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Identifying conditons with insufficient turbulence (indicated by low friction velocity u*), and marking those conditions as data gaps is important to avoid biases in fluxes measured by eddy covariance.
During stable stratification and low turbulent mixing the eddy covariance method faces several problems that introduce bias and uncertainties. These problems primarily happen during night and lead to an underestimation of the night-time flux, i.e. the ecosystem respiration. These problems can be detected via a micro-meteorological quality control that tests if the assumptions of the eddy covariance method are not too strongly violated for a particular half hour (e.g. Foken and Wichura, 1996; weblink). Under circumstances where the necessary information for those tests is not available, a heuristic class of methods is widely accepted that assumes that a treshhold of friction velocity (u*) can be site and season specifically established above that night-time fluxes are considered valid. This threshold is usually established by relating the night-time flux to friction velocity while accounting for temperature as a covariate (u*-filtering).
Here the minimum friction velocity u* is estimsted according to method described in Papaple et al. (2006), and the implementation in Papale et al. (In preparation). Alternatively, a Change Point Detection can be applied to detect saturation of NEE with increasing u*, similar to the method described in Barr et al. (2013).
With both methods work on data where solar radiation is below a threshold (default: Rg < 10 Wm2). They assume that photosynthesis is near zero. The data is subset to similar environmental conditions, aside from friction velocity: adjacent times (seasons) and temperature classes (default 7 classes). Within each season/temperature subclass the u* threshold is estimated at which NEE saturates. Next the u* threshold estimates of those subclasses are aggregated to a an u* threshold for each year.
With all methods, there is a minimum number of records in a season. If there are too few records, the data of the seasons within one year are combined, before estimating the u* threshold.
Papale et al. (2006) estimated u* threshold in each season/temperature subclass by binning the records to similar u* and computing the mean NEE amd mean u* for each class (default 20 classes). For each bin, a moving point test was applied to each bin, to determine the threshold. With the forward2-method, applied with the BGI online tool, for each bin is checked if the bin-NEE is higher than 0.95 times the mean of the following 10 bin-NEE values. If this holds true also for the next bin, the mean u* of the bin is reported as threshold.
There are slight differences in the Palale et al. (2006) and the BGI binning scheme: Both methods bin, so that the number of records in each bin is the same. If there are numerically equal u* values, they are sorted into the same bin, resulting in bins with more records with both methods. With the C-implementation by Papale et al. (In preparation), less and sometimes no records are sorted into the following bins. Contrary, the binninng with the REddyproc packages used by the BGI online tool ensures that there are a minimum of records in all bins. This often results in fewer bins than without numerically equal u* values. Moreover, differing from the Papale et al. (In preparation) C-implementation, the REddyproc package does not report a threshold in those data subsets where the NEE plateau (after the u* threshold bin) consists of less than three points, i.e. bins.
Barr et al. (2013) instead used Change Point Detection to infer the bin with the highest probability of being a change point in the u*-NEE relationship. REddyProc implements the similar RTw method. It estimates the breakpoint within the seasons/temperature classes based on the unbinned raw values using the segmented package. Hence, it avoids the sometimes very sensitive binning of u* values. Note also, that REddyProc differs from Barr (2013) by using the Papale 2006 aggregation scheme (described next) also with the Change Point Detection.
The online tool uses the same aggregation scheme as Papale et al. (2006). Within one season, the median is taken across the thresholds of different temperature classes. Within one year, the maximum is taken across the associated seasons.
Differeing from the Papale et al. (In preparation) C-implementation, the REddyproc package does not report a seasonal estimate if a threshold was found in less than 20% of the temperature subsets within the season.
Estimates of the u* threshold are often sensitive to the specifics of the methods, e.g. the binning, minimum number of records, criteria in aggregation, etc. Therefore, bootstrap (resampling with replacement) is applied to generate 200 pseudoreplicates of the dataset and on for each replicate the threshold is estimated. The 5%, 50% and 95% of the estimates are reported as a range of threshold estimates. The gapfilling and partitioning is then applied using those different thresholds to propagate the uncertainty of u* threshold estimation to NEE, GPP and Reco.
In difference to Papale et al. (In preparation) C-implementation, the REddyProc package resamples the data only within seasons instead across the entire dataset.
The REddyProc implementation has been benchmarked against the Papale et al. (In preparation) C-implementation.
Main conclusions are:
Details of the benchmark are reported.