Methods

Here, methods are only briefly described:

Gap-filling

The gap-filling of the eddy covariance and meteorological data will be performed through methods that are similar to Falge et al. (2001), but that consider both the co-variation of fluxes with meteorological variables and the temporal auto-correlation of the fluxes (Reichstein et al. 2005): In this algorithm, three different conditions are identified:
1) Only the data of direct interest are missing, but all meteorological data are available,
2) Also air temperature or VPD is missing, but radiation is available,
3) Also radiation data is missing.
In case 1), the missing value is replaced by the average value under similar meteorological conditions within a time-window of 7 days. Similar meteorological conditions are present when Rg, Tair and VPD do not deviate by more than 50 W m^-2, 2.5°C, and 5.0 hPa respectively. If no similar meteorological conditions are present within the time window, the averaging window is increased by 14 days.
In case 2) the same approach is taken, but similar meteorological conditions can only be defined via Rg deviation less than 50 W m^-2 and window size is not increased.
In case 3) the missing value is replaced by the average value at the same time of the day ( 1 hour), i.e. by the mean diurnal course. In this case the window size starts with 0.5 days (i.e. similar to a linear interpolation from available data at adjacent hours).
If after these steps the values could not be filled, the procedure is repeated with increased window sizes until the value can be filled. Both, the method, the window size, and the number and the standard deviation of values averaged is recorded then, so that for individual purposes appropriate data can be selected and e.g. uncertainties can estimated. Uncertainties are also calculated for actual measurements by simulating a gap and applying the gap-filling procedure and are found in the column fs_unc of the output file.
For convenience, the filled data is further classified into three categories (A, B, C) based on the method (1, 2, or 3) and the window size used (Table 1). The classification is based on the notion, that the estimation of the missing data improves with the knowledge on meteorological conditions and with the use of the temporal auto-correlation of the variable that favours smaller time-windows.

Table 1: Quality classification scheme for gap-filled values, according to method used and averaging time window. A: best; B: acceptable; C: dubious. The output file contains a variable fqc, with values 0: no gap-filled; 1-3: gap-filled category A-C.
Averaging-time window [days]	Method
	1	2	3
0.5	n.a.	n.a.	A
1.5-2.5	n.a.	n.a.	B
> 2.5	n.a.	n.a.	C
7	A	A	n.a
14	A	B	n.a.
> 28	B	C	n.a.
> 56	C	C	n.a.

Flux-partitioning

Only original data (not gap-filled) are used for the flux-partitioning, and all original data flagged with a quality indicator >1 (e.g., with non-turbulent conditions) are dismissed. Night-time data was selected according to a global radiation threshold of 20 W m^-2 (night below that threshold), cross-checked against sunrise and sunset data derived from the local time and standard sun-geometrical routines, and defined as ecosystem respiration (R_eco). Then the data set is split into consecutive periods of length x (days), and for each period it is checked where there are more than six data points available and whether the temperature range is more than 5 °C, since only under these conditions reasonable regressions of Reco versus temperature can be expected (x is a parameter of the algorithm and currently set to 10 days). For each of those periods where the criteria are met, the Lloyd-and-Taylor (1994) regression model

(Eq. 1)

is fitted to the scatter of ecosystem respiration (R_eco) versus either soil or air temperature (T). While the regression parameter T₀ is kept constant at -46.02°C as in Lloyd and Taylor (1994), the activation-energy kind of parameter (E₀), that essentially determines the temperature sensitivity was allowed to vary. The reference temperature (T_ref) was set to 10°C as in the original model.

For each period, the regression parameters and statistics are kept in memory and evaluated after regressions for all periods that have been performed. Only those periods where the standard error of the estimates of the parameter E₀ is less than 50% and where estimates are within realistic bounds are accepted. The three estimates of E₀ with the smallest standard error are then assumed to best represent the short-term temperature response of R_eco and are averaged resulting in an E_0,avg value for the data set. Subsequently, the respiration at reference temperature (R_eco,ref) is estimated from the night time data for consecutive intervals of y days using non-linear regression of the R_eco data versus temperature according to Eq. 1, where E₀ is fixed to the E_0,avg value (y is a parameter of the algorithm and currently set to 4). The estimated value R_eco,ref is then assigned to the central time-point of the period and linearly interpolated between periods. Thus, for each half hour the parameters E₀ and R_eco,ref are available and are used to estimate R_eco as a function of that one temperature (soil or air) that has been also used to derive the parameters.

u*-filtering

For the u*-filtering the data set (storage corrected flux is assumed) is split into 6 temperature classes of the same sample size (according to quantiles) and for each temperature class the set is split into 20 u*-classes. The threshold is defined as the u*-class where the night-time flux reaches more than 95% of the average flux at the higher u*-classes. The threshold is only accepted if for the temperature class if temperature and u* are not or only weakly correlated (|r| < 0.3). The final threshold is defined as the median of the thresholds of the (up-to) six temperature classed. This procedure is applied to the subsets of four three-months periods to account for seasonal variation of vegetation structure. For each period the u*-threshold is reported, but the whole data set is filtered according to the highest threshold found (conservative appraoch). In cases where no u*-threshold could be found it is set to 0.4 . A minimum threshold is set to 0.1. Each half-hourly value of NEE with the corresponding u*-below the threshold and each succeeding NEE value are removed.

How to use

Use of this service is quite simple.

Go to the data input form: Here you provide the file containing the data (as ASCII file) and some basic information that are necessary to read and process the data. As you imagine with automatic processing, it is absolutely important that the data format and the information you provide is exact and follows our minimum template
There are several options to choose from, e.g., uncertainties, u*-filtering and different methods for the flux-partitioning.
After you press the SUBMIT button the file is uploaded, the gap-filling, and, if selected, the flux-partitioning are performed. After a certain time depending on size of the data set, number of gaps, and server load and performance (2-15 min, maybe enough for getting a coffee) you are directed to the results page. Meanwhile, the log file of the processing is forwarded to your browser output and it refreshes automatically once the processing is finished.

If you follow the link (view whole output directory), you will find additional graphs and a zipped version of all the output files.