Map showing Australia with Search and Rescue Region post UN recommendation in April 2008 Map produced for Dfat for inclusion in IOR-SRC website. Developed from Geocat 72814 (2011)

Severe wind is one of the major natural hazards in Australia. The main contributors to economic loss in Australia are tropical cyclones, thunderstorms and sub-tropical (synoptic) storms. Geoscience Australia's Risk and Impact Analysis Group (RIAG) is developing mathematical models to study a number of natural hazards including wind hazard. This study examines synoptic wind hazard under current and future climate scenarios using RIAG's synoptic wind hazard model. This model can be used in non-cyclonic regions of Australia (Region A in the Australian-New Zealand Wind Loading Standard; AS/NZS 1170.2:2002) which are dominated by synoptic and thunderstorm winds. The methodology to study synoptic wind hazard involves a combination of three models: - a statistical model (ie. a model based on observed data) to quantify wind hazard using extreme value distributions; - a technique to extract and process wind speeds from a high-resolution regional climate model (RCM), which produces gridded hourly 'maximum time-step mean' wind speed and direction fields; and - a Monte Carlo method to generate gust wind speeds from the RCM mean winds. Gust wind speeds are generated by a numerical convolution of the modelled mean wind speed distribution and a distribution of observed 'regional' gust factor. To illustrate the methodology, wind hazard calculations under current and future climate conditions for the Australian state of Tasmania will be presented. The results show increases in synoptic wind hazard in some parts of the state especially at the end of this century.

These datasets cover approximately 1400 sq km along the eastern sector of the Cassowary Coast Regional Council and are part of the 2009 North Queensland LiDAR capture project. This project, undertaken by Photomapping Services on behalf of the Queensland Government captured highly accurate elevation data using LiDAR technology. Available dataset formats (in 1 kilometre tiles) are: - Classified las (LiDAR Data Exchange Format where strikes are classified as ground, vegetation or building) - 1 metre Digital Elevation Model (DEM) in ESRI grid - 1 metre Digital Elevation Model (DEM) in ASCII xyz - 1 metre Digital Elevation Model (DEM) in shapefile - 0.25 metre contours in ESRI Shape

Australia's rich neotectonic record provides an opportunity to better understand the characteristics of seismogenic intraplate deformation, both at the scale of a single 'active' fault and at the scale of the entire continent. Over the last decade knowledge of Australian intraplate faults has advanced significantly. Herein we review this knowledge and propose six preliminary seismicity source zones (domains) based upon neotectonic data. Each source zone contains 'active' faults that we contend share common recurrence and behavioural characteristics, in a similar way that source zones are defined using the historic record of seismicity. A seventh offshore domain is proposed based upon analogy with the eastern United States. The power of this domain approach lies in the ability to extrapolate characteristic behaviours from well characterised faults (few) to faults about which little is known (many), both nationally and to analogous regions elsewhere in the world. This data, and conceptual and numerical models describing the nature of the seismicity in each source zone, has the potential to significantly enhance our understanding of seismic hazard in Australia at a time scale more representative than the snapshot provided by the historic record of seismicity. This includes providing a means by which to estimate key parameters underpinning the next generation seismic hazard maps for Australia, such as maximum magnitude earthquake and seismic source zone b values.

This is a round-Australia fly through for the Land Cover Map of Australia from 2000-2008

A new approach for the 1D inversion of AEM data has been developed. We use a reversible jump Markov Chain Monte Carlo method to perform Bayesian inference. The Earth is partitioned by a variable number of non-overlapping cells defined by a 1D Voronoi tessellation. A cell is equivalent to a layer in conventional AEM inversion and has a corresponding conductivity value. The number and the position of the cells defining the geometry of the structure with depth, as well as their conductivities, are unknowns in the inversion. The inversion is carried out with a fully non-linear parameter search method based on a transdimensional Markov chain. Many conductivity models, with variable numbers of layers, are generated via the Markov chain and information is extracted from the ensemble as a whole. The variability of the individual models in the ensemble represents the posterior distribution. Spatially averaging results is a form of 'data-driven' smoothing, without the need to impose a specific number of layers, an explicit smoothing function, or choose regularization parameters. The ensemble can also be examined to ascertain the most probable depths of the layer interfaces in the vertical structure. The method is demonstrated with synthetic time-domain AEM data. The results show that an attractive feature of this method over conventional approaches is that rigorous information about the non-uniqueness and uncertainty of the solution is obtained. We also conclude that the method will also have utility for AEM system selection and investigation of calibration problems.

The Sustainable Management of Coastal Groundwater Resources (SMCGR) project aims to improve the management of groundwater in coastal dune aquifers, which is used to supply water for coastal communities in the Mid North Coast region. There is increasing pressure on groundwater resources from expanding urbanisation and tourism in this region, which has made sustainable management of existing groundwater supplies an important issue for coastal communities and councils. Over extraction from groundwater systems can affect the water available for ecosystems, which may be dependent on shallow groundwater resources. Withdrawal of groundwater resources in excess of the sustainable yield may also result in fresh water resources being degraded by seawater intrusion or by upcoming from underlying saline water bodies.

The National Geochemical Survey of Australia (NGSA) was initiated in late 2006, and details of progress were published, among others, in Caritat et al. (2009). The ultra-low density geochemical survey was facilitated by, and based on, overbank sediment sampling at strategic locations in 1186 catchments. Included in the analysis methods was a partial extraction method by the Mobile Metal Ion (MMI) technique (Mann, 2010) of sediment sampled at the depth of 0-10 cm, air-dried and sieved to <2 mm. The MMI method is based on solubilisation of adsorbed ions and potentially can provide a measure of bio-availability, as ions in natural soil pore waters are subject to solubility by solutions stronger in complexing ability than pure water, but not subject to soil phase dissolution as achieved by strong acid or total digestion methods. Of the ten elements considered essential for plant growth (Ca, Cu, Fe, K, Mg, Mn, N, P, S and Zn), only two (N and S) were not included in the 53 elements analysed after MMI extraction of the overbank samples. Comparison of a number of MMI concentrations for each element with the corresponding total analysis for the same soil samples provides an estimate of the recovery % by MMI in a similar manner to that used by Albanese (2008) to evaluate ammonium acetate-EDTA as a measure of bio-availability. Individual maps for the eight nutrients based on MMI analysis provide some very interesting and potentially useful information. For example, highest 'bio-available' Fe concentrations are not related to the Fe-rich soils and rocks of the Pilbara, but to high rainfall areas close to the coast, where processes akin to lateritisation are still taking place. The movement of Fe as Fe2+ and its subsequent oxidation to Fe3+ is not only important to agriculture, but on the east coast of Australia it has a number of environmental consequences in river systems. The distribution pattern for Mn .../...

Probabilistic seismic-hazard analyses (PSHAs) require an estimate of Mmax, the magnitude (M) of the largest earthquake that is thought possible within a specified area. In seismically active areas such as some plate boundaries, large earthquakes occur frequently enough that Mmax might have been observed directly during historic times. In less active regions like Australia, and most of the Central and Eastern United States and adjacent Canada (CEUSAC), large earthquakes are much less frequent and generally Mmax must be estimated indirectly. By virtue of a fortuitous combination of climatic conditions, geology and geomorphology, Australia boasts arguably the richest Quaternary faulting record of all the world's SCR crust. Extensive consultation amongst the geological community, and recent advances in digital elevation model coverage, have allowed the compilation of an inventory of over 200 landscape features consistent with fault scarps relating to Quaternary surface breaking earthquakes across Australia. Variations in the character of these scarps, when considered together with large-scale geological and geophysical variations, justify the division of the continent into six onshore 'neotectonic domains'. Within each domain, mean Mmax has been calculated from the 75th percentile scarp length by averaging the earthquake magnitudes predicted by several published relations. Results range between M7.0-7.5±0.2. While this approach is inherently conservative, extreme values relating to multiple event scarps, which cannot be confidently discriminated without field validation, are removed. Consequently, in several cases our data represent an underestimate of 0.1-0.2 magnitude units relative to calculations based upon rare palaeoseismic data. Nevertheless, our findings indicate the potential for M>7.0 earthquakes across Australia, and by proxy analogous crust in the CEUSAC and elsewhere, and thereby have the potential to significantly reduce uncertainty in PSHAs.

This metadata sheet refers to the following three shapefiles: flight_lines_ci11.shp photo_centres_ci11.shp photo_rectangles_ci2011.shp They can all be found in the following directory: \CIGIS\orthophoto\ortho2011 Together these datasets show the flight lines flown by AAM during the 2011 aerial survey of Christmas Island, the centre of each aerial photograph taken during flight and approximate photograph extent rectangles.