This data release consists of flux tower measurements of the exchange of energy and mass between the surface and the atmospheric boundary-layer in semi-arid eucalypt woodland using eddy covariance techniques. It been processed using PyFluxPro (v3.3.0) as described in Isaac et al. (2017), https://doi.org/10.5194/bg-14-2903-2017. 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 https://github.com/OzFlux/PyFluxPro/wiki.
Located in a 5 square kilometre block of relatively uniform open-forest savanna, the site is representative of high rainfall, frequently burnt tropical savanna.
Tropical savanna in Australia occupies 1.9 million square km across the north and given the extent of this biome, understanding biogeochemical cycles, impacts of fire on sequestration, vegetation and fauna is a national priority. In the NT, savanna ecosystems are largely intact in terms of tree cover, with only modest levels of land use change. Despite this, there is evidence of a loss of biodiversity, most likely due to shifts in fire regimes and a loss of patchiness in the landscape. Approximately 40% of the savanna burn every year and understanding fire impacts on fauna and flora is essential for effective land management.
Located in a 5 square kilometre block of relatively uniform open-forest savanna, the site is representative of high rainfall, frequently burnt tropical savanna.
Tropical savanna in Australia occupies 1.9 million square km across the north and given the extent of this biome, understanding biogeochemical cycles, impacts of fire on sequestration, vegetation and fauna is a national priority. In the NT, savanna ecosystems are largely intact in terms of tree cover, with only modest levels of land use change. Despite this, there is evidence of a loss of biodiversity, most likely due to shifts in fire regimes and a loss of patchiness in the landscape. Approximately 40% of the savanna burn every year and understanding fire impacts on fauna and flora is essential for effective land management.
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.
The site is managed by The University of Western Australia and Charles Darwin University. Stakeholders of the site include: Charles Darwin University, Maitec (Stefan Maier), CSIRO (Shaun Levick), Darwin Centre for Bushfire Research, Northern Territory Department of Natural Resources, Environment, The Arts and Sport (NRETAS) and the University of Western Australia. Flux data collection is funded by TERN. The flux station is part of the Australian OzFlux Network and contributes to the international FLUXNET Network.
Purpose
The Litchfield flux station will provide nationally consistent observations of vegetation dynamics, faunal biodiversity, micrometeorology (climate, radiation, fluxes of carbon and water), hydrology and biogeochemistry to examine the impacts of fire regime, climate on carbon stocks and GHG emissions, and impacts on habitat quality via ongoing monitoring of vegetation structure and fauna. A wide range of ground based observations of vegetation structure and floristics is planned and all will link to remote sensing of fire and vegetation change over time. Co-location of Flux observations and remote sensing systems will be co-located to provide TERN with a ground-/air-/space based remote sensing observation stream. This will enable the development of tools describing fire occurrence, severity and associated greenhouse gas emissions, evapotranspiration and carbon sequestration.
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) in the Publications section, https://doi.org/10.5194/bg-14-2903-2017 .
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