Eddy‐correlation measurements of benthic fluxes under complex flow conditions: Effects of coordinate transformations and averaging time scales

Lorke, Andreas; Mcginnis, Daniel Frank; Maeck, Andreas

doi:10.4319/lom.2013.11.425

Cited by 45 publications

(61 citation statements)

References 18 publications

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“…Through careful data quality control, sections of O 2 data were removed where the microsensors were impacted by floating debris or were fouled ). Rotation of the velocity field prior to flux extractions has been deemed important if the instrument is tilted relative to the sediment surface (Reimers et al 2012, Lorke et al 2013. However, caution must be taken when rotating the velocity data if significant surface waves are present (Reimers et al 2012) or when the mean current velocity is very small.…”

Section: Discussionmentioning

confidence: 99%

Multiple timescale processes drive ecosystem metabolism in eelgrass (Zostera marina) meadows

Rheuban¹,

Berg²,

McGlathery³

2014

Mar. Ecol. Prog. Ser.

106

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Section: Discussionmentioning

confidence: 99%

Multiple timescale processes drive ecosystem metabolism in eelgrass (Zostera marina) meadows

Rheuban¹,

Berg²,

McGlathery³

2014

Mar. Ecol. Prog. Ser.

106

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“…If needed, this software rotates the flow velocity field for each data segment to correct for any sensor tilt (Lee et al, 2004;Lorrai et al, 2010;Lorke et al, 2013) bringing the transverse and vertical mean velocities to zero. The vertical eddy flux was then calculated as (defined positive upward)…”

Section: Calculations Of Eddy Fluxesmentioning

confidence: 99%

Continuous measurement of air–water gas exchange by underwater eddy covariance

Berg¹,

Pace²

2017

Biogeosciences

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Abstract. Exchange of gases, such as O 2 , CO 2 , and CH 4 , over the air-water interface is an important component in aquatic ecosystem studies, but exchange rates are typically measured or estimated with substantial uncertainties. This diminishes the precision of common ecosystem assessments associated with gas exchanges such as primary production, respiration, and greenhouse gas emission. Here, we used the aquatic eddy covariance technique -originally developed for benthic O 2 flux measurements -right below the airwater interface (∼ 4 cm) to determine gas exchange rates and coefficients. Using an acoustic Doppler velocimeter and a fast-responding dual O 2 -temperature sensor mounted on a floating platform the 3-D water velocity, O 2 concentration, and temperature were measured at high-speed (64 Hz). By combining these data, concurrent vertical fluxes of O 2 and heat across the air-water interface were derived, and gas exchange coefficients were calculated from the former. Proofof-concept deployments at different river sites gave standard gas exchange coefficients (k 600 ) in the range of published values. A 40 h long deployment revealed a distinct diurnal pattern in air-water exchange of O 2 that was controlled largely by physical processes (e.g., diurnal variations in air temperature and associated air-water heat fluxes) and not by biological activity (primary production and respiration). This physical control of gas exchange can be prevalent in lotic systems and adds uncertainty to assessments of biological activity that are based on measured water column O 2 concentration changes. For example, in the 40 h deployment, there was near-constant river flow and insignificant windstwo main drivers of lotic gas exchange -but we found gas exchange coefficients that varied by several fold. This was presumably caused by the formation and erosion of vertical temperature-density gradients in the surface water driven by the heat flux into or out of the river that affected the turbulent mixing. This effect is unaccounted for in widely used empirical correlations for gas exchange coefficients and is another source of uncertainty in gas exchange estimates. The aquatic eddy covariance technique allows studies of air-water gas exchange processes and their controls at an unparalleled level of detail.A finding related to the new approach is that heat fluxes at the air-water interface can, contrary to those typically found in the benthic environment, be substantial and require correction of O 2 sensor readings using high-speed parallel temperature measurements. Fast-responding O 2 sensors are inherently sensitive to temperature changes, and if this correction is omitted, temperature fluctuations associated with the turbulent heat flux will mistakenly be recorded as O 2 fluctuations and bias the O 2 eddy flux calculation.

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“…5 were calculated without a traditional rotation (nullification of the transverse and vertical mean velocities for each 15 min based flux calculation; Lee et al, 2004;Lorrai et al, 2010;Lorke et al, 2013). The current velocities were too small to produce robust rotation estimates for most of the deployment.…”

Section: P Berg Et Al: Technical Note: Time Lag Correction Of Aquatmentioning

confidence: 99%

Technical note: Time lag correction of aquatic eddy covariance data measured in the presence of waves

et al. 2015

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Abstract. Extracting benthic oxygen fluxes from eddy covariance time series measured in the presence of surface gravity waves requires careful consideration of the temporal alignment of the vertical velocity and the oxygen concentration. Using a model based on linear wave theory and measured eddy covariance data, we show that a substantial error in flux can arise if these two variables are not aligned correctly in time. We refer to this error in flux as the time lag bias. In one example, produced with the wave model, we found that an offset of 0.25 s between the oxygen and the velocity data produced a 2-fold overestimation of the flux. In another example, relying on nighttime data measured over a seagrass meadow, a similar offset reversed the flux from an uptake of −50 mmol m −2 d −1 to a release of 40 mmol m −2 d −1 . The bias is most acute for data measured at shallow-water sites with short-period waves and low current velocities. At moderate or higher current velocities (> 5-10 cm s −1 ), the bias is usually insignificant. The widely used traditional time shift correction for data measured in unidirectional flows, where the maximum numerical flux is sought, should not be applied in the presence of waves because it tends to maximize the time lag bias or give unrealistic flux estimates. Based on wave model predictions and measured data, we propose a new time lag correction that minimizes the time lag bias. The correction requires that the time series of both vertical velocity and oxygen concentration contain a clear periodic wave signal. Because wave motions are often evident in eddy covariance data measured at shallow-water sites, we encourage more work on identifying new time lag corrections.

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Eddy‐correlation measurements of benthic fluxes under complex flow conditions: Effects of coordinate transformations and averaging time scales

Cited by 45 publications

References 18 publications

Multiple timescale processes drive ecosystem metabolism in eelgrass (Zostera marina) meadows

Multiple timescale processes drive ecosystem metabolism in eelgrass (Zostera marina) meadows

Continuous measurement of air–water gas exchange by underwater eddy covariance

Technical note: Time lag correction of aquatic eddy covariance data measured in the presence of waves

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