<br>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 (v3.4.7) 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).</br> <br>Silver Plains Flux Station was established in 2019 in Interlaken, on the Tasmanian Central Plateau, on land owned and managed by the Tasmanian Land Conservancy.</br>

<br>This data release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in high-altitude grassy peatland ecosystem using eddy covariance techniques. It been processed using PyFluxPro (v3.4.4) as described in Isaac et al. (2017), <a href="https://doi.org/10.5194/bg-14-2903-2017">https://doi.org/10.5194/bg-14-2903-2017</a>. PyFluxPro takes data recorded at the flux tower and process this data to a final, gap-filled product with Net Ecosystem Exchange (NEE) partitioned into Gross Primary Productivity (GPP) and Ecosystem Respiration (ER). For more information about the processing levels, see <a href="https://github.com/OzFlux/PyFluxPro/wiki">https://github.com/OzFlux/PyFluxPro/wiki</a>. </br> <br>Silver Plains Flux Station was established in 2019 in Interlaken, on the Tasmanian Central Plateau, on land owned and managed by the Tasmanian Land Conservancy. </br><br>This data is also available at http://data.ozflux.org.au</br>

This dataset consists of measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer at Silver Plains Station in Tasmania using eddy covariance techniques.</br> Silver Plains Flux Station was established in 2019 in Interlaken, on the Tasmanian Central Plateau, on land owned and managed by the Tasmanian Land Conservancy. <br />This data is also available at http://data.ozflux.org.au</br>

This dataset is modelled national pasture productivity. It describes the dynamics in grassland/pasture Gross Primary Production (GPP), Net Primary Production (NPP) and Carbon mass. GPP indicates total rate of carbon fixed through photosynthesis, in units gC/m2/day. It is the GPP of grasses only and so describes the production of grasslands and pastures. GPP is estimated separately for C3 and for C3 grasses using the Diffuse model (Donohue et al. 2014, see publication links). NPP is the net rate of carbon fixed through photosynthesis (GPP minus plant respiration) for grasses, in units of gC/m2/day. Grass carbon mass is the above-ground mass of grasslands and pastures, estimated using the CSP model. These are estimated using the unpublished CSP model (v2) for both live and senesced mass in units t/ha. Biomass is typically approximated as double the carbon mass. Inputs include MODIS MOD13Q1, minimum and maximum air temperature, elevation data and rainfall as described in the lineage section.

Heatwaves are defined as unusually high temperature events that occur for at least three consecutive days with major impacts to human health, economy, agriculture and ecosystems. This dataset provides time-series of heatwave characteristics such as peak temperature, number of events, frequency and duration from 1950 to 2016 in Australia. The analysis were based on daily minimum and maximum temperature obtained from the Australian Water Availability Project (AWAP). The data is available as spatial time-series (5km grid-cell) and aggregated time-series for all Local Government Areas in Australia.

Dynamically downscaled high-resolution (~10 km spatial resolution) climate change projection data for Queensland. Downscaling was completed using CSIRO Conformal Cubic Atmospheric Model (CCAM) for two RCPs (RCP4.5 and RCP8.5) from 11 CMIP5 global coarse resolution models for period 1980-2099. The Queensland Future Climate Dashboard (www.longpaddock.qld.gov.au/qld-future-climate/ ) provides easy access to climate projection for Queensland. The dashboard allows users to explore, visualize and download the latest high-resolution climate modelling data for specific regions, catchments, disaster areas, local government areas and grid squares. Underlying data is provided via TERN for easy access for each of 11 downscaled models. The Queensland Future Climate Dataset provides high resolution data for over 30 different metrics grouped in six climate themes: (i) Mean Climate; (ii) Heatwaves; (iii) Extreme Temperature Indices; (iv) Extreme Precipitation Indices; (v) Droughts; and (vi) Floods. In addition selected variables at daily and monthly intervals are also available.

<ul><li>1. Restoration of degraded landscapes has become increasingly important for conservation of species and their habitats owing to habitat destruction and rapid environmental change. An increasing focus for restoration activity are old-fields as agricultural land abandonment has expanded in the developed world. Studies examining outcomes of ecological restoration predominantly focus on vegetation structure and plant diversity, and sometimes vertebrate fauna. Fewer studies have systematically investigated effects of restoration efforts on soil chemical and biophysical condition or ground-dwelling invertebrates and there is limited synthesis of these data. </li> <li>2. This dataset comprised data for a global meta-analysis of published studies to assess the effects on soil properties and invertebrates of restoring land that was previously used for agriculture. Studies were included if the site had been either cropped or grazed, restoration was either active (planting) or passive (abandonment, fencing) and if adequate data on soil chemical or physical properties or invertebrate assemblages were reported for restored, control (cropped/grazed) or reference sites.</li> <li>3. The dataset includes 42 studies, published between 1994 and 2019 that met the inclusion criteria, covering 16 countries across all continents. More studies assessed passive restoration approaches than active planting, and native species were more commonly planted than exotic species.</li></ul>

<p>This dataset contains audio files from Gingin Banksia Woodland SuperSite. The site was established in 2011 and is located in a natural woodland of high species diversity with an overstorey dominated by Banksia species. For additional site information, see <a href="https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/gingin-banksia-woodland-supersite/">Gingin Banksia Woodland SuperSite</a></p> <p>In 2020 four acoustic recorders were set up to collect audio data continuously as part of the Australian Acoustic Observatory (A2O) project. Two recorders were placed in relatively wet habitats and two in relatively dry habitats.</p>

The Australian cosmic-ray soil moisture monitoring network was first established in 2010 to provide Australian and global researchers with spatially distributed intermediate scale soil moisture observations. A cosmic-ray sensor (CRS) provides continuous estimates of soil moisture over an area of approximately 30 hectares by measuring naturally generated fast neutrons (energy 10–1000 eV) that are produced by cosmic rays passing through the Earth’s atmosphere. The neutron intensity above the land surface is inversely correlated with soil moisture as it responds to the hydrogen contained in the soil and to a lesser degree to plant and soil carbon compounds. The cosmic-ray technique is also passive, non-contact, and is largely insensitive to bulk density, surface roughness, the physical state of water, and soil texture. The scale of CRS measurements fills the void between point scale sensor measurements and large scale satellite observations. The depth of measurements varies with the moisture content of the soil but is typically between 10-30 cm. The depth of observations is reported as ‘effective depth’. <br> The CosmOz network is expanding as new sensors are added over time. The initial network was funded by CSIRO Land and Water but more recently TERN has funded work to maintain the network add new sensors and deliver data more efficiently. The standard CRS installation includes; a cosmic-ray neutron tube, a rain gauge (2m high), temperature and humidity sensors, and an atmospheric pressure sensor. Measures of all parameters are reported at an hourly interval. Each CRS requires an in-field calibration across the footprint of measurements to convert neutron counts to soil moisture content. The calibration includes collection of soil samples for bulk density, lattice water content and soil organic carbon.<br> The Australia CosmOz network consists of <a href="https://cosmoz.csiro.au/sites">19 stations</a>. The extent of the network and available data can be seen at the CosmOz network web page: <a href="https://cosmoz.csiro.au/">https://cosmoz.csiro.au</a>. The data is also accessible from the <a href="https://landscapes-cosmoz-api.tern.org.au/rest/doc">TERN Cosmoz REST API</a>.<br> The calibration and correction procedures used by the network are described by <a href="https://doi.org/10.1002/2013WR015138">Hawdon et al. 2014 </a>.

<p>This dataset contains audio files for TERN Fletcherview Tropical Rangeland SuperSite. Long-term recordings of the environment can be used to identify sound sources of interest, characterise the soundscape, aid in the assessment of fauna biodiversity, monitor temporal trends and track environmental changes.</p> <p>Fletcherview Tropical Rangeland SuperSite was established in 2021 at James Cook University’s Fletcherview Research Station, a fully operational outback cattle station located 50&nbsp;km west of Townsville, Queensland. The site is used for cattle grazing and is characterised by tall open savanna. The vegetation is dominated by native grasses such as blackspear and kangaroo grasses, as well as introduced species like buffel grass, signal grass and leucaena. Fletcherview typically experiences a dry and wet season, with most rainfall occurring between January and April.</p> <p>In 2020 four acoustic recorders were set up to collect audio data continuously as part of the Australian Acoustic Observatory (A2O) project. Two recorders were placed in relatively wet habitats and two in relatively dry habitats.</p>