Beringer, J. & Walker, J. (2026). Yanco FLUXNET Release 2026_r1. Version 2026_r1. Terrestrial Ecosystem Research Network. Dataset. https://dx.doi.org/10.25901/k8zr-kn41
Yanco FLUXNET Release 2026_r1
Ver: 2026_r1
Status of Data: completed
Update Frequency: annually
Security Classification: unclassified
Record Last Modified: 2026-04-02
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Dataset Created: 2026-03-28
Dataset Published: 2026-04-02
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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 Yanco flux tower is located in the flat western plains of the Murrumbidgee Catchment in New South Wales, within a larger OzNet research area operating since 2001. The landscape consists mainly of sandy loams, clays, and red-brown soils with some stream valleys.
The 20 m tower measures fluxes slightly below its top in a climate with about 465 mm annual rainfall. Temperatures range from hot summers (up to 37.4°C) to cooler winters (around 16.6°C max).
The 20 m tower measures fluxes slightly below its top in a climate with about 465 mm annual rainfall. Temperatures range from hot summers (up to 37.4°C) to cooler winters (around 16.6°C max).
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
To provide additional validation data for GCOM-W1 and GOSAT-GW soil moisture products, which are components of a land surface data assimilation system. These data systems are important for land surface models, which rely on atmosphere-surface observations. Information provided by an existing network of soil moisture monitoring systems will also be used to validate the GCOM-W1 and GOSAT-GW soil moisture product.
For validation of the Advanced Microwave Scanning Radiometer-2 (AMSR2) and AMSR3 soil moisture product and resulting soil moisture and flux estimates.
For crop modelling, runoff, flood forecasting, Land Surface Model forcing data (i.e. input of AMSR2 soil moisture into the Joint UK Land Environment Simulator (JULES) model).
For validation of the Advanced Microwave Scanning Radiometer-2 (AMSR2) and AMSR3 soil moisture product and resulting soil moisture and flux estimates.
For crop modelling, runoff, flood forecasting, Land Surface Model forcing data (i.e. input of AMSR2 soil moisture into the Joint UK Land Environment Simulator (JULES) model).
Lineage
Data collected using standard eddy covariance and meteorological instrumentation on a 20m tower at the Yanco 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.
Method DocumentationPastorello, G., Trotta, C., Canfora, E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225 (2020).Isaac P., Cleverly J., McHugh I., van Gorsel E., Ewenz C. and Beringer, J. (2017). OzFlux data: network integration from collection to curation,PyFluxProONEFlux
Procedure Steps
1.
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.
Spatial Description
The Yanco flux tower is located approximately 37km south west of Narrandera, in the Yanco region of New South Wales.
Temporal Coverage
From 2012-01-01 to 2026-01-01
Spatial Resolution
Data not provided.
Vertical Extent
Data not provided.
Data Quality Assessment Scope
Isaac P., Cleverly J., McHugh I., van Gorsel E., Ewenz C. and Beringer, J. (2017). OzThe data have been quality controlled using the PyFluxPro software. Quality control checks applied to the data include:
Specific checks applied to the sonic and IRGA data including rejection of points based on the sonic and IRGA diagnostic values and on either automatic gain control (AGC) or CO2 and H2O signal strength, depending upon the configuration of the IRGA.
If the data quality is poor, the meteorological data is filled from ERA5 reanalysis data and fluxes are filled using the Marginal Distribution Sampling method. Filled data can be identified by the Quality Controls flags in the dataset.
The ONEFlux software used to gap fill and partition this data set also applies a Median Absolute Deviation (MAD) filter to the carbon dioxide, latent heat and sensible heat before the gap filling step.
- range checks for plausible limits
- spike detection and removal
- dependency on other variables
- manual rejection of date ranges
Specific checks applied to the sonic and IRGA data including rejection of points based on the sonic and IRGA diagnostic values and on either automatic gain control (AGC) or CO2 and H2O signal strength, depending upon the configuration of the IRGA.
If the data quality is poor, the meteorological data is filled from ERA5 reanalysis data and fluxes are filled using the Marginal Distribution Sampling method. Filled data can be identified by the Quality Controls flags in the dataset.
The ONEFlux software used to gap fill and partition this data set also applies a Median Absolute Deviation (MAD) filter to the carbon dioxide, latent heat and sensible heat before the gap filling step.
Data Quality Assessment Outcome
No anomalous data detected after quality control.
ANZSRC - FOR
GCMD Sciences
Horizontal Resolution
Parameters
Platforms
Temporal Resolution
User Defined
AU-Ync
FLUXNET ID
Author
Co-Author
Contact Point
Terrestrial Ecosystem Research Network
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