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 Fogg Dam flux station was located approximately 6km east of Black Jungle, Northern Territory.
It was established in February 2006 and decommissioned in September 2008. It was managed by Monash University and Charles Darwin University.
The flux tower site was classified as a seasonally flooded wetland. The vegetation was dominated by species Oryza rufipogon, Pseudoraphis spinescens and Eleocharis dulcis. Elevation of the site was close to 4m and mean annual precipitation from a nearby Bureau of Meteorology site measured 1411mm.
Maximum temperatures ranged from 31.3°C (in June and July) to 35.6°C (in October), while minimum temperatures ranged from 14.9°C (in July) to 23.9°C (in December and February). Maximum temperatures varied on a seasonal basis by approximately 4.3°C and minimum temperatures by 9.0°C.
The instrument mast was 15m tall. Heat, water vapour and carbon dioxide measurements are taken using the open-path eddy flux technique. Temperature, humidity, wind speed, wind direction, rainfall, incoming and reflected shortwave radiation and net radiation were measured above the canopy. Soil heat fluxes were measured and soil moisture content was gathered using time domain reflectometry.
Ancillary measurements taken at the site include LAI, leaf-scale physiological properties (gas exchange, leaf isotope ratios, N and chlorophyll concentrations), vegetation optical properties and soil physical properties. Airborne based remote sensing (Lidar and hyperspectral measurements) was carried out across the transect in September 2008.
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 Fogg Dam flux station was to: 1) Provide information as part of a larger network of flux stations established along the North Australian Tropical Transect (NATT) gradient, which extends ~1000km south from Darwin 12.5°S. 2) Examine spatial patterns and processes of land-surface-atmosphere exchanges (radiation, heat, moisture, CO2 and other trace gasses) across scales from leaf to landscape scales within Australian savannas. 3) Determine the climate and ecosystem characteristics (physical structure, species composition, physiological function) that drive spatial and temporal variations of carbon, water and energy fluxes from north Australian savanna. 4) Determine if fluxes of carbon, water vapour and heat over the various ecosystems as derived from the various measurement techniques can be combined to form a comprehensive and consistent estimate of the regional fluxes and budgets across the landscape.
Lineage
Data collected using standard eddy covariance and meteorological instrumentation on a 15m tower at the Fogg Dam 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.