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 Cow Bay flux station is located in lowland tropical rainforest at the Daintree Discovery Centre near Cow Bay in Far North Queensland (GPS coordinates: -16.238189, 145.427150; elevation: 86 m).
The Cow Bay flux station commenced continuous collection of data in December 2008. The flux system is located on an existing tourist tower at the Daintree Discovery Centre at Cow Bay, directly adjacent to Daintree National Park, within the Wet Tropics World Heritage Area.
The site is flanked to the south-west by coastal ranges rising to 400 m and to the east by the Coral Sea. The red clay loam podzolic soils are of metamorphic origin and have good drainage characteristics. The Cow Bay site is gently sloping but the fetch area makes the site one of very complex terrain.
The forest is classed as complex mesophyll vine forest (type 1a) and has an average canopy height of 25 m. The dominant canopy trees belong to the Arecaceae, Euphorbiaceae, Rutaceae, Meliaceae, Myristicaceae and Icacinaceae families. It is continuous for several kilometres around the Cow Bay Tower except for an area 800 m north-east of the tower, which is cleared agricultural land used for a cattle farm. A 1 Ha census plot is located in immediate proximity to the flux tower.
Annual average rainfall at the site is around 3700 mm and is strongly seasonal, with 68% falling between January and April (wet season). Daintree Discovery Centre
The flux station is located at the 23 m level viewing platform on the visitor tower at the Daintree Discovery Centre external link. The visitor centre has seen over 1 million visitors visit since it was set up in 1989 by Pam and Ron Burkett. The DDC is recognised as a leader in ecotourism in the Daintree and has won numerous state, national and international awards including the International Award for Ecotourism (Skal Congree, Budapest 2009). In 2016 the DDC was sold to the Aboriginal Development Benefits Trust (ADBT). The ADBT helps to develop indigenous community, youth and entrepreneurship programs within the Lower Gulf region.
The flux tower is managed by James Cook University.
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 Cow Bay flux station is to : 1) Measure exchanges of carbon dioxide, water vapour and energy between the tropical lowland rainforest in Far North Queensland and the atmosphere using micrometeorological techniques.
2) Quantify the changes in carbon and water balances of an Australian tropical rainforest on a long term basis in the face of climate change.
3) Present the results from the study in real time to the public and inform the public on what these results mean.
Lineage
Data collected using standard eddy covariance and meteorological instrumentation on a 23m tower at the Coy Bay 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.