Introduction
Background
Exchange rates of gases over the air–water interface in rivers, streams,
reservoirs, lakes, and estuaries are key parameters for estimating a number
of important ecosystem variables (Cole et al., 2010). Gas exchange rates are
used to estimate metabolism of aquatic systems (Hanson et al., 2004; Van de
Bogert et al., 2007, 2012), emission of greenhouse gases like CO2 and
CH4 to the atmosphere (Cole et al., 2010), and the role of inland and
near-shore waters in regional (Billett and Moore, 2008) and global (Cole et
al., 2007; Bastviken et al., 2011) carbon cycling. As a result, over several
decades, a tremendous effort among aquatic scientists has focused on
understanding and quantifying gas exchange processes at the air–water
interface and their controls under naturally occurring field conditions
(Whitman, 1923; Butman and Raymond, 2011; Raymond et al., 2013).
Multiple state variables and complex physical processes on both sides of the
air–water interface control gas exchange (MacIntyre et al., 1995, 2010).
Despite this complexity, the widely used expression for gas exchange rates
was formulated based on a conceptually simple model assuming that gas is
transported by molecular diffusion across intact boundary layers, or thin
films, found on each side of the interface (Whitman, 1923; Liss and Slater,
1974):
Jair–water=kCwater-Cair,
where Jair–water is the exchange rate, or vertical flux, of the gas
(positive upward), Cwater is the gas bulk concentration below the
film on the water side, Cair is the concentration above the film
on the air side, and k is the gas exchange coefficient, often also referred
to as the “gas transfer velocity” or “piston velocity”. For most gases,
Cwater and Cair are straight forward to measure with
modern sensors (Koopmans and Berg, 2015; Fritzsche et al., 2017), or
calculate from known functions, but the complexity of gas exchange and its
many controlling variables is contained in k (MacIntyre et al., 1995;
McKenna and McGillis, 2004; Cole et al., 2010).
For sparingly soluble gases such as O2, CO2, and CH4, the
ratio between the molecular diffusivity in air and water is on the order of
104. Consequently, the resistance to gas diffusion is associated with
the film on the water side, even if a substantially thicker film is found on
the air-side of the air–water interface. This means that in the case of
O2, Cair is simply the saturation concentration of O2
in water, which is a well-described function of the water temperature and
salinity (Garcia and Gordon, 1992) and the atmospheric pressure.
Turbulence, or turbulent-like motions, that affects or controls the thickness
of the film on the water side, and thus the diffusive resistance to gas
transport, can be driven by conditions both below and above the air–water
interface. In shallow streams and rivers, this turbulence is typically
generated by the water flow over an uneven or rough bottom. Substantial heat
loss from the water can similarly result in density-driven water motion that
erodes the film (Bannerjee and MacIntyre, 2004). On the contrary, in
reservoirs, lakes, and estuaries, the turbulence on the water side of the
interface is typically generated by wind, which makes wind speed the dominant
controlling variable for k for such systems (Marino and Howarth, 1993).
Despite the fact that typical conditions such as rough weather, surface
waves, and rain can rupture the film on the water side, the simple expression
for gas exchange (Eq. 1) is still applied with k values that are adjusted
accordingly (Watson et al., 1991). Keeping these multivariable, highly
dynamic, and complex controls in mind, it is evident that determination of
representative k values for specific sites is a challenging task.
Formulation of problem
A number of approaches have been used to study and determine values for k.
For smaller rivers and streams, they include targeted parallel up- and
across-stream additions of volatile tracers (e.g., propane) and hydrologic
tracers (e.g., dissolved chloride), where the latter is added to correct for
dilution of propane due to hyporheic mixing (Genereux and Hemond, 1992;
Koopmans and Berg, 2015). A common approach for smaller reservoirs and lakes
relies on additions of inert tracers, e.g., SF6 (Wanninkhof, 1985; Cole
et al., 2010), whereas floating chambers are often deployed in larger rivers,
reservoirs, lakes, and estuaries (Marino and Howarth, 1993). In a limited
number of studies of large reservoirs and lakes, tower-mounted atmospheric
eddy covariance systems have been used to measure air–water exchange, and
from that, k values were derived (Anderson et al., 1999; Jonsson et al.,
2008; Mammarella et al., 2015). Partly motivated by the substantial and often
methodologically challenging effort required to measure k at specific sites
with any of these approaches, many studies have simply relied on general
empirical correlations for k produced by fitting k values measured for
other similar aquatic systems (Raymond and Cole, 2001; Borges et al., 2004;
Cole et al., 2010). With the exception of atmospheric eddy covariance
measurements, none of these approaches represent a direct way of determining
k values because they rely on assumptions that often are difficult to
assess, or simply not fulfilled. As a result, gas exchange is viewed among
aquatic scientists as the primary source of uncertainty in many standard
estimates for aquatic systems such as gross primary production, respiration,
and net ecosystem metabolism (Wanninkhof et al., 1990; Raymond and Cole,
2001; Raymond et al., 2012).
Scope of work
The aquatic eddy covariance technique for O2 flux measurements under
undisturbed in situ conditions was originally developed for the benthic
environment (Berg et al., 2003). The approach has several significant
advantages over other flux methods, including its non-invasive nature (Lorrai
et al., 2010), high temporal resolution (Rheuban and Berg, 2013), and its
ability to integrate over a large benthic surface (Berg et al., 2007). As a
result, it has been used to measure whole-system fluxes for substrates such
as river bottoms (Lorke et al., 2012; Berg et al., 2013), seagrass meadows
(Hume et al., 2011; Rheuban et al., 2014), and coral reefs (Long et al.,
2013; Rovelli et al., 2015).
Here, we applied the aquatic eddy covariance technique “upside down” right
below the air–water interface to measure O2 fluxes. From
these fluxes, we derived exchange
coefficients for O2, and then standard gas exchange coefficients
(k600). All measurements were done from a floating platform, and because
we used a newly developed fast-responding dual O2–temperature sensor
(Berg et al., 2016), we were able to derive parallel fluxes of O2 and
thermal energy, or sensible heat. We conducted proof-of-concept tests that
were up to 40 h long at three river sites.
Methods
Floating measurements platform
All measurements were made from a 1.2 m × 0.9 m floating platform
with a catamaran-shaped hull (Fig. 1) that was kept at a fixed position at
the river sites by two upstream anchors. The modular design and the
catamaran-shaped hull allow the platform to be collapsed for storage and easy
shipment in a standard sturdy polymer case (Pelican Products, USA).
Floating platform for determining air–water gas exchange.
(a) The 1.2 m × 0.9 m wide platform with a
catamaran-shaped hull being prepared for deployment. Four inflatable fenders
provide flotation. (b) The platform deployed in the Hardware River
and anchored to both river banks. A dive weight is used to level the
platform. (c) Close-up look at: (1) the three-pronged upward-facing
sensor head of the cabled acoustic Doppler velocimeter (cabled Vector, Nortek
AS, Norway), (2) the fast-responding dual O2–temperature sensor (RINKO
EC, JFE Advantech, Japan), and (3) two stable independent dual
O2–temperature sensors used for calibration (miniDOT, PME, USA).
The 3-D velocity field was measured with an acoustic Doppler velocimeter
(ADV) with a cabled sensor head (cabled Vector, Nortek AS, Norway). This type
of ADV allowed the sensor head to be positioned facing upwards (Fig. 1) while
recording the velocity field right below the air–water interface (typically
∼ 4 cm). This distance was determined from standard ADV output. Data
were collected continuously at a rate of 64 Hz and represent water velocity
values averaged over the ADV's cylindrical measuring volume
(h ∼ 1.4 cm, Ø ∼ 1.4 cm) located 15.7 cm above the
sensor head (Fig. 1).
The O2 concentration was measured with a new fast-responding dual
O2–temperature sensor (RINKO EC, JFE Advantech, Japan) developed
specifically for eddy covariance measurements (Berg et al., 2016). This
sensor allows for deriving simultaneous fluxes of O2 and heat. It also
allows instantaneous temperature correction of the O2 concentration. The
sensor was designed to interface with our standard ADVs (Vector, Nortek AS,
Norway) through a single cable supplying power to the sensor and also
transmitting its two outputs, one for O2 and one for temperature, to the
ADV's data logger to be recorded along with velocities to ensure perfect time
alignment of all data. The O2 measuring part of this new sensor is a
small 6 mm diameter planar optode and concentrations are determined from
fluorescence life-time measurements (Klimant et al., 1995; Holst et al.,
1997, 1998). The tip of the sensor, which contains both the temperature
thermistor and the O2 sensing foil, has a diameter of 8.0 mm, which
makes it far more robust than O2 microsensors typically used for aquatic
eddy covariance measurements. Yet because the sensor's tip is still only half
the size of the ADV's measuring volume, it will not limit the eddy size that
can be measured by the system. The sensor's response times (t90%)
were measured to be 0.51 ± 0.01 s (SE, n=7) for O2 and
0.34 ± 0.01 s (SE, n=9) for temperature (Berg et al., 2016). The
same response time for O2 was consistently found when the O2
sensing foil was replaced (an easy user performed operation typically needed
after ∼ 10 days of continuous use). The edge of the sensor tip was
positioned ∼ 2.0 cm downstream of the edge of the ADV's measuring
volume so that water passed through this volume before sweeping over the
angled O2 sensing tip (Fig. 1a). This setup ensured undisturbed
measurements of the natural current flow. Power was supplied from an external
battery (Fig. 1a) with a capacity that allowed 64 Hz data to be collected
continuously for at least 48 h. Because all instrument components were
designed for underwater use they were not affected by rain or humid conditions.
Measurement of supporting environmental variables during each deployment
allowed verification of recorded data and assisted in the interpretation of
the derived eddy fluxes. These variables included mean O2 concentration
and temperature at the measuring depth recorded every 1 min with one or two
stable independent dual O2–temperature sensors (miniDOT, PME, USA;
referred to as the independent sensor below). In most deployments,
photosynthetically active radiation (PAR) was recorded at the measuring depth
every 5 min using an independent submersible PAR sensor (Odyssey, Dataflow
Systems, New Zealand). For one deployment, light data were
used from nearby meteorological
weather stations.
Field tests
The new approach for determining air–water gas exchange rates and associated
exchange coefficients from underwater eddy covariance measurements was tested
at three river sites, all in Virginia (US); one in the Hardware River, one in
the Mechums River, and one in the Moormans River. All sites had a fairly
linear run with a water depth between ∼ 0.3 and ∼ 1 m and smooth
and quietly flowing water without standing riffles or waves. As a result of
this, the two-point anchoring system, and the current's constant pull on the
hull, the platform was stationary during measurements. Typical surface flow
velocities ranged from 6 to 30 cm s-1. The ADV and the fast-responding
O2–temperature sensor were adjusted to record data ∼ 4 cm below
the air–water interface. Four deployments lasting up to 40 h were initiated
on 22 November 2015 and 14 September 2016 in the Hardware River, on
21 December 2016 in the Mechums River, and on 18 January 2017 in the Moormans River. Using a level and by placing dive weights
on the platform (Fig. 1b) care was taken to ensure that the platform was
horizontal within the tolerance of the level to minimize post-processing
rotations of the velocity field to correct for sensor tilt.
Calculations of eddy fluxes
Fluxes of O2 were extracted from the raw eddy covariance data following
the multi-step process briefly described below.
Using the two simultaneously measured outputs from the fast-responding dual
O2–temperature sensor, one for O2 and one for temperature, the
O2 concentration was calculated from the calibration equation provided
by the manufacturer. Because this equation contains both outputs, this
calculation includes instantaneous temperature correction of the O2
concentration evaluated in detail below. If needed, the O2 concentration
was calibrated against the independent sensor data. All 64 Hz data were then
reduced to 8 Hz data, which reduces noise while providing sufficient
resolution to contain the full frequency spectrum carrying the detectable
flux signal (Berg et al., 2009). This assumption was validated by comparing
fluxes calculated from both 8 and 64 Hz data for a subset of the data.
A 40 h long test deployment initiated at 16:00 LT in the afternoon as indicated on the x axis. (a) Three
velocity components at 8 Hz (x, y, z; z is vertical) and 15 min
mean current velocity. (b) O2 concentration at 8 Hz measured
with the dual O2–temperature sensor and at 1 min measured with an
independent sensor. (c) Cumulative flux over 15 min time intervals
with clear linear trends. (d) Hourly O2 flux (positive values
represent a release from the river), each value based on 15 min flux
extractions (n=4, SE) and day light measured at a nearby weather station.
(e) Hourly standard gas exchange coefficient (k600) based on
15 min estimates (n=4, SE). The few gaps in the data are for the times
when the driving O2 concentration difference changes sign (c).
O2 fluxes, one for each 15 min data segment, were extracted from the
8 Hz data using the software package EddyFlux version 3.1 (P. Berg,
unpublished data). If needed, this software rotates the flow velocity field
for each data segment to correct for any sensor tilt (Lee et al., 2004;
Lorrai et al., 2010; Lorke et al., 2013) bringing the transverse and vertical
mean velocities to zero. The vertical eddy flux was then calculated as
(defined positive upward)
Jeddy=w′C′‾,
where the overbar symbolizes averaging over the 15 min data segment, and w′ and C′ are the
fluctuating vertical velocity and the fluctuating O2 concentration,
respectively. These fluctuating components were calculated as w-w‾ and C-C‾ where w and
C are measured values (at 8 Hz), and w‾ and C‾ are
mean values defined as least square linear fits to all w and C values
within the 15 min time segment, a procedure usually referred to as linear
de-trending (Lee et al., 2004; Berg et al., 2009).
Due to the response time of the dual O2–temperature sensor and its
position downstream from the ADV's measuring volume, a time shift correction
was applied. This was done by repeating the outlined flux calculation, while
shifting the 8 Hz O2 concentration data back in time, 0.125 s
(1/8 s) at a time, until the numerically largest flux was found.
Estimating the gas exchange coefficient requires the O2 flux over the
air–water interface to be known. However, the eddy flux, Jeddy
(Eq. 2), is measured ∼ 4 cm below the interface. By using the linear
fit to the measured O2 concentrations in each 15 min data segment,
defined as C‾ above, Jeddy is corrected for storage
of O2 in the ∼ 4 cm column of water to give the flux at the
air–water interface:
Jeddy, air–water=Jeddy-∫0hdC‾dtdz,
where h is the ∼ 4 cm tall water column, and the integral represents
the change in time of O2 stored in this column. For further details on
this flux extraction protocol included in EddyFlux version 3.1, see Lorrai et
al. (2010), Hume et al. (2011), and Rheuban et al. (2014). For presentation,
the 15 min fluxes were lumped in groups of four to give hourly values.
To examine the eddy frequencies that carried the flux signal, cumulative
co-spectra of the O2 concentration and the vertical velocity were
calculated for representative periods in each deployment using the software
package Spectra version 1.2 (P. Berg, unpublished data). This software
essentially performs the identical flux calculation in the frequency domain
after fast Fourier transforming the de-trended data as EddyFlux does in the
time domain. Both software packages rely on the same methods for de-trending
and time shifting data.
Heat fluxes and associated co-spectra were extracted from the raw eddy
covariance data following the same multi-step process.
Calculations of gas exchange coefficients
The saturation concentration of O2 (Cair in Eq. 1) was
calculated from Garcia and Gordon (1992) as a function of salinity (here
0 ‰) and surface water temperature measured with the fast-responding
dual O2–temperature sensor ∼ 4 cm below the air–water interface
and then corrected for actual atmospheric pressure (average sea-level pressure of
1013.25 mbar corrected for elevation). The water column O2 bulk
concentration (Cwater in Eq. 1) was measured with the same
sensor. By substituting Jair–water (Eq. 1) with the 15 min values
for Jeddy, air–water (Eq. 3), a gas exchange coefficient for
O2 was calculated from Eq. (1) and converted to the standard exchange
coefficient, k600, for CO2 at 20 ∘C (Cole et al., 2010).
For presentation, the 15 min k600 values were lumped in groups of four
to give hourly values.
Results
All four deployments resulted in high-quality time series of the velocity
field, the O2 concentration, and the temperature ∼ 4 cm below the
air–water interface, and derived from those, air–water fluxes of O2 and
heat, and gas exchange coefficients. These data and their interpretation are
presented below.
The same deployment as in Fig. 2, but with results for temperature
and heat. The deployment was initiated at 16:00 LT in the afternoon as
indicated on the x axis. (a) Three velocity components at 8 Hz
(x, y, z; z is vertical) and 15 min mean current velocity.
(b) Temperature at 8 Hz measured with the dual O2–temperature
sensor and at 1 min measured with an independent
sensor. (c) Cumulative flux
over 15 min time intervals with clear linear trends. (d) Hourly
heat flux, each value based on 15 min flux extractions (n=4, SE) and day
light measured at a nearby weather station. Positive flux values represent a
release of heat from the river.
Data example
For a 40 h long deployment initiated on 18 January 2017 in the Moormans
River, the 15 min mean current velocity (Fig. 2a) was relatively constant
(averaging 20.5 cm s-1). The O2 concentration measured with the
fast-responding dual O2–temperature sensor (Fig. 2b) agreed closely
with the concentration recorded with the independent sensor and showed a
distinct diurnal pattern. During most of the first night of the deployment,
the O2 concentration increased linearly (h 19 to h 32), whereas a
smaller and non-linear increase that tapered off was measured during the
second night (h 45 to h 56). A diurnal pattern was also seen in the
calculated O2 saturation concentration (Fig. 2b) reflecting variation in
water temperature. The cumulative O2 flux (Fig. 2c), with each data
segment covering a 15 min time interval, had clear linear trends indicating
a strong eddy flux signal in the data. The hourly O2 flux (Fig. 2d),
representing means of four successive 15 min flux estimates, also exhibited
a clear diurnal pattern with a nighttime average uptake by the river of
16.4 mmol m-2 d-1 for the first night,
9.1 mmol m-2 d-1 for the second night, and an average daytime
release of 11.1 mmol m-2 d-1. As observed for the O2
concentration (Fig. 2b), the hourly O2 flux differed during the two
nighttime periods with a near-constant flux during the first night and a flux
that tapered off during the second night. The hourly standard gas exchange
coefficient (k600; Fig. 2e) derived from the 15 min O2 flux and
the O2 concentration difference over the air–water interface (Fig. 2b)
was almost constant over the first night of the deployment with an average of
3.9 m d-1. After that, k600 diminished almost 3-fold to a value
of 1.4 m d-1 during the daytime. During the second night, k600
tapered off markedly from a level found for the first night to almost
0.89 m d-1 during the last 4 h of the deployment. This pattern was
unexpected given the almost constant mean current
velocity (Fig. 2a) and insignificant winds and the similar O2
concentration difference (Fig. 2b) for the two nighttime periods. The pattern
suggests that gas exchange was controlled by at least one driver apart from
the river current velocity or winds (see Discussion section below).
The parallel temperature data measured with the fast-responding dual
O2–temperature sensor agreed perfectly with the temperature recorded
with the independent sensor (Fig. 3b) and had, as with the O2
concentration, a distinct diurnal pattern. A near-linear decrease occurred
during the first night (h 18 to h 32), whereas a smaller and non-linear
decrease that tapered off was recorded during the second night (h 45 to
h 56). During the daytime the temperature increased. Unfortunately, we do
not have reliable on-site measurements of the air temperature, but we infer
that it, together with short-wave (sunlight during day) and long-wave
(nighttime) thermal radiation, controlled the recorded water temperature
variations (Fig. 3b). The cumulative heat flux (Fig. 3c) had, as for O2,
clear linear trends indicating a strong flux signal in the data. The hourly
heat flux (Fig. 3d) also exhibited a clear diurnal pattern with a nighttime
average release of heat of 60.6 W m-2 for the first night and
27.5 W m-2 for the second night. As was observed for the temperature
(Fig. 3b), the hourly heat flux showed different trends for the two nights
with a near-constant flux during the first night and a flux that tapered off
during the second night.
Ignoring differences in the sign, representative cumulative co-spectra for
the O2 and heat fluxes (Fig. 4) during the first night (Figs. 2, 3) were
similar in the 0.1 to 1 Hz frequency band with all substantial flux
contributions for both the O2 and heat fluxes having frequencies lower
than ∼ 0.9 Hz.
Representative standard gas exchange coefficients (k600) along
with current velocity and O2 flux for four deployments at three
different sites. The third column (n) specifies the number of 15 min time
intervals included in the averages. Values from the last deployment (Moormans
River) are depicted in Figs. 2 and 3.
Deployment
Start date
n
Current velocity
O2 flux
k600
–
–
–
cm s-1
mmol m-2 d-1
m d-1
Hardware River, deployment 1
22 Nov 2015
20
28.4
9.1
1.6
Hardware River, deployment 1
22 Nov 2015
39
27.5
12.0
2.7
Hardware River, deployment 1
22 Nov 2015
13
27.6
-10.7
2.5
Hardware River, deployment 2
14 Sep 2016
20
8.7
7.0
0.4
Hardware River, deployment 2
14 Sep 2016
4
8.3
9.4
0.7
Mechums River
21 Dec 2016
23
9.4
-42.9
2.3
Mechums River
21 Dec 2016
36
9.3
-29.2
1.7
Moormans River
18 Jan 2017
4
25.6
-8.9
1.9
Moormans River
18 Jan 2017
51
18.4
16.8
3.9
Moormans River
18 Jan 2017
34
20.4
-11.8
1.3
Moormans River
18 Jan 2017
3
22.9
19.3
5.1
Moormans River
18 Jan 2017
16
23.4
10.1
2.1
Moormans River
18 Jan 2017
26
21.3
5.8
1.0
Nighttime normalized cumulative co-spectra for the vertical velocity
combined with the O2 concentration and the temperature, respectively,
revealing which frequencies carried the eddy flux signal.
Due to careful leveling of the platform prior to data collection (Fig. 1b),
rotation of the velocity field to correct for sensor tilt was minimal with an
average of only 1.3∘ from horizontal. This rotation had an
insignificant effect on the flux calculation. The applied time shift averaged
1.3 and 1.2 s for the O2 and heat flux calculations, respectively,
whereas the average storage correction (Eq. 3) amounted to 11 % for the
O2 flux and 15 % for the heat flux.
Representative gas exchange coefficients
The three other test deployments were shorter than the one presented in
Figs. 2 and 3 but results were of comparable quality. Average values for
selected parameters covering periods of time with several successive 15 min
time intervals from all four deployments are given in Table 1. These periods
were identified by containing consecutive time intervals with consistent
standard gas exchange coefficient values, k600, that had little
variation and appeared to represent a particular field condition. The longest
period (n=51) covers the first full night of the deployment shown in
Fig. 2 (h 19 to h 32). Overall, the average current velocity varied from
8.3 to 28.4 cm s-1 while k600 ranged from 0.4 to
5.1 m d-1, or more than a factor of 12.
There was no significant relationship (R=0.37, p=0.22) between river
current velocity and k600 values (Fig. 5) for all of the data in
Table 1. Substantial variations in k600 values were found for some
individual deployments even though the current velocity did not change
markedly. Most prominently in the Moormans River deployment (Figs. 2, 3),
where the k600 values varied more than a factor of 5. As we discuss
below, this suggest that, at least for some sites and under some field
conditions, other drivers of air–water gas exchange than river flow and winds
are more important.
Standard gas exchange coefficient, k600, plotted against river
current velocity. The dotted line is a linear fit to all data (R=0.37, p=0.22).
Temperature effects on O2 readings – a possible
methodological bias
In the benthic environment the vertical turbulent heat flux is usually small
relative to the O2 flux due to slowly and modestly varying mean
temperatures in the bottom water. At the air–water interface, however, the
heat flux is typically larger due to substantial variations in air
temperature and short- and long-wave thermal radiation, and the associated
turbulent temperature fluctuations can
represent a challenge in O2 flux measurements by eddy covariance.
All highly sensitive fast-responding O2 sensors that can be used for
aquatic eddy covariance measurements are to the best of our knowledge
inherently sensitive to temperature changes, and thus give variable O2 readings at the same molar O2
concentration if the temperature varies. Typical temperature coefficients
(% change in O2 concentration reading caused by a temperature change
of 1 ∘C) for Clark-type microelectrodes, the most common sensor type
used for aquatic eddy covariance, have values of ∼ 3 % (Gundersen
et al., 1998). Lab measurements in which the O2 concentration was held
constant but temperature varied showed that the fast-responding dual
O2–temperature sensor used in this study has a temperature coefficient
of 2.9 % if temperature correction was omitted. This characteristic of
fast-responding O2 sensors implies that rapid temperature fluctuations
associated with any turbulent heat flux will mistakenly be recorded as
fluctuations in O2 concentration and bias the O2 flux calculation
unless an instantaneous temperature correction of the signal is
performed. In this study, this correction was done using the fast-responding
dual O2–temperature sensor's temperature reading from within a few
millimeters of the O2 sensing foil. Below, we exemplify the nature and
magnitude of this potential bias using data measured during the first night
(h 18 to h 32) of the deployment shown in Figs. 2 and 3.
The turbulent temperature fluctuations for a 3 min period shown in Fig. 6a
are associated with a vertical heat flux of ∼ 60 W m-2 (Fig. 3d)
and amount to ± ∼ 0.015 ∘C. Based on a temperature
coefficient of ∼ 3 %, this translates into fluctuations in O2
concentration readings of ± ∼ 0.2 µmol L-1
(Fig. 6a; right axis). Using such “simulated” O2 data, derived from
the 8 Hz nighttime temperature data (Fig. 3; h 18 to h 32), representing
solely temperature sensitivity effects and no true O2 reading, produced
an O2 release, or flux bias, of 11.9 mmol m-2 d-1 (blue
bar; Fig. 6b). Using the instantaneous temperature corrected O2 data, as
was done for all other calculations we present, gives an oppositely directed
O2 uptake of 16.9 mmol m-2 d-1 (red bar; Fig. 6b). Using
the sensor's O2 readings, but without the instantaneous temperature
correction, gives an update of only
4.4 mmol m-2 d-1 (green bar; Fig. 6b).
Bias that can arise if O2 concentration sensor readings are not
corrected using rapid parallel temperature measurements.
(a) Recorded 8 Hz data of temperature fluctuations and their mean
(left axis) through 3 min and the resulting fluctuations in O2
concentration that would be recorded solely due to temperature sensitivity by
a sensor with a temperature coefficient of 3 % (right
axis).
(b) Average air–water O2 fluxes, all for the
same period of the first night (h 18 to h 32) of the
deployment depicted in Figs. 2 and 3, calculated using instantaneous
temperature corrected data (red bar), data without temperature correction
(green bar), and “simulated” data produced from 8 Hz temperature
recordings as shown in panel (a) and assuming a temperature
coefficient of 3 % (blue bar).
The magnitude of this O2 flux bias, if temperature correction is omitted,
scales with the heat flux and is proportional to the O2 sensor's
temperature coefficient and the actual O2 concentration. Given the
millimeter-close proximity of the temperature thermistor and the O2 sensing
foil, and the relatively small difference between the fast-responding dual
O2–temperature sensor's response times (0.51 for O2 and 0.34 s for
temperature; Berg et al., 2016), we conclude that the effects of temperature
sensitivity were removed from our O2 flux calculations. This point is
supported by the high-frequency end (∼ 0.9 Hz) of the co-spectra for
the O2 and heat fluxes (Fig. 4).
Discussion
Deploying the aquatic eddy covariance technique right below the air–water
interface provided a feasible way to determine gas exchange rates and
coefficients. Relative to what is possible with traditional methods, this
new approach gives gas exchange rates and coefficients with an improved
precision and at a higher spatial and temporal resolution. For those
reasons, the approach has the potential to enhance our knowledge of the
dynamics and controls of gas exchange and thus benefit aquatic ecosystem
studies and pave the way for new lines of ecosystem research.
These points are exemplified in our longest test deployment that lasted 40 h
(Figs. 2, 3) and resulted in aquatic eddy covariance data for both O2
and temperature of a quality and internal consistency that fully match those
published for many benthic environments (see review by Berg et al., 2017).
Specifically, the 8 Hz velocity, O2, and temperature data (Figs. 2a, b,
3b) were recorded with low noise and the O2 and temperature data
perfectly matched measurements with the stable independent sensor (Figs. 2b,
3b). Furthermore, the cumulative fluxes (Figs. 2c, 3c) had clear linear
trends that indicate a strong and consistent flux signal in the data, and the
times when the hourly O2 flux changed direction (Fig. 2d; positive
values represent a release), matched exactly the times when the driving
O2 concentration difference changed sign (Fig. 2b). Moreover, the
cumulative co-spectra for the O2 and heat fluxes (Fig. 4) have the shape
typically seen for shallow-water environments (Lorrai et al., 2010; Berg et
al., 2013). The fact that all flux contributions for both the O2 and
heat fluxes had frequencies lower than ∼ 0.9 Hz, combined with the
fast-responding dual O2–temperature sensor's response times
(t90%) of 0.51 s for O2 and 0.34 s for temperature (Berg et
al., 2016), indicates that the entire flux signal over all frequencies was
captured. Finally, for both O2 and temperature there was a clear
relationship between the flux over the air–water interface (Figs. 2d, 3d)
and the observed change in the water column (Figs. 2b, 3b). For O2, for
example, the ratio between the averaged fluxes for the two nights (Fig. 2d;
h 21 to h 30 vs. h 45 to h 54) equals 2.0, which is close to the ratio of
2.2 between the changes in water column concentrations (Fig. 2b) for the same
two periods.
Both the O2 and temperature data (Figs. 2b, d, 3b, d) contained a clear
diurnal signal overall. For O2, however, this was not driven by
biological processes, i.e., net primary O2 production during daytime and
respiration during nighttime, as this would have resulted in an increase in
mean water column O2 concentration during daytime and a decrease at
nighttime. The fact that the opposite pattern was found indicates that physical
processes related to thermal conditions were controlling the O2
dynamics. Specifically, colder nighttime air temperatures and possibly also
long-wave thermal radiation to the atmosphere were driving the substantial
heat flux out of the river (Fig. 3d), which resulted in falling water
temperatures (Fig. 3b). This, in turn, changed the O2 saturation
concentration (Cair in Eq. 1) and thus the driving concentration
difference of O2 exchange over the air–water interface (Fig. 2b). During
the daytime, the reverse pattern was in place. This rather complex
relationship, or linkage via physical processes, is the only mechanism that
can explain the overall pattern found for this deployment (Figs. 2, 3).
Considering that these measurements were done under conditions that did not
include any uncommon or extreme weather conditions suggests that physical
processes, and not biological processes, are often an important, or even the
main, driver of O2 dynamics in shallow-water rivers and streams. An
unfortunate consequence of this dominance or control by physical conditions,
which we believe is not yet fully recognized, is that it adds substantial
uncertainty to the widely used approach of deriving metabolic estimates
(e.g., gross primary production, respiration, net ecosystem metabolism) from
time series of measured water column O2 concentrations (Odum, 1956; Hall
et al., 2016).
The standard gas exchange coefficients (k600) for all of our four
deployments (distributed on three different river sites, all with smooth
quietly flowing water without standing riffles or waves; Fig. 1) did not show
a significant relationship with river current velocity (Fig. 5; Table 1).
This is in line with previously published results from across-site
comparisons (Hall et al., 2016), but the substantial variation among
k600 values for some individual deployments (in particular for the
Moormans River deployment; Fig. 2) despite only moderately varying river flow
velocity and insignificant winds is surprising. For example, k600 varied
from a near-constant value of 3.9 m d-1 during the first night
(Fig. 2e; h 19 to h 32), followed by an almost 3 times smaller daytime
value of 1.4 m d-1 (h 33 to h 42), and then increased again at the
onset of the second night before finally tapering off to a small value of
0.9 m d-1 (h 52 to h 56) at the end of the deployment. The
co-variance of the heat exchange (Fig. 3d) suggests that turbulence, or
turbulent-like motions (which stimulates gas exchange) was generated by
natural convective forces driven by the substantial heat loss from the river
during the nighttime (Fig. 3d). Conversely, during the daytime, when the heat
flux was directed into the river (Fig. 3d), turbulent motions were presumably
dampened by vertical temperature stratification in the surface water. Given the “low-energy” smooth and
quietly flowing water, we find this explanation for the varying k600
values (Fig. 2e) likely and note that this controlling factor has been
described before (Bannerjee and MacIntyre, 2004; MacIntyre et al., 2010). We
also note that this observed complex pattern illustrates the difficulties
that can be associated with determining accurate air–water gas exchange
rates and coefficients without direct site- and time-specific measurements.
An important methodological finding linked to the new approach is that
O2 sensor readings should, at least in some cases, be corrected for
temperature sensitivity using concurrent high-speed temperature readings as
was done here for all O2 fluxes used to estimate air–water gas exchange
coefficients (Fig. 2e; Table 1). In the benthic environment the vertical
turbulent heat flux is usually small relative to the O2 flux due to
slowly and modestly varying mean temperatures in the bottom water. However,
results presented here show that rapid temperature fluctuations associated
with the substantial turbulent heat flux below the air–water interface can
mistakenly be recorded as fluctuations in the O2 concentration and bias
the O2 flux calculation significantly if instantaneous temperature
correction is omitted (Fig. 6). It is unclear how widespread this problem is
– more studies are needed to determine that – but in the example included
here, this bias alters the flux by more than a factor of 3 (Fig. 6b). Our
data were recorded during winter and one could argue that the O2
exchange would be much larger during summer due to extensive primary
production and respiration, which would reduce the relative magnitude of this
bias. But as the O2 flux is indeed likely to be more pronounced during
summer than during winter, so is the heat flux.
Summary and recommendations
Based on our proof-of-concept deployments, the aquatic eddy covariance
technique applied right below the air–water interface should be particularly
useful in detailed studies of gas exchange that evaluate its dynamics and
controls. The approach can consequently help reduce the generally recognized
problem of large uncertainties linked to gas exchange estimates in
traditional aquatic ecosystem studies.
The floating platform we used here for measuring aquatic eddy covariance
fluxes right below the air–water interface (Fig. 1) can easily be reproduced
as it relies exclusively on standard materials and commercially available
instrumentation, the latter designed with plug-and-play capabilities.
Furthermore, standard software for eddy flux extractions developed for the
benthic environment or for the atmospheric boundary layer can be used to
estimate air–water fluxes.
We recommend that eddy covariance data are recorded close to the air–water
interface (Fig. 1c) to minimize the effects of the O2 storage in the
water between the measuring point and the surface and because gradients of
both O2 and temperature can form in the upper water column. We also
recommend that simultaneous high-speed temperature measurements are performed
within a few millimeters of the O2 concentration recordings to allow for
instantaneous temperature correction of the O2 signal (Fig. 6b).
Finally, our results illustrate that the O2 concentration difference
driving the air–water gas exchange is often small (Fig. 2b), here less than
2 % of the absolute concentration. This emphasizes the importance of
relying both on accurately calibrated sensors to measure the water bulk
concentration (Cwater in Eq. 1) and precise determinations of the
saturation concentration (Cair in Eq. 1) that is corrected for
temperature, salinity, and atmospheric pressure.