
estimates of the contribution of lakes to the carbon cycle are highly
variable and uncertain
(; ; ; ), but they are significant
compared to the terrestrial sources and sinks.
Global estimates are usually based on the boundary layer method (BLM, also known
as boundary layer model) that uses wind speed (via gas transfer velocity k)
and concentration gradient between the air and surface water as the only
factors driving the gas exchange . According to recent
studies, this upscaling approach strongly underestimates current emissions
from lakes and improved methods are needed (e.g.
). and
suggest k models based also on heat flux and water
turbulence measurements for more accurate estimates.
A widely used direct flux measurement technique is the floating chamber (FC)
method, where the vertical flux at the air–water interface is calculated from
the concentration increase within the chamber during the measurement period
. This method has a small source area and is
representative of the measurement point only. On the other hand, it can be
used to quantify the spatial variability of the gas emissions
. FC method is laborious, but inexpensive, and does
not need extensive data post-processing. However, similar to BLM, it requires
automatic data loggers or access to a gas analyser, such as a gas
chromatograph, in the case of manual sampling. FC measurements also disturb
the air–water interface and might affect the gas exchange by creating
artificial turbulence, especially with anchored chambers in running waters
. However, these effects are minor for drifting chambers
following the water . FC measurements on standing water can
also correspond well with non-invasive methods for certain chamber types and
deployment methods .
Recently, also direct eddy covariance (EC) flux measurements have grown their
popularity in lake studies, but there are still only a few sites with long data
sets (e.g. ; ). Instead of measuring
just a specific point of the lake, the EC method provides flux estimates over
a much larger source area, also known as footprint ,
and as opposed to chamber measurements, it does not disturb the air–water
interface. EC measurements are, however, quite expensive and require
extensive data post-processing.
In this study, we compared these three flux measurement methods, including
three different gas transfer velocities for BLM approach, over a boreal lake
in southern Finland for both CH4 and CO2 during an intensive field
campaign from 11 to 26 September 2014. We also studied spatial variation of
CH4 and CO2 fluxes over the EC footprint area with manual floating
chambers, while simultaneously estimating fluxes with the EC method and BLM. Our
aim is to compare the three methods and make recommendations for future
measurements based on our results. Because current upscaling estimates are
based on these methods, comparison is needed to reduce the uncertainties in
current estimates of the role of freshwaters in global carbon cycle. Such a
comparison also gives valuable information on measurement technique
development needs, and so far there is only one comparative study including
all three methods for CH4 in a temperate lake . This
is, to our knowledge, the first study including the three measurement methods
for both CH4 and CO2 in a boreal lake, even though the boreal zone
harbours a large fraction of the global lakes .
Materials and methods
Site description and measurements
The study site was the humic, oblong Lake Kuivajärvi situated in southern
Finland (61∘50′ N, 24∘17′ E), in the middle of a managed mixed
coniferous forest, close to the SMEAR II station (Station for Measuring
Ecosystem Atmosphere Relations; ). The lake has a maximum
depth of 13.2 m, mean depth of 6.3 m, length of 2.6 km and surface area of
0.62 km2 (Fig. a). Due to the oblong shape, the wind
usually blows along the longest fetch . Lake
Kuivajärvi has two separate basins and a measurement raft is mounted on the
south basin, near the deepest part of the lake. Lake Kuivajärvi has median
light extinction coefficient Kd=0.59 m-1 as estimated in
. The low water clarity is mainly due to high dissolved
organic carbon (DOC) concentration in the lake. Lake Kuivajärvi is a
dimictic lake that mixes thoroughly right after ice-out usually in the
beginning of May, stratifies for summertime and then mixes again at the latest in
October, until it freezes and stratifies again underneath the ice cover for
5–6 months . These spring and autumn mixing periods
usually bring high amounts of CH4 and CO2 from the hypolimnion and
bottom sediments of the lake to the atmosphere .
Continuous measurements of carbon exchange between water and air started
in 2010 and the lake belongs to the ICOS (Integrated Carbon Observation
System) network. Flux measurement apparatus with the EC system on the raft
consists of an ultrasonic anemometer (USA-1, Metek GmbH, Elmshorn, Germany),
a closed-path infrared gas analyser (LI-7200, LI-COR Inc., Nebraska, USA) for
measuring CO2 and water vapour (H2O) mixing ratios and a closed-path
gas analyser (Picarro G1301-f, Picarro Inc., California, USA) for measuring
CH4 and H2O mixing ratios. EC measurement height was 1.8 m above the
lake surface. Measurement frequency was 10 Hz and a 30 min averaging period
was used in this study. CO2 measurements with LI-7200 were stopped on 25 September. Air temperature and relative humidity were measured using
a Rotronic MP102H/HC2-S3 (Rotronic Instrument Corp., NY), while radiation
components were measured with a CNR1 net radiometer (Kipp & Zonen, Delft,
Netherlands). These data were collected every 5 s and averaged over 30 min.
Water temperature at depths 0.2, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 5.0, 6.0,
7.0, 8.0, 10.0 and 12.0 m was measured with a chain of Pt100 temperature
sensors. Water column CO2 concentration was measured at depths 0.2, 1.5,
2.5 and 7.0 m using semipermeable silicone tubing in the water and
circulating air in a closed loop continuously to the analyser
(CARBOCAP® GMP343, Vaisala Oyj, Vantaa, Finland). The
measurement system is explained in detail in ,
and . Water column temperature
and CO2 data were collected at the raft every 5 s and averaged over 30 min
periods.
(a) Bathymetry of Lake Kuivajärvi and (b) floating chamber
measurement spots (white squares) around the EC measurement raft (white
star).
Another gas analyser (Ultraportable Greenhouse Gas Analyzer, Los Gatos Inc.,
USA) was used for measuring CH4 and CO2 concentrations in the air at
1 m height and in the water at depths 0.2 and 11 m. The analyser was connected
step-wise to three different intakes – one in air and two in water – and a dryer,
consisting of a container filled with silica gel. For all levels, air was
circulated in closed loop between the gas analyser and the different intakes.
The internal pump of the gas analyser was used for this circulation of air at
a rate of 1.2 L min-1. The air intake consisted of a ca. 10 cm long
diffusive membrane (Accurel S6/2, PP, AKZO NOBEL) that was placed under a
protective rain cover. The water intakes at each level consisted of a 4.1 m long, 8 mm diameter silicon tube that was bundled and attached to a metal
disc ca. 25 cm in diameter, to give a well-defined measurement depth. The
dryer was added to the system to remove excess moisture that could have
entered into the tubing system by condensation. The air intake was located 1
m above the lake surface and the water intakes were located at 0.2 and 11 m depths. A full measurement cycle was completed over 2 h. The air
intake was connected to the gas analyser for 10 min, while the water intakes
were connected for 45 min each, but data were averaged only during the
last 5 min of each connection period in order to allow equilibration to
the new concentration after a change of intake. After each measurement cycle
for the water intakes, the air was circulated through the dryer. The gas
analyser was checked against a standard after the measurement campaign and
found to be accurate within the specifications of the standard.
Manual floating chamber measurements of CH4 and CO2 fluxes were done
with two replicate chambers at eight different spots (Fig. b)
in the EC footprint area 2–3 times a day (morning, afternoon and night/early
morning) during the period 11–22 September. Unfortunately, multiple daily
measurements were only possible in the first 11 days of the campaign and only
a few measurements were done during 22–26 September due to high wind and hard
weather conditions towards the end. Measurement lines were perpendicular to
the shoreline. The line north of the raft was chosen when the wind was
blowing from north, and south line was chosen during southerly winds.
Measurement spots N2/S2 and N3/S3 were about 10 m deep, and points N1/S1 and
N4/S4 were about 3 m deep. They were chosen so that the distance to the
measurement raft was about 50 m and the points were marked with buoys.
Chambers used in this study were polyethylene/plexiglas plastic buckets
equipped with styrofoam floats and sampling outlets .
Chambers reached approximately 3 cm into the water and their height above
water was about 9.6 cm. The closing time for the chambers was 20 min and
sampling interval 5 min. Air samples were taken with syringes and injected
into 12 mL Labco Exetainer® vials (Labco Ltd., Lampeter,
Ceredigion, UK) and analysed with gas chromatograph (GC). The GC system
consisted of a Gilson GX-271 liquid handler (Gilson Inc., Middleton, USA), a
1 mL Valco 10-port valve (VICI Valco Instrument Co. Inc., Houston, USA) and
an Agilent 7890A GC system (Agilent Technologies, Santa Clara, USA) equipped
with a flame-ionization detector (temperature 210 ∘C).
In addition to automatic water concentration measurements, we took manual
water samples for comparison. Two replicate water samples were taken into
60 mL plastic syringes. After sampling, 30 mL of water was pushed out and
replaced by 30 mL of N2 gas. The syringes were placed in a water bath at
20 ∘C temperature for 30 min. Then the samples were equilibrated by
shaking the syringes vigorously for 3 min. The samples of the syringe
headspace gas were injected into 12 mL Labco Exetainer® vials
(Labco Ltd., Lampeter, Ceredigion, UK) and analysed with the same GC as
manual air samples. Final gas concentrations in the water were calculated
using Henry's law. Henry's law solubility constants at 298.15 K were
1.4×10-3 mol dm-3 bar-1 for CH4
and 3.4×10-2 mol dm-3 bar-1 for CO2
.
Data processing and quality criteria
Eddy covariance data
EC data were processed using EddyUH software according
to the approaches in . Briefly, spikes in the data were
removed on the basis of a maximum difference being allowed between two
adjacent points, and 2-D coordinate rotation was done so that the wind
component u is directed parallel to the mean horizontal wind. Linear
detrending was used for calculating the turbulent fluctuations. Lag time was
determined from the maximum of the cross-covariance function and cross-wind
correction was applied to sonic temperature data . High-frequency spectral corrections were calculated according to
.
Data quality was ensured with tests for flux stationarity (FST≤1 was
approved) and limits for kurtosis (1<Ku<8) and skewness (-2<Sk<2)
. Wind directions other than along the lake were ignored
to ensure that only fluxes from the lake were included. Accepted wind
directions were 130∘<WD<180∘ and 320∘<WD<350∘.
For gas fluxes, a criterion for standard deviation of the mixing ratios
was also used. During night-time, the standard deviation often increased,
indicating that there was advection of CH4 and CO2 from the forest
uphill to the lake causing scatter in the flux measurements. This scatter was
found to be small when the standard deviation of CO2 was less than 3 ppm
and thus CO2 mixing ratio (and flux) data with standard deviation larger
than 3 ppm were removed. The same procedure was also done for CH4, with
the threshold value for standard deviation being 0.003 ppm. After all data
quality criteria, the data coverage was 27 and 32 % of the original data
for CO2 and CH4 fluxes, and 83 and 80 % for latent and sensible heat
fluxes, respectively. The EC flux detection limit was determined as
3σ, where σ is the total random uncertainty estimated according
to . This estimate for the detection limit takes into
account both instrumental noise and one-point sampling random error
. On average, detection limit of 30 min averaged CH4
flux was 0.81 nmol m-2 s-1 and CO2 flux 0.84 µmol m-2 s-1. Average detection limits scaled for the daily median fluxes were
0.12 nmol m-2 s-1 and 0.12 µmol m-2 s-1 for CH4
and CO2, respectively. The average source area of the EC system reaches
100–300 m from the measurement raft, depending on the stability conditions
.
Heat fluxes measured with the EC system were gap-filled using a bulk model
depending on water–air temperature difference multiplied by wind speed and
vapour pressure difference multiplied by wind speed for sensible and latent
heat fluxes, respectively. The coefficients for these relationships were
found from a linear fit between measured EC fluxes and the parameters,
similar to .
Chamber flux calculations
The gas concentration increase inside the chambers was linear over a short
closure time (20 min) combined with low flux levels. Flux calculation was
conducted according to :
F=dχdtpaVRTA,
where dχdt is the slope of the linear fit to
concentration increase inside the chamber during the closure time (µL L-1 s-1), pa ambient pressure (Pa), V chamber volume (m3),
A the area of the surface that the chamber covers (m2), R universal
gas constant (J mol-1 K-1), and T ambient temperature (K).
Measurements were accepted when there were no leakages during the chamber
closure. If measurements from both replicate chambers (located within 1 m distance from each other) were successful, then an average flux from these
two chambers was used.
Boundary layer method
Diffusive gas exchange F between the air and water was determined according
to the boundary layer model
F=k(caq-ceq),
where k is the gas transfer velocity (m s-1), caq the gas
concentration (mol m-3) in surface water and ceq the concentration
(mol m-3) that the surface water would have if it was in equilibrium
with the above air . Equilibrium gas concentrations were
calculated from measurements of mixing ratio χc and air pressure pa
and corrected with Henry's constant kH according to the solubility of the
gas in the water:
ceq=χcpakH.
For this study, gas transfer velocity was calculated according to
, and . Gas
concentrations for flux calculations were measured automatically at the
measurement raft. Wind speed, sensible and latent heat fluxes, and air
friction velocity were measured with the EC system.
Gas transfer velocity
The most simple and the most often used model for gas transfer velocity k is the one proposed by
:
kCC=(2.07+0.215U101.7)Sc600-0.5,
where U10 represents the wind speed at 10 m height (in m s-1,
approximated by U10=1.22U, where U is the measured wind speed at 1.5 m height) and Sc is the Schmidt number calculated for local conditions. This
model considers wind as the only factor causing water turbulence and driving
the gas exchange.
A model by , on the other hand, suggests the importance of
the buoyancy flux β driven turbulence during cooling periods, so that
the turbulent dissipation rate εTE becomes
εTE=c1u*w3κz+c2|β|if β<0,c3u*w3κzif β≥0,
where c1=0.56, c2=0.77 and c3=0.6 are dimensionless constants,
u*w is the friction velocity in the water, κ=0.41 is the von
Kármán constant and depth z is here used as constant 0.15 m (; ). Friction velocity in the water
u*w was calculated from direct EC measurements of air friction velocity
u*a, so that
u*w=u*aρaρw,
where ρa is the air density and ρw water density. Buoyancy flux β was calculated according to :
β=gαtHeffρwCp,
where g is the gravitational acceleration, αt coefficient of
thermal expansion of water, Heff the effective heat flux (i.e. latent
and sensible heat fluxes and portion of shortwave radiation that is not
trapped to the mixing layer are subtracted from the net radiation), and Cp
the specific heat of water. Buoyancy flux is positive when the effective heat
flux is positive and the lake is heating, whereas negative buoyancy and
effective heat fluxes indicate cooling of the lake. Gas transfer velocity k
can then be calculated according to the surface renewal model
kTE=c4(εTEν)1/4Sc-1/2,
where c4=0.5 is a dimensionless constant and ν kinematic viscosity of water (m2 s-1).
Another k model that takes heat flux into account as a factor creating
turbulence was developed by :
kHE=(C1U)2+(C2w*)2Sc-12,
Here C1=0.00015 and C2=0.07 are dimensionless constants defined for
Lake Kuivajärvi , w* is the convective velocity,
defined as
w*=-βzAML3,
and zAML is the depth of the actively mixing layer (m), where
temperature varies within 0.25 ∘C of the surface water temperature.
This model was developed in Lake Kuivajärvi for CO2 fluxes but had not
been tested for CH4 before this study.
Half-hour averages of (a) measured air temperature (black) and lake
surface water temperature (red), (b) sensible (black) and latent (red) heat
fluxes measured with the EC system and gap-filled using a bulk formula (see
Sect. and , for details), (c) wind speed,
(d) wind direction, (e) daily rainfall, (f) incoming shortwave radiation and
(g) effective heat flux measured at the measurement raft. Time ticks
represent midnight and the vertical black line the start of the lake mixing
period.
Half-hour averages of (a) temperature, (b) CH4 concentration and
(c) CO2 concentration in the water column at different depths. The red
line is the equilibrium concentration of CH4 and CO2 at the surface in
subplots (b) and (c), respectively. The orange triangles are manual headspace
samples taken from the surface water at chamber measurement locations. Time
ticks represent midnight and the vertical black line the start of the lake
mixing period. Note that CH4 concentration at 11 m depth (blue line) is
read from the right y axis.
All three k models are hereafter referred to as they are presented in the formulas.
Results and discussion
The results of the measurement campaign are divided into two sub-periods (11
days of stratified period 11–21 September and 5 days of lake mixing period
22–26 September 2014) according to lake stratification and environmental
conditions during the campaign, since gas transfer processes differ between
these two periods. The water column started its autumn turnover on 22
September, but the mixing did not yet reach the lake bottom. Measurements of
CH4 and CO2 fluxes with BLM, EC and the more sporadic FC method are
first compared by examining daily median as well as daytime and night-time
fluxes. Spatial variation is then studied by checking median FC fluxes in
different measurement points against simultaneous EC fluxes.
Environmental conditions and water column temperature
Weather at the beginning of the measurement campaign in September 2014 was
warm with a maximum air temperature of 18 ∘C (Fig. ).
Sensible and latent heat fluxes were low, less than 100 W m-2 and winds
were weak, around 2 m s-1, and mostly from south. Air temperature
exceeded surface water temperature during the afternoons causing negative
sensible heat fluxes. Night-time air temperatures were more than 10 ∘C
colder than during daytime. The lake was clearly stratified with bottom
temperature around 9 ∘C, and surface water temperature about 16 ∘C (Fig. a). On 14 September, the mixing layer of
the lake deepened from 5 m to around 6–7 m due to night-time cooling. Warm
daytime air temperature then caused the surface water to stratify again.
Similar occasions of night-time cooling were experienced on 16 and 17
September. The sun rose at 05:45 and set at 18:45 during the stratified
period.
On 22 September, a cold front turned winds north bringing cold air and rain
(11 mm on 22 September). Air temperature dropped to even 0 ∘C on 24
September and wind speeds as high as 8 m s-1 were measured at the lake.
A drop in the air temperature caused a large temperature difference between
air and lake surface water that together with high wind speed caused high,
even 200 W m-2, positive (upward) sensible and latent heat fluxes on 22
and 23 September and a large negative (-400 W m-2) effective heat flux,
resulting in a negative buoyancy flux during this cooling period. Cooling
also caused the starting of the autumn mixing of Lake Kuivajärvi and the
thermocline reached a depth of 8 m on 22 September. Mixing reached 11 m depth in the end of the measurement campaign on 25 September but did not yet
mix the bottom waters. During the mixing period sunrise was at 06:15 and
sunset at 18:15.
Water column gas concentration profiles
CH4 concentration profile
During the stratified period CH4 concentration according to the automatic
measurements at the surface was small, only around 0.02 mmol m-3 (Fig. b). Manual measurements, on the other hand, show surface
water concentrations of 0.07 mmol m-3 on average during the stratified
period. Manual CH4 concentration measurements were always higher than
automatic measurements, which might be caused by insufficient equilibration
time for CH4 in the automatic measurement system or by spatial variation
only caught by manual measurements. At 11 m depth CH4 concentration was
almost 10 times higher than at the surface. Diel variation of CH4
concentration at 11 m could be caused by lake-side cooling and convection
or,
more likely, by internal waves , triggering the lake-bottom CH4-rich sediments.
On 22 September, thermocline tilting due to high wind speed caused a rapid
increase in 11 m CH4 concentration and the concentration reached its
maximum of 9.6 mmol m-3 on 24 September. CH4 accumulation near the
bottom usually happens in the anoxic conditions in late autumn
. CH4 concentration at 11 m depth was still three
times lower than the maximum concentration found in in
late September and 2 times lower than found at 12 m depth in
in September. A clear increase in CH4 surface water
concentration is seen on 23 September due to upwelling and concentration up
to 0.19 mmol m-3 was measured with the automatic system on 24 September.
Manual measurements show concentrations up to 0.47 mmol m-3 on 25
September.
Median of all CH4 fluxes and average daytime and night-time
CH4 fluxes during lake stratification and mixing periods using different
measurement methods. Results of Mann–Whitney U test comparing differences
between daytime and night-time fluxes are given in U test column. Note that
FC fluxes are averaged also over different measurement spots. Mixing period
did not include enough FC measurements for this analysis. Uncertainties are
given as 25th and 75th percentiles for median fluxes and as standard errors
for the flux averages.
Stratified period
CH4 flux (nmol m-2 s-1)
All
Day
Night
U test
BLM kHE
0.21-0.06+0.12
0.177 (±0.005)
0.431 (±0.008)
h=1, p=0.0004
BLM kTE
0.26-0.13+0.16
0.370 (±0.011)
0.439 (±0.007)
h=0
BLM kCC
0.12-0.04+0.05
0.128 (±0.003)
0.186 (±0.004)
h=1, p=0.02
EC
0.51-0.34+0.34
0.41 (±0.04)
0.34 (±0.04)
h=0
FC
1.77-0.78+0.82
2.4 (±0.3)
1.1 (±0.2)
h=1, p=0.002
Mixing period
CH4 flux (nmol m-2 s-1)
All
Day
Night
U test
BLM kHE
4.34-3.35+9.81
7.1 (±0.6)
6.6 (± 0.5)
h=0
BLM kTE
4.73-3.15+9.41
7.7 (±0.6)
7.1 (± 0.5)
h=0
BLM kCC
1.65-1.04+5.50
3.7 (±0.3)
2.8 (± 0.2)
h=0
EC
4.80-2.28+3.34
5.9 (±0.3)
5.0 (±0.4)
h=1, p=0.02
Daily median CH4 flux from BLM, EC and FC methods. The black
whiskers indicate the 25th and 75th percentiles, respectively. The vertical
black line represents the start of the lake mixing period. Fluxes during (a)
the stratified period (11–21 September) are read from the left and (b)
mixing period fluxes (22–26 September) from the right y axis.
CO2 concentration profile
CO2 concentration at the surface was 47 mmol m-3 on average as
measured with the automatic system during the stratified period, while manual
measurements show CO2 concentration of 110 mmol m-3 at the water
surface on average, similar to (Fig. c). On 14 September, surface layer mixing reached 7 m depth
and brought CO2-rich water from deeper waters to the surface causing a
drop in CO2 concentration at 7 m depth and manual samples show a rapid
increase in the surface water concentration. Similar occasions on 16 and 17
September induced further decrease in CO2 concentration at 7 m depth and
also
an increase in the surface water CO2 concentration. After 16
September, the automatic and manual CO2 concentration measurements agree
better with each other, as the average difference between the measured
concentrations decreases from 114 to 16 mmol m-3. CO2 is more soluble
in water than CH4 and thus equilibration time of 40 min should be enough
for automatic CO2 measurements, and two different automatic systems
compared well with each other on CO2 concentration at the surface (results
not shown). We thereby conclude the difference between automatic and manual
CO2 concentration measurements to be caused by spatial variation rather
than the measurement system. We point out, however, that choosing the
measurement method as well as the measurement spot has an effect on the
observed concentrations and thus fluxes calculated with the BLM, as a
larger concentration difference between the water surface and air would
result in a larger flux in general (Eq. ). CO2
concentration at 11 m depth was 10 times higher than at the surface and
comparable to those measured in at 12 m depth. Diel
variation observed in CO2 concentration at 11 m could be caused by either
lake-side cooling and convection or by internal waves .
Decreasing CO2 concentration from 390 to 63 mmol m-3 at 11 m depth
observed on 23–24 September was probably due to upwelling. However, this
amount of upwelling was not enough to cause a notable increase in the
surface water CO2 concentration since CO2 concentration difference
between the bottom and the surface is not as drastic as that of CH4, and
the gas gets diluted in a large water volume on its way to the surface.
CH4 flux comparison
CH4 fluxes during the stratified period were small (less than 2 nmol m-2 s-1), estimated both with EC and BLM (Fig. ). The
EC fluxes during the stratified period were close to the detection limit
(approximately 0.12 nmol m-2 s-1 for daily median flux) and are
thus partly uncertain. FC fluxes were highest, reaching a maximum daily
median flux of 4 nmol m-2 s-1 on 12 September. The median of all FC
CH4 flux measurements during the stratified period still remained at
1.77-0.78+0.82 nmol m-2 s-1 (where the lower and upper
limits represent the 25th and 75th percentiles, respectively, Table ). Median CH4 flux according to all three
methods during the stratified period was considerably lower than 4 nmol m-2 s-1 reported in , who used BLM with k
calculated from FC measurements, for Lake Kuivajärvi in autumn 2011 and
2012.
Linear fit y=ax+b parameters for comparison between EC and BLM
fluxes according to different models for k, and between EC and FC, when EC
flux estimates were on the x axis. Uncertainties are given by the standard
errors of the parameters. The last column gives the results of Mann–Whitney
U test for each method compared with EC. The comparison was made using daily
median fluxes.
Method
a
b
r2
RMSE
U test
(nmol m-2 s-1)
(nmol m-2 s-1)
CH4
BLM kHE
0.9 ± 0.2
-0.3 ± 0.8
0.50
2.62
h=1, p=8×10-5
BLM kTE
1.0 ± 0.2
-0.3 ± 0.8
0.53
2.58
h=1, p=0.0007
BLM kCC
0.5 ± 0.1
-0.2 ± 0.4
0.48
1.38
h=1, p=1×10-8
FC
2.0 ± 0.5
1.1 ± 0.5
0.62
1.35
h=1, p=3×10-8
CO2
BLM kHE
0.6 ± 0.3
0.3 ± 0.2
0.27
0.58
h=1, p=0.02
BLM kTE
0.6 ± 0.3
0.4 ± 0.2
0.26
0.59
h=1, p=6×10-5
BLM kCC
0.3 ± 0.1
0.2 ± 0.1
0.20
0.30
h=1, p=0.01
FC
0.2 ± 0.2
0.50 ± 0.12
0.13
0.32
h=1, p=0.002
During the stratified period, EC and BLM with kTE model show no
statistical difference between daytime and night-time fluxes, whereas BLM
fluxes measured with kHE and kCC are slightly higher during
night-time than daytime (Table ). As the CH4
concentration difference (Δ[CH4]) between the surface water and air
is lower in night-time than daytime, higher night-time fluxes are caused by
gas transport coefficients kHE and kCC giving highest values
at
night-time (Fig. ). The differences between daytime and
night-time fluxes still remain lower than 0.3 nmol m-2 s-1. FC
fluxes, however, are higher during daytime, when the concentration
difference also has its maximum value.
After the mixing started on 22 September, daily median CH4 fluxes
increased rapidly from 1.5 to even 15 nmol m-2 s-1 in one day due to
effective mixing and gas transport from deeper waters to the surface. This
increase is clearly visible in both EC and BLM fluxes, although BLM flux
calculated with kCC remains lower than other BLM fluxes and is closest
to EC median flux on 23 September. The flux peak in the beginning of the
mixing period was over 2-fold higher compared to the 6 nmol m-2 s-1 reported in
, probably due to rougher weather conditions during our
field campaign. , on the other hand, report high CH4
emissions (6 nmol m-2 s-1) after heavy rain events. Rain on 22
September could have also played a role here, enhancing the lateral transport
from the catchment to the lake (; ).
However, in comparison to the situation described by Ojala et al. (2011), the
rain episode in Lake Kuivajärvi was very short in duration.
During the mixing period, EC measurements show a diurnal pattern in CH4
flux with higher daytime than night-time fluxes, as was found in
, and . BLM
measurements do not show a statistical difference between daytime and
night-time (Table ). Higher daytime fluxes are
expected due to higher wind speed and enhanced shear during the afternoon
as well as upwelling of CH4 from deeper layer (Fig. d). We find a lower concentration
difference, Δ[CH4], during night-time. This may be caused by higher oxidation rate in
dark, which lowers CH4 concentration in the water, and thus also the concentration difference . During daytime solar radiation, the oxidation rate would then
be lower, resulting in an increase in water CH4 concentration towards the
afternoon. Another possible explanation for larger concentration difference
Δ[CH4] in the afternoon, in addition to CH4 feeding from the
deeper waters and lower oxidation rate, is enhanced resuspension from the
sediments in the littoral zone during periods of high wind speed
. EC and BLM fluxes by kHE and kTE are also
similar in magnitude (5.9 ± 0.3, 7.1 ± 0.6 and 7.7 ± 0.6 nmol m-2 s-1 daytime averages, respectively), whereas kCC gives
clearly lower fluxes (3.7 ± 0.3 nmol m-2 s-1 daytime average,
Table ). ,
and also report highest daytime
fluxes for CH4 probably caused by more effective turbulent transfer during
daytime, while report higher night-time fluxes
and suggest it to be caused by water-side convection. However, we find that
both surface water concentration changes and more effective daytime gas
transfer are likely explanations to the higher daytime CH4 fluxes in Lake
Kuivajärvi.
Median (a) CH4 and (b) CO2 FC fluxes (grey bars) at different
measurement spots and median of simultaneous EC measurements (blue bars)
during lake stratification. Black whiskers represent the 25th and 75th
percentiles.
Linear fit parameters for the EC and BLM flux comparison for CH4 show that
kTE (r2=0.53) and kHE (r2=0.50) were comparable to EC
measurements, but kCC (r2=0.48) resulted in clearly lower fluxes than
EC measurements (p<0.05, Table ). Ebullition is
not an important gas transport mechanism in the EC footprint area as found in
and thus BLM including only diffusive gas flux is
expected to give results close to EC. A similar result with kCC giving
the lowest flux estimate was also found in , where EC and
FC methods gave 8 and 7 times higher cumulative fluxes than BLM with
kCC. Also, report seasonal changes in CH4 flux due
to cooling and changes in buoyancy flux. This further encourages to prefer
up-to-date k models instead of kCC in CH4 flux estimates. FC measured
daily median CH4 fluxes 2 times higher than EC (p<0.05, Table ), as was also observed in , and
thus gave highest flux estimates from all three methods. A reason behind the
result might be that these low fluxes are very difficult to detect with the
EC method, since the CH4 fluxes were very close to the detection limit of
the EC measurement system. Higher fluxes during the mixing period could have
been more suitable for a comparison between the two methods.
did not find systematically higher fluxes
with EC or FC and found quite good agreement between these two methods for
CH4 fluxes. The EC method has a larger source area (flux footprint) than FC
method, which might also affect the flux. Windy conditions during the mixing
period could have made the comparison better, but manual FC measurements are
difficult to do during high wind and rough weather conditions.
In addition to comparison between FC and EC measurements on a temporal scale,
spatial variation of CH4 flux within the EC footprint area was also
studied with floating chambers at different parts of the lake during the
stratified period 11–21 September 2014. The measurement spots were chosen
upwind from the measurement raft to ensure being within the EC footprint
area. Results are shown in Fig. , where the median of FC
measurements at different spots are compared with the median of simultaneous
EC measurements.
Measurement points N3 and N4 showed slightly higher median FC CH4 fluxes
than elsewhere, although the 25th and 75th percentiles fall within the same
range in all locations (Fig. a). Since the two measurement
locations are of different depth and other locations measure similar fluxes
compared to each other, we cannot make any conclusions about depth or wind
direction dependencies. EC measurements do not show any difference in CH4
fluxes measured from the south side or the north side of the measurement
raft. FC measured CH4 fluxes were systematically higher than simultaneous
EC fluxes, independent from the measurement location.
CO2 flux comparison
CO2 flux was small (below 1 µmol m-2 s-1) at the beginning of
the measurement campaign and similar to those reported in
, and due
to low wind speeds and thermal stratification of the lake (Fig. ). Negative daily median EC fluxes on 11, 12 and 14 September
were not statistically different from zero (p<0.05, tested with
Mann–Whitney U test) and denote very small fluxes close to the detection
limit of the measurement system (0.12 µmol m-2 s-1), rather
than uptake, which would be very unlikely in September in a boreal lake.
Median of all CO2 fluxes and average daytime and night-time
CO2 fluxes during lake stratification and mixing periods using different
measurement methods. Results of Mann–Whitney U test comparing differences
between daytime and night-time fluxes are given in U test column. Note that
FC fluxes are averaged also over different measurement spots. Mixing period
did not include enough FC measurements for this analysis. Uncertainties are
given as 25th and 75th percentiles for median fluxes and as standard errors
for the flux averages.
Stratified period
CO2 flux (µmol m-2 s-1)
All
Day
Night
U test
BLM kHE
0.31-0.08+0.17
0.305 (±0.009)
0.410 (±0.008)
h=1, p=0.0008
BLM kTE
0.44-0.11+0.13
0.545 (±0.014)
0.396 (±0.010)
h=1, p=0.01
BLM kCC
0.19-0.04+0.05
0.201 (±0.004)
0.180 (±0.004)
h=0
EC
0.35-0.69+0.48
0.31 (±0.04)
0.28 (±0.08)
h=0
FC
0.50-0.27+0.20
0.62 (±0.08)
0.29 (±0.04)
h=1, p=0.01
Mixing period
CO2 flux (µmol m-2 s-1)
All
Day
Night
U test
BLM kHE
1.80-0.65+0.86
2.15 (±0.06)
1.43 (±0.05)
h=1, p=0.0002
BLM kTE
2.15-0.91+0.61
2.37 (±0.06)
1.54 (±0.05)
h=1, p=5×10-5
BLM kCC
0.73-0.21+0.65
1.11 (±0.04)
0.58 (±0.02)
h=1, p=7×10-6
EC
1.09-0.95+0.74
1.3 (±0.2)
0.88 (±0.14)
h=0
Daily median CO2 flux from BLM, EC and FC methods. The black
whiskers indicate the 25th and 75th percentiles, respectively. The vertical
black line represents the start of the lake mixing period. Fluxes during (a)
the stratified period (11–21 September) are read from the left and (b)
mixing period fluxes (22–26 September) from the right y axis.
In the stratified period, BLM with kTE and FC methods results in a similar
diurnal pattern with higher fluxes detected during daytime than night-time,
while BLM with kTE shows the opposite and EC and BLM with kCC show
no statistical difference between daytime and night-time fluxes (Table ). Low BLM flux in the daytime
(0.305 ± 0.009 µmol m-2 s-1 on average with kHE model) is probably caused
by photosynthetic activity of algae in the lake that reduces the CO2
concentration difference between air and water (Δ[CO2]) right after
sunrise (Fig. d, Table ).
Also, the convective term (C2w*) in kHE is zero during daytime, when
the lake is heating due to higher air temperature, resulting in a lower
kHE (Fig. a). Higher flux during night-time
(0.410±0.008 on average with kHE model) is probably caused by
turbulence created by waterside cooling . This is seen
in Fig. a as the convective term C2w* increases
towards night-time causing higher gas transfer coefficient kHE and thus
higher flux as well. argued that the main driver for
enhanced night-time gas exchange is convection, and they did not find a
correlation with the concentration difference Δ[CO2]. However, we
find that Δ[CO2] also increases during night-time due to the
absence of algal photosynthesis. BLM by kTE gives highest fluxes at
noon,
when friction velocity also gains its maximum value (Fig. c), even though Δ[CO2] is at its minimum. In
the absence of buoyancy term in daytime, the gas transfer velocity kTE
is solely composed of the shear term. The BLM flux by kTE is thus also
larger in the daytime (0.545 ± 0.014 µmol m-2 s-1 on average,
Table ) despite the lower Δ[CO2], and
night-time flux (0.396 ± 0.010 µmol m-2 s-1) is 27 % smaller
than the daytime flux during the stratified period. Water friction velocity,
that was used in kTE, was calculated from direct EC measurements in the
air (Eq. ). Friction velocity calculated from wind speed
measurements (with a drag coefficient 0.001 for a water surface) instead of
direct u*a measurements gave similar diurnal variation to model kHE
(data not shown) but resulted in a lower u*w than with direct u*a
measurements. BLM with kTE could give better results with direct
turbulence measurements in the water. The buoyancy term (β) in kTE
is low compared to the shear term (u*3/(κz)) even during night-time
(Fig. c). EC and BLM with kCC methods do not show
any diurnal variation for CO2 exchange over the lake when the lake is
stratified. did not detect diurnal variation in CO2 EC
flux in September either over a small humic lake in Finland with fluxes
usually under 1 µmol m-2 s-1 during the stratified period.
Overall, kHE and EC measurements agree well on the magnitude of CO2
flux during daytime, while FC measured CO2 fluxes closest to EC during
night-time in the stratified period.
The flux increased almost 3-fold when the lake started mixing with higher
wind speeds and was larger (3 µmol m-2 s-1) than reported in
other studies from Lake Kuivajärvi (less than
2 µmol m-2 s-1;
; ). EC, on the other hand, measured
daily median CO2 flux less than 2 µmol m-2 s-1, as reported
in other studies.
Average daytime CO2 fluxes were 1.3 ± 0.2, 2.15 ± 0.06, 2.37 ± 0.06
and 1.11 ± 0.04 µmol m-2 s-1 with the EC method and BLM by
kHE, kTE and kCC, respectively. Night-time average fluxes were
notably smaller, as 0.88 ± 0.14, 1.43 ± 0.05, 1.54 ± 0.05 and
0.58 ± 0.02 µmol m-2 s-1 with the EC method and BLM by kHE,
kTE and kCC, respectively (Table ).
Highest flux according to BLM with all three k models was measured at noon,
when wind speeds are highest. Shear terms C1U and u*3/(κz) in
kHE and kTE models, respectively, have diurnal variations with
highest values at noon as well (Fig. a and
c), which results in higher daytime BLM fluxes with
kHE and kTE. BLM by kCC, however, shows considerably lower
fluxes than kHE and kTE both during daytime and night-time on
average. Higher fluxes during daytime than night-time in the mixing period
are expected due to enhanced gas transfer during stronger winds in the
daytime. The buoyancy term β in kTE is still almost a magnitude
smaller than the shear term and does not influence the kTE much, even
during lake mixing (Fig. c). The maximum and minimum
concentration differences Δ[CO2] were 1.4 to 1.6 times higher
during the mixing period than in the stratified period. This may be caused by
upwelling of CO2 from deep waters to the surface during the mixing period
and more effective algal photosynthesis during the stratified period. This
indicates that selectively using only daytime gas concentration measurements
in BLMs systematically biases the estimates of the long-term carbon budget.
Linear fit parameters for the comparison of BLM and FC methods with EC
measurements show that kTE (r2=0.26) and kHE (r2=0.27) give
the best results when compared with EC (60 % of the measured EC flux). BLM
CO2 flux based on kCC was clearly underestimated, being only about
30 % of the measured EC flux (r2=0.20) and FC fluxes were also generally
lower than EC (20 %, r2=0.13, Table ). The same
result of kCC giving lower fluxes than EC was found also in other
studies (e.g. ; ;
) and the use of this model in global carbon budget
estimates may therefore be questionable (e.g. ). During
lake stratification, kCC gives the general flux level quite well, while
during lake mixing and rain events it is clearly lower than the other
measured fluxes. However, on an annual scale, these special occasions might
contribute significantly to the CH4 and CO2 budgets and should be noted in upscaled
flux estimates.
During the stratified period, CO2 fluxes were almost always higher when
measured with FC than simultaneous EC measurements, as also found in
and (statistical
significance tested with Mann–Whitney U test, p<0.05), although daily
median values were, on average, higher when measured with EC than FC (Table ). Lower daily median FC fluxes might thus result
from discontinuous FC measurements missing important episodic flux events, as
suggested by . However, from the north side
of the measurement raft (measurement spots N1–N4), FC fluxes do not differ
statistically from EC CO2 fluxes.
The FC measurements did not show spatial variation in CO2 flux but there
is a clear difference between EC measurements from the south and north sides
of the lake (tested with Mann–Whitney U test, p<0.05) with approximately
0.1 µmol m-2 s-1 higher CO2 fluxes measured from the south
than from north (Fig. b). The south side of the raft is
shallower than the north side (Fig. a) and thus more prone
for the mixing to reach bottom even during the stratified period. The EC
footprint area of 100–300 m from the raft reaches
further to the shallow areas than the FC measurements that were done
approximately 50 m south from the raft. EC is thus more likely to catch the
higher gas fluxes resulting from upwelling of gas-rich waters from the
bottom. Higher CH4 flux from the south side was not detected possibly due
to CH4 oxidation in the water column into CO2. This oxidation would not
increase the CO2 efflux, as CH4 flux is so much smaller than that of
CO2. The footprint area north from the raft is over significantly deeper water
and mixing from the deeper waters during stratified period is unlikely.
Conclusions
We found that all gas transfer velocity, k, models used in BLM calculation
gave mainly lower flux estimates of both CH4 and CO2 compared to EC,
while FC measurements were mostly higher than EC. For CH4 fluxes, this
difference between the FC and EC methods is probably caused by the fact that,
during lake stratification, the measured fluxes were very small, close to the
detection limit of the EC system. For CO2, there was no statistical
difference between the FC and EC methods over the north side of the lake, and
night-time average fluxes were almost the same with these two methods. Gas
transfer velocity models by (kTE) and
(kHE) showed very similar fluxes both for CH4
and CO2, and the k model by (kCC) resulted
in clearly lower gas fluxes especially during the lake mixing period. A
comparison between BLM and EC fluxes showed that, on average, the kTE
model is the most similar and the kCC model the lowest, when compared to
EC fluxes. For global upscaling, it would be preferable to use up-to-date
k models instead of kCC to reduce the risk of systematic biases. The
simple kCC model underestimates the flux especially during special
occasions of, for example, lake mixing and rain events, which may vastly contribute to
the annual flux estimate.
During the stratified period, CO2 flux by kTE showed higher daytime
than night-time fluxes, opposite to other models, due to higher air friction
velocity during daytime. This model could work better with direct friction
velocity measurements in the water. The buoyancy term included in kTE
model was not significant compared to the shear term even in night-time, and
does not affect the diurnal variation of the flux. CO2 concentration
difference between the surface water and air was found to have a diurnal
cycle with lower values during daytime, probably due to algal photosynthesis
reducing surface water concentration of CO2. An opposite diurnal cycle was
found for CH4 concentration difference with highest values reached in the
afternoon. This might be due to CH4 feeding from the deeper waters, lower
oxidation rate in daylight in the water column, or more effective
lateral transport from the littoral zone during higher wind speeds in the
daytime. As we observe a clear diurnal cycle in the concentration difference
for both CH4 and CO2, it is important to note that using only daytime
concentration (and wind speed) measurements for upscaling with BLM affects
the resulting flux estimate.
Including the effect of lake cooling clearly improves the flux estimate both
for CH4 and CO2, although these models are not as simple to use as wind-speed-based models. In the absence of an extensive measurement system, the
use of e.g. bulk formulas for estimating latent and sensible heat fluxes for
kHE and kTE would result in better flux estimates than the use of
kCC. This would require an estimate for the depth of the actively mixing
layer, light extinction coefficient, radiation data, and wind speed, as well as
temperature and moisture differences between the air and water surface. With
this information, it is possible to calculate the effective heat flux and
buoyancy flux, after which estimating kHE and kTE is
straightforward, keeping in mind that the water-side friction velocity for
kTE model may be estimated from wind speed measurements by scaling it
with an appropriate drag coefficient.
FC measurements did not show a spatial variation in either CH4 or CO2
flux. CO2 EC flux was clearly higher from the south side of the
measurement raft than north, due to the shallower lake area within the EC
footprint on the south side. This was not detected with CH4, possibly due
to oxidation in the water column.
FC measurements are generally used for studying spatial variation, but our
results suggest that EC measurements are also able to detect differences
between different wind sectors. EC measurement systems are set up in one
place, often on the shore or on a raft near the deepest parts of the lake to
have a large footprint area for measurements. This is due to one of the
limitations in the EC method, because it requires a homogeneous surface and
favourable wind conditions but leads to possibly biased flux estimations,
especially if flux is only measured over a particularly deep or shallow area
not representative of the lake. The FC method is good for detecting spatial
variation but has its limitations regarding temporal and spatial data
coverage and challenging measurements in windy and wavy weather conditions.
As we find clear differences between night-time and daytime flux measurements
as well as between stratified and lake mixing periods, it is advisable to
prefer frequent and diverse sampling over daytime-only measurements, which can
lead to biases in greenhouse gas budget estimates.