The Gross Domestic Product (GDP) in Australia expanded 0.20 percent in the first quarter of 2025 over the previous quarter. This dataset provides - Australia GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Gross Domestic Product (GDP) in Australia was worth 1728.06 billion US dollars in 2023, according to official data from the World Bank. The GDP value of Australia represents 1.64 percent of the world economy. This dataset provides - Australia GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains estimated Material Footprint (MF) for Australia per US Dollar of GDP (constant year 2005 basis) generated, disaggregated by the four main material categories used in Economy Wide Material Flows Accounting i.e. biomass, fossil fuels metal ores, and non-metallic minerals.

1. The rate of elongation and thickening of individual branches (shoots) varies across plant species. This variation is important for the outcome of competition and other plant-plant interactions. Here we compared rates of shoot growth across 44 species from tropical, warm temperate, and cool temperate forests of eastern Australia. 2. Shoot growth rate was found to correlate with a suite of traits including the potential height of the species, xylem-specific conductivity, leaf size, leaf area per xylem cross-section, twig diameter (at 40 cm length), wood density and modulus of elasticity. 3. Within this suite of traits, maximum plant height was the clearest correlate of growth rates, explaining 50 to 67% of the variation in growth overall (p < 0.0001), and 23 to 32% of the variation (p < 0.05) in growth when holding the influence of the other traits constant. Structural equation models suggest that traits associated with ‘hydraulics’, ‘biomechanics’, and the ‘leaf economics spectrum’ represent three clearly separated axes of variation, with the hydraulic axis exhibiting the strongest alignment with height and largest independent contribution to growth (in the case of branch thickening). However most of the capacity of these axes to predict growth was also associated with maximum height, presumably reflecting coordinated selection on multiple traits that together influence life histories. 4. Growth rates were not strongly correlated with leaf nitrogen or leaf mass per unit leaf area. 5. Correlations between growth and maximum height arose both across latitude (47%, p < 0.0001) and from within-site differences between species (30%, p < 0.0001). Covariation between growth and maximum height was driven in part by variation in irradiance across sites as well as among canopy positions within sites (23%, p < 0.0001). A significant fraction of this shared variation was independent of irradiance (45%, p < 0.0001), reflecting intrinsic differences across species and sites.

Usage Notes trait_data_Gleason_et_al_2017Leaf, stem, xylem, height, shoot growth data for 44 Australian woody dicotyledon species. Trait descriptions and units are given in the last column.trait_publish.csv

This dataset contains estimated Domestic Material Consumption (DMC) for Australia per US Dollar of GDP (on a constant year 2005 basis) generated, disaggregated by the four main material categories used in Economy Wide Material Flows Accounting i.e. biomass, fossil fuels metal ores, and non-metallic minerals. This indicator is often referred to in the literature as Material Intensity, and is the reciprocal of Material Productivity.

Overview

The report presents updated estimates of household food expenditure trends and examines further issues relating to Australia's household food expenditure. The analysis builds on a June 2017 ABARES report that examined recent trends in food demand in Australia and a range of food security issues.

Key Issues

Between 2009-10 and 2016-17, the key drivers of Australia's household food demand growth were, in order of importance, population growth, changes in tastes and preferences (including lifestyle choices), lower real food prices and real income growth. While population growth is important, increasing the number of people seeking to meet their energy and nutrition requirements, there has also been a broadly-based shift toward spending on meals out and fast foods, with the share of meals out and fast foods in household food expenditure in Australia increasing from 31 per cent in 2009-10 to 34 per cent in 2015-16. This increases food expenditure per person, all else constant.

Domestic household consumption is still the most important market for food producers (based on value), but food exports have recovered strongly in recent years, from $25 billion in 2009-10 to $39 billion in 2016-17 (in 2015-16 prices); the share of exports in Australia's indicative food production increased from a recent low of 25 per cent in 2009-10 to 33 per cent in 2016-17.

Two key questions posed in the report relate to food security across population sub-groups and economic opportunities for farmers and other food product and service providers. • Food security-based on average outcomes in population sub-groups in 2015-16 using HES data, the Australian Government's transfer system is important in ensuring a high level of food security across households in Australia; some households, such as those highly reliant on family support payments, may require complementary support, for example, from non-government organisations.

• Economic opportunities in the domestic food supply chain-future food demand growth in Australia will be underpinned by population and income growth. For people living in higher income and/or net worth households, there is a demonstrated willingness to pay a premium for quality attributes of food products and services, including convenience factors. Food labelling is a key approach to inform consumers about quality attributes that may earn a price premium.

A key challenge in the long-term trend toward increased demand for meals out and fast foods is to ensure people have information about food attributes such as nutrition content. Reliable and well understood food product and service labelling may enhance nutrition security in Australia, and allow consumers to make food choices that are more closely aligned with their tastes and preferences (including in relation to nutrition and health), and wider circumstances, as well as contributing to reducing food waste.

The National Indicative Aggregated Fire Extent Dataset has been developed rapidly to support the immediate needs of the Department of Climate Change, Energy, the Environment and Water (DCCEEW, previously DAWE) in:quantifying the potential impacts of the 2019/20 bushfires on wildlife, plants and ecological communities; and,identifying appropriate response and recovery actions.The intent was to derive a reliable, agreed, fit for purpose and repeatable national dataset of burnt areas across Australia for the 2019/20 bushfire season.The NIAFED was first published on 13 February 2020 and was updated several times during 2020 to reflect updates to fire extent datasets from state and territory agencies. Most changes across these versions, after February (end of summer), reflect refinements on previous extent mapping, rather than new burnt areas. Fire analyses and decision making within the department after June 2020 has been based on the GEEBAM dataset. The GEEBAM dataset reports on fire severity within the NIAFED v20200225 extent envelope and includes some areas determined to be unburnt within NIAFED areas.NOTE: previous versions of this dataset are available on request to geospatial@dcceew.gov.auThe dataset takes the national Emergency Management Spatial Information Network Australia (EMSINA) data service, which is the official fire extent currently used by the Commonwealth and adds supplementary data from other sources to form a cumulative national view of fire extent. This EMSINA data service shows the current active fire incidents, and the Department map shows the total fire extent from 1 July 2019 to the 22 June 2020.EMSINA have been instrumental in providing advice on access to data and where to make contact in the early stages of developing the National Indicative Aggregated Fire Extent Dataset.This dataset is released on behalf of the Commonwealth Government and endorsed by the National Burnt Area Dataset Working Group, convened by the National Bushfire Recovery Agency.Known Issues:The dataset has a number of known issues, both in its conceptual design and in the quality of its inputs. These are outlined below and should be taken into account in interpreting the data and developing any derived analyses.The list of known issues below is not comprehensive: it is anticipated that further issues will be identified in the future, and the Department welcomes feedback on this. We will seek as far as possible to continuously improve the dataset in future versions.In addition, the 2019/20 bushfire season is ongoing and it can be expected that the fire extent will increase.Future versions of the dataset will therefore document and distinguish between changes arising from methodological improvement, as distinct from changes to the actual fire extent.The dataset draws data together from multiple different sources, including from state and territory agencies responsible for emergency and natural resource management, and from the Northern Australian Fire Information website. The variety of mapping methods means that conceptually the dataset lacks national coherency. The limitations associated with the input datasets are carried through to this dataset. Users are advised to refer to the input datasets’ documentation to better understand limitations.The dataset is intentionally precautionary and the rulesets for its creation elect to accept the risk of overstating the size of particular burnt areas. If and when there are overlapping polygons for an area, the internal boundaries have been dissolved.The dataset shows only the outline of burnt areas and lacks information on fire severity in these areas, which may often include areas within them that are completely unburnt. For the intended purpose this may limit the usability of the data, particularly informing on local environmental impacts and response. This issue will be given priority, either for future versions of the dataset or for development of a separate, but related, fire severity product.This continental dataset includes large burnt areas, particularly in northern Australia, which can be considered part of the natural landscape dynamics. For the intended purpose of informing on fire of potential environmental impact, some interpretation and filtering may be required. There are a variety of ways to do this, including by limiting the analysis to southern Australia, as was done for recent Wildlife and Threatened Species Bushfire Recovery Expert Panel’s preliminary analysis of 13 January 2020. For that preliminary analysis area, boundaries from the Interim Biogeographic Regionalisation of Australia version 7 were used by the Department to delineate an area of southern Australia encompassing the emergency bushfire areas of the southern summer. The Department will work in consultation with the expert panel and other relevant bodies in the future on alternative approaches to defining, spatially or otherwise, fire of potential environmental impact.The dataset cannot be used to reliably recreate what the national burnt area extent was at a given date prior to the date of release. Reasons for this include that information on the date/time on individual fires may or may not have been provided in the input datasets, and then lost as part of the dissolve process discussed in issue 2 above.With fires still burning extents are not yet refined.Fire extents are downloaded daily, and datasets are aggregated. This results in an overlap of polygon extents and raises the issue that refined extents are disregarded at this early stage.The Northern Australian Fire Information (NAFI) dataset is only current to 19 June 2020.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Geofabric Surface Cartography product provides a set of related feature classes to be used as the basis for the production of consistent hydrological cartographic maps. This product contains a geometric representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs, canals and other hydrographic features).

The product is fully topologically correct which means that all the stream segments flow in the correct direction.

This product contains fifteen feature types including: Waterbody, Mapped Stream, Mapped Node, Mapped Connectivity (Upstream), Mapped Connectivity (Downstream), Sea, Estuary, Dam, Structure, Canal Line, Water Pipeline, Terrain Break Line, Hydro Point, Hydro Line and Hydro Area.

Purpose

This product contains a geometric representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for the production of consistent hydrological cartographic map products, as well as the visualisation of surface hydrology within a GIS to support the selection of features for inclusion in cartographic map production.

This product can also be used for stream tracing operations both upstream and downstream however, as this is a mapped representation, streams may be represented as interrupted or intermittent features. In contrast, the Geofabric Surface Network product represents the same stream as a continuous connected feature, that is, the path that stream would take (according to the terrain model) if sufficient water were available for flow. Therefore, for stream tracing operations where full stream connectivity is required, the Geofabric Surface Network product should be used.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Geofabric Surface Cartography is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The source data input for the Geofabric Surface Cartography product is the AusHydro v1.7.2 (AusHydro) surface hydrology data set. The AusHydro database provides a seamless surface hydrology layer for Australia at a nominal scale of 1:250,000. It consists of lines, points and polygons representing natural and man-made features such as watercourses, lakes, dams and other water bodies. The natural watercourse layer consists of a linear network with a consistent topology of links and nodes that provide directional flow paths through the network for hydrological analysis.

This network was used to produce the GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3 of Australia (https://www.ga.gov.au/products/servlet/controller?event=GEOCAT_DETAILS&catno=66006).

Geofabric Surface Cartography is an amalgamation of two primary datasets. The first is the hydrographic component of the GEODATA TOPO 250K Series 3 (GEODATA 3) product released by Geoscience Australia (GA) in 2006. The GEODATA 3 dataset contains the following hydrographic features: canal lines, locks, rapid lines, spillways, waterfall points, bores, canal areas, flats, lakes, pondage areas, rapid areas, reservoirs, springs, watercourse areas, waterholes, water points, marine hazard areas, marine hazard points and foreshore flats.

It also provides information on naming, hierarchy and perenniality. The dataset also contains cultural and transport features that may intersect with hydrographic features. These include: railway tunnels, rail crossings, railway bridges, road tunnels, road bridges, road crossings, water pipelines.

Refer to the GEODATA 3 User Guide http://www.ga.gov.au/meta/ANZCW0703008969.html for additional information.

The second primary dataset is based on the GEODATA TOPO-250K Series 1 (GEODATA 1) watercourse lines completed by GA in 1994, which was supplemented by additional line work captured by the Australian National University (ANU) during the production of the DEM-9S to improve the representation of surface water flow. This natural watercourse dataset consists of directional flow paths and provides a direct link to the flow paths derived from the DEM. There are approximately 700,000 more line segments in this version of the data.

AusHydro uses the natural watercourse geometry from the ANU enhanced GEODATA 1 data, and the attributes (names, perenniality and hierarchy) associated with GEODATA 3 to produce a fully attributed data set with topologically correct flow paths. The attributes from GEODATA 3 were attached using spatial queries to identify common features between the two datasets. Additional semi-automated and manual editing was undertaken to ensure consistent attribution along the entire network.

AusHydro dataset includes a unique identifier for each line, point and polygon. AusHydro-ID will be used to maintain the dataset and to incorporate higher resolution datasets in the future. The AusHydro-ID will be linked to the ANUDEM streams through a common segment identifier and ultimately to a set of National Catchments Boundaries (NCBs).

Changes at v2.1

! New Water Storages in the WaterBody FC.

Changes at v2.1.1

! 16 New BoM Water Storages attributed in the AHGFWaterBody feature class

and 1 completely new water storage feature added.



- Correction to spelling of Numeralla river in AHGFMappedStream (formerly

Numaralla).



- Flow direction of Geometric Network set.

Processing steps:

1. AusHydro Surface Hydrology dataset is received and loaded into the Geofabric development GIS environment

2. feature classes from AusHydro are recomposed into composited Geofabric hydrography dataset feature classes in the Geofabric Maintenance Geodatabase.

3. re-composited feature classes in the Geofabric Maintenance Geodatabase Hydrography Dataset are assigned unique Hydro-IDs using ESRI ArcHydro for Surface Water (ArcHydro: 1.4.0.180 and ApFramework: 3.1.0.84)

4. feature classes from the Geofabric Maintenance Geodatabase hydrography dataset are extracted and reassigned to the Geofabric Surface Cartography Feature Dataset within the Geofabric Surface Cartography Geodatabase.

A complete set of data mappings, from input source data to Geofabric Products, is included in the Geofabric Product Guide, Appendices.

Dataset Citation

Bureau of Meteorology (2014) Geofabric Surface Cartography - V2.1.1. Bioregional Assessment Source Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/ce5b77bf-5a02-4cf8-9cf2-be4a2cee2677.

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the May 2025 release

• Nationally, the May 2025 update of G-NAF shows an overall increase of 47,194 addresses (0.30%). The total number of addresses in G-NAF now stands at 15,753,927 of which 14,909,770 or 94.64% are principal.

• At some locations, there are unit-numbered addresses that appear to be duplicate addresses. Geoscape is working to identify these locations and include these addresses as separate addresses in G-NAF. To facilitate this process, some secondary addresses have had the word RETAIL added to their building names. In the first instance, this process is being progressively rolled out to identified locations, but it is expected that the requirement for this will become ongoing.

• There is one new locality in G-NAF: Keswick Island, QLD.

• The source data used for generating G-NAF STREET_LOCALITY_POINT data in New South Wales has an updated datum and changed from GDA94 to GDA2020. This has resulted in updates to the STREET_LOCALITY_POINT geometry for approximately 91,000 records, however, more than 95% of these have moved less than a metre.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

Historical chart and dataset showing Australia GDP by year from 1960 to 2023.

Spatial predictions of plant available water capacity (PAWC), drained upper limit (DUL) and crop lower limit (CLL) for grain-growing regions of NSW and Queensland, Australia, from Padarian Campusano pedotransfer functions and Soil and Landscape Grid of Australia datasets.

PAWC is the amount of water a soil can hold against gravity (i.e. water which does not freely drain) that is available to plants through their roots. This soil property is very important in dryland cropping areas which rely on rainfall. The maximum amount of water which can be held by a soil against gravity is called the DUL. The water that remains in a soil after plants have extracted all that is available to them is called the CLL. PAWC is calculated as DUL minus CLL.

Digital soil mapping (DSM) allows the spatial prediction of soil properties across large areas using modelling techniques which combine point data measured in the field and continuous datasets related to soil forming processes such as climate, topography, land cover, existing soil mapping and lithology. Pedotransfer functions (PTFs) are equations which use the easier to measure soil attributes, e.g. sand, clay, bulk density, to model the harder to measure attributes like DUL and CLL. DSM techniques such as Latin Hypercube (LHC) sampling can be used to incorporate the uncertainties associated with the input datasets in the modelling, and to produce estimates of model output precision and reliability.

This data collection consists of spatially predicted PAWC, DUL and CLL for the grain-growing regions of New South Wales and Queensland, Australia, as defined by the boundary of the Grains Research and Development Corporation's Northern Region. PAWC was modelled using PTFs for DUL and CLL from Padarian Campusano, with LHC sampling to incorporate the uncertainties associated with the input datasets. The PAWC, DUL and CLL were modelled at the six Global Soil Map depths of 0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. The top five depths have been aggregated to create a PAWC prediction for 0-100 cm.

Lineage: INPUT DATASETS 1. Soil attribute layers from the Soil and Landscape Grid of Australia (SLGA): clay (%), sand (%), bulk density (BD; g cm-3), and effective cation exchange capacity (CEC; meq/100 g). The estimated value (mean) and the confidence interval limits (5th and 95th percentiles) were used for all six Global Soil Map depths (0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm). https://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html 2. The Northern Region boundary from the Grains Research and Development Corporation (GRDC)

PEDOTRANSFER FUNCTIONS DUL and CLL equations from Padarian Campusano (2014), which used a subset of 806 soil profiles from the APSoil database that included field measurements of DUL and CLL: 1. DUL = 0.2739 + 0.005033*clay + 3.158 x 10^-5*sand*CEC – 1.96 x 10^-5*sand^2 – 0.00256*clay*BD 2. CLL = 0.6151*DUL – 0.02192 3. PAWC = DUL – CLL

METHODS These methods are available from Austin et al. (2019), see Related Links section.

The SLGA input datasets were clipped to the study area boundary and divided into tiles of 200 x 200 grid cells prior to parallel processing in a supercomputer environment. Except for the LHC sampling and correlation matrices, all code was written in Python. Layer thickness for each of the six soil depths was calculated in mm from the depth layer upper and lower bounds (e.g. 5 to 15 cm).

A correlation matrix was generated in the R package for the SLGA clay, sand, BD and CEC input datasets for each of the six depths, with correlation values derived using data for the whole study area for each of the inputs.

Each of the six soil depth layers was modelled separately. For every grid cell in each depth layer, the following steps were used to calculate DUL, CLL and PAWC: 1. Standard deviation (SD) was calculated from the 5th and 95th percentiles for the clay, sand, CEC and BD input variables using the following equation from Malone et al. (2011): SDi = (UPLi – LPLi) / 2 x z where SDi is the variance associated with prediction i, UPL and LPL are the upper and lower prediction limits, and z is the z-value used for a confidence interval (CI) which in this case is 90% and z = 1.64. A normal distribution is assumed

1. LHC sampling with a correlation matrix (from the R pse library; Chalom and Prado, 2014), using means, SDs and a correlation matrix as inputs, produced fifty realisations of each input variable. Fifty realisations were chosen following the work of Malone et al. (2015) who found that there was little difference in outcome when using more than 50 samples

2. 50 DUL and CLL values were calculated from the 50 input variable realisations using the DUL and CLL equations from Padarian Campusano (2014)

3. 50 PAWC values were calculated from the DUL and CLL values, constrained by the depth layer thickness, with units of mm

4. From the 50 DUL, CLL and PAWC values for each grid cell, the mean, median, 5th and 95th percentiles, and SD were calculated and written to file as geotiffs

The tiled outputs were merged to form single rasters of the study area for DUL, CLL and PAWC at each of the six depths. Additionally, the 0-5, 5-15, 15-30, 30-60 and 60-100 cm soil depth layers were used to calculate 0-1 m versions of DUL, CLL and PAWC. The mean, median, 5th and 95th percentile values were summed to produce the 0-1 m DUL, CLL or PAWC prediction for each grid cell. This aggregation of depths assumes high correlation between layers – for example, the 95th percentile for the 0 – 1 m layer is the sum of the 95th percentiles for each contributing layer. If the layers were uncorrelated, the 95th percentile would end up closer to the mean. The SD for each of the 0-1 m DUL, CLL and PAWC layers was calculated from the summed 5th and 95th percentiles, as per the equation from Malone et al. (2011).

This dataset is a spatial represention of a database of landslides occurring within Australia, based on published and unpublished information and field observations. The database is under constant development.

The ISOTOPE database stores compiled age and isotopic data from a range of published and unpublished (GA and non-GA) sources. This internal database is only publicly accessible through the webservices given as links on this page. This data compilation includes sample and bibliographic links. The data structure currently supports summary ages (e.g., U-Pb and Ar/Ar) through the INTERPRETED_AGES tables, as well as extended system-specific tables for Sm-Nd, Pb-Pb, Lu-Hf and O- isotopes. The data structure is designed to be extensible to adapt to evolving requirements for the storage of isotopic data. ISOTOPE and the data holdings were initially developed as part of the Exploring for the Future (EFTF) program. During development of ISOTOPE, some key considerations in compiling and storing diverse, multi-purpose isotopic datasets were developed: 1) Improved sample characterisation and bibliographic links. Often, the usefulness of an isotopic dataset is limited by the metadata available for the parent sample. Better harvesting of fundamental sample data (and better integration with related national datasets such as Australian Geological Provinces and the Australian Stratigraphic Units Database) simplifies the process of filtering an isotopic data compilation using spatial, geological and bibliographic criteria, as well as facilitating ‘audits’ targeting missing isotopic data. 2) Generalised, extensible structures for isotopic data. The need for system-specific tables for isotopic analyses does not preclude the development of generalised data-structures that reflect universal relationships. GA has modelled relational tables linking system-specific Sessions, Analyses, and interpreted data-Groups, which has proven adequate for all of the Isotopic Atlas layers developed thus far. 3) Dual delivery of ‘derived’ isotopic data. In some systems, it is critical to capture the published data (i.e. isotopic measurements and derived values, as presented by the original author) and generate an additional set of derived values from the same measurements, calculated using a single set of reference parameters (e.g. decay constant, depleted-mantle values, etc.) that permit ‘normalised’ portrayal of the data compilation-wide. 4) Flexibility in data delivery mode. In radiogenic isotope geochronology (e.g. U-Pb, Ar-Ar), careful compilation and attribution of ‘interpreted ages’ can meet the needs of much of the user-base, even without an explicit link to the constituent analyses. In contrast, isotope geochemistry (especially microbeam-based methods such as Lu-Hf via laser ablation) is usually focused on the individual measurements, without which interpreted ‘sample-averages’ have limited value. Data delivery should reflect key differences of this kind.

Measures of predictive performance of the model at ~80% sensitivity.

The Gross Domestic Product per capita in Australia was last recorded at 61583.92 US dollars in 2023. The GDP per Capita in Australia is equivalent to 488 percent of the world's average. This dataset provides - Australia GDP per capita - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Retail Sales in Australia decreased 0.10 percent in April of 2025 over the previous month. This dataset provides - Australia Retail Sales MoM - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

The Geofabric Surface Network product provides a set of related feature classes to be used as the basis for production of consistent hydrological surface stream network analysis. This product contains a topographically consistent representation of the (major) surface water features of Australia (excluding external territories). Primarily, these are natural surface hydrology features but the product also contains some man-made features (notably reservoirs and other hydrographic features).

The Geofabric Surface Network product is based upon the input from ANUDEM Derived Streams V1.1.2 (ANUDEM Streams) which is the vectorised version of the nine second ANUDEM derived raster steams product. The product is related to, but distinct from, the stream network contained in the Geofabric Surface Cartography product. The network product represents the flow direction of streams over the surface of the terrain, based on the GEODATA Nine Second Digital Elevation Model (DEM-9S) Version 3. This product is more generalised than the Geofabric Surface Cartography and represents the main channels of the stream, particularly in areas where streams are heavily anabranched or disconnected.

In addition, the stream connectivity represents a stream flow over the terrain, regardless of the presence of a corresponding Geofabric Surface Cartography stream segment. This means that the Geofabric Surface Cartography product may represent a stream as an interrupted or intermittent feature, whereas this product represents the same stream as a continuous connected feature. That is, the path that a stream would take (according to the terrain model) if sufficient water were available for flow. This product is fully topologically correct which means that all the stream segments flow in the correct direction. It also has full connectivity based on the flow of water across a terrain model.

This product contains six feature types including: Waterbody, Network Stream, Network Node, Catchment, Network Connectivity (Upstream) and Network Connectivity (Downstream).

Purpose

This product contains a topographic representation of the (major) surface water features of 'geographic Australia' excluding external territories. It is intended to be used as the basis for production of consistent surface stream network analysis.

Geofabric Surface Network is intended to be used in stream flow tracing operations, using its full topological connection. The product can support the spatial selection of associated hydrological features as inputs for spatial analysis/modelling.

This product is intended to supplement the Geofabric Surface Cartography, Geofabric Surface Catchments and Geofabric Hydrology Reporting Catchments data products. This product is also used to support the definition of the Geofabric Surface Catchments and Geofabric Hydrology Reporting Catchments products and provides a spatial framework for analysis and assessment of streams and their catchments.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied:

Lineage statement: Geofabric Surface Network is part of a suite of Geofabric products produced by the Australian Bureau of Meteorology. The geometry of this product is largely derived from the ANUDEM Derived Streams V1.1.2 (ANUDEM Streams). It consists of water bodies such as swamps, reservoirs, lakes, etc as derived from AusHydro V1, as well as the stream lines and stream line connectors through these water bodies. The ANUDEM Streams are firstly vectorised to be usable in vector line feature format and are then informed and modified by the coincident locations of the AHGFMappedStream feature class. The features are organised into specific feature class subtypes, based upon both the inputs from the AusHydro V1.7.2 and their behaviour within the AHGF Network Stream relationships. All of the AHGFNetworkStream and AHGFWaterbody features participate in the connected stream flow topology.

This product also contains the AHGFCatchment features that are derived from the National Catchment Boundaries V1.1.4. The AGHFCatchment feature class consist of the lowest level stream flow catchments based upon the inputs from ANUDEM Streams. The catchment boundaries are based upon a single AHGFNetworkStream extent over GEODATA National 9 Second DEM grid. These catchments form the basis of aggregated catchment boundaries, either by Contracted Nodes or by Pfafstetter ID Levels.

All of these features participate in the connected stream flow topology.

Changes at v2.1

! Addition of Beta Monitoring Point Table including 479 ghost nodes

 connected to the network.

- New Water Storages in the WaterBody FC.

Changes at v2.1.1

! Replacement of Beta Monitoring Point Table and inclusion of 3,310

(formerly 479) ghost nodes connected to the stream network.



! 16 New BoM Water Storages attributed in the AHGFWaterBody feature class

and 1 completely new water storage feature added.



- SegnoLink attribute update to fix single catchment feature in Tasmania.



- Correction to spelling of Numeralla river in AHGFMappedStream (formerly

Numaralla).



! Metadata updated adding explanation of AHGFNetworkStream AusHydroEr codes

and revision made to description of DrainID field.



- Fixed a series of NoFlow catchments (small internally draining catchments

not related to a stream segment) in Murray-Darling were incorrectly

attributed as externally draining via the ExtrnlBasn field in

AHGFCatchments.



! Usage of the MergedSink attribute changed from v2.1 (see

HR_Catchments_Technical_Overview.pdf for more info).

Processing steps:

1. ANUDEM Streams dataset is received and loaded into the Geofabric development GIS environment.

2. Feature classes from ANUDEM Streams are recomposed into composited Geofabric Feature Dataset Feature Classes in the Geofabric Maintenance Geodatabase.

3. Re-composited feature classes in the Geofabric Maintenance Geodatabase Feature Dataset are assigned unique Hydro-IDs using ESRI ArcHydro for Surface Water (ArcHydro: 1.4.0.180 and ApFramework: 3.1.0.84).

4. Feature classes from the Geofabric Maintenance Geodatabase Feature Dataset are extracted and reassigned to the Geofabric Surface Network Feature Dataset within the Geofabric Surface Network Geodatabase.

A complete set of data mappings, from input source data to Geofabric Products, is included in the Geofabric Product Guide, Appendices.

Dataset Citation

Bureau of Meteorology (2014) Geofabric Surface Network - V2.1.1. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/d84e51f0-c1c1-4cf9-a23c-591f66be0d40.

These reports are provided as a historic archive only and are not current. If you have an ongoing requirement to use the statistics, to meet a legislative or policy requirement, please seek advice from the relevant WA Government Department or Authority rather than contacting the data custodians listed under Key Information. If you use the reports, please include a caveat stating the following: (1) year of currency, (2) a statement that the statistics presented may now be out of date, and (3) the report citation. Overview of the Report: From 2007 to 2018 DBCA provided regular updates of statistics on the pre-European and current extent of the vegetation associations of Western Australia within IBRA or IBRA sub-regions. The reporting is based on Beards (pre-European) vegetation mapping of systems and associations at 1:250,000. The statistics were used for several purposes including conservation planning, land use planning and when assessing development applications. The statistics provided a general overview of the status of vegetation associations, within IBRA bioregions or sub-regions, noting the limitations detailed in the README document relating to scale, remnant vegetation mapping and currency of the analysis. They were intended to be used in conjunction with other information on the biodiversity values of an area and with input and advice from people familiar with the vegetation association and the vegetation condition of an area of interest. Included in the report are statistics on the progress towards achieving a conservation reserve system, for WA, that is comprehensive, adequate and representative (CAR). The CAR reserve system is based on three principles: Comprehensive - includes the full range of ecological/forest communities recognised at an appropriate scale within and across each bioregion, Adequate – level (extent) of reservation that will ensure viability and integrity of populations, species, and ecological communities, and Representative – those areas reserved should reasonably reflect the biotic diversity of the communities The system of reserves helps conserve our biodiversity. Please contact DBCA for advice on the CAR statistics. In 2011 the name of the report changed from "CAR Analysis" to "Statewide Vegetation Statistics". The CAR analysis statistics are still included in the report.

The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level.

Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness.

Phase 1 of the project focused on developing validated and verified checklists of countries that are Party to the Convention on Biological Diversity (CBD). Phase 2 aimed to achieve global coverage including non-party countries and all overseas territories of countries, e.g. those of the Netherlands, France, and the United Kingdom.

All kingdoms of organisms occurring in all environments and systems are covered.

Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.

Radiogenic isotopes decay at known rates and can be used to interpret ages for minerals, rocks and geologic processes. Different isotopic systems provide information related to different time periods and geologic processes, systems include: U-Pb and Ar/Ar, Sm-Nd, Pb-Pb, Lu-Hf, Rb-Sr and Re-Os isotopes. The GEOCHRON database stores full analytical U-Pb age data from Geoscience Australia's (GA) Sensitive High Resolution Ion Micro-Probe (SHRIMP) program. The ISOTOPE database is designed to expand GA's ability to deliver isotopic datasets, and stores compiled age and isotopic data from a range of published and unpublished (GA and non-GA) sources. OZCHRON is a depreciated predecessor to GEOCHRON and ISOTOPE, the information once available in OZCHRON is in the process of migration to the two current databases.

The ISOTOPE compilation includes sample and bibliographic links through the A, FGDM, and GEOREF databases. The data structure currently supports summary ages (e.g., U-Pb and Ar/Ar) through the INTERPRETED_AGES tables, as well as extended system-specific tables for Sm-Nd, Pb-Pb, Lu-Hf and O- isotopes. The data structure is designed to be extensible to adapt to evolving requirements for the storage of isotopic data. ISOTOPE and the data holdings were initially developed as part of the Exploring for the Future (EFTF) program - particularly to support the delivery of an Isotopic Atlas of Australia.

During development of ISOTOPE, some key considerations in compiling and storing diverse, multi-purpose isotopic datasets were developed:

1) Improved sample characterisation and bibliographic links. Often, the usefulness of an isotopic dataset is limited by the metadata available for the parent sample. Better harvesting of fundamental sample data (and better integration with related national datasets such as Australian Geological Provinces and the Australian Stratigraphic Units Database) simplifies the process of filtering an isotopic data compilation using spatial, geological and bibliographic criteria, as well as facilitating 'audits' targeting missing isotopic data.

2) Generalised, extensible structures for isotopic data. The need for system-specific tables for isotopic analyses does not preclude the development of generalised data-structures that reflect universal relationships. GA has modelled relational tables linking system-specific Sessions, Analyses, and interpreted data-Groups, which has proven adequate for all of the Isotopic Atlas layers developed thus far.

3) Dual delivery of 'derived' isotopic data. In some systems, it is critical to capture the published data (i.e. isotopic measurements and derived values, as presented by the original author) and generate an additional set of derived values from the same measurements, calculated using a single set of reference parameters (e.g. decay constant, depleted-mantle values, etc.) that permit 'normalised' portrayal of the data compilation-wide.

4) Flexibility in data delivery mode. In radiogenic isotope geochronology (e.g. U-Pb, Ar-Ar), careful compilation and attribution of 'interpreted ages' can meet the needs of much of the user-base, even without an explicit link to the constituent analyses. In contrast, isotope geochemistry (especially microbeam-based methods such as Lu-Hf via laser ablation) is usually focused on the individual measurements, without which interpreted 'sample-averages' have limited value. Data delivery should reflect key differences of this kind.

Value: Used to provide ages and isotope geochemistry data for minerals, rocks and geologic processes.

Scope: Australian jurisdictions and international collaborative programs involving Geoscience Australia