Performance of eddy-covariance measurements in fetch-limited applications

Nicolini, Giacomo; Fratini, Gerardo; Avilov, Vitaly; Kurbatova, S. A.; Vasenev, Ivan; Валентини, Р.

doi:10.1007/s00704-015-1673-x

Cited by 21 publications

(18 citation statements)

References 58 publications

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“…The concentration of CO 2 before (natural flux) and following the tracer release is in the range of 637 ± 10 and 800 ± 100 mg/m 3 , respectively. Fully developed turbulent flow field along with unstable atmospheric conditions resulted in a low, yet significantly deviated CO 2 fluxes, that are good for evaluation (Nicolini et al, 2017). The conventional averaging interval of 30 min for flux data processing is not suitable as steady state conditions during the flux calculation period were not achieved (Coates et al, 2017; Felber et al, 2015; Schaller et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study

2020

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Flux footprint models simulate the source area of scalar fluxes from a measurement site but are hindered by the assumptions that are rarely satisfied in real field conditions. We conducted artificial tracer (CO2) experiments in unstable conditions over an open field, to evaluate three analytical footprint models (Kormann and Meixner‐KM, Hsieh‐HS, and Schuepp‐SP) under variable source‐receptor settings. Experimental configurations include (i) source length (point to semi‐infinite), (ii) emission height (below and above zero‐plane displacement), (iii) measurement height (1.5 and 2.8 m above ground level), and (iv) offset from the principal wind direction (±60°). Eddy covariance (EC) fluxes were measured using one source‐multiple sampling system approach. Results indicate that KM model is in good agreement with the EC measurements under ideal conditions (R2 = 0.81, root‐mean‐square error (RMSE) = 0.008 m−1) compared to HS (R2 = 0.60, RMSE = 0.014 m−1) and SP (R2 = 0.72, RMSE = 0.009 m−1) models. The KM model captured both footprint maximum and its location. Uncertainties in the model estimates were ascertained by considering random ensembles of wind speed, friction velocity, and Obukhov length. The KM model has resulted in the least uncertainty band containing all the flux observations. Error propagation into model simulations resulting from a progressive deviation of the underlying assumptions was assessed. Model‐to‐measurement discrepancies were positive (flux overestimation) for offset deviations up to ±30° and negative beyond. Modeled fluxes were sensitive to source length and emission height in comparison to the offset deviation. Overall, the KM model showed the least error (9.6 ± 8.12%) for different source‐receptor deviations. Study findings can help in evaluating and improving the analytical models for use under nonideal source‐receptor configurations and scaling measurements for different applications.

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Section: Methodsmentioning

confidence: 99%

Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study

2020

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“…is the local Obukhov length, determined using values of fluxes of momentum |τ | and temperature Q at the given height z (local scaling in the stable ABL (Nieuwstadt, 1984)). The measurements suggest that the mean value of the normalized RMS σ w ≈ 1.33 if the value ξ is small.…”

Section: Variances Of Velocity Componentsmentioning

confidence: 99%

“…The measurements of the scalar flux footprint functions in natural environment are restricted (e.g., Finn et al, 1996;Leclerc et al, 1997Leclerc et al, , 2003Nicolini et al, 2015) due to the necessity to conduct the emission and detection of artificial tracers. Besides, such measurements are not available for the stably stratified ABL, where the area of the surface influencing the point of measurements increases.…”

Section: Introductionmentioning

confidence: 99%

Large-eddy simulation and stochastic modeling of Lagrangian particles for footprint determination in the stable boundary layer

et al. 2016

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Large-eddy simulation (LES) and Lagrangian stochastic modeling of passive particle dispersion were applied to the scalar flux footprint determination in the stable atmospheric boundary layer. The sensitivity of the LES results to the spatial resolution and to the parameterizations of small-scale turbulence was investigated. It was shown that the resolved and partially resolved ("subfilter-scale") eddies are mainly responsible for particle dispersion in LES, implying that substantial improvement may be achieved by using recovering of small-scale velocity fluctuations. In LES with the explicit filtering, this recovering consists of the application of the known inverse filter operator. The footprint functions obtained in LES were compared with the functions calculated with the use of first-order single-particle Lagrangian stochastic models (LSMs) and zeroth-order Lagrangian stochastic models -the random displacement models (RDMs). According to the presented LES, the source area and footprints in the stable boundary layer can be substantially more extended than those predicted by the modern LSMs.A. Glazunov et al.: LES and LS modeling of Lagrangian particles for footprint determination in the SBL

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“…Despite the large number of small peatlands in mountain ranges (Pullens et al, 2016), there are only few EC studies conducted on these ecosystems as a result of their limited area and their complex local topography (Batzer & Baldwin, 2012). These site-specific characteristics may result in an influence of advection on measured fluxes (Etzold et al, 2010) and therefore possibly lead to temporary violations of the Monin-Obukhov similarity theory which is the basis for the EC flux calculation (Stiperski & Rotach, 2016) and in turn can lead to mixed flux signals for measurements in the transition layer (Nicolini et al, 2015). Nonetheless, previous studies successfully applied EC at sites with challenging turbulence characteristics (Heusinger & Weber, 2017;Weber & Kordowski, 2010).…”

Section: Introductionmentioning

confidence: 99%

Robust Inverse Modeling of Growing Season Net Ecosystem Exchange in a Mountainous Peatland: Influence of Distributional Assumptions on Estimated Parameters and Total Carbon Fluxes

Weber

Gerling

Reineke

et al. 2018

J Adv Model Earth Syst

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While boreal lowland bogs have been extensively studied using the eddy‐covariance (EC) technique, less knowledge exists on mountainous peatlands. Hence, half‐hourly CO2 fluxes of an ombrotrophic peat bog in the Harz Mountains, Germany, were measured with the EC technique during a growing season with exceptionally dry weather spells. A common biophysical process model for net ecosystem exchange was used to describe measured CO2 fluxes and to fill data gaps. Model parameters and uncertainties were estimated by robust inverse modelling in a Bayesian framework using a population‐based Markov Chain Monte Carlo sampler. The focus of this study was on the correct statistical description of error, i.e. the differences between the measured and simulated carbon fluxes, and the influence of distributional assumptions on parameter estimates, cumulative carbon fluxes, and uncertainties. We tested the Gaussian, Laplace, and Student's t distribution as error models. The t‐distribution was identified as best error model by the deviance information criterion. Its use led to markedly different parameter estimates, a reduction of parameter uncertainty by about 40%, and, most importantly, to a 5% higher estimated cumulative CO2 uptake as compared to the commonly assumed Gaussian error distribution. As open‐path measurement systems have larger measurement error at high humidity, the standard deviation of the error was modeled as a function of measured vapor pressure deficit. Overall, this paper demonstrates the importance of critically assessing the influence of distributional assumptions on estimated model parameters and cumulative carbon fluxes between the land surface and the atmosphere.

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Performance of eddy-covariance measurements in fetch-limited applications

Cited by 21 publications

References 58 publications

Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study

Sensitivity of Analytical Flux Footprint Models in Diverse Source‐Receptor Configurations: A Field Experimental Study

Large-eddy simulation and stochastic modeling of Lagrangian particles for footprint determination in the stable boundary layer

Robust Inverse Modeling of Growing Season Net Ecosystem Exchange in a Mountainous Peatland: Influence of Distributional Assumptions on Estimated Parameters and Total Carbon Fluxes

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