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 (v3.5.0) 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).
Alice Springs Mulga flux station is located on Pine Hill cattle station, near Alice Springs in the Northern Territory. The woodland is characterized by the Acacia aneura canopy, which is 6.5 m tall on average. Elevation of the site is 606 m above sea level, and the terrain is flat. Mean annual precipitation at the nearby (45 km distant) Bureau of Meteorology station is 305.9 mm but ranges between 100 mm in 2009 to 750 mm in 2010. Predominant wind directions are from the southeast and east. The extent of the woodland is 11 km to the east of the flux station and 16 km to the south. The soil is red sandy clay (50:50 sand:clay) overlying a 49 m deep water table. Pine Hill Station is a functioning cattle station that has been in operation for longer than 50 years. The instrument mast is 13.7 m tall. Fluxes of heat, water vapour and carbon are measured using the open-path eddy covariance technique at 11.6 m. Supplementary measurements above the canopy include temperature and humidity (11.6 m), windspeed and wind direction (9.25 m), downwelling and upwelling shortwave and longwave radiation (12.2 m). Precipitation is monitored in a canopy gap (2.5 m). Supplementary measurements within and below the canopy include barometric pressure (1 m), wind speed (2 m, 4.25 m and 6.5 m), and temperature and humidity (2 m, 4.25 m and 6 m). Below ground soil measurements are made in bare soil, mulga, and understory habitats and include ground heat flux (0.08 m), soil temperature (0.02 m – 0.06 m) and soil moisture (0 – 0.1 m, 0.1 – 0.3 m, 0.6 – 0.8 m and 1.0 – 1.2 m). Ancillary measurements include soil water and carbon fluxes, leaf water potential, leaf gas exchange, stem basal area, stem growth, litter production, leaf area index, stem hydraulic conductance, and carbon and water stable isotope ratios.
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 productivity, 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.
The TERN Alice Springs Mulga site is managed by the University of Technology Sydney, and is funded by TERN.
The purpose of the Alice Springs Mulga flux station is to:
- measure the exchanges of carbon dioxide, water vapour and energy between a semi-arid mulga (Acacia aneura) ecosystem and the atmosphere using micrometeorological techniques
- study ecosystem, hydrologic and ecophysiologic responses to rainfall variability
- evaluate the evapotranspiratory cost of assimilation
- study the partitioning of ecosystem metabolism between the mulga canopy, a seasonal mixed understory (C3 and C4, grass and shrub) and soil components
- utilise the measurements for paramterising a Soil-Vegetation-Atmosphere Transfer (SVAT) model to evaluate climate change scenarios in North-Central Australia
- utilise the measurements for parameterising and validating remote sensing measurements over semi-arid mulga ecosystems
- utilise the measurements for parmaterising and validating the Community Atmosphere-Biosphere Land Exchange (CABLE) model.
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>
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: 2023-03-31
Issued: 2023-05-04
Modified: 2024-05-12
Data time period: 2010-09-03
text: Pine Hill cattle station, near Alice Springs in the Northern Territory.
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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/fc10d99e-7f15-4864-9ebb-5188a8cbb2f4
- global : fc10d99e-7f15-4864-9ebb-5188a8cbb2f4
