Spatial and temporal variability of CO2 and CH4 gas transfer velocitiesand quantification of the CH4 microbubble flux in mangrovedominated estuaries

J. A. Rosentreter,1* D. T. Maher,1,2 D. T. Ho,3 M. Call,1,2 J. G. Barr,4 B. D. Eyre1

1Centre for Coastal Biogeochemistry Research, School of Environment, Science and Engineering, Southern Cross University,Lismore, New South Wales, Australia

2National Marine Science Centre Southern Cross University, Coffs Harbour, New South Wales, Australia3Department of Oceanography, University of Hawai‘i at M�anoa, Honolulu, Hawaii4South Florida Natural Resource Center, Everglades National Park, Homestead, Florida

Abstract

Gas transfer velocities (k) of CO2 and CH4 were determined from 209 deployments of a newly designed

floating chamber in six mangrove dominated estuaries in Australia and the United States to estimate man-

grove system specific k. k600-CO2 and k600-CH4 (k normalized to the Schmidt number of 600) varied greatly

within and between mangrove creeks, ranging from 0.9 cm h21 to 28.3 cm h21. The gas transfer velocity cor-

related well with current velocity at all study sites suggesting current generated turbulence was the main

driver controlling k. An empirical relationship that accounts for current velocity and a linearly additive con-

tribution of wind speed and water depth was a good predictor of k600-CO2 (R2 5 0.67) and k600-CH4

(R2 5 0.57) in the mangrove creeks in Australia. In a side-by-side study, good agreement was found between k

determined from this new floating chamber and a 3He/SF6 dual tracer release experiment (�5% discrepancy).

k600-CH4 correlated well with k600-CO2 (R2 5 0.81), however, k600-CH4 was on average 1.2 times higher than

k600-CO2, most likely reflecting a microbubble flux contribution. The microbubble flux contributed up to

73% of the total CH4 flux and was best predicted by a model that included CH4 supersaturation, temperature,

and current velocity. A large overestimation was found for both CO2 and CH4 fluxes when calculated using

empirically derived k models from previous studies in estuaries. The high temporal and spatial variabilities of

kCO2 and kCH4 highlights the importance of site specific transfer velocity measurements in dynamic ecosys-

tems such as mangrove estuaries.

The exchange of gases between the water and atmosphere

is of great scientific interest because of its importance in bio-

geochemical cycling and more recently due to the realization

that aquatic systems play a large role in the global cycling of

greenhouse gases such as CO2 and CH4. The flux of a gas

across the water-atmosphere interface (F) can be computed as

F5k K0 pwater–pairð Þ (1)

where k is the gas transfer velocity of a given gas, K0 is the

solubility coefficient expressed as in units of mol/(kg atm)

(Wanninkhof 1992), which is a function of temperature and

salinity, and pwater and pair are the partial pressures of a given

gas in water and atmosphere, respectively. Today, the mea-

surement of gas partial pressure in water and the atmosphere

are very precise and accurate, therefore the major challenge

and highest uncertainty in the flux computation remains in

the estimate of k (Zappa et al. 2007; Rutgersson et al. 2008;

Wanninkhof et al. 2009).

At the aqueous boundary layer, k of sparingly soluble gas-

es such as CO2 and CH4 depends on water-side turbulence

and the Schmidt number (Sc) of the gas (i.e., kinematic vis-

cosity of the water divided by molecular diffusion coefficient

of the gas in water) (Liss and Merlivat 1986; MacIntyre et al.

1995). The factors controlling turbulence at the water-side

(and therefore k) and the relative importance of these fac-

tors, however, vary greatly between aquatic systems. For

example, in areas of large fetch such as the open ocean,

wind is the major driver controlling the gas transfer velocity

(Wanninkhof 1992; Woolf 2005; Ho et al. 2011). In lakes,

temperature driven convection (Podgrajsek et al. 2014) and

*Correspondence: judith.rosentreter@scu.edu.au

Additional Supporting Information may be found in the online versionof this article.

561

LIMNOLOGYand

OCEANOGRAPHY Limnol. Oceanogr. 62, 2017, 561–578VC 2016 Association for the Sciences of Limnology and Oceanography

doi: 10.1002/lno.10444

microbubbles (Prairie and del Giorgio 2013; McGinnis et al.

2015) can have additionally strong effect on the transfer of

some gases such as CH4. In shallow streams and estuaries

with strong water turbulence, k is mostly controlled by bot-

tom friction and depends on water depth, current speed and

bed roughness (Zappa et al. 2003; Borges et al. 2004; Upstill-

Goddard 2006). While many studies in the past have

focussed on k over the open ocean, more system specific k

measurements and models are required in rivers and estuar-

ies for accurate assessment of water-atmosphere gas fluxes.

Mangrove dominated estuaries play an important role in

coastal carbon budgets (Jennerjahn and Ittekkot 2002; Ditt-

mar et al. 2006; Eyre et al. 2011). Yet, estimates of CO2 and

CH4 fluxes in mangrove waters have inherent uncertainties

due to high variability within and between mangrove ecosys-

tems and limited data availability (Bouillon et al. 2008;

Alongi 2014; Ho et al. 2014). Estimating CO2 and CH4 fluxes

in mangrove waters using k values calculated from empirical

models derived from non-mangrove estuaries or the ocean

may not be appropriate, as k might be affected by different

factors in different settings. To our knowledge there are only

two previous studies and these measured gas transfer velocity

in the same mangrove dominated estuary (Shark River estu-

ary, Everglades National Park, U.S.A.; Ho et al. 2014, 2016).

Considering the disproportionately large role that mangroves

play in the global carbon cycle (Dittmar et al. 2006; Bouillon

et al. 2008; Sippo et al. 2016) more data are needed for

mangrove specific k estimates. Furthermore, CH4 fluxes in

mangrove estuaries are usually calculated from models based

on CO2 gas transfer velocity (Kristensen et al. 2008; Call et al.

2015; Sadat-Noori et al. 2015). The two gases, however, can

behave quite differently at the water-atmosphere interface

and CH4 fluxes may be underestimated when using models

intended for CO2 because of a “microbubble flux” that

enhances the CH4 flux relative to CO2 (Prairie and del Giorgio

2013; McGinnis et al. 2015). Microbubbles are generated at

the water surface by wind induced breaking waves, spray, pre-

cipitation or gas supersaturation and should not be confused

with rising gas bubbles from sediments (ebullition) (Turner

1961; Vagle et al. 2010; McGinnis et al. 2015). The introduc-

tion of microbubbles into surface waters induces an inhomo-

geneous distribution of gases in the water column, adding a

non-Fickian diffusion process to the generally accepted

Fickian transport of gases at the water-atmosphere interface.

The presence of a microbubble flux has been invoked to

explain an increased transfer velocity (normalized to a

Schmidt number of 600) of CH4 by up to 2.1 m d21 (Prairie

and del Giorgio 2013), and �2.5-fold increase (McGinnis

et al. 2015) vs. CO2 (also normalized to a Schmidt number of

600) in lakes. Yet, the role of microbubbles in enhancing CH4

transfer velocities in estuaries remains unexplored.

A number of methods are available to estimate k for CO2

and CH4. Early mass balance techniques using naturally

occurring tracers such as O2 (O’Connor and Dobbins 1958)

and radon (Elsinger and Moore 1983; Kromer and Roether

1983; Hartman and Hammond 1984) were replaced with SF6

in deliberate tracer release experiments (Wanninkhof et al.

1987; Cole and Caraco 1998; Ho et al. 2014), turbulent

kinetic energy-dissipation rate (E) measurements using acous-

tic Doppler velocimeters (ADV) (Zappa et al. 2003; Vachon

et al. 2010; Tokoro et al. 2014), micrometrological techni-

ques such as eddy covariance (Prytherch et al. 2010; Mørk

et al. 2014), and the floating chamber method (Frank-

ignoulle 1988; Marino and Howarth 1993; Frankignoulle and

Borges 2001). Each method has advantages and disadvan-

tages and the choice of the most appropriate technique for

estimating k depends on study site conditions, and the spa-

tial and temporal scale of interest.

The main criticism of the floating chamber method has

been that the chamber disturbs the water turbulence regime,

which can alter k and therefore gas fluxes (Broecker and

Peng 1984; Raymond and Cole 2001; Vachon et al. 2010).

However, Tokoro et al. (2008) found no significant difference

between the ADV-based energy-dissipation rate (E) (a param-

eter for turbulence) measured inside and outside a floating

chamber suggesting that it can be a valid technique when

there is little wave breaking. Galfalk et al. (2013) found good

agreement between the floating chamber method and the

dissipation method (E) during a diel cycle with varying wind

conditions and wave heights and no overestimation of k

derived from the chamber method. An artificial increase in

temperature and pressure inside the chamber was found dur-

ing deployments over several hours in a study of Belanger

and Korzun (1991), however, short-term deployments

(minutes) can minimize these effects. Furthermore, not all

methods are appropriate under all conditions. For example,

the eddy covariance method requires sufficient spatial uni-

formity and measures a direct flux over a large footprint

within a study area (Tokoro et al. 2014). Deliberate gas tracer

release experiments are very precise for average k estimates

over large spatial and time scales of days to weeks in the

ocean (Nightingale et al. 2000; Ho et al. 2011) and in rivers

(Clark et al. 1995; Ho et al. 2002), however, it is a relatively

time and resource intensive method to employ. Furthermore,

due to the time scale over which the measurements are

made (hours to days), this method may not capture short

term variability in k on the minutes scale, which can be of

interest in dynamic systems such as estuaries.

The various designs of floating chambers found in the lit-

erature make them difficult to compare, as chamber types

can differ in geometric shape, height, penetration depth of

the chamber walls, surrounding floats, and volume, ranging

from simple bucket deployments to well-constructed cham-

ber designs (Mazot and Taran 2009; Xiao et al. 2014; Lorke

et al. 2015). The performance of the floating chamber tech-

nique depends on the chamber design, therefore, an appro-

priate design is essential for accurate k estimates. Here, we

introduce a new design of the floating chamber that aims to

Rosentreter et al. Gas transfers in estuaries

562

minimize interference with the water turbulence regime by

using flexible submerged walls and a chamber frame with

minimal contact to the water surface. The floating chamber

method is relatively easy to employ compared to the other

methods and has been shown to be a reliable technique pro-

viding accurate k estimates on short time scale under

Fig. 1. Map showing study locations. (a) Tidal mangrove creeks in Queensland, Australia: Jacobs Well (JW), Fitzroy River Estuary (FR), Constant CreekEstuary (CC), Burdekin River Estuary (BR), Johnstone River Estuary (JR); (b) Everglades National Park (ENP) in Florida, United States; and (c) Shark River

study site in ENP: main channel, lake, creeks and Tarpon Bay site. Gauge station Gunboat Island.

Table 1. Characterization and location of mangrove creek study sites.

Creek Latitude, longitude Tidal regime

Tidal

amplitude (m)

Depth*

(m)

Width†

(m)

pCO2

water‡ (latm)

pCH4

water‡ (latm)

JW 27.7808S, 153.3808E Semidiurnal 1.5 0.98 23 1473.5 47.6

FR 23.5238S, 150.8758E Semidiurnal 4 1.50 37 3115.5 247.6

CC 20.9828S, 149.0318E Semidiurnal 4.1 5.48 43 1896.4 349.2

BR 19.6878S, 147.6118E Diurnal 2.3 1.30 30 6862.7 126.5

JR 17.5098S, 146.0668E Semidiurnal 2.1 3.07 42 3476.2 245.4

SR 25.3818N, 81.0258W Semidiurnal 1 2.41 118 5728.6 na

*Mean depth in FR, CC, BR, and JR are average values of study 2 and 3. Mean depth in SR is average of the main channel and Tarpon Bay.†Mean width (m) is derived from GIS Lidar data.‡pCO2/pCH4 water in FR, CC, BR, and JR are average values of study 2 and 3. pCO2 in SR is the average of the main channel, creeks, lake and TarponBay.

Rosentreter et al. Gas transfers in estuaries

563

appropriate conditions (Frankignoulle et al. 1996; Borges et al.

2004; Tokoro et al. 2007). In this study, we used the newly

designed chamber to reveal temporal and spatial variability of

k over short time scale in estuarine mangrove systems. In

addition, the floating chamber deployments were undertaken

side-by-side with a 3He/SF6 tracer release experiment in a

mangrove estuary in one of the four field campaigns (gas trac-

er results are published in Ho et al. 2016), which for the first

time allowed a direct comparison of the two methods.

We hypothesize that spatial and temporal variability of

CO2 and CH4 gas transfer velocities within and between

mangrove estuaries will be primarily due to variations in cur-

rent, wind speed and water depth leading to the inapplica-

bility of wind speed only k parameterizations for these

systems. We further hypothesize that an effect of microbub-

bles will lead to a systematically higher kCH4 than kCO2.

Methods

Study sites

Gas transfer velocities of CO2 and CH4 were estimated

from 209 individual floating chamber deployments and

simultaneous water column pCO2/pCH4 measurements over

four field campaigns in five mangrove dominated estuaries in

Australia and the United States (Table 1; Fig. 1). To reveal

temporal variation in k, 28 floating chamber deployments

were undertaken in study 1 (Nov 2013) over a whole tidal

cycle (12.1 h) in a single location in a small mangrove creek

in the subtropical Southern Moreton Bay, Australia. Study 2

(Feb/Mar 2014) and study 3 (Sep/Oct 2014) were conducted

in four tidal mangrove creeks in relatively shallow subtropical

estuaries along the north-eastern coast of Queensland (NQ),

Australia (Fitzroy River Estuary, Burdekin River Estuary,

Constant Creek Estuary and Johnstone River Estuary) (Fig. 1a;

Table 1); all characterized by episodic, large freshwater inputs

during the wet season and low or no discharge and high

evaporation rates during the dry season (Eyre 1998). In study

1, 2 and 3 CO2 and CH4 gas transfer velocities were deter-

mined simultaneously using the floating chamber technique.

A spatial survey (study 4, Oct 2014) of CO2 gas transfer veloci-

ty was undertaken in Shark River in the Everglades National

Park (ENP), Florida, United States, a semi-diurnal tidal river

with a mean tidal amplitude of 0.5–1 m, surrounded by the

largest contiguous mangrove forest (144,447 ha) in North

America (Fig. 1b). Within the Shark River catchment, we con-

ducted floating chamber deployments in the main channel, a

nearby lake, bay and a number of small tributaries of varying

size (Fig. 1c). The floating chamber deployments in Shark Riv-

er were undertaken concurrently with a 3He/SF6 tracer release

experiment, which was conducted over 7 d in the main chan-

nel in Shark River and described in detail in Ho et al. (2016).

Floating chamber design

The new floating chamber was designed to minimize dis-

turbance of the water turbulence regime by allowing free ver-

tical and horizontal movement of the chamber through

loose attachment to a spider-like frame, via a stainless steel

chain that suspended the chamber from the frame (Fig.

2a,b). The frame floats on the water by the placement of

foam buoys on each of its four legs, creating minimal distur-

bance of the water flow. The chamber further consists of two

different types of walls. Flexible walls, constructed from

polyvinyl fluoride film, were used at the air-water interface

to a depth of �15 cm to allow for the propagation of water-

side turbulence into the chamber. Rigid walls were used for

the above water section of the main chamber, and were

Fig. 2. Floating chamber design. (a) The floating chamber is connected through a thin chain to four stainless steel legs with floats that sit in thewater. Flexible submerged walls minimize interference with water turbulence regime; (b) Picture of the floating chamber in the field.

Rosentreter et al. Gas transfers in estuaries

564

made of polycarbonate sheets with a low profile (50 cm wide

3 50 cm long 3 23 cm high) and a large ratio of water sur-

face area (0.25 m2) to chamber volume (58.6 L). Both flexible

and rigid walls were sealed with silicone (marine proof). A

polystyrene cover was built around the chamber to reduce

any artificial internal temperature increase. The temperature

difference was tested in an experiment using two tempera-

ture loggers (HOBO, 6 0.18C) attached inside and outside the

chamber, respectively. The experiment showed a minor

change of 0.18C during 10 min deployments, over a range of

temperatures from 348C to 388C, hence temperature artifacts

did not affect our k measurements. A small fan was attached

at the top inside the chamber to ensure evenly dispersed air

circulation. A direct comparison between fan-on and fan-off

inside the chamber in study 1 showed that there was no arti-

ficial increase of kCO2 or kCH4 during deployments (paired

t-test, t 5 20.13, df 5 16, p 5 0.89, n 5 28) and all following

floating chamber deployments were carried out with fan-on.

Gas transfer velocity calculations for CO2 and CH4

k of CO2 and CH4 were derived from gas fluxes (F) and

partial pressure differences in the water and atmosphere by

rearranging Eq. 1

k5F= K0 pwater–pairð Þð Þ (2)

The flux F was estimated using the equation

F5 s V=RTairAð Þ½ �t (3)

where s is the regression slope of the respective gas over 10

min chamber deployments expressed in ppm s21, V is the

chamber volume (m3), R is the universal gas constant (8.2 3

1025 m3 atm mol21 K21), Tair is the air temperature mea-

sured inside the chamber (K), A is the surface area of the

chamber (m2), and t is the conversion factor from seconds to

day and lmol to mmol. The concentration change in the

chamber was linear over the short term deployments (Fig.

3a,b) and therefore we opted for this linear model as

opposed to a non-linear model used for longer deployments

(e.g., Cole et al. 2010).

Although not observed during chamber deployments,

there was a chance of gas ebullition (in addition to diffusive

fluxes) contributing to the observed fluxes, which would

have been reflected as a non-linear increase in CO2 and CH4

concentrations inside the chamber. Therefore, we only used

the slopes of linear regression lines over time intervals of

between 5 min and 10 min with a R2>0.98 to calculate

kCO2 and kCH4 (e.g., Fig. 3a,b). k was normalized to Sc of

600 as a function of temperature and salinity using the equa-

tions given by Wanninkhof (2014), assuming the Schmidt

exponent (n) was 20.5 regardless of different wind speeds

because water turbulences generated by tidal currents were

generally high in the water (Abril et al. 2009).

k6005k 600=Scð Þn (4)

For the NQ deployments, air pCO2 and pCH4 increase inside

the chamber was measured by connecting a closed recirculating

loop to a Cavity-Ring-Down-Spectroscopy (CRDS) analyzer (Pic-

arro; G2308 or G2201-i), and in a second recirculating loop to a

CO2 gas analyzer (LI-COR; LI-820). The G2308 CRDS analyzer

has a precision of 10 ppb1 0.05% of reading for CH4 concen-

trations. The G2201-i CRDS analyzer has a precision of 210

ppb1 0.05% of reading and a 60 ppb1 0.05% of reading for

CO2 and CH4, respectively. The CO2 gas analyzer (LI-COR LI-

820) has a precision of 1 ppm with 1 s signal averaging. Gas

concentrations of CO2 and CH4 were recorded every second

over the 10 min deployments. The CO2 concentrations increase

inside the chamber always exceeded the precision of the instru-

ments. The minimum detectable flux for the setup used was

1.4 mmol m22 d21 of CO2 and 0.01 mmol m22 d21 of CH4.

Water column CO2 and CH4 concentrations were deter-

mined by pumping water from a depth of �30 cm with a

Fig. 3. Typical example of (a) increasing air pCH4 inside the chamber,

increasing pCH4 in the water column, and temperature measured insidethe chamber with a minor temperature change of 0.18C over a 10 min

chamber deployment during incoming tide; and (b) increasing air pCO2

inside the chamber, decreasing pCO2 in the water column, and baromet-ric pressure over a 10 min chamber deployment during outgoing tide.

Rosentreter et al. Gas transfers in estuaries

565

submersible bilge pump to a shower-head air-water equilibra-

tor, where water was sprayed into a chamber creating fine

droplets that maximize gas equilibration. From the shower-

head equilibrator a continuous loop was linked to a CRDS

(Picarro; G2308 or G2201-i), where the dried gas stream was

detected in real-time before returning back to the equilibra-

tor (details see Maher et al. 2013b). pCO2 and pCH4 were

averaged over 10 min chamber deployments. A temperature

logger (HOBO, 6 0.18C) was attached at the top inside the

chamber to measure air temperature during deployments.

Water was pumped continuously into a flow-through cham-

ber, where salinity (6 0.1) and water temperature (6 0.18C)

were recorded every 2 min using a calibrated Hydrolab DS5X

Sonde. Water depth, current velocity, and direction were

measured with an acoustic Doppler current profiler (ADCP-

Argonaut-XR-Flowmeter, SonTek), deployed a minimum of

5 m away from the boat to ensure no interference with boat

movements. A weather station (150 WX, Airmar) was

attached on top of the boat (3 m above water surface) to

measure wind speed at minute intervals during chamber

deployments. Wind speed was recalculated for a 10 m height

(U10) according to Amorocho and DeVries (1980) using the

following equation

Uz5U10 12ðC10Þ1=2

jln

10

z

� �" #(5)

where C10 is the surface drag coefficient for wind at 10 m

height, j is the Von Karman constant (0.41) and z is the

wind speed measured (m) above the water surface. The sur-

face drag coefficient is usually in a range between 1.3 and

1.5 3 1023 for U10 (Stauffer 1980). Because the wind drag

coefficient is reduced over shallow water sites (depth<3 m)

and in the presence of surface films (Van Dorn 1953; Baines

1974; Hicks et al. 1974), we assumed the lower end of the

surface drag coefficient of 1.3 3 1023 for all deployment in

our tidal mangrove creeks (Table 1).

For the Shark River deployments, water column CO2 con-

centration was measured using a membrane contactor

(Liqui-Cel) (Hales et al. 2004) coupled to a CO2 gas analyzer

(LI-COR; LI-820) on a small boat. The CO2 concentration in

the floating chamber was measured every second using a sep-

arate CO2 gas analyzer (LI-COR; LI-820). A weather station

(150 WX, Airmar) was attached 2 m above the water surface

on top of the boat, and wind speed was corrected to U10.

Current velocity data for the main channel and Tarpon Bay

were obtained from the National Water Information System

(USGS). Water depth was determined using bathymetry data

from a previous study in Shark River (see Ho et al. 2014).

Temperature and salinity data were derived from a nearby

station Gunboat Island (25.37888N, 81.02948W; Fig. 1c).

Note all errors presented throughout refer to the standard

error.

Results

A total of 209 k600 measurements (k600-CO2 5 135; k600-

CH4 5 74) were undertaken across large gradients of salinity

(0.1–36), water depth (0.3–6.3 m), and current velocity

(0.01–0.5 m s21). However, wind speed (U10) in all deploy-

ments was low to moderate, ranging from 0.6 m s21 to

7.3 m s21, due to mangrove tree canopy and fetch limitation

Table 2. Average (range) of salinity, water depth, current velocity, wind speed (U10), k600-CO2 and k600-CH4 at study sites in NQ,Australia (study 1, 2, 3) and Shark River main channel, lake, creeks and Tarpon Bay in ENP, Florida, United States (study 4).

Study

location

Salinity

(&)

Water

depth (m)

Current

velocity (m s21)

U10

(m s21)

k600-CO2

(cm h21)

k600-CH4

(cm h21)

Study 1, NQ

JW (n 5 28) 34.8 (34.0–35.5) 0.89 (0.25–1.55) 0.12 (0.01–0.29) 3.03 (1.87–5.21) 6.48 (3.10–12.96) 6.95 (0.89–20.46)

Study 2, NQ

FR (n 5 3) 0.12 (0.12–0.13) 2.13 (1.98–2.29) 0.21 (0.19–0.22) 4.48 (3.97–5.23) 8.32 (7.89–8.55) 13.55 (12.6–14.6)

BR (n 5 10) 29.5 (26.5–30.9) 1.77 (1.65–1.98) 0.14 (0.03–0.27) 1.77 (1.11–2.93) 6.25 (1.31–15.94) 7.58 (1.65–19.51)

JR (n 5 8) 5.6 (3.1–6.3) 2.43 (2.23–2.70) 0.20 (0.10–0.32) 1.22 (0.84–1.96) 4.60 (2.93–8.24) 6.34 (2.99–12.02)

Study 3, NQ

FR (n 5 7) 34.1 (33.7–34.4) 0.97 (0.58–1.63) 0.13 (0.03–0.17) 1.52 (0.62–2.63) 6.45 (4.20–11.62) 2.56 (1.03–5.90)

CC (n 5 6) 30.9 (29.5–32.0) 5.65 (4.95–6.28) 0.40 (0.33–0.45) 4.71 (4.43–4.96) 18.90 (13.80–23.98) 21.65 (10.11–28.27)

BR (n 5 10) 36.6 (36.4–36.7) 0.84 (0.72–1.19) 0.12 (0.06–0.26) 5.21 (4.38–7.30) 8.46 (4.31–14.70) 9.73 (4.74–15.43)

JR (n 5 7) 8.8 (6.8–10.3) 3.81 (3.48–4.14) 0.22 (0.14–0.31) 1.87 (0.98–2.81) 10.07 (2.42–21.83) 11.07 (5.24–16.72)

Study 4, ENP

Main channel (n 5 27) 10.6 (2.2–17.4) 2.50 (1.97–3.75) 0.34 (0.08–0.48) 2.68 (0.67–5.19) 4.59 (1.28–10.13) na

Creeks (n 5 28) 7.1 (2.8–13.1) na na 1.55 (0.60–4.50) 3.99 (1.50–9.21) na

Lake (n 5 4) 9.9 (9.1–11.0) na na 4.89 (3.44–5.64) 5.26 (3.06–7.81) na

Tarpon Bay (n 5 4) 7.0 (6.4–7.5) 1.79 (1.54–2.06) 0.37 (0.20–0.50) 4.07 (2.78–4.62) 13.67 (8.56–17.07) na

Rosentreter et al. Gas transfers in estuaries

566

(Table 2). At all study locations, surface water CO2 and CH4

were supersaturated with respect to the atmosphere. In NQ,

water pCO2 values and CH4 concentrations ranged from 566

to 9945 latm and 23 to 507 nM, respectively, and gas fluxes

were directed toward the atmosphere ranging from 9 to 728

(mean 186.0 6 19.2) mmol m22 d21 for CO2 and 0.02 to 2.7

(mean 0.48 6 0.07) mmol m22 d21 for CH4 (see Supporting

Information S1, S2). ENP CO2 fluxes were also toward the

atmosphere, with values ranging from 56 to 450 (mean

217.7 6 12.8) mmol m22 d21 with water pCO2 values from

2873 to 7844 latm (see Supporting Information S2). Table 2

shows the mean and range of ancillary background parame-

ters, k600-CO2 and k600-CH4 in studies 1 to 4. A similar range

of k600-CO2 values was measured in NQ (1.3–24.0 cm h21,

Fig. 4. Temporal variability of k600-CO2 and k600-CH4 over a 12.1 h tidal cycle in JW. k600-CO2 ranged from 3.1 cm h21 to 13.0 cm h21 and k600-CH4 ranged from 0.9 cm h21 to 20.5 cm h21 with highest values observed at mid-ebb tide.

Fig. 5. Boxplots with median, minimum and maximum observation, and lower and upper quartiles illustrate the spatial variability of k600-CO2 withinand between the main channel, lake, creek and Tarpon Bay in the Shark River catchment, ENP.

Rosentreter et al. Gas transfers in estuaries

567

Fig. 6. Relationship between k600-CO2, k600-CH4 and wind speed (U10), current velocity and water depth (study 1–3). The best fit to the data isgiven by k600-CO2 5 20.08 1 0.26v 1 0.83u 1 0.59h and k600-CH4 5 21.07 1 0.36v 1 0.99u 1 0.87h, using a multiple linear regression model

including current velocity (v), U10 (u), and depth (h).

mean 7.80 6 0.56, n 5 72) and ENP (1.3–17.1 cm h21, mean

4.94 6 0.40, n 5 63). k600-CH4 values, however, were only

estimated from chamber deployments in NQ and showed

higher variability (1.0–28.3 cm h21, mean 8.83 6 0.74,

n 5 74) when compared to k600-CO2. k600-CH4 was on aver-

age 1.2 times higher than k600-CO2 with ratios ranging from

0.26 to 2.31 (expressed as the ratio k600-CH4 : k600-CO2).

Temporal variability of k600 over a tidal cycle

During the deployments over a tidal cycle at Southern

Moreton Bay, k600-CO2 and k600-CH4 were generally highest

at mid-ebb tide (k600-CO2: 13.0 cm h21; k600-CH4: 20.5 cm h21)

when current velocities were highest (> 0.2 m s21), and k val-

ues were lowest at mid-flood tide with a current velocity of

0.092 m s21 (k600-CO2: 3.1 cm h21; k600-CH4: 0.9 cm h21)

(Fig. 4). The mean k600 over the tidal cycle was 6.5 6 0.5 cm

h21 for CO2 and 6.9 6 1.0 cm h21 for CH4 (Table 2). k600 for

CO2 and CH4 followed generally the same trend, however,

k600-CH4 showed higher variability over the tidal cycle than

k600-CO2 (Fig. 4).

Spatial variability of k600-CO2 in the Everglades National

Park

Overall k600-CO2 in ENP ranged from 1.3 cm h21 to

17.1 cm h21 (mean 4.9 6 0.4 cm h21) (Table 2). In the main

channel of Shark River, the mean k600-CO2 was 4.6 6 0.4 cm

h21, while in the small, sheltered creeks around Shark River

k600-CO2 was slightly lower (4.0 6 0.4 cm h21) and at the

lake site k600-CO2 was slightly higher (5.3 6 1.1 cm h21). In

Tarpon Bay, k600-CO2 values were higher ranging from 8.6 to

17.1 cm h21 (mean 13.7 6 1.8 cm h21) (Fig. 5). Wind speeds

(U10) were generally higher in Tarpon Bay (4.1 6 0.4 m s21)

and the lake (4.9 6 0.5 cm h21) than the river channel

(2.7 6 0.3 m s21) and small creeks (1.6 6 0.2 m s21). The cur-

rent velocity in the main channel and Tarpon Bay ranged

between 0.08 m s21 and 0.5 m s21.

Discussion

Factors controlling k600

Overall, the gas transfer velocities of CO2 and CH4 varied

greatly within and between the different mangrove creeks

with k600 ranging from 1.0 cm h21 to 28.3 cm h21 across all

deployments in Australia and the United States (Table 2).

Figure 6 summarizes the relationships between k600-CO2,

k600-CH4 and U10, current velocity and water depth for all

deployments in NQ. Linear, quadratic, exponential, and

cubic functions were tested for best-fit relationships between

k600 and U10 but only a weak exponential relationship was

found for k600-CO2 (R2 5 0.14, p<0.01, n5 67) and k600-CH4

(R2 5 0.09, p<0.01, n5 68). k600-CO2 and k600-CH4 followed the

same trend with weakest correlation to U10<depth< current

velocity. For both CO2 and CH4, the strongest relationship was

found between k600 and current velocity (k600-CO2, R2 5 0.58,

p<0.001, n5 70; k600-CH4, R2 5 0.48, p<0.001, n5 68) (Fig. 6),

suggesting current generated turbulence is the main driver con-

trolling k600 in the mangrove dominated estuaries in NQ. A

stepwise (backward) multiple linear regression analysis showed

the best prediction of k600 was provided by an additive contribu-

tion of current velocity, depth and U10 (Eqs. 10 and 13, Table 3;

Fig. 6). In Table 3, we present our three best empirical models

for prediction of k600-CO2 and k600-CH4, using current velocity,

U10 and depth (Eqs. 8–13). Statistically, there was no significant-

ly difference between Eqs. 9 and 10 for k600-CO2 and between

Eqs. 12 and 13 for k600-CH4 (AIC<2) and usually the simpler

model is chosen over the more complicated. However, when

water depth was included in the model the prediction was

slightly better, therefore we present both equations in Table 3.

Various k600 parameterizations from previously published

relationships were tested to see how well they predicted k600

in this study. The best prediction of k600 in NQ derived from

a combination of the oxygen reaeration coefficient parame-

terization of O’Connor and Dobbins (1958) that accounts for

current velocity and depth expressed as k600 5 1.539 v0.5 h20.5

(Ho et al. 2014) and the quadratic relationship between U10

and k600 5 0.266u2 of Ho et al. (2006) (Table 3). When com-

bined (k600 5 0.266u2 1 1.539v0.5 h20.5) this model predicted

k600 to average 7.99 6 0.4 cm h21 close to the measured

mean k600 of CO2 (7.80 6 0.6 cm h21). The wind speed rela-

tionship of Raymond and Cole (2001) using their floating

chamber data only (k600 5 2.06 e0.37u) also showed good

agreement (7.27 6 0.6 cm h21) with k600-CO2. The combined

parameterization of O’Connor and Dobbins (1958) and Ho

et al. (2006), and Raymond and Cole (2001) based on CO2,

natural or deliberate gas tracer studies, however, underesti-

mated k600-CH4 (8.83 6 0.7 cm h21) (Table 3).

k600-CH4 vs. k600-CO2 and the contribution of a

microbubble flux

There are far fewer studies on CH4 gas transfer velocity

than CO2 (Sebacher et al. 1983; Wanninkhof and Knox 1996;

McGinnis et al. 2015) and many studies use general k parame-

terizations to calculate CH4 (Bastviken et al. 2004; Call et al.

2015; Maher et al. 2015) or N2O flux rates (Harley et al. 2015;

O’Reilly et al. 2015; Maher et al. 2016) with no gas corrections

other than Schmidt number normalization. However, k600-

CH4 has been found to be 1.4–2.9 times larger in 90% of 260

floating chamber measurements in boreal lakes in Canada

(Prairie and del Giorgio 2013). Similarly, k600-CH4 was on

average 2.5 times higher than k600-CO2 using the floating

chamber method in an oligotrophic lake in Germany (McGin-

nis et al. 2015). This study also found generally higher k600-

CH4 than k600-CO2 with ratios ranging from 0.26 to 2.31 (Fig.

7) with an average of 1.2. CH4 flux rates may therefore be

underestimated when calculated based on generic k600 param-

eterizations estimated using other gases (e.g., CO2 or 3He/SF6)

because k600-CH4 was higher when compared to k600-CO2 in

our study and previous studies (Prairie and del Giorgio 2013;

McGinnis et al. 2015). This suggests that Fickian diffusive

Rosentreter et al. Gas transfers in estuaries

569

Tab

le3

.M

ean

(6SE)

gas

tran

sfer

velo

citi

es

of

CO

2an

dC

H4

dete

rmin

ed

from

para

mete

riza

tion

sof

this

stud

yco

mp

are

dto

oth

er

pub

lish

ed

stud

ies.

Stu

dy

loca

tio

nM

eth

od

Eq

uati

on

†V

ari

ab

les

k 600-C

O2*

(cm

h2

1)

k 600-C

H4*

(cm

h2

1)

Th

isst

ud

y(E

q.

8)

Man

gro

veest

uar

ies

FCk 6

00,

CO

25

2.1

51

0.3

5v

(R2

50.5

8)

Curr

ent

velo

city

7.9

7(6

0.4

3)

Th

isst

ud

y(E

q.

9)

Man

gro

veest

uar

ies

FCk 6

00,

CO

25

0.1

11

0.3

2v

10.7

9u

(R2

50.6

7)

Curr

ent

velo

city

,U

10

7.6

1(6

0.4

3)

Th

isst

ud

y(E

q.

10)

Man

gro

veest

uar

ies

FCk 6

00,

CO

25

20.0

81

0.2

6v

10.8

3u

10.5

9h

(R2

50.6

7)

Curr

ent

velo

city

,

U10,

dep

th

7.6

2(6

0.4

4)

O’C

on

nor

an

d

Dob

bin

s

(1958)1

Ho

et

al.

(2006)

Riv

ers

,op

en

oce

an

O2,

3H

e/S

F 6k 6

00,

CO

25

1.5

39

v0.5

h2

0.5

10.2

66

u2

Curr

ent

velo

city

,

U10,

dep

th

7.9

9(6

0.4

2)

Raym

on

dan

dC

ole

(2001)

Riv

ers

an

dest

uaries

FConly

k 600,

CO

25

2.0

6e

0.3

7u

U10

7.2

7(6

0.6

1)

Ho

et

al.

(2016)

Man

gro

veest

uar

y3H

e/S

F 6k 6

00,

CO

25

0.7

7v0

.5h

20.5

10.2

66

u2

Curr

ent

velo

city

,

U10,

dep

th

5.4

9(6

0.3

8)

Borg

es

et

al.

(2004)

Macr

otid

alest

uar

yFC

k 600,

CO

25

1.0

11.7

19

v0.5

h2

0.5

12.5

8u

Curr

ent

velo

city

,

U10,

dep

th

13.9

9(6

0.5

8)

Th

isst

ud

y(E

q.

11)

Man

gro

veest

uar

ies

FCk 6

00,

CH

45

2.0

31

0.4

3v

(R2

50.4

8)

Curr

ent

velo

city

8.9

8(6

0.5

4)

Th

isst

ud

y(E

q.

12)

Man

gro

veest

uar

ies

FCk 6

00,

CH

45

20.7

71

0.4

5v

10.9

2u

(R2

50.5

4)

Curr

ent

velo

city

,U

10

9.0

5(6

0.5

8)

Th

isst

ud

y(E

q.

13)

Man

gro

veest

uar

ies

FCk 6

00,

CH

45

21.0

71

0.3

6v

10.9

9u

10.8

7h

(R2

50.5

7)

Curr

ent

velo

city

,

U10,

dep

th

9.1

0(6

0.6

0)

McG

inn

iset

al.

(2015)

Lake

FCk 6

00,C

H4

53.2

k 600,C

O2

-3.4

Bas

ed

on

k 600,

CO

2

reg

ress

ion

19.2

8(6

1.6

4)

Pra

irie

an

dd

el

Gio

rgio

(2013)

Lake

FCk 6

00,C

H4

5F C

H4/K

0[(

pC

H4) w

ate

r-

(pC

H4) a

ir]‡

Diffu

sive

,

mic

rob

ub

ble

16.8

8(6

0.7

7)

*Measu

red

k 600-C

O2

at

stud

ysi

tes

1–3

inN

Qw

as

7.8

0(6

0.5

6)

cmh

21,

measu

red

k 600-C

H4

was

8.8

3(6

0.7

4)

cmh

21.

†C

urr

en

tve

loci

ty(v

)in

cms2

1,

U10

(u)

inm

s21,

wate

rd

ep

th(h

)in

m.

‡k 6

00-C

H4

incl

ud

es

Pra

irie

an

dd

elG

iorg

io’s

(2013)

mic

rob

ub

ble

com

pon

en

tk D

52.1

md

21.

Rosentreter et al. Gas transfers in estuaries

570

transport may not be the only process driving CH4 gas transfer

at the water-atmosphere interface.

Prairie and del Giorgio (2013) first described the CH4

non-Fickian diffusive component as a “microbubble flux,”

where microbubbles enter the water surface by atmospheric

bubble entrainment or are formed in situ under surface films

or on organic compounds in gas supersaturated environ-

ments (Turner 1961; D’Arrigo 2011). Surface microbubbles

can persist for long periods of time (hours to days) and

depending on their size and depth distribution can raise to

the water surface, dissolve in the upper water layer or stabi-

lize with the surrounding water when equalized (Turner

1961; Vagle et al. 2010). The presence of microbubbles in

the water surface can cause an inhomogeneous distribution

of the gases in the water column, which invalidates the gen-

eral assumption of only Fickian-diffusion processes (and

therefore k600) at the water-atmosphere interface. Because of

the higher solubility of CO2 compared to CH4 it has been

suggested that the CO2 flux is less affected by the microbub-

bles than CH4, implying an enhanced CH4 transfer velocity

when compared to CO2 (Prairie and del Giorgio 2013;

McGinnis et al. 2015). Generally, CH4 bubbles are formed

relatively easily in the water column because of its low solu-

bility and low partial pressure in the atmosphere. According

to Prairie and del Giorgio (2013) the microbubble flux is

only marginally related to wind speed but significantly relat-

ed to the degree of CH4 supersaturation (pCH4water/pCH4air).

We tested the relationship between the estimated micro-

bubble flux and degree of CH4 supersaturation in our study

to see if the microbubble flux component was related to CH4

supersaturation. To estimate the microbubble flux we used

the following equation

FMB5FCH4– kCH4ðcalc: fromkCO2ÞK0 pCH4water–pCH4airð Þ� �

(6)

where the microbubble flux FMB (mmol m22 d21) is computed

from FCH4, the observed CH4 flux calculated from Eq. 3, kCO2

(m d21), the gas transfer velocity calculated from Eq. 2 using

CO2 values corrected to CH4 using Eq. 4. Using Eq. 6 the cal-

culated FMB ranged from 20.13 to 0.95 mmol m22 d21

(mean 5 0.156 0.03 mmol m22 d21) for study 1 to 3. Note,

negative FMB values were derived from 15 pairs where k600-

CO2 was slightly higher than k600-CH4, suggesting no micro-

bubble flux in these cases. The slightly higher k600-CO2 values

relative to k600-CH4 were found predominantly at low tide,

however, there was no clear trend between the k600-CO2 : k600-

CH4 ratio and tidal stage. Another explanation for higher k600-

CO2 values may be chemical enhancement of CO2 in the sur-

face boundary layer, which has been found to have a pro-

nounced effect in equatorial regions with low wind speeds

and large CO2 gas exchange fluxes (Wanninkhof and Knox

1996; Xiao et al. 2014). However, low wind speed (U10<4 m

s21) did not explain the higher k600-CO2 values compared to

k600-CH4 in this study and the discrepancy is most likely

attributed to equilibration time differences in the gas

exchange equilibrator device (Webb et al. 2016). Overall, the

results showed CH4 supersaturation (CH4sat) was significantly

correlated to the microbubble flux (R2 5 0.26, p<0.001,

n 5 72). However, a multiple linear regression model (Eq. 7)

including temperature (T; 8C) and current (v; cm s21) was a

better predictor of FMB (adjR2 5 0.53, df5 71, p<0.001).

FMB5 21:568 1 0:0017CH4sat 1 0:053v 1 0:011T (7)

Figure 8 shows good agreement between the predicted FMB

from this model (Eq. 7) and the calculated FMB from Eq. 6.

(R2 5 0.55, p<0.001, n 5 72). We further computed the per-

centage contribution of the microbubble flux (FMB) to the

Fig. 7. Relationship between k600-CH4 and k600-CO2. k600-CH4 was onaverage 1.2 times higher than k600-CO2. The dashed line represents the

1 : 1 line of equality, and the black line represents the linear regressionline.

Fig. 8. Observed microbubble flux of CH4 using Eq. 6 vs. the predictedmicrobubble flux of CH4 using Eq. 7 for study sites in NQ, Australia.

Rosentreter et al. Gas transfers in estuaries

571

total flux CH4 (FCH4) and found that the FMB portion of the

total flux ranged from 8% to 73% (mean 35%, excluding

negative FMB values, implying no microbubble flux). The

high contribution of the microbubble flux is important for

CH4 flux estimates and FCH4 may be significantly underesti-

mated when calculated based on k600-CO2 and corrected to

k600-CH4. Here, we present a first estimate of a microbubble

flux contribution to total flux CH4 in estuarine systems. The

average contribution of 35% to the total CH4 flux found in

this study is lower than found in a previous study performed

in a freshwater lake (50%) (Prairie and del Giorgio 2013),

which may be due to higher sulphate availability causing

generally lower CH4 saturation levels in estuarine systems

(Burdige 2012). However, the microbubble flux may still be

an important pathway for water to air exchange of CH4 in

estuarine systems and should be taken into account in future

studies estimating air-water CH4 gas exchange.

Another study conducted in a lake extended the microbub-

ble hypothesis and suggested enhanced CH4 flux relative to

CO2 may be due to the presence of microbubbles in the sur-

face layer of the lake (McGinnis et al. 2015). The authors

found microbubbles formed in situ or introduced by atmo-

spheric bubble entrainment (or both) were significantly relat-

ed to water and atmosphere turbulence and the difference

between k600-CH4 and k600-CO2 increased with wind speed

(McGinnis et al. 2015). Our study showed that wind speed

alone was a poor predictor of k600-CH4 (Fig. 6) and the differ-

ence between k600-CO2 and k600-CH4 did not increase signifi-

cantly with U10 (R2 5 0.00002, p>0.5, n 5 66). In tide-

dominated mangrove estuaries with a small fetch, current

generated turbulence appears to be a more important driver

than wind speed (Fig. 6; Table 3, Ho et al. 2016). The differ-

ence between k600-CH4 and k600-CO2, however, only increased

slightly with current velocity (R2 5 0.11, p<0.01), suggesting

the approach of McGinnis et al. (2015) using the difference of

the linear regressions of measured FCH4 and diffusive CH4 flux

based on k600-CO2 (in their case as a function of wind speed)

may not be applicable to estimate the microbubble flux con-

tribution in mangrove dominated estuaries.

In this paper, we present three new equations (Eqs. 11–13,

Table 3) that can be used to calculate k600-CH4 for similar

mangrove estuaries (Table 1) and a microbubble flux model

(Eq. 7) depending on CH4 supersaturation, temperature, and

current that can be added to the diffusive flux estimate of

CH4. However, choosing the most appropriate model for FCH4

may be dependent on site specific conditions. In macro-tidal

mangrove estuaries with relatively high tidal amplitudes and

strong water currents we suggest using one of the Eqs. 11–13

(Table 3), because they include current velocity combined

with wind speed and/or water depth. In estuaries with rela-

tively high CH4 supersaturation or when only pCH4 data is

available, adding a microbubble flux component (Eq. 7) to a

diffusive flux estimate may be a more applicable approach as

this model includes CH4sat.

Temporal variability of k600-CO2 and k600-CH4

Both, k600-CO2 and k600-CH4 varied tidally and were low-

est at mid-flood tide due to the reduced current velocity

(Fig. 4). In JW k600-CO2 correlated well with current velocity

(R2 5 0.62, p<0.001, n 5 24). The same trend was found for

k600-CH4, implying the variability of k600 over the 12.1 h tid-

al cycle was mainly controlled by the tide induced current

turbulence as could be found at all other study sites in NQ.

Figure 9a,b shows the measured gas fluxes in JW com-

pared to predicted CH4 and CO2 fluxes calculated from six

empirically derived models of k600 from previous studies.

First, we tested three k600-CO2 parameterizations from stud-

ies also performed in estuaries: Ho et al. (2016), Borges et al.

(2004), Raymond and Cole (2001), and the combined param-

eterization of O’Connor and Dobbins (1958) and Ho et al.

(2006), which was on average closest to our estimated k600 in

NQ. Observed temporal CH4 fluxes (Fig. 9b) were further

compared to the parameterizations of Prairie and del Giorgio

(2013) and McGinnis et al. (2015), which include the micro-

bubble approach.

The dual gas tracer experiment of Ho et al. (2016) was

conducted concurrently with the floating chamber deploy-

ments in the tidal Shark River, ENP (field campaign study 4).

We used their best fit model (k600 5 0.77v0.5 h20.5 1 0.266u2),

which is a modification of the O’Connor and Dobbins

(1958) and Ho et al. (2006) parameterization to calculate

fluxes in JW. Although this parameterization was derived

from a similar mangrove system the model underestimated

the CO2 flux on average by 33% ranging from 83% underes-

timation to 49% overestimation, and underestimated the

CH4 flux on average by 5% ranging from 76% underestima-

tion to 84% overestimation (Fig. 9a,b). Borges et al. (2004)

suggested a k600-CO2 parameterization (k600 5 1.0 1 1.719v0.5

h20.5 1 2.58u), also a function of wind speed, current velocity,

and depth, derived from floating chamber deployments in

the macro-tidal estuary Scheldt. This parameterization has

been used as a model for estimating k600 in recent mangrove

estuary CO2 and CH4 flux studies (Atkins et al. 2013; Linto

et al. 2014; Call et al. 2015). This model overestimated CO2

(55%) and CH4 (50%) emissions from our mangrove creek

(Fig. 9a,b). Raymond and Cole’s (2001) parameterization

(k600 5 1.91 e0.35u) has also been used in recent mangrove CO2

flux studies (Bouillon et al. 2007; Maher et al. 2013a; Ruiz-

Halpern et al. 2015) and was a good fit to our data, but the

estimated fluxes still varied from an underestimation of 67%

to an overestimation of 65% (mean 5 10% underestimation)

and estimated CH4 fluxes from an underestimation of 79% to

an overestimation of 86% (mean 5 5% underestimation). The

combined parameterization of O’Connor and Dobbins (1958)

and Ho et al. (2006) was also a good fit to our measured

fluxes and overestimated the CO2 and CH4 flux on average by

20% with a variation between 36% underestimation and 66%

overestimation for CO2 and an underestimation of 59% and

overestimation of 87% for CH4 (Fig. 9a,b).

Rosentreter et al. Gas transfers in estuaries

572

Observed temporal CH4 fluxes were further compared to

Prairie and del Giorgio’s (2013) microbubble approach,

which adds a microbubble component of k 5 2.1 m d21 to a

diffusive flux, and McGinnis et al. (2015) k600-CH4-CO2

regression fit (k600-CH4 5 3.2 k600-CO2 - 3.4). Both studies

were derived from floating chamber deployments in lakes

and highly overestimated the observed CH4 flux in our man-

grove creek over the whole tidal cycle (Fig. 9b). CH4 flux

overestimation ranged from 28% to 90% (mean 5 57%) for

the Prairie and del Giorgio (2013) model and was even

higher when we used the regression fit of McGinnis et al.

(2015) (24–97%, mean 5 73%). We attribute this high overes-

timation to a smaller contribution of the microbubble flux

in tidal mangrove estuaries (35%) than in freshwater lakes

(50%) (Prairie and del Giorgio 2013).

Spatial variability of k600 CO2

A total of 63 floating chamber deployments were carried

out in Shark River to investigate spatial variability of k with-

in a mangrove dominated estuary. On average Tarpon Bay

was characterized by a significantly higher k600-CO2 (13.7 6

1.8 cm h21) than was observed in the nearby main channel

(4.6 6 0.4 cm h21), lake (5.3 6 1.1 cm h21), and small creeks

(4.0 6 0.4 cm h21) (Fig. 5). Wind speed was higher in Tarpon

Bay (U10 5 4.1 m s21) when compared to the main channel

(U10 5 2.7 m s21), likely inducing stronger water surface tur-

bulences during floating chamber deployments in Tarpon

Bay. Similarly, wind speed at the lake site was higher

(U10 5 4.9 m s21) when compared to the nearby creeks

(U10 5 1.6 m s21), however, U10 alone was a poor predictor of

k600 including all study sites in ENP (R2 5 0.19, p<0.001,

n 5 63). Current velocity and depth data were only available

for the main channel and Tarpon Bay and although k600-

CO2 had the strongest correlation with current velocity, as

could be found in NQ, due to the lack of site specific current

velocity data (current velocity was only measured at Gun-

boat Island; Fig. 1c) we are not confident in any empirical

relationships fitted to k600-CO2.

Ho et al. (2014) conducted two SF6 tracer release experi-

ments in the Shark River in 2010 (SharkTREx 1) and in 2011

(SharkTREx 2). A third 3He/SF6 tracer release experiment in

the Shark River estuary (SharkTREx 3) was conducted concur-

rently with the floating chamber deployments in this study

(Ho et al. 2016). The revised mean k600 for SharkTREx 1 and

2 were 3.5 6 1.0 cm h21 and 4.2 6 1.8 cm h21, respectively,

and the mean k600 for SharkTREx 3 was 3.3 6 0.2 cm h21 (Ho

et al. 2016). In comparison, we found k600 was on average

4.9 6 0.4 cm h21 including all deployments from surround-

ing creeks, the lake, main channel and Tarpon Bay within

the Shark River estuary. The mean k600 for the main channel

only was 4.6 6 0.4 cm h21. This is the first time the floating

chamber technique can be directly compared to a 3He/SF6

tracer release experiment.

The two methods show good agreement and both studies

found that current generated turbulence is the main driver

for k in Shark River. The discrepancy of 18% (using the

mean k600 of SharkTREx 1, 2, and 3) may be explained by

the diurnal variability of k600 due to higher wind speeds

Fig. 9. Observed flux of CO2 (a) and CH4 (b) in JW vs. a predicted flux

of CO2 and CH4 based on empirically derived models from previousstudies. Parameterizations of Borges et al. (2004), Raymond and Cole(2001), McGinnis et al. (2015), and Prairie and del Giorgio (2013)

derived from floating chamber deployments; Ho et al. (2014) from twoSF6 gas tracer experiments, Ho et al. (2006) from 3He/SF6 dual gas trac-er technique, and O’Connor and Dobbins (1958) from the reaeration

rate of oxygen.

Rosentreter et al. Gas transfers in estuaries

573

during the day than night. The deliberate gas tracer release

experiments SharkTREX 1, 2, and 3 are averages over day

and night (1 week) and during SharkTREx 3 the daytime k600

(3.8 6 1.2 cm h21) was 36% higher than the nighttime k600

(2.8 6 0.8 cm h21) (Ho et al. 2016). The floating chamber

deployments were conducted solely during the day and the

mean k600 (4.6 6 0.4 cm h21) was close to the daytime mean

k600 of SharkTREx 3 (3.8 6 1.2 cm h21). The discrepancy is

even smaller when accounting for the CO2 chemical

enhancement effect in Shark River during SharkTREx 3,

which increases k600 by �1 cm h21 at the observed transfer

velocities, based on a pH of 7.2, temperature of 278C and

salinity of 20 (similar to the conditions observed in Shark

River during the deployments, unpubl. data), using the mod-

el of Hoover and Berkshire (1969), as validated by Wannink-

hof and Knox (1996). This would increase the daytime gas

tracer k600 to �4.8 cm h21, i.e., within 5% of the floating

chamber estimates.

When using an appropriate chamber design the floating

chamber technique can provide accurate site specific k values

at high temporal and spatial resolution that are required in

dynamic ecosystems such as mangrove estuaries. However,

for overall ecosystem gas exchange fluxes k should be mea-

sured over a whole diurnal cycle because using daytime gas

transfer velocities only may overestimate the overall gas

exchange flux. The suggested empirical model derived from

SharkTREx 3 (k600 5 0.77v0.5 h20.5 1 0.266 u2; Ho et al. 2016)

was a good fit (5.1 6 0.2 cm h21) to the k600-CO2 found in

ENP in this study including the main channel, lake, small

creek, and Tarpon Bay (4.9 6 0.4 cm h21). However, this

parameterization underestimated k600 and fluxes of CO2 and

CH4 in NQ (Table 3), which is most likely due to a higher

contribution of U10 to the Ho et al. (2016) model when com-

pared to the models found for NQ (Table 3; Fig. 6).

Putting the effect of spatial variability of k600 into con-

text, we calculated an average CO2 flux (mmol m22 d21) for

Shark River from the mean water pCO2 of 5000 latm and

mean air pCO2 of 400 latm using different k600 values from

the main channel, lake, bay, and creeks. A high flux range of

141 mmol m22 d21 (based on a k600 value of 3.9 cm h21 as

measured in the small creeks) to 496 mmol m22 d21 (based

on a k600 value of 13.7 cm h21 in Tarpon Bay) is estimated

depending on the k600 used. This highlights the differing

water turbulence regime within a single system, resulting in

a high spatial variability of k, which should be considered in

future studies, especially when flux rates are upscaled from

point measurements to estuary scale.

Floating chamber evaluation

The optimized floating chamber design presented in this

paper (Fig. 2) attempts to minimize disturbance of the water

turbulence regime. A similar “flying chamber” design with

submerged flexible chamber walls was tested in a recent study

by Lorke et al. (2015) and suggested as a reliable method with

reduced interference of the flow field underneath the cham-

ber. Although the floating chamber method has been criti-

cized in the past, the side-by-side comparison of the floating

chamber technique and the 3He/SF6 tracer release experiment

in this study (and other studies comparing the floating cham-

ber method, e.g., Vachon et al. 2010; Galfalk et al. 2013)

showed good agreement between the methods. When using

the floating chamber method we suggest a design that

includes flexible submerged chamber walls, a frame with min-

imal contact to the water surface, best possible free move-

ment of the chamber on the water surface, a large ratio of

water surface area to chamber volume, insulation around the

chamber to prevent temperature changes, and a fan attached

inside for evenly dispersed air circulation.

We revealed high spatial and temporal variability of CO2

and CH4 gas transfer velocities within and between six differ-

ent mangrove creeks in Australia and the United States using

the floating chamber method. Overall the empirical models

for k600 (Table 3) demonstrate that the variability of the gas

transfer velocity in shallow mangrove dominated estuaries

under low to moderate wind conditions is driven by current-

generated turbulences. In a side-by-side study, good agree-

ment was found between k determined from this newly

designed floating chamber and a 3He/SF6 dual tracer release

experiment (�5% discrepancy). A direct comparison of k600-

CO2 and CH4 measurement pairs showed k600 values derived

from CH4 were on average 1.2 times higher than those

derived from CO2, most likely reflecting a microbubble flux

contribution. A model that includes CH4 supersaturation,

temperature, and current velocity was a good predictor of

this additional microbubble flux of CH4 found in mangrove

estuaries. The potential for underestimating CH4 evasion

rates due to the presence of a microbubble flux contribution

should be considered in future CH4 flux studies, especially in

ecosystems with high CH4 saturation levels. Ultimately,

mangrove estuaries are dynamic ecosystems and given the

high spatial and temporal variability of k600 and the different

processes driving k600-CO2 and k600-CH4, most accurate esti-

mates of CO2 and CH4 flux rates derive from simultaneous

pCO2, pCH4 and site specific in situ measurements of CO2

and CH4 gas transfer velocities.

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Acknowledgments

We would like to thank Dirk Erler, Ashly McMahon and Rachel Murray forassistance in the field and Iain Alexander for technical support. We further

thank Rik Wanninkhof for the estimate of the chemical enhancement effectin Shark River. Current velocity data for Shark River was obtained from theNational Water Information System (USGS). This project was funded by the

Great Barrier Reef Foundation’s Resilient Coral Reefs Successfully Adaptingto Climate Change research and development program in collaboration

with the Australian Government, and ARC Projects DE150100581,DP160100248, LP110200975, LP150100519 and LE120100156. Funding

for DTH was provided by the National Aeronautics and Space Administra-tion (NNX14AJ92G) under the Carbon Cycle Science Program.

Conflict of Interest

None declared.

Submitted 07 March 2016

Revised 22 July 2016

Accepted 29 August 2016

Associate editor: Leila Hamdan

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