A new robust oxygen‐temperature sensor for aquatic eddy covariance measurements

Berg, Peter; Koopmans, Dirk; Huettel, Markus; Li, Hua; Mori, Kosuke; Wüest, Alfred

doi:10.1002/lom3.10071

Cited by 41 publications

(71 citation statements)

References 51 publications

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“…The somewhat slower response found in the field, where the sensor was permanently under water, was likely caused by oxygen concentration equilibration through the 6730 P. Berg et al: Technical Note: Time lag correction of aquatic eddy covariance data thin boundary layer flow that forms over the oxygen sensing foil . Through detailed model calculations, Berg et al (2015) showed that a sensor with this response time will virtually capture the entire flux signal if a time lag correction is applied. Specifically, the underestimation of the flux was found to be less than 5 %, even in challenging situations at shallow-water sites with substantial unidirectional current flow, where small rapid eddies dominated the vertical turbulent mixing.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, Berg et al (2015) showed through modeling that when, for example, a 0.5 Hz sinusoidal wave signal in oxygen concentration is measured with a sensor with a response time (t 90% ) of 0.5 s, a 0.20 s phase shift, or time lag, is introduced in the recorded data. While this inevitably will add substantial time lag bias to calculated fluxes unless a time lag correction is applied (Figs.…”

Section: Discussionmentioning

confidence: 99%

“…Clark-type oxygen microelectrodes used for eddy covariance typically have response times (t 90% ) of 0.2 to 0.5 s Attard et al, 2015;Donis et al, 2015). Newer optical sensors that have been developed in recent years have comparable or somewhat longer response times of 0.2 to 0.8 s (Chipman et al, 2012;Murniati et al, 2015;Berg et al, 2015). This means that the time series of the two key variables from which eddy fluxes are derived, the vertical velocity and oxygen concentration, are never perfectly aligned in time.…”

Section: Formulation Of Problemmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Formulation Of Problemmentioning

confidence: 99%

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

et al. 2015

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“…From these fluxes, we derived exchange coefficients for O 2 , and then standard gas exchange coefficients (k 600 ). All measurements were done from a floating platform, and because we used a newly developed fast-responding dual O 2 -temperature sensor (Berg et al, 2016), we were able to derive parallel fluxes of O 2 and thermal energy, or sensible heat. We conducted proof-of-concept tests that were up to 40 h long at three river sites.…”

Section: Scope Of Workmentioning

confidence: 99%

“…The O 2 concentration was measured with a new fastresponding dual O 2 -temperature sensor (RINKO EC, JFE Advantech, Japan) developed specifically for eddy covariance measurements (Berg et al, 2016). This sensor allows for deriving simultaneous fluxes of O 2 and heat.…”

Section: Floating Measurements Platformmentioning

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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Surface gas exchange determined from an aquatic eddy covariance floating platform

Long

Nicholson

2017

Limnology & Ocean Methods

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We present a new approach to quantifying air-water flux and gas transfer velocity (k 600 ) from underwater eddy covariance (EC) of dissolved oxygen. EC fluxes were measured 35 cm below the air-water interface using an acoustic Doppler velocimeter (ADV) coupled with a fast-responding dissolved oxygen optode probe, integrated on a floating platform. A micro-electro-mechanical system (MEMS)-based inertial motion unit integrated with the ADV enabled compensation of measured velocities for platform motion and changing sensor orientation. Deployments of 16 h and 30 h were conducted under low to moderate wind conditions in Sage Lot Pond, a small estuarine embayment. We evaluate air-sea flux parameterizations based on wind speed, current speed, and turbulent kinetic energy dissipation rate compared to our directly measured EC flux. Total kinetic energy was linearly correlated with EC-determined k 600 , indicative of a more direct relationship to near surface turbulence compared to wind and current speed based parameterizations, which were subject to biases attributable to directional differences in fetch and low current speed. Our observations, which encompassed a period of low turbulent energy, suggest that existing parameterizations are not well constrained for these conditions. This new aquatic EC technique is highly advantageous for the determination of air-water exchange, especially in dynamic near-shore and inland systems. The system presented here has the potential for wide applicability for lake, riverine, and open-ocean air-water exchange and the results can be extrapolated for use with a wide range of biogeochemically relevant gases.

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A new robust oxygen‐temperature sensor for aquatic eddy covariance measurements

Cited by 41 publications

References 51 publications

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

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

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

Surface gas exchange determined from an aquatic eddy covariance floating platform

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