FLUXNET-CH4 Data Variables

This webpage describes data variables and formatting for the FLUXNET-CH4 Community Product.

1. Data Variables: Base names

Base names indicate fundamental quantities that are either measured or calculated/derived. They can also indicate quantified quality information.

Table 1. Base names for data variables

2. Data Variables: Qualifiers

Qualifiers are suffixes appended to variable base names that provide additional information about the variable. For example, the _DT qualifier in the variable label GPP_DT indicates that gross primary production (GPP) has been partitioned using the flux partitioning method from Lasslop et al. 2010.

Multiple qualifiers can be added, and they must follow the order in which they are presented here.

2.1. Qualifiers: General

General qualifiers indicate additional information about a variable.

• _F: Variable has been gap-filled by the FLUXNET-CH4 team. Gaps in meteorological variables (including air temperature (TA), incoming shortwave (SW_IN) and longwave (LW_IN) radiation, vapor pressure deficit (VPD), pressure (PA), precipitation (P), and wind speed (WS) ) were filled with ERA-Interim (ERA-I) reanalysis data ((Vuichard and Papale 2015)). Other variables were filled using the MDS approach in REddyProc (see Delwiche et al. 2021 for more details).
• _DT: Variable acquired using the flux partitioning method from (Lasslop et al. 2010), with values estimated by fitting the light-response curve.
• _NT: Variable acquired using the flux partitioning method from (Reichstein et al. 2005), with values estimated from night-time data and extrapolated to day time.
• _RANDUNC: Random uncertainty introduced from several different sources including errors associated with the flux measurement system (gas analyzer, sonic anemometer, data acquisition system, flux calculations), errors associated with turbulent transport, and statistical errors relating to the location and activity of the sites of flux exchange (“footprint heterogeneity”) (Hollinger and Richardson 2005).
• _ANNOPTLM: Gap-filled variable using an artificial neural net routine from Matlab with the Levenberg-Marquardt algorithm as the training function, and parameters optimized across runs (more detail in (Sara Helen Knox et al. 2016; Sara H. Knox et al. 2019)).
• _UNC: Uncertainty introduced from ANNOPTLM gap-filling routine, as described in Knox et al. 2016 and Knox et al. 2019.
• _QC: Reports quality checks on FCH4 gap-filled data (_ANNOPTLM) based on length of data gap. 1 = data gap shorter than 2 months, 3 = data gap exceeds 2 months which could lead to poor quality gap-filled data. Nondimensional.

2.2. Qualifiers: Positional (_V)

Positional qualifiers are used to indicate relative positions of observations at the site. For FLUXNET-CH4, positional qualifiers are used to distinguish soil temperature probes for sites with more than one probe. Probe depths for each positional qualifier per site are included in the metadata file included with data download and also in Table B6 of Delwiche et al. 2021. For sites where the original database file release in AmeriFlux, AsiaFlux, or EuroFlux contains multiple probes at the same _V depth, we average values and report only the average for each _V position. The one exception to this is site US-UAF where the original positional qualifier from the data we downloaded from AmeriFlux had different depths for the same qualifier. We still averaged the probe data, so _V qualifiers from US-UAF represent an average of more than one depth.

3. Missing data

Missing data are reported using -9999. Data for all days in a leap year are reported.

References

Delwiche et al., 2021. “FLUXNET-CH4: A global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands.” Earth System Science Data Discuss https://doi.org/10.5194/essd-13-3607-2021.

Hollinger, D. Y., and A. D. Richardson. 2005. “Uncertainty in Eddy Covariance Measurements and Its Application to Physiological Models.” Tree Physiology 25 (7): 873–85.

Knox, Sara Helen, Jaclyn Hatala Matthes, Cove Sturtevant, Patricia Y. Oikawa, Joseph Verfaillie, and Dennis Baldocchi. 2016. “Biophysical Controls on Interannual Variability in Ecosystem-Scale CO2and CH4exchange in a California Rice Paddy.” Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1002/2015jg003247.

Knox, Sara H., Robert B. Jackson, Benjamin Poulter, Gavin McNicol, Etienne Fluet-Chouinard, Zhen Zhang, Gustaf Hugelius, et al. 2019. “FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and Future Directions.” Bulletin of the American Meteorological Society 100 (12): 2607–32.

Lasslop, Gitta, Markus Reichstein, Dario Papale, Andrew D. Richardson, Almut Arneth, Alan Barr, Paul Stoy, and Georg Wohlfahrt. 2010. “Separation of Net Ecosystem Exchange into Assimilation and Respiration Using a Light Response Curve Approach: Critical Issues and Global Evaluation.” Global Change Biology. https://doi.org/10.1111/j.1365-2486.2009.02041.x.

Reichstein, Markus, Eva Falge, Dennis Baldocchi, Dario Papale, Marc Aubinet, Paul Berbigier, Christian Bernhofer, et al. 2005. “On the Separation of Net Ecosystem Exchange into Assimilation and Ecosystem Respiration: Review and Improved Algorithm.” Global Change Biology. https://doi.org/10.1111/j.1365-2486.2005.001002.x.

Vuichard, N., and D. Papale. 2015. “Filling the Gaps in Meteorological Continuous Data Measured at FLUXNET Sites with ERA-Interim Reanalysis.” Earth System Science Data. https://doi.org/10.5194/essd-7-157-2015.