The coverage of this dataset is over the Taree region . The C3 LAS data set contains point data in LAS 1.2 format sourced from a LiDAR ( Light Detection and Ranging ) from an ALS50 ( Airborne Laser Scanner ) sensor . The processed data has been manually edited to achieve LPI classification level 3 whereby the ground class contains minimal non-ground points such as vegetation , water , bridges , temporary features , jetties etc . Purpose: To provide fit-for-purpose elevation data for use in applications related to coastal vulnerability assessment, natural resource management ( especially water and forests) , transportation and urban planning . Additional lineage information: This data has an accuracy of 0.3m ( 95 confidence ) horizontal with a minimum point density of one laser pulse per square metre. For more information on the data's accuracy, refer to the lineage provided in the data history .

Recently, continental-scale geochemical surveys of Europe and Australia were completed. Thanks to having exchanged internal project standards prior to analysing the samples, we can demonstrate direct comparability between these datasets for 10 major oxides (Al2O3, CaO, Fe2O3, K2O, MgO, MnO, Na2O, P2O5, SiO2 and TiO2), 16 total trace elements (As, Ba, Ce, Co, Cr, Ga, Nb, Ni, Pb, Rb, Sr, Th, V, Y, Zn and Zr), 14 aqua regia extracted elements (Ag, As, Bi, Cd, Ce, Co, Cs, Cu, Fe, La, Li, Mn, Mo and Pb), Loss On Ignition (LOI) and pH. By comparing these new datasets to one another, we can learn lessons about continental-scale controls on soil geochemistry and about critical requirements for global geochemical mapping. Although the median soil compositions of both continents are overall quite similar, the Australian median values are systematically lower, except for SiO2 and Zr. This reflects the generally longer and, locally more intense weathering in Australia (median Chemical Index of Alteration values are 72 and 60% for Australia and Europe, respectively). We found that element concentrations typically span 3 (and up to 5) orders of magnitude on each continent. The comparison of 2 continental geochemical surveys shows that the most critical requirement for global geochemical mapping is good analytical quality. Only where a comprehensive quality control program, including field and laboratory duplicates, internal project standards and Certified Reference Materials, is implemented and documented, are the results credible and comparable with other datasets.

In order to compare Australia's national inventory of mineral resources with those of other countries and estimate total global stocks, it is useful to map different systems onto a common international classification template. The UNFC-2009 is an internationally applicable scheme for the classification and reporting of fossil energy and mineral resources. It can be applied directly but is also designed to align with other widely used mineral resource classification schemes such as the Template of the Committee for Mineral Reserves International Reporting Standards (CRIRSCO). Geoscience Australia maintains an inventory of Australia's mineral resources according to its National Mineral Resources classification scheme. The compilation of national resource stocks is derived from companies listed on the Australian Securities Exchange who are required to report publicly on ore reserves and mineral resources using the Joint Ore Reserves Committee (JORC) Code - the Australian version of the CRIRSCO Template. The highest category in the national system is 'Economic Demonstrated Resources' (EDR) which is considered to provide a reasonable estimate of what is likely to be available for mining in the long term. In summary: EDR comprises mainly current JORC Reserves and Resources where, (i) The JORC Reserves component of EDR correlates with UNFC's class of 'Commercial Projects', and (ii) The JORC Resources component correlates with UNFC's class of 'Potentially Commercial Projects'. Subeconomic Resources in the national system are largely from historic company reports, but include minor proportions of resources from latest company reports, and correlate with a subclass of the UNFC's 'Non-Commercial Projects'.

Extended abstracts from various authors compiled as the Proceedings volume of the 2012 Australian Geothermal Energy Conference, 14-16 November 2012, Crown Plaza, Coogee Beach, Sydney.

Map showing Australias Maritime Jurisdiction post UN recommendations in April 2008. Map produced for GA submission for senate inquiry by Mark Alcock. Developed from Geocat 72584

Source The data was sourced from CSIRO (Victoria) in 2012 by Bob Cechet. It is not known specifically which division of CSIRO, although it is likely to have been the Marine and Atmospheric Research Division (Aspendale), nor the contact details of the person who provided the data to Bob. The data was originally produced by CSIRO for their input into the South-East Queensland Climate Adaptation Research Initiative (SEQCARI). Reference, from an email of 16 March 2012 sent from Bob Cechet to Chris Thomas (Appendix 1 of the README doc stored at the parent folder level with the data), is made to 'download NCEP AVN/GFS files' or to source them from the CSIRO archive. Content The data is compressed into 'tar' files. The name content is separated by a dot where the first section is the climatic variable as outlined in the table format below: Name Translation rain 24 hr accumulated precipitation rh1_3PM Relative humidity at 3pm local time tmax Maximum temperature tmin Minimum temperature tscr_3PM Screen temperature (2 m above ground) at 3pm local time u10_3PM 10-metre above ground eastward wind speed at 3pm local time v10_3PM 10-metre above ground northward wind speed at 3pm local time The second part of the name is the General Circulation Model (GCM) applied: Name Translation gfdlcm21 GFDL CM2.1 miroc3_2_medres MIROC 3.2 (medres) mpi_echam5 MPI ECHAM5 ncep NCEP The third, and final, part of the tarball name is the year range that the results relate to: 1961-2000, 1971-2000, 2001-2040 and 2041-2099 Data format and extent Inside each of the tarball files is a collection of NetCDF files covering each simulation that constitutes the year range (12 simulations for each year). A similar naming protocol is used for the NetCDF files with a two digit extension added to the year for each of the simulations for that year (e.g 01-12). The spatial coverage of the NetCDF files is shown in the bounding box extents as shown below. Max X: -9.92459297180176 Min X: -50.0749073028564 Max Y: 155.149784088135 Min Y: 134.924812316895 The cell size is 0.15 degrees by 0.15 degrees (approximately 17 km square at the equator) The data is stored relative to the WGS 1984 Geographic Coordinate System. The GCMs were forced with the Intergovernmental Panel on Climate Change (IPCC) A2 emission scenario as described in the IPCC Special Report on Emissions Scenarios (SRES) inputs for the future climate. The GCM results were then downscaled from a 2 degree cell resolution by CSIRO using their Cubic Conformal Atmospheric Model (CCAM) to the 0.15 degree cell resolution. Use This data was used within the Rockhampton Project to identify the future climate changes based on the IPCC A2 SRES emissions scenario. The relative difference of the current climate GCM results to the future climate results was applied to the results of higher resolution current climate natural hazard modelling. Refer to GeoCat # 75085 for the details relating to the report and the 59 attached ANZLIC metadata entries for data outputs.

The effect of ground motion models, site response and recurrence parameters (a, b, Mmax) on the uncertainty in estimating earthquake hazard have been widely discussed. There has been less discussion on the effect of the choice of source zones and the implied seismicity model. In the current Australian national seismic hazard map we have adopted a 3 layer source zone model. This attempts to capture the variability of the spatial distribution of the seismicity in the stable continental crust of Australia. PSHA has an implied assumption that the spatial distribution of earthquakes within a source zone is either uniform or random - with the random distribution approaching uniformity as it becomes sufficiently dense. At almost any scale in no area of Australia does the seismicity conform to either a random (single Poisson model) or a uniform distribution - it is more clustered. Generally, at least three Poisson models are required to match the observed spatial statistical distribution; typically zones of low, moderate and very high seismicity. Using the full (not declustered catalogue) at least 4 Poisson models are required. In all cases examined there are more bins than expected with <1 and >3 earthquakes and a deficit of bins with 1 or 2 earthquakes. This observaion is consistent with emerging models of earthquakes in stable continents being a non-stationary or episodic, rather than a steady state, process. In order to account for this observation, we use a three layer source zone model, consisting of: a Background layer, with three zones covering 100% of the continent, based on the geological and geophysical properties; a Regional layer, of 25 zones covering ~50% of the continent, based on the pattern of earthquake density; and a Hotspot layer, of 44 zones covering 2% of the continent, based on the areas of sustained intense seismicity. In the final hazard model the maximum of the three hazard values is used, not a weighted average of the three layers.

A study of the consistency of gust wind speed records from two types of anemometers has been undertaken by Geoscience Australia. The study examined the Bureau of Meteorology's (BoM) wind speed records in order to establish the existence of bias between measurements obtained by the old pressure-tube Dines anemometers and the new cup anemometers. The study is part of a wider study of this problem undertaken by a number of research institutions using historical wind records, theoretical modelling of the anemometers and experimental testing (Ginger et al. 2011).

The impacts of climate change, including sea level rise and the increased frequency of storm surge events, will adversely affect infrastructure in a significant number of Australian coastal communities. In order to quantify this risk, Geoscience Australia in collaboration with the Department of Climate Change and Energy Efficiency, have undertaken a first-pass national assessment which has identified the extent and value of infrastructure that are potentially vulnerable to impacts of climate change. We have utilised the best available national scale information to assess the vulnerability of Australia's coastal zone to the impacts of climate change. In addition to assessing coastal vulnerability assuming the current population, we also examined the changes in exposure under a range of future population scenarios provided by the Australian Bureau of Statistics. Continuation of the current trend for significant development in the coastal zone increases the number and value of residential buildings potentially vulnerable by 2100. We found that over 270,000 residential buildings are potentially vulnerable to the combined impacts of inundation and recession by 2100. This equates to a replacement value of approximately AUD$72 billion. Nearly 250,000 residential buildings were found to be potentially vulnerable to inundation only, which equates to AUD$64 billion. Queensland and New South Wales have the largest vulnerability (considering both value and number of buildings affected). Nationally, approximately 33,000 km of road and 1,500 km of rail infrastructure are potentially at risk by 2100. These results are influencing policy and adaptation planning decisions made by federal, state and local government.

This job is part of the town capture program