MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 307: 59–68, 2006

Published January 24

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Oxygen exchange flux between sediment and waterin an intertidal sandflat, measured in situ by theeddy-correlation method
Tomohiro Kuwae1,*, Koichiro Kamio2, Tetsunori Inoue1, Eiichi Miyoshi1,
Yusuke Uchiyama3
1

Coastal and Estuarine Environment Research Group, Port and Airport Research Institute, 3-1-1, Nagase,
Yokosuka 239-0826, Japan
2

Tokyo-Kyuei Co., 6906-10, Shiba-tsurugamaru, Kawaguchi 333-0866, Japan

3

Institute of Geophysics and Planetary Physics, University of California at Los Angeles, 3637 Geology, Los Angeles,
California 90095-1567, USA

ABSTRACT: High temporal–resolution fluctuations in oxygen concentration and vertical velocity
were measured over an intertidal sandflat (water depth < 69 cm) using an oxygen microelectrode andan acoustic Doppler velocimeter, in order to estimate oxygen flux across the sediment –water interface using the eddy-correlation method. The effect of flux estimate procedures, including noise
removal and extraction of fluctuating components, was investigated. The estimated oxygen effluxes
from the sediment ranged from –3.2 to 6.6 mmol O2 m–2 h–1 in the light and from –14.5 to –6.6 mmol
O2 m–2 h–1 in the dark. The oxygen-uptake fluxes in the dark were markedly higher than those measured by a conventional enclosure technique. High-frequency turbulence and/or noise (> 5 Hz)
observed in the vertical velocity and oxygen concentration data made little contribution to the total
oxygen flux (0 to 7%). However, trends (steady change over a longer time scale) caused significant
artifacts in the estimated fluxes for several cases. Thus, removal of trends from raw time-series data
is recommended. The co-spectrum of the fluctuating components of vertical velocity and oxygen concentration revealed that the oxygen flux at a frequency band between 0.3 and 1.4 Hz (at a period
from 0.7 to 3.3 s) was a major contributor to the total oxygen flux. This frequency was consistent withthe dominant frequency of vertical velocity, indicating that transport and exchange of porewater andoverlying water by wind-induced waves may be crucial processes to dissolved oxygen flux between
permeable sandy sediments and shallow waters.
KEY WORDS: Sedimentary oxygen flux · Microelectrode · Acoustic Doppler velocimeter · ADV ·Permeable sediment · Banzu tidal flat
Resale or republication not permitted without written consent of the publisher

Conventionally, oxygen-exchange fluxes across thesediment –water interface are measured by 2 approaches: (1) tracking changes in oxygen concentration in
the overlying water of enclosed benthic systems,
including intact sediment core incubation in the laboratory and in situ benthic chamber deployment; and(2) determining oxygen concentration profiles in thebenthic boundary layer by microsensors (e.g. Jørgensen & Boudreau 2001, Reimers et al. 2001, Wenzhöfer & Glud 2004, de Beer et al. 2005, and references
therein). However, both approaches have beenreported to have limitations. Hydrodynamic forcing,such as waves and currents, is almost never represented in enclosed benthic systems, even though it is
known to have a significant effect on solute fluxes
between sediment and water (e.g. Jørgensen & Des*Email: kuwae@ipc.pari.go.jp© Inter-Research 2006 · www.int-res.com

INTRODUCTION

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Mar Ecol Prog Ser 307: 59–68, 2006

Marais 1990, Huettel & Webster 2001). Moreover, complete enclosing of benthic systems may enhance artifacts for permeable sandy sediments, because thewater exchange generated by horizontal pressure gradients and/or turbulence is blocked (e.g. Huettel &Gust 1992). The oxygen concentration profiling
method, in turn, measures a local point flux using a
1-dimensional concentration gradient; hence, theeffects of bioturbation and bioirrigation occurringaway from the oxygen microsensor cannot be determined.
A newly developed technique to measure exchange
fluxes is the eddy-correlation method. This method has
been used to measure such energy and material fluxes
in coastal marine environments as heat under sea ice(Shirasawa et al. 1997) and suspended sediment in an
estuary (Dyer et al. 2004). Berg et al. (2003) first
applied the eddy-correlation method to measure oxygen-uptake fluxes by aquatic sediments. The eddycorrelation method can overcome the disadvantages ofthe 2 conventional methods because of its (1) applicability for in situ measurements without creating artifacts (no alterations of in situ environment) and (2) lackof limitations regarding seabed characteristics such as
sediment permeability, topography, and vegetation.
The eddy-correlation method is based on the principlethat vertical flux is expressed by an average of the
product of fluctuating vertical velocity and passive
scalar, such as dissolved oxygen. However, a practical
protocol and quality controls have not yet been investigated thoroughly for the eddy-correlation method
applied to oxygen flux, particularly to extracting fluctuating components from the raw time series of verticalvelocity and oxygen concentration and to determining
sufficient sampling time.
Here, we report on high time-resolution fluctuationsof oxygen concentration and vertical current velocity
in the overlying water during both daytime and nighttime in an intertidal sandflat, and the oxygen flux
across the sediment –water interface estimated by theeddy-correlation method. The calculation methodology, including fluctuating component extraction andthe length of sampling time required, is discussed inorder to estimate the oxygen flux accurately. We also
describe the importance of wind-induced waves as adriving force of oxygen transport and flux in the permeable sandy sediment ecosystem, by comparing thespectral peak frequencies.

MATERIALS AND METHODS
Study site. The study site, Banzu intertidal sandflat,
is located on the east coast of Tokyo Bay (Japan) andcovers an area of 7.6 km2. Tokyo Bay receives nutrient loading from a human population of ca. 26 million and is subjected to heavy eutrophication andanoxia in bottom waters over a wide area in summer.
Tides are semi-diurnal, with amplitudes from 0.5 to
1.6 m (Guo & Yanagi 1994). The site (35° 24.2’ N,
139° 53.4’ E) experiences emersion and immersion
during each tide.
We conducted 6 series of in situ measurementsfrom daytime (Cases 1 to 4) to nighttime (Cases 5
and 6) on February 25, 2004 (Table 1). The slope ofthe seabed at the site is gentle (0.07 cm m–1). Sediments are characterized by well-sorted fine sand(> 99.5%), with a median grain size of 188 to 217 µm
and an intrinsic permeability (k) of ca. 7 × 10–12 m2.
Total organic carbon and total nitrogen contents inthe top 3 cm of sediment on the measurement day
were 0.630 ± 0.022 mg C g–1 dry wt (mean ± SE, n =12) and 0.110 ± 0.004 mg N g–1 dry wt (mean ± SE,
n = 12), respectively.
There is no macro-vegetation, and pennate diatoms
dominate the epibenthic microalgal flora. Chlorophyll a content in the top 1 cm of sediment was 5.7 ±0.7 µg cm– 3 bulk sed. (mean ± SE, n = 12) on the measurement day. Bubble formation on the sediment surface by microalgal photosynthesis, which can affectoxygen-flux estimation, was not observed at the site.
The ghost shrimp Callianassa japonica (Decapoda:
Thalassinidea) dominated 28% of the macrofauna on adensity basis (41 ± 24 ind. m–2) (mean ± SE, n = 8) and
91% of the macrofauna on a soft body dry weight basis(0.7 ± 0.7 g dry wt m–2) (mean ± SE, n = 8).

Table 1. Ranges of overlying water parameters during each time series. Mean values are indicated in parentheses

Case 1Case 2
Case 3
Case 4
Case 5
Case 6

Measurement
period (min)

Depth
(cm)

Temperature
(°C)

18
12
13
40
16
35

38–46 (42)
63–69 (65)
53–60 (56)
39–57 (49)
36–42 (39)
36–38 (37)

10.7–10.9 (10.8)
11.0–11.8 (11.4)
11.3–11.6 (11.4)
11.6–12.5 (11.8)
13.3–14.0 (13.7)
11.8–12.0 (11.8)

Dissolved oxygen(µM O2)(% air saturation)
288.6–298.6 (293.5)
297.2–311.0 (304.0)
301.3–305.8 (303.6)
282.5–289.9 (286.0)
264.0–266.6 (267.0)
271.3–275.5 (273.5)

95–98 (97)102–103 (102)97–97 (97)
100–103 (101)
98–99 (98)
97–98 (98)61

Kuwae et al.: O2 flux measurement using eddy-correlation

Measurements. Current velocity was measured
using an acoustic Doppler velocimeter (ADV) (Vector,
Nortek). The ADV measures 3-dimensional velocity ina cylindrical measurement volume (14 mm height,
14 mm in diameter) located on the centerline, 157 mm
from the base of 3 acoustic transducers (Fig. 1). The
velocity was corrected against the speed of soundvarying with temperature and salinity of the ambient
seawater. The accuracy documented by the manufacturer is ± 0.5% of the measured value ± 0.1 cm s–1.
Oxygen concentration was measured using a
Clark-type oxygen microelectrode (OX-10, Unisense).
The sensor has a 4 µm diameter tip and a < 0.3 s
response time for a 90% response to concentration
changes (documented by the manufacturer). The sensor was connected to a pico-ampere meter (PA-2000,
Unisense). The pico-ampere meter has a responsetime of < 0.05 s (documented by the manufacturer).
Preliminary laboratory experiments confirmed that a
combination of the pico-ampere meter and the oxygen microelectrode was capable of detecting small
concentration changes stably with a < 0.3 s responsetime.
The ADV and the oxygen microelectrode were
mounted on a rigid stepladder (Fig. 1). The stepladder
was installed at a different location for each case. In
addition, the stepladder was placed in a direction perpendicular to the dominant direction of tidal currents
for each case, in order to avoid artifacts in flux estimates due to disturbance of current fields by the
stepladder pods. The position of the measurement volume was adjusted 7 to 17 cm above the sediment sur-face. The tip of the microelectrode was positioned
immediately adjacent (< 5 mm) to the measurement
volume of the ADV. Current velocity and oxygen concentration were simultaneously measured at a frequency of 20 Hz and recorded on a data logger (NR2000, Keyence). The ADV, the pico-ampere meter, andthe data logger were powered individually by internal
batteries to minimize synchronized noise.
To exclude errors in oxygen concentration data due
to possible drift or sensitivity variation in the oxygen
microelectrode during measurement periods, the oxygen data were crosschecked with the alternative oxygen data simultaneously obtained using a galvaniccell–type sensor (DO meter) (MDO-I, Sanyo-Sokki)
mounted on the stepladder (Fig. 1). Only the timeseries data that were consistent with the DO meterdata were used for oxygen-flux calculation (Fig. 2).
Flux calculation. Procedure for flux calculation issummarized in Fig. 2. In addition, details and the principle of the eddy-correlation method are described by
Berg et al. (2003). Briefly, advection is supposed to be
the dominant solute transport in the water column
above the diffusive boundary layer. Under such a
condition, the vertical solute flux is:
J = wC(1)where J is the instantaneous flux, w is the vertical current velocity, and C is the solute concentration. Both w
and C are also decomposed into 2 components: w = w
3+
3 + C’, where w
w ’ and C = C
3 is the mean vertical veloc3 is the mean
ity, w ’ is the vertical fluctuating velocity, C
solute concentration, and C ’ is the fluctuating soluteconcentration. The separations are substituted into Eq. (1), and then averaged
over a period of time significantly longer
than turbulent fluctuations; averages ofboth w ’ and C ’ become equal to zero bythis operation. The mean vertical velocity (w3) is assumed to vanish, then Eq. (1)can be expressed as:
J - = w ’C ’

(2)

where J - is the mean flux over time.

Fig. 1. Schematic diagram of sensor installation (ADV: acoustic Doppler velocimeter; DO: dissolved oxygen)

Therefore, the extraction methodologyof these 2 fluctuating components, w ’and C ’, from measured time-series datais critical for accurate quantification ofoxygen flux, which is discussed indetail below. Since J - is calculated as
the time-averaged flux, changes in the
instantaneous flux, varying with environmental conditions such as light andtemperature, are averaged over theduration of the time series used for
calculation.

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Mar Ecol Prog Ser 307: 59–68, 2006

Raw time series of oxygen concentration measured by microelectrode (C t1, C t2, C t3, ..., C tn )Compare with time series of oxygen concentration measured by DO meterand select the duration in which both data are identical (from t1 to tm )Selected time series of oxygen concentration (C t1, C t2, C t3, ..., C tm )Corresponding time series of velocity measured by ADV (w t1, w t2, w t3, ..., w tm )Remove highNo treatment or frequency fluctuation or
and spike by filtering

Remove trendorby linear fitting

Remove high frequency
fluctuation and spikeand then remove trend

Time series of processed oxygen data C
Time series of processed velocity data w
Extract fluctuating component C’
by calculating C – C, where

ADV

period because of inevitable inhomogeneities and ‘folding’ errors in the lowfrequency spectrum when using a fastFourier transformation (Frieche et al.
1991, von Storch & Zwiers 1999).

RESULTS AND DISCUSSION
Environmental conditions

Value

The duration of the time series used
for flux calculations ranged from 12 to
40 min (Table 1). Changes in water
Extract fluctuating component w’
Oxygenby calculating w – w, where
microelectrodedepth during each measurement werew = w t1 + w t2 + w t3 + ... + w tm
2 to 18 cm. Changes in temperature andt1tmtn
oxygen concentration in the overlying
Time
Time series of C’ (C’ t1, C’ t2, C’ t3, ..., C’ tm )Time series of w’ (w’ t1, w’ t2, w’ t3, ..., w’ tm )water during each measurement wereSpectrum analysis
Time series of w’C’ (w’ t1C’ t1, w’ t2C’ t2, w’ t3C’ t3, ..., w’ tmC’ tm )0.2 to 0.9°C and 2.6 to 13.8 µM O2,
respectively. Percent air saturation of
Calculate w’ t1C’ t1 + w’ t2C’ t2 + w’ t3C’ t3 + ... + w’ tmC’ tm
oxygen exhibited almost saturated
Oxygen flux J (mean flux from t1 to tm )values for all cases.
Maximum wave height ranged from
Fig. 2. Procedure for flux calculation
3 to 9 cm (Table 2). Directional changes
against major current direction (perpenTo investigate the effects of turbulence and noise indicular to the stepladder) during each measurement
high-frequency components, w ’ and C ’ were low passranged from ± 4 to ±10°. The mean velocities of horizonfiltered to 5 Hz (Dyer et al. 2004), and spikes were retal and upward vertical currents ranged from 13.8 to
moved from filtered data by replacing variations > 3 SD
24.1 cm s–1 and from –1.2 to 1.0 cm s–1, respectively.
with an interpolated value (cf. French & Clifford 1992).
Few data were removed as spikes. The calculated fluxes
using the filtered data were compared with those using
Time series of vertical velocity andthe unfiltered data. To examine the fluctuation characoxygen concentration
teristics of vertical velocity, oxygen concentration, andTemporal variation in oxygen concentration
oxygen flux, the spectra of w ’ and C ’ and the 1-sided cospectrum of w ’C ’ were analyzed using a fast-Fourier
throughout the measurement period showed a significant linear change over time (trend) for all cases (n =transformation of the time series of w ’ and C ’. The relative contribution of different frequencies of oxygen flux
14 000 to 47 400, p < 0.0001) (Fig. 3), although the coefto the total oxygen flux can be evaluated by the co-specficient of determination in linear fitting was variable
trum analysis of w ’C ’ (Frieche et al. 1991, Berg et al.
(r2 = 0.02 to 0.97). Since the microelectrode data were2003). We described only the spectra and co-spectra withconsistent with the DO meter data, errors due to drift or
sensitivity variation did not account for the trend.
frequencies >1/30 of the length of each measurement
C = C t1 + C t2 + C t3 + ... + C tm

DO meterTable 2. Maximum wave height and ranges of current direction and velocity during each time series. Sensor position indicates distance from the sediment surface. Angles against the major current direction (perpendicular to the stepladder) are given as current
direction. Velocity values are averages for 1 min; the data are filtered but not detrended. Mean values are indicated in parentheses

Case 1Case 2
Case 3
Case 4
Case 5
Case 6

Maximum
wave height (cm)Sensorposition (cm)Current
direction (°)

Horizontal
velocity (cm s–1)

Upward verticalvelocity (cm s–1)

5
3
4
5
9
9

17
17
17
17
14
7

±7
±8
±4
±9
±6
±10

9.8–19.4 (16.9)
11.8–18.1 (13.8)
20.4–26.3 (24.1)
11.8–17.0 (13.9)
14.9–16.8 (15.5)
14.3–18.9 (16.1)–0.7 to –0.1 (–0.4)
0.0 to 1.4 (1.0)–1.4 to –0.7 (–1.2)–0.6 to 0.3 (–0.2)–1.2 to –0.5 (–0.9)–1.4 to –0.7 (–1.0)

63Vertical velocity (cm s–1)

Kuwae et al.: O2 flux measurement using eddy-correlation

10

5

0
–5

Oxygen conc. (µM O2)

Vertical velocity (cm s–1)(a)Oxygen concentration (µM O2)

–10
300

(b)

298
296

6

2
0
–2
–4
–6
296

(b)

294

292

290
0

294

(a)

4

2

4

6

8

10

Time (s)

292
290
288
0

2

4

6

8
10 12
Time (min)

14

16

18

20

Fig. 3. Raw time series of (a) vertical velocity and (b) oxygen
concentration throughout the measurement period (Case 1).
Data were re-sampled with frequencies <1 Hz to improve
clarity. Positive velocity values indicate upward direction. A
non-smooth inflection point is seen in the time series ofoxygen concentration (5 to 6 min)Hence, the trend presumably reflected a long-term net
change in oxygen concentration in the water column.
Vertical velocity did not show a steady change
throughout the measurement period (r2 < 0.02) (Fig. 3),
yet linear fitting was significant for all cases (n = 14 000
to 47 400, p < 0.0001).
The vertical velocity and the oxygen concentration
exhibited a synchronized rhythmic fluctuation, with aperiod of 1 to 3 s on a short time scale for all cases(Fig. 4). For example, in Case 1, the oxygen concentration was high when the direction of the vertical velocity was upward, indicating that oxygenrich water was rhythmically advected upward fromthe benthic boundary layer. This correlation resulted
in a net oxygen transport from the sediment. Obviously, oxygen was supplied through active photosynthesis by microphytobenthos on the sediment surface under a sufficient amount of irradiance for Case
1. Conversely, there was an inverse correlation in thefluctuation patterns between the oxygen concentration and the vertical velocity in the dark (Cases
5 & 6), i.e. the oxygen concentration was low when
the direction of the vertical velocity was upward, due
to oxygen consumption by the sediment (data notshown).

Fig. 4. Raw time series of (a) vertical velocity and (b) oxygen
concentration measured 17 cm above the sediment at a frequency of 20 Hz (Case 1). Positive velocity values indicate
upward direction. Irradiance in air was 650 to 970 µmol
photons m–2 s–1

Effects of high- and low-frequency fluctuations
and trends
High-frequency components (> 5 Hz) were detectedin the vertical velocity and oxygen concentration fluctuating at a period of 1 to 3 s (Fig. 4). Possibly both real
turbulence and noise were included in the highfrequency components for the vertical velocity. However, most of the high-frequency components in theoxygen concentration were caused by noise, because
the oxygen microelectrode used here had a < 0.3 s response time. Spectrum analysis revealed that the contribution of high-frequency components to the total
power spectral density of w ’ was low (6.8 ± 3.0%)(mean ± SE, n = 6) and that of high-frequency components of C ’ to their total spectral density was moderate(13.7 ± 2.2%) (mean ± SE, n = 6) for all cases (Fig. 5). To
investigate the relative contribution of high-frequency
components to the total flux, oxygen fluxes were calculated for both the raw data and the filtered data
(Table 3). Extraction of w ’ and C ’ was performed by
3 for the raw data or filtered datasubtracting w
3 and C
from the corresponding w and C (see Fig. 2). Results
showed that high-frequency components (> 5 Hz) in w ’
and C’ had little effect on the total oxygen flux (0 to 7%).
Analysis of cumulative co-spectrum of w ’C ’ allows us
to determine the relative contribution of different frequencies to the total flux (Frieche et al. 1991, Berg et al.
2003). The co-spectrum of w ’C ’ confirmed that the contribution of high frequencies to the total flux was negligible for all cases (0.9 ± 0.5%) (mean ± SE, n = 6)

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Mar Ecol Prog Ser 307: 59–68, 2006

1.2

1.2

0.8

0.6
0.4
0.2

0.6
0.4
0.2

Normalized spectrum

Normalized spectrum

(a)

1.0
0.8

(a)

1.0

0.0
1.2

(b)

1.0
0.8
0.6
0.4
0.2
0.0
10–2

10–1

100

101

(b)

0.5
0.0
–0.5
–1.0
–1.5
0.2
0.0
–0.2

Frequency (Hz)

–0.4
–0.6
–0.8

Fig. 5. Representative data for the cumulative spectra of (a) w ’and (b) C ’ (Case 4). The spectra were cumulated from thehigh-frequency side. The frequency band with a steep gradient indicates a major contributor to the total spectrum. Detrended but unfiltered data were used for spectrum calculation(Fig. 6). Noise contained in the high-frequency band of
the velocity and oxygen concentration was presumably
random and non-correlated (white noise), resulting in
the negligible contribution to the total flux. In addition,
little contribution of high-frequency components suggested that the oxygen microelectrode used here, witha < 0.3 s response time, would capture importantfluctuations (Berg et al. 2003).
Steady changes in velocity and concentration overlonger time scales (trends) have been reported tocause errors in extraction of fluctuating components
from time-series data, because of overestimation of turbulent fluctuations (Panofsky & Dutton 1984). Sincetime-variant fluctuations in velocity and concentration
seem to be often observed in in situ environments,
detrending may be a prerequisite to calculate fluxes.

0.0
–0.2
1.5
1.0

–1.0
–1.2
10–2

(c)
10–1
100
Frequency (Hz)101

Fig. 6. Cumulative co-spectrum of w ’C ’ in (a) the dominantperiod of oxygen production (Case 1), (b) the intermediate
period (Case 4), (c) and the dominant period of oxygen consumption (Case 5). The spectrum was cumulated from thehigh-frequency side. The frequency band with a steep gradient indicates a major contributor to the total co-spectrum(total oxygen flux). Positive values indicate effluxes from thesediment. Detrended but unfiltered data were used forco-spectrum calculation for each caseFor example, in our case, the trend observed in theoxygen concentration (Fig. 3) was probably caused bya long-term net change in the oxygen concentration in
the overlying water, but was not the real turbulent
fluctuation needed for eddy-correlation. Technically,
trends have been removed by subtracting lowfrequency variations, evaluated by linear or curve fit-

Table 3. Oxygen fluxes (mmol O2 m–2 h–1) calculated after various data treatments. Positive values indicate effluxes fromthe sediment

Case 1Case 2
Case 3
Case 4
Case 5
Case 6aNon-treatment

Filtering

Detrending

Filtering& detrending

11.0
46.5
–3.9
–3.3
–14.7
–6.5

10.3
46.0
–3.9
–3.3
–14.7
–6.4

7.0
2.9
0.5
–3.2
–14.5
–6.6

6.6
2.6
0.5
–3.2
–14.5
–6.6

The last segment (< 2 min) for each case was excluded from calculation

Filtering & block averaginga
Mean ± SE
n5.1 ± 0.8
0.1 ± 1.7
0.4 ± 0.9
–2.5 ± 0.6–13.8 ± 1.1–6.5 ± 0.4

8
5
6
19
8
17

Kuwae et al.: O2 flux measurement using eddy-correlationting or by moving averages, from the original data. In
this study, detrending was performed using linear fitting, and the calculated fluxes using the detrended
data were compared with those using the raw data.
Calculation of w ’ and C ’ was performed by subtracting
3 for the raw data or detrended data from thew
3 and C
corresponding w and C (see Fig. 2). A marked difference in the calculated fluxes was observed between
detrending and non-treatment for several cases (0 to94%) (Table 3). In particular, for Case 2, the steep
slope of linear fitting and high fitness (r2 = 0.97) in theoxygen concentration appeared to cause substantial
differences in the estimated fluxes. In contrast, nomarked difference was found for Case 6, in which theslope of linear fitting and fitness were low. Nevertheless, our results suggest that detrending is needed
when the eddy-correlation method is applied forstrong time-variant conditions (Berg et al. 2003), which
frequently occur in intertidal flat systems.
Detrending with linear or curve fitting, as well aswith a moving average, has a limitation for the timeseries data that includes non-smooth inflection points,
as seen in Fig. 3. Short-term flux measurements or
averaging procedures would be an alternative strategy
to minimize the effect of trends without using the fittings or moving averages, because the temporal variances of velocity and solute concentration may besmaller for shorter times. Therefore, we calculated
oxygen fluxes by ‘block averaging’ (Table 3). Time
series of filtered data for each case were divided into
2 min segments, and the flux for each segment wascalculated according to Fig. 2. For instance, in Case 1(total length of time series: 11.8 min), 5 fluxes were calculated from 5 segments of 2 min, and the last segment(1.8 min) was not used for the flux calculation. It should
be noted that these flux calculation results neverinclude the contribution of the < 0.008 Hz frequency (aperiod of > 2 min) component, that is, block averaging
can lead to an error in total flux estimates due to theneglect of low-frequency contributions. Thus, a difference in the estimated flux between the block-averagemethod and the filtering and detrending method could
suggest a contribution of the < 0.008 Hz frequency tothe total flux. Nevertheless, it is impossible to accurately separate low-frequency components into thereal turbulent fluctuations that reflect sediment –water
flux and the apparent time-variant fluctuations that are
independent from the flux (e.g. Frieche et al. 1991,
Dyer et al. 2004). Therefore, we have to compromise
between exclusion of low-frequency flux contributions
and the increase of uncertainty in estimated fluxes due
to inclusion of time-variant fluctuations.
We described only the co-spectra with frequencies>1/30 of the duration of the time series because of
inevitable inhomogeneities and ‘folding’ errors in the65

low-frequency spectrum when using a fast-Fouriertransformation (Frieche et al. 1991, von Storch &Zwiers 1999). However, the possibility of a lowfrequency (<1/30 of the duration of the time series)
contribution to the total oxygen flux was indicated by
the difference in the estimated flux between the blockaverage method and the filtering and detrending
method. One explanation for the processes responsible
for the low-frequency fluxes can be bioirrigationinduced flux, including ventilation by bivalves, polychaetes, and borrow-building shrimps (e.g. Koike &Mukai 1983, Mukai & Koike 1984, Kinoshita et al.
2003). Intermittent and periodic burrow irrigation isoften observed for deposit feeders (e.g. Kristensen
1988, Forster & Graf 1995, Stanzel & Finelli 2004).
Thus, the thalassinidean shrimp Callianassa japonica,
which dominated our site, is a candidate for explaining
the low-frequency oxygen flux. However, if the entire
population within a footprint (the location where contributions to the oxygen flux are detected by the eddycorrelation method) is considered, the irrigationinduced oxygen fluctuation caused by individual
C. japonica should be overlapped and more frequent,unless the irrigation of all individuals are in phase.
Therefore, we concluded that the low-frequency oxygen flux was not caused by irrigation of C. japonica,
but by unknown mechanisms.

Evaluation of estimated fluxes
The oxygen exchange fluxes observed in this study
were comparable to those observed in previous studies
at the site using the core-incubation method, in thelight (range: 1.9 to 19.3 mmol O2 m–2 h–1, mean ± SE:
7.7 ± 1.0 mmol O2 m–2 h–1, n = 32) and in the dark
(range: –14.5 to –0.5 mmol O2 m–2 h–1, mean ± SE: –3.2
± 0.6 mmol O2 m–2 h–1, n = 32) (Kuwae et al. 1998,
T. Kuwae et al. unpubl. data). The high oxygen-uptake
rates observed in Cases 5 and 6 were also comparable
to results from the permeable sandy sediments of the
Wümme River using the eddy-correlation method(mean ± SE: 8.8 ± 0.7 mmol O2 m–2 h–1) (Berg et al.
2003). However, there was great variation between
cases in our results. The variation is attributed to either(1) true variability or (2) artifacts caused by our measurement system. For the former, differences in themeasurement conditions, including irradiation andtemperature, would affect the fluxes and induce thevariation between cases. Moreover, the position of
footprints differed between cases, because of differences in the installed position and direction of the
stepladder. Hence, the great variation between cases
may account for the horizontal and temporal heterogeneity of the actual oxygen fluxes.

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Mar Ecol Prog Ser 307: 59–68, 2006

The possibility of artifacts may arise from sensor
capabilities and the effect of the stepladder on the flow
field of footprints. As for the sensor capabilities, ourresults should not be affected by the sensor specification, because (1) the contribution of high-frequency
components (> 5 Hz) to the total flux was small, which
has been discussed above, and (2) a capacity for
detecting small concentration changes with a < 0.3 s
response time was provided by our pico-ampere meterand oxygen microelectrode, as was confirmed bypreliminary laboratory experiments.
A footprint has been reported to be located upstream
from the sensor position and to be oval shaped, withthe longer axis being parallel to major current direction; factors affecting the area of footprints include thevertical position of the sensor and bed roughness (e.g.
Kljun et al. 2002). Adjustment of the direction of the
stepladder perpendicular to the dominant direction of
tidal currents for each case would have avoided creating artifacts in the flow field of the footprints. However,since we did not examine the accurate location andarea of the footprint using numerical simulations, weare unable to absolutely demonstrate that the stepladder did not have an effect on the estimated fluxes.
The result of the block-average method does, however, suggest that the effect of the stepladder was negligible and that our system can be applied for sites
influenced by waves. This is because, even though theenvironmental conditions during the measurement
were not stationary for all cases, the standard error of
the fluxes calculated by block averaging was relatively
small and comparable to that in previous studies at thesite.
Another potential source of error might be that themean vertical velocity was slightly off zero (Table 2).
This probably occurs because the sediment surface is
not completely flat and horizontal in natural environments. In addition, (1) use of the burst average insteadof the ensemble average for mean velocity calculationsand (2) the accuracy of the ADV could also be sourcesof error. However, the problem of non-zero verticalvelocity has been reported to be negligible whenapplied for scalar flux calculation (e.g. Anthoni et al.
2004, Dyer et al. 2004).

Importance of waves
The cumulative spectrum of w ’ showed that the frequency band of 0.3 to 1.4 Hz (0.7 to 3.3 s) had a steep
gradient (Fig. 5) and was a major contributor to thetotal spectrum power. This pattern was also detected in
the time-series data (Fig. 4) and spectra of w ’ for allcases; the fluctuations of w ’ in the frequency band of
0.3 to 1.4 Hz accounted for 60.8 ± 5.1% (mean ± SE, n =6) of the total variances. These frequencies were consistent with those in the power spectra of wave heightat the site (the peak periods increase up to 4 s), indicating that the orbital velocity induced by wind-driven
waves dominated the hydrodynamics at our site
(Uchiyama 2006). Moreover, the cumulative co-spectraof w ’C ’ in both the dominant periods of oxygen production (Cases 1 and 2) and the dominant periods ofoxygen consumption (Cases 5 and 6) also had peaks inalmost the same band as w ’ (Fig. 6). The 0.3 to 1.4 Hz
frequency flux (co-spectrum) accounted for 84.0 ±
9.1% (mean ± SE, n = 4) of the total fluxes. Therefore,
we conclude that wave-induced vertical velocity witha predominant frequency of 0.3 to 1.4 Hz seems to be
the most important process to control the oxygen flux
between the sediment and the overlying water. Waveinduced oscillating flow drives advective transport ofporewater and exchange between porewater andoverlying water (Webb & Theodor 1968, Riedl et al.
1972). Shallow-water waves can increase exchange
between porewater and overlying water up to 3 ordersof magnitude more than the exchange by molecular
diffusion (Precht & Huettel 2003, 2004). As a result ofwave action, porewater is released from ripple crests,
and overlying water is inundated into ripple troughs(Shum 1992, 1993, Ziebis et al. 1996a, Precht & Huettel
2003, Precht et al. 2004). Possibly, our results directly
captured these processes using the ADV and the oxygen microelectrode. In the dominant period of oxygen
production, the oxygen-rich porewater formed by photosynthesis in the surface sediment may be forced into
the overlying water and then transported upward as apassive solute by the vertical flow created by windinduced waves, with the predominant frequencies
observed here. In the same manner, in the dominantperiod of oxygen consumption, the oxygen-poor porewater formed by respiration and chemical oxidation in
the surface sediment may be forced into the overlying
water by wave-induced flow. Moreover, release ofporewater would be enhanced when sediment resuspension occurs, which may affect the oxygen flux.
The importance of wind-induced waves, with a peakfrequency around 0.1 to 2 Hz, as transporters is likely
to depend on water depth, because orbital flow velocities vary with (1) water depth, according to the infinitesimal wave theory (Phillips 1977), and (2) wave
height, which is also affected by water depth in shallow waters (e.g. Uchiyama 2006).
A characteristic pattern was observed when oxygen
production and consumption were close to being balanced (Cases 3 and 4) (Table 3, Fig. 6b). The cumulative co-spectrum of w ’C ’ showed that the direction offlux inverted within the spectrum; the oxygen-release
flux occurred in the 1 to 2 Hz frequency band, and theoxygen-uptake flux occurred in the 0.01 to 0.5 Hz fre-

Kuwae et al.: O2 flux measurement using eddy-correlationquency band. The observation that the oxygen-release
flux peaked at a frequency of 1 to 2 Hz was consistent
with the peak spectrum of w ’ (Fig. 5); thus, it may be
attributed to the wave-induced vertical transport ofoxygen formed by the microphytobenthos on the sediment surface, which is the same mechanism as
described above. However, to understand more about
the oxygen-uptake flux that peaked at a frequency of
0.01 to 0.5 Hz further investigation is needed. Webster(1992) showed that the presence of waves leads tooscillatory motion within macrofaunal burrows. The
motion is driven by the different pressures at the openings of burrows. Since thalassinidean shrimps, which
dominated our site, often form a complex burrow system with multi-chambers and multi-openings (e.g.
Mukai & Koike 1984, Ziebis et al. 1996b, Shimoda &Tamaki 2004), flow through the burrows would be very
complicated. The friction to flow in the complex burrow and the geometry of the burrow, such as opening
diameter and passage length, may affect the oscillatory burrow flow and could lower the flow frequency.
Moreover, burrow walls are characterized by both
higher microbial abundance and higher oxygen
uptake than non-burrow sediments (e.g. Aller 1988,
2001, Kinoshita et al. 2003, Wenzhöfer & Glud 2004).
Thus, oxygen-poor burrow water could be released tothe overlying water. These processes may explain why
the oxygen-uptake flux peaked at a frequency of 0.01
to 0.5 Hz. However, this effect, if any, may be dependent on the locality characterized by burrow densities
and burrow structures.
Comparison of the results from the eddy-correlation
method with those from conventional methods can also
reveal the importance of waves and advective porewater transport for oxygen flux between permeable sediments and overlying water. The oxygen-uptake flux inthe dark, measured by the core-incubation method
using intact sediment cores taken from the same siteon the following day of this study, was 1.0 to 1.5 mmol
O2 m–2 h–1 (T. Inoue unpubl. data), indicating only 23%of the flux measured in the dark by the eddycorrelation method at its maximum (Case 6, Table 3).
This underestimate could be due to the exclusion ofporewater pressure fluctuations by waves and ripple
topography in the enclosed system (Precht & Huettel
2003), even though wave-induced advective porewater transport supports a high oxygen-uptake rate associated with mineralization of dissolved and particulateorganic matter in sandy sediments (Malan & McLachlan 1991, Huettel & Rusch 2000, Rusch & Huettel 2000,
de Beer et al. 2005).
In summary, the eddy-correlation method has
marked advantages over conventional enclosure
methods or vertical concentration profiling methods for
accurate measurement of solute fluxes across the sedi-

67

ment –water interface, particularly in shallow water
ecosystems and/or sandy sediment ecosystems. Rigorous pre-analyses and extraction of fluctuating components from the time series of vertical velocity andoxygen concentration, by noise- and trend-removal
operations, are needed in the eddy-correlation
method. Determination of the length of sampling time
and the appropriate detrending operation require acompromise between exclusion of low-frequency flux
contributions and inclusion of the apparent fluctuations that are independent of the flux. The oxygen flux
at a frequency band between 0.3 and 1.4 Hz is a major
contributor to the total oxygen flux, and this result is ingood agreement with Berg et al. (2003), being <1 Hz.
Oscillating vertical flows induced by wind-driven
waves are of importance for transport and exchange
between porewater and overlying water, which contribute greatly to total oxygen flux between permeable
sandy sediment and overlying water.
Acknowledgements. We thank S. Konuma and S. Hosokawa
for their kind help during fieldwork. We also thank R. W.
Elner and R. C. Ydenberg for helpful comments. The manuscript was greatly improved by the valuable comments of 5
anonymous reviewers. This research was supported by grants
to T.K. from the Japan Railway Construction, Transport andTechnology Agency (JRTT) Program for Promoting Fundamental Transport Technology Research and the Japan Society
for the Promotion of Science (JSPS) Postdoctoral Fellowships
for Research Abroad (FY2004).

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Editorial responsibility: Otto Kinne (Editor-in-Chief),
Oldendorf/Luhe, Germany

Submitted: March 2, 2005; Accepted: August 18, 2005
Proofs received from author(s): December 19, 2005