Uncertainty analysis of eddy covariance CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux measurements for different EC tower distances using an extended two-tower approach

Post, H.; Franssen, Harrie‐Jan Hendricks; Graf, Alexander; Schmidt, Marius; Vereecken, Harry

doi:10.5194/bg-12-1205-2015

Cited by 51 publications

(51 citation statements)

References 39 publications

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“…Rannik et al, 2006), we suggest that the one-point statistical approach provides rigorous but more convenient method to estimate the flux random errors. Nevertheless, the two-tower approach was shown to give close results to the method by Finkelstein and Sims (2001) when similar weather conditions at the two sites were included in the analysis (Post et al, 2015).…”

Section: Discussionmentioning

confidence: 66%

Random uncertainties of flux measurements by the eddy covariance technique

2016

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Abstract. Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated artificial records. We recommend two flux errors with clear physical meaning: the flux error of the covariance, defining the error of the measured flux as 1 standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 h duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The filtering method proposed by Salesky et al. (2012) is an alternative to the method by Finkelstein and Sims (2001) producing consistent, but somewhat lower, estimates. The method proposed by Wienhold et al. (1995) provides a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error, depending on the conditions and method of estimation.

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Section: Discussionmentioning

confidence: 66%

Random uncertainties of flux measurements by the eddy covariance technique

2016

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“…restrial compartments at the regional level. Therefore, comprehensive input and evaluation data are available for the catchment, including information on land use (Lussem and Waldhoff, 2013), LAIs (Ali et al, 2015;Reichenau et al, 2016) and EC data (Schmidt et al, 2012;Graf et al, 2014;Kessomkiat et al, 2013;Post et al, 2015).…”

Section: The Rur Catchmentmentioning

confidence: 99%

“…EC data at EN and NZ were measured by a roving station, which was installed for 2 to 3 months at each of the three sites (Table 1). The complete processing of the raw data was performed with the TK3.1 software (Mauder and Foken, 2011), using the quality flagging and uncertainty estimation scheme by Mauder et al (2013) as outlined in Post et al (2015). Only nongap-filled data with quality flag 0 (high-quality data) and 1 (moderate-quality data) were included in this study.…”

Section: Eddy Covariance Datamentioning

confidence: 99%

“…Only nongap-filled data with quality flag 0 (high-quality data) and 1 (moderate-quality data) were included in this study. Rollesbroich (RO) is an extensively used grassland site (Korres et al, 2010;Post et al, 2015). Wüstebach (WÜ; Graf et al 2014) is located in the Eifel National Park and is largely covered by evergreen coniferous forest, particularly spruce.…”

Section: Eddy Covariance Datamentioning

confidence: 99%

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Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

Post

Franssen²,

Han³

et al. 2018

Biogeosciences

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Abstract. Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM (zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C 3 grass and C 3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ NEE ) for the different ensemble members from ∼ 2 to 3 g C m −2 yr −1 (with uncertain parameters) to ∼ 45 g C m −2 yr −1 (C 3 grass) and ∼ 75 g C m −2 yr −1 (C 3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (σ NEE ∼ 4.0-13.5 g C m −2 yr −1 with perturbed parameters, meteorological forcings and initial states). We conclude that LAI and NEE uncertainty with CLM is clearly underestimated if uncertain meteorological forcings and initial states are not taken into account.

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“…In this study, only data of high and moderate quality were used. A more detailed description of the treatment of EC data can be found elsewhere (Gebler et al, 2015;Post et al, 2015). …”

Section: Meteorological Data and Latent Heat Fluxmentioning

confidence: 99%

The integrated water balance and soil data set of the Rollesbroich hydrological observatory

Bogena

Huisman

et al. 2016

Earth Syst. Sci. Data

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Abstract. The Rollesbroich headwater catchment located in western Germany is a densely instrumented hydrological observatory and part of the TERENO (Terrestrial Environmental Observatories) initiative. The measurements acquired in this observatory present a comprehensive data set that contains key hydrological fluxes in addition to important hydrological states and properties. Meteorological data (i.e., precipitation, air temperature, air humidity, radiation components, and wind speed) are continuously recorded and actual evapotranspiration is measured using the eddy covariance technique. Runoff is measured at the catchment outlet with a gauging station. In addition, spatiotemporal variations in soil water content and temperature are measured at high resolution with a wireless sensor network (SoilNet). Soil physical properties were determined using standard laboratory procedures from samples taken at a large number of locations in the catchment. This comprehensive data set can be used to validate remote sensing retrievals and hydrological models, to improve the understanding of spatial temporal dynamics of soil water content, to optimize data assimilation and inverse techniques for hydrological models, and to develop upscaling and downscaling procedures of soil water content information. The complete data set is freely available online (http://www.tereno.net, doi:10.5880/ TERENO.2016.001, doi:10.5880/TERENO.2016.004, doi:10.5880/TERENO.2016 and additionally referenced by three persistent identifiers securing the long-term data and metadata availability.

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Uncertainty analysis of eddy covariance CO<sub>2</sub> flux measurements for different EC tower distances using an extended two-tower approach

Cited by 51 publications

References 39 publications

Random uncertainties of flux measurements by the eddy covariance technique

Random uncertainties of flux measurements by the eddy covariance technique

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

The integrated water balance and soil data set of the Rollesbroich hydrological observatory

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