Recent climate conditions experienced in Australia certainly ring true with the famous words from author, Dorothea Mackellar, ‘of droughts and flooding rains’.

In the last century coastal erosion has caused significant damage to property and infrastructure in NSW. Extreme erosion can be caused by individual extreme storms, or by multi-storm 'clusters' which induce disproportionate erosion by limiting the time for inter-storm shoreline recovery. Statistical changes in storm wave properties also occur in association with seasonal and ENSO (El-Nino Southern Oscillation) cycles, and a number of studies suggest the latter affects the mean shoreline position and likelihood of extreme erosion in NSW. Quantification of site-specific erosion hazards is necessary to support coastal management, with probabilistic or risk-based approaches being particularly attractive because they avoid reliance on arbitrarily chosen 'design' events. Callaghan et al. (2008) developed a methodology for probabilistic erosion hazard assessment on sandy shorelines, combining a probabilistic model of storm waves with a deterministic shoreline evolution model. The probability of the shoreline eroding past a given position (over a given timeframe) may be quantified, and epistemic uncertainties associated with e.g., our limited knowledge of the frequency of very large storms, are accounted for with bootstrapping. Herein we develop a probabilistic model of the storm wave climate at Old Bar, NSW, for use in a coastal erosion hazard assessment. A novel aspect of the model is that it accounts for the impacts of ENSO and seasonality on the storm wave properties, and the frequency of storm events. We establish relationships between ENSO, seasonality, and storm waves in the area using 30 years of wave observations, and extend the statistical framework of Callaghan et al. (2008) to account for these factors. This study is a key component of the Bushfire and Natural Hazards CRC Project "Resilience to clustered disaster events on the coast ¿ storm surge". References: Callaghan et al., (2008) Statistical Simulation of wave climate and extreme beach erosion. Coastal Engineering 2008, 55, 375-390.

The main aim of this study is to use petroleum systems analysis to improve the understanding of the petroleum systems present on the Lawn Hill Platform of the Isa Superbasin. Part A of this report series reported the results of burial and thermal modelling of two wells (Desert Creek 1 and Egilabria 1). Results from the 1-D modelling help other aspects of interest such as the hydrocarbon generation potential and distribution of hydrocarbons by source rock which this publication presents. Modelling uncertainties are reported and described, highlighting knowledge gaps and areas for further work.

This Agreements ontology is designed to model 'agreements' which are social contracts that include: licenses, laws, contracts, Memoranda of Understanding, standards and definitional metadata. Its purpose is to support data sharing by making explicit the relationships between agreements and data and agreements and Agents (people and organisations). Eventually it will also help with the interplay between different classes of agreements. We think of this ontology as a 'middle' ontology, that is one which specializes well-known, abstract, upper ontologies and is able to be used fairly widely but is expected to be used particular contexts in conjunction with detailed, domain-specific, lower ontologies. We have tried to rely on: existing agent, data manipulation, metadata and licence ontologies where possible. As such we specialise the ORG and FOAF ontologies; the PROV ontology; the Dublin Core Terms RDF schema & DCAT ontology; and the ODRS vocabulary & Creative Commons RDF data models for those areas, respectively

<p>The Roebuck Basin is considered a new and relatively untested hydrocarbon province in the central North West Shelf of Australia. Inconsistent results from drilling for hydrocarbons highlights the need to better understand the deep structures along this rifted margin that initially formed as an intra-continental, failed rift during Late Permian. Recent wells penetrated the previously unknown Lower-Middle Triassic fluvio-deltaic sedimentary package in the Bedout Sub-basin (inboard part of the Roebuck Basin), including intervals with major oil and gas discoveries. Another two wells, Anhalt 1 and Hannover South 1, only penetrated the top of this succession and they encountered volcanics in the Rowley Sub-basin (outboard part of the Roebuck Basin). Steeply dipping clinoforms observed in the seismic data in the Rowley Sub-basin have been interpreted either as a lava delta complex associated with a failed triple junction; or as a series of back-stepping, Late Permian carbonate ramps and banks, interpreted to have developed on a thermally subsiding rift flank. The implication for prospectivity between the two scenarios is significant. Geoscience Australia undertook a Triassic regional basin analyses, including potential field modelling to validate whether the two proposed models are a plausible solution. A combination of magnetic and gravity 2.5D modelling along nine key regional seismic lines, considered the distribution of potential intrabasinal volcanic rocks and the crustal structure, including Moho depth and depth to top crystalline basement. <p>New seismic interpretation correlated to recent wells, including 2D and 3D seismic reflection surveys was integrated with deep seismic reflection and refraction data resulting in an improved geometry and lithology model that was input into the potential field analyses. The results show that the combined Jurassic and Triassic successions reach up to 16 km deep in the central North West Shelf. The Lower-Middle Triassic sediment package in the Rowley Sub-basin correlates with up to 10 km of dense material (about 2.7 g/cm3 density) and contains magnetic features partially sourced from basalts at the top of the section, as intersected in Anhalt 1 and Hannover South 1. Combined with other causative sources within basement, the basalts correlate with a spatially large positive magnetic anomaly that extends north onto the Scott Plateau and into the Barcoo Sub-basin; in the offshore southwest part of the Browse Basin, where Warrabkook 1 intersected Late Jurassic volcanoclastics at its total depth. The presence of high density and high positive magnetic anomalies in the Lower-Middle Triassic and basement supports the presence of a large igneous province in this area. This interpretation in the outer Rowley Sub-basin downgrades the petroleum prospectivity in this area for this Lower-Middle Triassic interval. Petroleum prospectivity remains in the area due to the overlying sediments containing good source rocks which have been identified to have good to excellent generative potential. <p>The Lower-Middle Triassic sediment package in the adjacent northern Carnarvon Basin has been intersected only on the Lambert Shelf; encountering fluvio-deltaic sediments. In the offshore part of the northern Carnarvon Basin, the nature of this sediment package still remains enigmatic. It correlates with low density sediments (about 2.5 g/cm3 density) that include magnetic bodies on the outboard Exmouth Plateau. The basement and crust show crustal thinning with the presence of a thick layer of interpreted hyper-extended continental crust or exhumed lithospheric mantle. This crustal domain is overlain by thick onlapping Lower-Middle Triassic sediments which form a triangular shape depocentre in the inboard northern Carnarvon Basin, wrapping around the edge of the Pilbara Craton. The location of this initial thick sediment package suggests that it was controlled by the inherited thermal structure of the Late Permian-early Triassic rift architecture that is associated with some volcanics related to a large igneous province extending across the central North West Shelf. As described in the Rowley Sub-basin, the petroleum prospectivity of the northern Carnarvon Basin remains in the overlying sediments showing similar characteristics and indicating good to excellent hydrocarbon generative potential.

Major oxides provide valuable information about the composition, origin, and properties of rocks and regolith. Analysing major oxides contributes significantly to understanding the nature of geological materials and processes (i.e. physical and chemical weathering) – with potential applications in resource exploration, engineering, environmental assessments, agriculture, and other fields. Traditionally most measurements of oxide concentrations are obtained by laboratory assay, often using X-ray fluorescence, on rock or regolith samples. To expand beyond the point measurements of the geochemical data, we have used a machine learning approach to produce seamless national scale grids for each of the major oxides. This approach builds predictive models by learning relationships between the site measurements of an oxide concentration (sourced from Geoscience Australia’s OZCHEM database and selected sites from state survey databases) and a comprehensive library of covariates (features). These covariates include: terrain derivatives; climate surfaces; geological maps; gamma-ray radiometric, magnetic, and gravity grids; and satellite imagery. This approach is used to derive national predictions for 10 major oxide concentrations at the resolution of the covariates (nominally 80 m). The models include the oxides of silicon (SiO2), aluminium (Al2O3), iron (Fe2O3tot), calcium (CaO), magnesium (MgO), manganese (MnO), potassium (K2O), sodium (Na2O), titanium (TiO2), and phosphorus (P2O5). The grids of oxide concentrations provided include the median of multiple models run as the prediction, and lower and upper (5th and 95th) percentiles as measures of the prediction’s uncertainty. Higher uncertainties correlate with greater spreads of model values. Differences in the features used in the model compared with the full feature space covering the entire continent are captured in the ‘covariate shift’ map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful, when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map. Details of the modelling approach, model uncertainties and datasets are describe in an attached word document “Model approach uncertainties”. This work is part of Geoscience Australia’s Exploring for the Future program that provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. These data are published with the permission of the CEO, Geoscience Australia.