Abiotic surrogates for marine biodiversity have been identified across multiple ecosystems and vary according to spatial scale, region, habitat, and biodiversity measures. Compared to other regions, our knowledge of the relationships between abiotic and biotic factors in northern Australian waters is limited. As part of the Australian Government's program of collecting pre-competitive regional information on seabed habitats, Geoscience Australia recently collaborated with the Australian Institute of Marine Science to conduct a survey along a representative channel of the Van Diemen Rise in the Joseph Bonaparte Gulf (50 - 250 km off the coast of Darwin). We used a range of methods to collect physical and biological data including multibeam sonar, towed underwater video, oceanographic moorings, sediment sampling, and epibenthic sampling. Depth was a major driver in epibenthic biomass and richness. Sponge and octocoral gardens were common on almost all banks surveyed but rarely found on other geomorphic features, suggesting that biodiversity of epifauna is linked to geomorphology and depth. Infaunal assemblages were extremely diverse in soft sediment plains and correlated to some geochemical factors. Species-level identifications will show whether these biological communities are different across sites and thereby reveal potentially unique habitats in the region. Results from this survey will identify key environmental drivers of biological assemblages in a representative region of the Van Diemen Rise to produce regional-scale information on seabed habitats in northern Australia for resource management purposes.

Crust predating 3.0 Ga within the Australian continent has previously been identified only in relatively restricted areas of the Yilgarn and Pilbara Cratons of Western Australia. Here we report the discovery of early Mesoarchean (~3150 Ma) rocks in the eastern Gawler Craton of South Australia. Rocks of broadly Mesoarchean age have been inferred by some authors to exist at depth beneath the Gawler Craton (Creaser and Fanning, 1993; Daly and Fanning, 1993), but no rocks of this age have been identified previously at the surface. The newly identified Mesoarchean granites and gneisses crop out across at least ~20 x 30 km and, on the basis of inherited zircon and Nd-isotopic compositions, are inferred to be present at depth beneath a region of at least ~1500 km2.

North Queensland Geodynamic and Mineral System Synthesis

Geoscience Australia has developed two free and open source models; the Earthquake Risk Model (EQRM) and in collaboration with the Australian National University, the hydrodynamic model, ANUGA. Both models estimate damage and loss to residential communities from earthquake and a range of hydrodynamic hazards, such as flood and tsunami. Both models have been developed in python using scientific and GIS packages, such as Shapely, Numeric and SciPy. Both rely on an underlying geospatial data-structure to model natural hazards. EQRM estimates the ground motion and damage at a set of locations for a suite of earthquakes, representing all plausible events. Modelling the earthquake risk involves estimating the probability of losses due to building damage from earthquakes. EQRM development began in 2001 and was released as open source software on Sourceforge in 2007. ANUGA solves the conservative form of the shallow water wave equation, using a finite-volume method. This method allows the study area to be represented by an unstructured mesh with variable resolution to suit the particular problem. Development of ANUGA began in 2004 and it was released as open source in 2006 on Sourceforge. It is now being used by academic, government and commercial organisations world wide to assess tsunami and flood inundation. This presentation will outline key lessons learnt in releasing these models as free and open-source software. Examples of where these models have been used to support the government's and the community's understanding of the economic impact of earthquakes and hydrodynamic hazards will be briefly described before the current development activities outlined.

Australia is the world's ninth largest energy producer and its economic wealth is built on abundant, high quality and diverse energy resources, including oil and gas, coal and uranium. In March 2010 the federal government released a comprehensive and integrated assessment of Australia's non-renewable and renewable energy resources. The assessment covers crude oil, condensate, LPG (Liquefied petroleum gas) and oil shale; conventional gas, coal seam gas, tight gas, shale gas and gas hydrates; as well as coal, uranium and thorium, geothermal, hydro, wind, solar, wave and tidal, and bioenergy. The Australian Energy Resource Assessment (AERA) was undertaken jointly by Geoscience Australia and the Australian Bureau of Agricultural and Resource Economics (ABARE). It documents the current resource base and market, and the outlook to 2030 for each of the energy commodities and is a useful compilation for investors in the Australian petroleum sector.

This map shows the boundary of the Maritime Security Zones for each port for the purpose of the Maritime Transport & Office Security Act 2003. 1 Sheet (Colour) May 2010 Not for sale or public distribution Contact Manager LOSAMBA project, PMD

This map shows the boundary of the security regulated port for the purpose of Maritime Transport & Office Security Act 2003 1 Sheet (Colour) May 2010 Not for sale or public distribution Contact Manager LOSAMBA project, PMD

This map shows the boundary of the Maritime Security Zones for each port for the purpose of the Maritime Transport & Office Security Act 2003. 1 Sheet (Colour) May 2010 Not for sale or public distribution Contact Manager LOSAMBA project, PMD

Tropical Cyclone Tracy impacted Darwin early on Christmas Day, 1974. The magnitude of damage was such that Tracy remains deeply ingrained in the Australian psyche. Since 1974, the population of Darwin has grown rapidly. As such it is desirable to know what impact an event similar to Tracy would have on the present day built environment. To assess the impacts in 1974 and the present day, we apply the Tropical Cyclone Risk Model (TCRM) developed at Geoscience Australia. The TCRM is used to produce a wind field consistent with the sparse meteorological observations made in Darwin during the passage of TC Tracy. A parametric wind field is calculated using the Holland radial wind profile coupled with the Kepert boundary layer model, and is further modified by incorporating localised windfield multiplier effects. The resulting wind field predicts maximum wind gusts at Darwin Airport of 72 m.s-1, which matches the estimated maximum wind speed at that location. The wind field generated with TCRM is applied to building damage models in an attempt to reproduce the widespread damage to residential structures associated with TC Tracy in 1974. Employing these models yields a mean damage estimate of 36%; a figure lower than that determined by the post-event damage survey. The unaccounted impact of large windborne debris is one possible explanation for the discrepancy between the observed and simulated damage. Based on the satisfactory replication of the damage associated with the historical impacts of TC Tracy, the wind field is then applied to the current day residential building database. We find that the mean damage to Darwin for the same urban footprint as the 1974 analysis in the present day would be around 5%. This represents an approximately 90% reduction in the modelled damage, and a significant portion of this reduction can be attributed to building code improvements.

A Dynamic Land Cover Map (DLCM) for Australia has been developed to provide comprehensive and consistent land cover information to inform national and state level priority setting monitoring and reporting for sustainable farming practices, management of water resources, air quality, soil erosion, and forests. The relatively long term time series observations available in the DLCM can be used to assess the land cover dynamics of forests, woodlands, rangelands and cropping systems. The DLCM is based on an analysis of 16-day Enhanced Vegetation Index (EVI) composites collected at a 250-metre resolution using the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period from 2000 to 2008. The MODIS time series for each pixel was analysed using an innovative technique that reduced each time series into 12 coefficients based on the statistical, phenological and seasonal characteristics of each pixel. These coefficients were then clustered and the resultant clusters labelled using Catchment Scale Land Use and the National Vegetation Information System datasets. The classification scheme used to describe land cover categories in the DLCM conforms to the 2007 International Standards Organisation (ISO) land cover standard (19144-2). Land covers including all land and vegetation types are clustered into 34 ISO classes. An accuracy assessment based on around 26,000 independent sites was used to validate the DLCM. As land cover classes are not generally clear-cut, but merge gradually from one to the other, a fuzzy-logic system was used to compare the 34 DLCM classes with the field data on a sliding scale. The match between the field data and the DLCM was exact in 30% of cases, very similar in 35% of cases, moderately similar in 10% of cases, somewhat similar in 18% of cases and a complete mismatch in 7% of cases. These results show a high degree of consistency between the DLCM and the site-based dataset.