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Australian Terrestrial and Coastal Marine (Blue) Carbon Stocks 

Ver: 0.01
Status of Data: completed
Update Frequency: notPlanned
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 770 times
Accessed 264 times
Dataset Created: 2022-12-01
Dataset Published: 2023-05-31
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFsWMSs30cuHTTPLandscape Data VisualiserHTTPAustralia's terrestrial and coastal marine (blue: mangroves, tidal marshes, seagrasses) soil organic carbon stocks and uncertaintyHTTPro-crate-metadata.json
How to cite this collection:
Viscarra Rossel, R. & Walden, L. (2023). Australian Terrestrial and Coastal Marine (Blue) Carbon Stocks. Version 0.01. Terrestrial Ecosystem Research Network. Dataset. https://dx.doi.org/10.25901/6kn5-1020 
The soil in terrestrial and blue carbon ecosystems (BCE; mangroves, tidal marshes, seagrasses) is a significant carbon (C) sink. National assessments of C inventories are needed to protect them and aid nature-based strategies to sequester atmospheric carbon dioxide. We harmonised measurements from Australia's terrestrial and BCE and, using consistent multi-scale spatial machine learning, unravelled the drivers of soil organic carbon (SOC) variation and digitally mapped their stocks. The modelling shows that climate and vegetation are continentally the primary drivers of SOC variation. But the underlying regional drivers are ecosystem type, terrain, clay content, mineralogy, and nutrients. The digital soil maps indicate that in the 0-30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6-39.0 Gt), and BCE 0.35 Gt (0.20-0.62 Gt). Tall open eucalypt and mangrove forests have the largest mean SOC per unit area. Eucalypt woodlands and hummock grassland, which occupy vast areas, store the largest total SOC stock. These ecosystems constitute important regions for conservation, emissions avoidance, and preservation because they also provide additional co-benefits. 
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. Authors thank the Australian Government for funding this research via grant ACSRIV000077. We thank all co-authors and their contributions, and the many colleagues who contributed to the collection of soil samples and data used in this research. This work is also supported by the use of (i) Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS), and (ii) computational resources in the Pawsey Supercomputing Centre, which is funded by the Australian Government and the Government of Western Australia. 
Purpose
A fine spatial resolution, spatially explicit dataset on the soil organic carbon stocks (t/ha) in Australia's terrestrial and coastal marine (or blue carbon: mangroves, tidal marshes, seagrasses) ecosystems. The data provides a consistently-derived and up-to-date baseline of Australia's 0-30 cm soil carbon stocks. Can be used by land managers, researchers, and policy-makers. 
Lineage
The data was produced using a compilation of various regional datasets. They were analysed and harmonised using statistical methods that are described in the publication that describes the research - Walden et al. (2023) in Communications Earth & Environment. 
Method DocumentationData not provided.
Procedure StepsData not provided.
All of Australia, including terrestrial and coastal marine (mangroves, tidal marshes, seagrasses) ecosystems.
Temporal Coverage
From 2005-01-01 to 2015-01-01 
Spatial Resolution

Data not provided.

Vertical Extent

Data not provided.

Data Quality Assessment Scope
Data not provided. 
Data Quality Report
Data not provided. 
Data Quality Assessment Outcome
Multi-scale spatial machine learning.
The modelling and validation accuracies are as follows.

Model training:
  • Root mean squared error (RMSE) = 0.20 (min, max: 0.18 - 0.22) - log10 t/ha
  • Lin's concordance correlation = 0.76 (0.72-0.8).


Model validation:
  • RMSE = 0.20 (0.19-0.21) - log10 t/ha
  • Mean error (ME) = -0.01 (-0.03 - -0.004)
  • Standard deviation of error (SDE) = 0.20*(0.19-0.21)
  • Lin's concordance correlation = 0.76 (0.72-0.8).
 
ANZSRC - FOR
Agricultural spatial analysis and modelling
Carbon sequestration science
Machine learning
Soil chemistry and soil carbon sequestration (excl. carbon sequestration science)
Soil sciences
Spatial data and applications
Spatial statistics
GCMD Sciences
LAND SURFACE - SOIL ORGANIC CARBON (SOC)
LAND SURFACE - SOILS
Horizontal Resolution
30 meters - < 100 meters
Parameters
soil carbon content
Temporal Resolution
one off
Topic
environment
geoscientificInformation
Vertical Resolution
< 1 meter
Author
Viscarra Rossel, Raphael A.
Walden, Lewis
Contact Point
Viscarra Rossel, Raphael A.
Publisher
Terrestrial Ecosystem Research Network
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Terrestrial Ecosystem Research Network
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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/
TERN services are provided on an "as-is" and "as available" basis. Users use any TERN services at their discretion and risk. They will be solely responsible for any damage or loss whatsoever that results from such use including use of any data obtained through TERN and any analysis performed using the TERN infrastructure.
Web links to and from external, third party websites should not be construed as implying any relationships with and/or endorsement of the external site or its content by TERN.

Please advise any work or publications that use this data via the online form at https://www.tern.org.au/research-publications/#reporting 
Please cite this dataset as {Author} ({PublicationYear}). {Title}. {Version, as appropriate}. Terrestrial Ecosystem Research Network. Dataset. {Identifier}. 

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Version:6.2.22