The Historical Bushfire Boundaries service represents the aggregation of jurisdictional supplied burnt areas polygons stemming from the early 1900's through to 2022 (excluding the Northern Territory). The burnt area data represents curated jurisdictional owned polygons of both bushfires and prescribed (planned) burns. To ensure the dataset adhered to the nationally approved and agreed data dictionary for fire history Geoscience Australia had to modify some of the attributes presented. The information provided within this service is reflective only of data supplied by participating authoritative agencies and may or may not represent all fire history within a state.

This study explored the full potential of high-resolution multibeam data for an automatic and accurate mapping of complex seabed under a predictive modelling framework. Despite of the extremely complex distributions of various hard substrata at the inner-shelf of the study area, we achieved a nearly perfect prediction of 'hard vs soft' classification with an AUC close to 1.0. The predictions were also satisfactory for four out of five sediment properties, with R2 values range from 0.55 to 0.73. In general, this study demonstrated that both bathymetry and backscatter information (from the multibeam data) should be fully utilised to maximise the accuracy of seabed mapping. From the modelled relationships between sediment properties and multibeam data, we found that coarser sediment generally generates stronger backscatter return and that deeper water with its low energy favours the deposition of mud content. Sorting was also found to be a better sediment composite property than mean grain size. In addition, the results proved one again the advantages of applying proper feature extraction approaches over original backscatter angular response curves.

Wildfires are one of the major natural hazards facing the Australian continent. Chen (2004) rated wildfires as the third largest cause of building damage in Australia during the 20th Century. Most of this damage was due to a few extreme wildfire events. For a vast country like Australia with its sparse network of weather observation sites and short temporal length of records, it is important to employ a range of modelling techniques that involve both observed and modelled data in order to produce fire hazard and risk information/products with utility. This presentation details the use of statistical and deterministic modelling of both observations and synthetic climate model output (downscaled gridded reanalysis information) in the development of extreme fire weather potential maps. Fire danger indices such as the McArthur Fire Forest Danger Index (FFDI) are widely used by fire management agencies to assess fire weather conditions and issue public warnings. FFDI is regularly calculated at weather stations using measurements of weather variables and fuel information. As it has been shown that relatively few extreme events cause most of the impacts, the ability to derive the spatial distribution of the return period of extreme FFDI values contributes important information to the understanding of how potential risk is distributed across the continent. The long-term spatial tendency FFDI has been assessed by calculating the return period of its extreme values from point-based observational data. The frequency and intensity as well as the spatial distribution of FFDI extremes were obtained by applying an advanced spatial interpolation algorithm to the recording stations' measurements. As an illustration maps of 50 and 100-year return-period (RP) of FFDI under current climate conditions are presented (based on both observations and reanalysis climate model output). MODSIM 2013 Conference

An integrated analysis of geoscience information and benthos data has been used to identify benthic biotopes (seafloor habitats and associated communities) in the nearshore marine environment of the Vestfold Hills, East Antarctica. High-resolution bathymetry and backscatter data were collected over 42km2 to depths of 215 m using a multibeam sonar system. Epibenthic community data and in situ observations of seafloor morphology, substrate composition and bedforms were obtained from towed underwater video. Analysis of the datasets was used to identify statistically distinct benthic assemblages and describe the physical habitat characteristics related to each assemblage, with seven discrete biotopes identified. The biotopes include a range of habitat types including shallow coastal embayments and rocky outcrops which are dominated by dense macroalgae communities, and deep muddy basins which are dominated by mixed invertebrate communities. Transition zones comprising steep slopes provide habitat for sessile invertebrate communities. Areas of flat sandy plains are relatively barren. The relationship between benthic community composition and environmental parameters is complex with many variables (e.g. depth, substrate type, longitude, latitude and slope) contributing to differences in community composition. Depth and substrate type were identified as the main drivers of benthic community composition, however, depth is likely a proxy for other unmeasured depth-dependent parameters such as light availability, frequency of disturbance by ice, currents and/or food availability. Sea ice cover is also an important driver and the benthic community in areas of extended sea ice cover is comprised of sessile invertebrates and devoid of macroalgae. This is the first study that has used an integrated sampling approach based on multibeam sonar and towed underwater video to investigate benthic assemblages across a range of habitats in a nearshore marine environment in East Antarctica. This study demonstrates the efficacy of using multibeam sonar and towed video systems to survey large areas of the seafloor and to collect non-destructive high-resolution data in the sensitive Antarctic marine environment. The multibeam data provide a physical framework for understanding benthic habitats and the distribution of benthic communities. This research provides a baseline for assessing natural variability and human induced change on nearshore marine benthic communities (Australian Antarctic Science Project AAS-2201), contributes to Geoscience Australia's Marine Environmental Baseline Program, and supports Australian Government objectives to manage and protect the Antarctic marine environment.

These datasets contain legacy data from the decommissioned MapConnect/AMSIS2 application. It contains legacy data for Fisheries, Regulatory, Offshore Minerals and Environment. It is not authoritative and has not been updated since 2006. These datasets contain legacy data found in the Australian Marine Spatial Information System (AMSIS) between 2006 and 2015, with a currency date of 2006. . Users will need to contact the agency responsible for the data to check current validity and spatial precision.

Geoscience Australia defines a sample as a feature observed, measured or collected in the field. A specimen is a physical individual sample collected during the field work. This data set represents a subset of all Sampling data held by Geoscience Australia that have been collected as part of drilling activities (ie relate to Australian Boreholes). The data will be utilised by other data domains by providing Sampling context to various Observation & Measurement data.

A collection of mining and explotation tenements supplied by the individual state and territory bodies. Loaded monthly to an Oracle database from shapefiles given to Geoscience Australia.

This presentation will provide an overview of geological storage projects and research in Australia.

Wind multipliers are factors that transform regional wind speeds into local wind speeds, accounting for the local effects which include topographical, terrain and shielding influences. Wind multipliers have been successfully utilized in various wind related activities such as wind hazard assessment (engineering building code applications), event-based wind impact assessments (tropical cyclones), and also national scale wind risk assessment. The work of McArthur in developing the Forest Fire Danger Index (FFDI: Luke and McArthur, 1978) indicates that the contribution of wind speed to the FFDI is about 45% of the magnitude, indicating the importance of determining an accurate local wind speed in bushfire hazard and spread calculations. For bushfire spread modeling, local site variation (@ 100 metre and also 25 metre horizontal resolution) have been considered through the use of wind multipliers, and this has resulted in a significant difference to the currently utilized regional '10 metre height' wind speed (and further to the impact analysis). A series of wind multipliers have been developed for three historic bushfire case study areas; the 2009 Victorian fires (Kilmore fire), the 2005 Wangary fire (Eyre Peninsula), and the 2001 Warragamba - Mt. Hall fire (Western Sydney). This paper describes the development of wind multiplier computation methodology and the application of wind multipliers to bushfire hazard and impact analysis. The efficacy of using wind multipliers within a bushfire spread hazard model is evaluated by considering case study comparisons of fire extent, shape and impact against post-disaster impact assessments. The analysis has determined that it is important to consider wind multipliers for local wind speed determination in order to achieve reliable fire spread and impact results. From AMSA 2013 conference

Spatially continuous information is often required for environmental planning and conservation. Spatial modelling methods are essential for generating such information from point samples. The accuracy of spatial predictions is crucial for evidence-based decision making and often affected by many factors. Spatial reference systems can alter the features of spatial data and thus are expected to affect the predictions of spatial modelling methods. However, the degree to which such systems can affect the predictions has not been examined yet. It is not clear whether such effect changes with spatial modelling methods neither. In this study, we aim to test how sensitive spatial modelling methods are to different spatial reference systems. On the basis of a review of different spatial reference systems, we select eight systems that are suitable for environmental variables for the Australian Exclusive Economic Zone. We apply two most commonly used spatial interpolation methods to a marine dataset that is projected using the eight systems. Finally we assess the accuracy of the methods using leave-one-out cross validation in terms of their predictive errors. The sensitivities of the spatial modelling methods to the eight spatial reference systems are then analyzed. The data manipulation and modelling work are implemented in ArcGIS and R. In this paper, we discuss the testing results; examine the spatial predictions visually; and discuss the implications of the findings on spatial predictions in the marine environmental sciences. The outcomes of this study can be applied to the spatial predictions of both marine and terrestrial environmental variables. ModSim 2013, Adelaide, South Australia