Brief description
This release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer using eddy covariance techniques. Data were processed using PyFluxPro (v3.4.15) as described by Isaac et al. (2017). PyFluxPro produces a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER).
The Digby Plantation flux station was installed in a recently planted blue gum (Eucalyptus globulus) plantation near the town of Digby in western Victoria, Australia. The plantatiopn was establlished in August 2017 with a tree density of approximately 1000 trees per hectare. The seedlings were about 30 cm tall and the trees were about 11 m tall in July 2021. The equipment was installed on an extendable tower that was moved from 5 m at the beginning of the monitoring period to about 15 m at the end of the experiment, following the growth of the trees.
Notes
Data ProcessingFile naming convention
The NetCDF files follow the naming convention below:
SiteName_ProcessingLevel_FromDate_ToDate_Type.nc
- SiteName: short name of the site
- ProcessingLevel: file processing level (L3, L4, L5, L6)
- FromDate: temporal interval (start), YYYYMMDD
- ToDate: temporal interval (end), YYYYMMDD
- Type (Level 6 only): Summary, Monthly, Daily, Cumulative, Annual
- Summary: This file is a summary of the L6 data for daily, monthly, annual and cumulative data. The files Monthly to Annual below are combined together in one file.
- Monthly: This file shows L6 monthly averages of the respective variables, e.g. AH, Fc, NEE, etc.
- Daily: same as Monthly but with daily averages.
- Cumulative: File showing cumulative values for ecosystem respiration, evapo-transpiration, gross primary product, net ecosystem exchange and production as well as precipitation.
- Annual: same as Monthly but with annual averages.
Lineage
All flux raw data is subject to the quality control process OzFlux QA/QC to generate data from L1 to L6. Levels 3 to 6 are available for re-use. Datasets contain Quality Controls flags which will indicate when data quality is poor and has been filled from alternative sources. For more details, refer to Isaac et al. (2017).
Notes
CreditWe at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging.
This research was supported by the Australian Research Council (grant no. DP180101229). PF Olsen Australia is thankfully acknowledged for providing access to the plantation site.
The purpose of the Digby Plantation station was to monitor the fluxes in a blue gum (Eucalyptus globulus) plantation during the first few years after its establishment. The aim was to understand how the water efficiency of the plantation changes as trees rapidly grow in the years after being planted.
Data Quality Assessment Scope
local :
dataset
<br>Processing levels</br>
<br>Under each of the data release directories, the netcdf files are organised by processing levels (L3, L4, L5 and L6):<ul style="list-style-type: disc;">
<li>L3 (Level 3) processing applies a range of quality assurance/quality control measures (QA/QC) to the L1 data. The variable names are mapped to the standard variable names (CF 1.8) as part of this step. The L3 netCDF file is then the starting point for all further processing stages.</li>
<li>L4 (Level 4) processing fills gaps in the radiation, meteorological and soil quantities utilising AWS (automated weather station), ACCESS-G (Australian Community Climate and Earth-System Simulator) and ERA5 (the fifth generation ECMWF atmospheric reanalysis of the global climate).</li>
<li>L5 (Level 5) processing fills gaps in the flux data employing the artificial neural network SOLO (self-organising linear output map).</li>
<li>L6 (Level 6) processing partitions the gap-filled NEE into GPP and ER.</li></ul>
Each processing level has two sub-folders ‘default’ and ‘site_pi’:<ul style="list-style-type: disc;">
<li>default: contains files processed using PyFluxPro</li>
<li>site_pi: contains files processed by the principal investigators of the site.</li></ul>
If the data quality is poor, the data is filled from alternative sources. Filled data can be identified by the Quality Controls flags in the dataset. Quality control checks include: <ul style="list-style-type: disc;">
<li>range checks for plausible limits</li>
<li>spike detection</li>
<li>dependency on other variables</li>
<li>manual rejection of date ranges</li></ul>
Specific checks applied to the sonic and IRGA data include rejection of points based on the sonic and IRGA diagnostic values and on either automatic gain control (AGC) or CO<sub>2</sub> and H<sub>2</sub>O signal strength, depending upon the configuration of the IRGA.</br>
<br>Digby Plantation Flux Tower was established in December 2017 and was decommissioned in February 2022; data after August 2021 might be affected by the height of the trees, which were close to the equipment installed at the top of the tower extended at its maximum height. The processed data release is currently ongoing, biannually.</br>
<br></br>
Isaac P., Cleverly J., McHugh I., van Gorsel E., Ewenz C. and Beringer, J. (2017). OzFlux data: network integration from collection to curation, Biogeosciences, 14: 2903-2928
doi :
https://doi.org/10.5194/bg-14-2903-2017
Created: 2023-10-06
Issued: 2023-12-21
Modified: 2024-05-03
Data time period: 2018-01-01 to 2021-07-07
text: The Digby Plantation flux station is located in blue gum plantation in southwestern Victoria, approximately 300 km west of Melbourne.
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Point-of-truth metadata URL
Isaac P., Cleverly J., McHugh I., van Gorsel E., Ewenz C. and Beringer, J. (2017). OzFlux data: network integration from collection to curation, Biogeosciences, 14: 2903-2928
doi :
https://doi.org/10.5194/bg-14-2903-2017
PyFluxPro
- URI : geonetwork.tern.org.au/geonetwork/srv/eng/catalog.search#/metadata/868e5485-67bb-4424-af73-66c6ec5fa247
- global : 868e5485-67bb-4424-af73-66c6ec5fa247
