The protocol is a step-by-step description of the eddy covariance site setup and measurements performance from the scratch, which can be successfully applied in spatially limited ecosystems. We believe the protocol make it easier to realize that, however, strict requirements need to be met. The covariance technique can be satisfactorily applied also in not non-ideal locations.
Once it is presented in a visual form, the protocol can be used as a first and relatively easy choice for non-specialists forced or willing to use the eddy covariance technique. To begin, on a height map, choose a measuring site location in relatively homogeneous and flat terrain to meet the basic requirements of the EC method. Select a location with no obstacles and ensure that the area to be investigated extends in each direction at least 100 times the height of the sensor to be placed.
At the site, use an anemometer to investigate prevailing wind directions for one year or analyze data from the nearest meteorological station. Decide which EC system to use. Open the path infrared gas analyzer with lower power consumption or a closed path analyzer with a short intake tube if there are no limitations to power supply or if in harsh environments.
At the site, place a tripod with a vertical pole to mount the EC system on top. Position the infrared gas analyzer and the 3D sonic anemometer close to each other. Place the sonic anemometer at a perfectly vertical position.
Tilt the gas analyzer slightly to allow rainwater to run off easily. Elevate the instruments to a height twice the canopy height from the soil surface and at least one and a half to two meters above the top of the canopy. Avoid mounting any unnecessary elements close to the EC system, which can distort the airflow.
For further computation and flux analysis, measure some auxiliary variables at the same time, including at least air and soil temperature, relative humidity of the air, photosynthetic photon flux density, incoming solar radiation, and precipitation. To compute carbon dioxide flux, use commercially available free software EddyPro that includes correction applications for EC flux computation. First, create a new project and then in the project info tab specify the raw data file format and choose a metadata file.
Go to the flux info tab, choose the dataset and output directories, specify the raw file name format, and check the list of items for flux computation. Then, go to the processing options tab and choose raw data processing settings. Choose the rotation method for correction of the anemometer's measurements, which allows accounting for any misalignment of the sonic anemometer with respect to the local wind streamline.
Tick the first planar fit approach for non-deal heterogeneous locations. Choose the 012 type of flagging policy. Select the preferred footprint method for the area of the influence on measured fluxes.
Leave all other settings unchanged. Click run in advanced mode to start flux computations at the end. Create a spreadsheet that contains the results from the flux calculation software and auxiliary measurements.
Use filtering tools in the spreadsheet to filter out carbon dioxide fluxes measured during unfavorable weather conditions and instrument malfunctions. For an enclosed path analyzer, check the average signal strength value. Then, mark and discard all fluxes measured with ASS lower than the 60%threshold suggested in the instrument's manual.
Discard the fluxes measured during any rain events with P greater than or equal to 0.1 millimeters. To account for inappropriate conditions for eddy covariance method application, discard the flux data with poor quality having carbon dioxide flagged values greater than one in the common results file. Use the nighttime period indicator, daytime equaling zero, given in the output file to filter out the carbon dioxide flux values measured at night.
Plot the nighttime carbon dioxide fluxes against the corresponding friction velocity values and find the U-star value at which these fluxes stopped increasing. Mark the obtained value as the friction velocity threshold to be used as a measure of insufficient turbulence conditions. Discard from the dataset all carbon dioxide fluxes having a U-star value less than the threshold, indicating insufficient turbulence.
Now, plot the wind rose on the map of investigated area for flux spatial representativeness constraints. According to the estimation of the cross-wind integrated footprints, choose 70%as the probability for spatially limited sites to be used for further analysis. Next, orient a map and a wind rose the same way and using north direction as an indicator, check if any direction at the area of interest has obstacles, for example, other kinds of ecosystems, and mark them as not representative.
Choose the wind direction sectors and the footprint values that are most representative of the measuring site, check the dimension, and specify the maximum length. Filter out flux values that do not meet both requirements. To perform gap filling for carbon dioxide data, choose the method for quality checked carbon dioxide flux gap filling and partitioning into absorption and respiration from three basic groups:process-based approach, statistical methods, and the use of neural networks.
The weakest point of the protocol is the gap filling and flux partitioning description since suggested methods were individually developed by other specialists and only implemented here as proposed techniques. An example of the process-based approach is from the FLUXNET Canada Research Network. To fill the gaps, not only in the carbon dioxide but also other EasyFlux values, such as sensible and latent heat, as well as in the important meteorological elements, use the REddyProc online tool, which is also available as an R software package.
Next, in, for example, R software, calculate daily, monthly, and annual totals of all gap-filled carbon dioxide fluxes, including net ecosystem production, gross ecosystem production, and ecosystem respiration. The wind rose plot on the background of the TLEN 1 site area shows the blue-shaded polygons for the chosen wind direction and the red-shaded polygons within them as sectors of a circle with a radius of 200 meters, representing maximal acceptable extent of flux footprints. This figure shows the results of a filtering procedure on the example of one year of net ecosystem production fluxes measurements from the TLEN 1 wind throw site.
The smallest number of data points was discarded due to unfavorable weather conditions and instrument malfunctions. While the last part of the quality assurance protocol, considering flux spatial representativeness constraints, yielded a final data coverage of only 1/3 of all raw net ecosystem production fluxes measured by EC.The relationship between net ecosystem production fluxes, gap filled for the process-based method, and a statistical approach shows a simple linear regression, suggesting that in general, both techniques are comparable and thus can be used for net ecosystem production fluxes gap filling. By using the two methods, daily ecosystem respiration flux totals were also obtained from the partitioning procedure.
It has to be remembered that one of the crucial steps in data filtering and quality control at non-ideal sites is the assessment of measured fluxes'spatial representativeness.
