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 as described by Isaac et al. (2017) for the quality control and post-processing steps. The final, gap-filled product containing Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER) has been produced using the ONEFlux software as described in Pastorello et al. (2020). This data set has been produced as part of the FLUXNET Shuttle project.
The Silver Plains flux tower is located at Interlaken, on the Tasmanian central plateau, Australia (GPS coordinates -42.15, 147.13, elevation 880m), on land owned and managed by the Tasmanian Land Conservancy. The site is a natural grassy sedgeland with an average summer temperature of 16°C, average winter temperature of 6°C, and average annual rainfall of approximately 900mm. Soil at the site is peaty, being an organosol containing on average 8 kgCm−2 in the top 10cm, overlying black vertosol on Jurassic dolerite. The vegetation at the site is heavily grazed year round by a range of native vertebrate herbivores, including wallabies, pademelons and wombats, as well as by feral fallow deer, resulting in an extremely low vegetation stature of a few centimetres, with the exception of inflorescences which can extend up to 30cm above the ground. The instrument tower is 3m tall, with carbon dioxide, water and heat fluxes being measured using an open-path eddy flux technique at 2.5m. Additional measurements include wind speed and direction, air temperature, humidity, incoming and outgoing short and longwave radiation, rainfall, soil temperature and soil water content.
Credit
We 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.
Purpose
The purpose of the Silver Plains flux tower is to: - Monitor exchanges of carbon dioxide, water vapour and energy in a high-altitude grassy peatland ecosystem on the Tasmanian Central Plateau.
- Quantify the carbon balance of the ecosystem, along with the key components of net ecosystem exchange, gross primary productivity and ecosystem respiration.
- Identify key environmental and climatic drivers of carbon, water and energy fluxes.
- Complement manual chamber measurements of net ecosystem CO2 exchange in an adjacent climate change experiment.
- Utilise measurements alongside manual chamber measurements and ancillary environmental variables to model ecosystem carbon dynamics under a future climate.
- Utilise the measurements for parameterising forage and grazing models.
- Utilise the measurements for parameterising and validating remote sensing measurements over semi-arid savanna ecosystems.
- Utilise the measurements for parameterising and validating the Earth System models to better understand the effects of climate change. Additionally the Silver Plains flux site will monitor changes in ecosystem carbon dynamics in response to hydrological remediation works occurring from 2027-2028, with adjacent flux tower Wedgetail (AU-Wgt) acting as a control.
Lineage
Data collected using standard eddy covariance and meteorological instrumentation on a <2.5m tower at the Silverplains site. The data were quality controlled using the PyFluxPro software package, see Isaac et al. (2017), which is available at
https://github.com/OzFlux/PyFluxPro. Gap filling and partitioning has been done using the ONEFlux software package, see Pastorello et al. 2020, which is available at
https://github.com/fluxnet/ONEFlux.
Procedure Steps1.
Data is measured using standard micro-meteorological instrumentation on a flux tower.
2.
Data is recorded on a data logger and is collected by the site PI.
3.
Data quality control including removal of data outside plausible ranges, removal of spikes, exclusion of particular date ranges and removal of data based on the dependence of one variable on another is done using PyFluxPro.
4.
Filtering for low-ustar conditions, gap filling and partitioning of NEE into GPP and ER are done using ONEFlux.