Prerequisites for application of hyperbolic relaxed eddy accumulation on managed grasslands and alternative net ecosystem exchange flux partitioning

Riederer, Michael; Hübner, Jörg; Ruppert, Johannes; Brand, Willi A.; Foken, Thomas

doi:10.5194/amt-7-4237-2014

“…Ignoring these spikes, a satisfactory agreement to eddy covariance was determined, but the unsystematic errors made the results critical for detailed investigations. Conditional sampling may be applied for nearly steady-state data sets and can be used as a tool to see the effects of periodic coherent structures on the flux as was done by Thomas and Foken (2007) or to analyse the coefficients for relaxed eddy accumulation (Riederer et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Flux calculation of short turbulent events – comparison of three methods

Schaller

¹

,

Göckede

²

,

Foken

³

2017

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Abstract. The eddy covariance method is commonly used to calculate vertical turbulent exchange fluxes between ecosystems and the atmosphere. Besides other assumptions, it requires steady-state flow conditions. If this requirement is not fulfilled over the averaging interval of, for example, 30 min, the fluxes might be miscalculated. Here two further calculation methods, conditional sampling and wavelet analysis, which do not need the steady-state assumption, were implemented and compared to eddy covariance. All fluxes were calculated for 30 min averaging periods, while the wavelet method -using both the Mexican hat and the Morlet wavelet -additionally allowed us to obtain a 1 min averaged flux.The results of all three methods were compared against each other for times with best steady-state conditions and well-developed turbulence. An excellent agreement of the wavelet results to the eddy covariance reference was found, where the deviations to eddy covariance were of the order of < 2 % for Morlet as well as < 7 % for Mexican hat and thus within the typical error range of eddy covariance measurements. The conditional sampling flux also showed a very good agreement to the eddy covariance reference, but the occurrence of outliers and the necessary condition of a zero mean vertical wind velocity reduced its general reliability. Using the Mexican hat wavelet flux in a case study, it was possible to locate a nightly short time turbulent event exactly in time, while the Morlet wavelet gave a trustworthy flux over a longer period, e.g. 30 min, under consideration of this short-time event.At a glance, the Mexican hat wavelet flux offers the possibility of a detailed analysis of non-stationary times, where the classical eddy covariance method fails. Additionally, the Morlet wavelet should be used to provide a trustworthy flux in those 30 min periods where the eddy covariance method provides low-quality data due to instationarities.

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“…Ignoring these spikes, a satisfactory agreement to eddy covariance was determined, but the unsystematic errors made the results critical for detailed investigations. Conditional sampling may be applied for nearly steady-state data sets and can be used as a tool to see the effects of periodic coherent structures on the flux as was done by Thomas and Foken (2007) or to analyse the coefficients for relaxed eddy accumulation (Riederer et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…First they were used to detect jumps of turbulent motions (Mahrt, 1991) or the duration of turbulent events including filtering analysis (Collineau and Brunet, 1993a, b). The application of the spectra of the wavelet coefficient (Collineau and Brunet, 1993b;Treviño and Andreas, 1996) offered also the possibility to determine turbulent fluxes (Katul and Parlange, 1995;Handorf and Foken, 1997;Saito and Asanuma, 2008). With the availability of wavelet software packages in the last 20 years all these methods have become more popular, and have been used for applications such as the detection of turbulent structures (Thomas and Foken, 2005) or turbulent fluxes of aircraft measurements (Strunin and Hiyama, 2004) and at forested sites (Thomas and Foken, 2007).…”

Section: Introductionmentioning

confidence: 99%

Flux calculation of short turbulent events – comparison of three methods

Schaller¹,

Göckede²,

Foken³

2016

Preprint

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Abstract. The eddy covariance method is commonly used to calculate vertical turbulent exchange fluxes between ecosystems and the atmosphere. Besides other assumptions, it requires steady state flow conditions. If this requirement is not fulfilled over the averaging interval of, e.g., 30 min, the fluxes might be mis-calculated. Here two further calculation methods, conditional sampling and wavelet analysis, which do not need the steady state assumption, were implemented and compared to eddy covariance. All fluxes were calculated for 30 in averaging periods, while the wavelet method - using both the Mexican hat and the Morlet wavelet – additionally allowed to obtain a 1 min averaged flux. The results of all three methods were validated against each other for times with best steady state conditions and well developed turbulence. An excellent agreement of the wavelet results to the eddy covariance reference was found, where the deviations to eddy covariance were on the order of on the order of

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“…The β 0 method is based on the strong assumption that the proxy scalar and the scalar of interest behave similarly in their exchange mechanism, which requires the vertical and horizontal distribution of the sinks and sources of both scalars to be identical. A violation of this assumption will inevitably lead to large errors in the REA flux estimate (Katul et al, 1995;Katul and Hsieh, 1999;Ruppert et al, 2006;Riederer et al, 2014). The similarity between the scalar of interest and the scalar chosen as a proxy can be evaluated by examining the correlation coefficients between the highresolution time series of two scalars, if available.…”

Section: Introductionmentioning

confidence: 99%

“…The scalar-scalar correlation coefficients, r cs , as used in other publications (e.g. Gao, 1995;Katul and Hsieh, 1999;Ruppert et al, 2006;Riederer et al, 2014), are defined as follows:…”

Section: Introductionmentioning

confidence: 99%

Choosing an Optimal β Factor for Relaxed Eddy Accumulation Applications Across Vegetated and non-Vegetated Surfaces

Vogl¹,

Hrdina²,

Thomas³

2020

Preprint

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Abstract. Accurately measuring the turbulent transport of reactive and conservative greenhouse gases, heat, and organic compounds between the surface and the atmosphere is critical for understanding trace gas exchange and its response to changes in climate and anthropogenic activities. The Relaxed Eddy Accumulation (REA) method enables measuring the land surface exchange when fast-response sensors are not available, broadening the suite of trace gases that can be investigated. This study evaluates a variety of different REA approaches with the goal of formulating universally applicable recommendations for an optimal choice of the β factor in combination with a suitable deadband. The β factor scales the concentration differences to the flux, and its choice is central to successfully using REA. Deadbands are used to select only certain turbulent motions to compute the flux. Observations were collected across three contrasting ecosystems offering stark differences in scalar transport and dynamics: A mid-latitude grassland ecosystem in Europe, a loose gravel surface of the Dry Valleys of Antarctica, and a spruce forest site in the European mid-range mountains. We tested a total of three different REA models for the β factor: The first method derives β0 based on a proxy for which high-frequency observations are available (sensible heat). The second method employs the approach of Baker et al. (1992), which computes βw solely based upon the vertical wind statistics. The third method uses a constant β derived from long-term averaging of the proxy-based β0 factor. Each β model was optimized with respect to deadband type and size before intercomparison. Concerning deadband form and size, we found an optimum in RMSE for linear deadbands with sizes of 0.5 and 0.9σw. These deadband widths make this method approximately equal to the use of a constant β factor. With respect to overall REA performance, we found that the βw and constant β from long-term measurements performed more robustly than the proxy-dependent approach β0. The latter model still performed well when scalar similarity between the proxy (here sensible heat) and the scalar of interest (here latent heat) show strong statistical correlation, i.e. during periods when the distribution and temporal behavior of sources and sinks were similar. With respect to sensitivity of β to atmospheric stability, we observed that β0 slightly increased with increasing stability parameter z / L when no deadband is applied, but this trend vanished with increasing deadband size. βw was independent of z / L. To explain these surprising differences, we separated the contribution of w' kurtosis to the flux uncertainty, which can be expressed by the median ratio of the REA flux compared to that from classical eddy covariance FREAFEC. Results showed a strong sensitivity to site conditions: While the kurtosis of w' seems to have no effect on the flux estimate at the grassland site, decreasing trends with increasing kurtosis can be observed for the loose gravel and forests sites and could explain the variability of FREAFEC within 10 %. For REA applications without deeper site-specific knowledge of the turbulent transport and degree of scalar similarity, we recommend using either the constant β or βw models when REA scalar fluxes are not expected to be limited by the detection limit of the instrument. For conditions close to the instrument detection limit, the β0 models using a hyperbolic deadband are the optimum choice.

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Prerequisites for application of hyperbolic relaxed eddy accumulation on managed grasslands and alternative net ecosystem exchange flux partitioning

Cited by 14 publications

References 105 publications

Flux calculation of short turbulent events – comparison of three methods

Flux calculation of short turbulent events – comparison of three methods

Flux calculation of short turbulent events – comparison of three methods

Choosing an Optimal β Factor for Relaxed Eddy Accumulation Applications Across Vegetated and non-Vegetated Surfaces

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