Inland sulfidic soils have recently formed throughout wetlands of the Murray River floodplain associated with increased salinity and river regulation (Lamontagne et al., 2006). Sulfides have the potential to cause widespread environmental degradation both within sulfidic soils and down stream depending on the amount of carbonate available to neutralise acidity (Dent, 1986). Sulfate reduction is facilitated by organic carbon decomposition, however, little is known about the sources of sedimentary organic carbon and carbonate or the process of sulfide accumulation within inland sulfidic wetlands. This investigation uses stable isotopes from organic carbon (13C and 15N), inorganic sulfur (34S) and carbonate (13C and 18O) to elucidate the sources and cycling of sulfur and carbon within sulfidic soils of the Loveday Disposal Basin.

pH is one of the more fundamental soil properties governing nutrient availability, metal mobility, elemental toxicity, microbial activity and plant growth. The field pH of topsoil (0-10 cm depth) and subsoil (~60-80 cm depth) was measured on floodplain soils collected near the outlet of 1186 catchments covering over 6 M km2 or ~80% of Australia. Field pH duplicate data, obtained at 124 randomly selected sites, indicates a precision of 0.5 pH unit (or 7%) and mapped pH patterns are consistent and meaningful. The median topsoil pH is 6.5, while the subsoil pH has a median pH of 7 but is strongly bimodal (6-6.5 and 8-8.5). In most cases (64%) the topsoil and subsoil pH values are similar, whilst, among the sites exhibiting a pH contrast, those with more acidic topsoils are more common (28%) than those with more alkaline topsoils (7%). The distribution of soil pH at the national scale indicates the strong controls exerted by precipitation and ensuing leaching (e.g., low pH along the coastal fringe, high pH in the dry centre), aridity (e.g., high pH where calcrete is common in the regolith), vegetation (e.g., low pH reflecting abundant soil organic matter), and subsurface lithology (e.g., high pH over limestone bedrock). The new data, together with existing soil pH datasets, can support regional-scale decision-making relating to agricultural, environmental, infrastructural and mineral exploration decisions.

The Petrel Sub-basin Marine Survey GA-0335 (SOL5463) was acquired by the RV Solander during May 2012 as part of the Commonwealth Government's National Low Emission Coal Initiative (NLECI). The survey was undertaken as a collaboration between the Australian Institute of Marine Science (AIMS) and GA. The purpose was to acquire geophysical and biophysical data on shallow (less then 100m water depth) seabed environments within two targeted areas in the Petrel Sub-basin to support investigation for CO2 storage potential in these areas. The survey mapped two targeted areas of the Petrel-Sub-basin located within the Ptrl-01 2009 Greenhouse Gas acreage release area (now closed). Data acquired onboard the AIMS research vessel, Solander included multibeam sonar bathymetry (471.2 km2 in Area 1 and 181.1 km2 in Area 2) to enable geomorphic mapping, and multi-channel sub-bottom profiles (558 line-kilometres in Area 1 and 97 line-kilometres in Area 2) to investigate possible fluid pathways in the shallow subsurface geology. Sampling sites covering a range of seabed features were identified from the preliminary analysis of multibeam bathymetry and shallow seismic reflection data. Sampling equipment deployed during the survey included surface sediment grabs, vibrocores, towed underwater video, conductivity-temperature-depth (CTD) profilers and ocean moorings. A total of 14 stations were examined in Area 1 (the priority study area) and one station in Area 2. This report provides a comprehensive overview of the survey activities and preliminary results from survey SOL5463. Detailed analyses and interpretation of the data acquired during the survey will be integrated with new and existing seismic data. This new information will support the regional assessment of CO2 storage prospectivity in the Petrel Sub-basin and contribute to the nation's knowledge of its marine environmental assets.

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.

Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, especially its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.

The National Geochemical Survey of Australia (NGSA) project (www.ga.gov.au/ngsa) was part of Geoscience Australia's Onshore Energy Security Program 2006-2011 and was carried out in collaboration with the geological surveys of all States and the Northern Territory. It delivered (1) Australia's first national geochemical atlas, (2) an underpinning geochemical database, and (3) a series of reports. Catchment outlet sediments (similar to floodplain sediments in most cases) were sampled in 1186 catchments covering ~80% of the country (average sample density 1 sample per 5500 km2). Samples were collected at 2 depths each sieved to 2 grain size fractions. Chemical analyses carried out on the samples fall into 3 main categories: (1) total (using mainly XRF and total digestion ICP-MS), (2) aqua regia, and (3) Mobile Metal Ion® (MMI) element contents. The MMI analyses were conducted on the surface (0-10 cm) samples sieved to <2 mm, in one single batch, by ICP-MS. Concentrations of 54 elements (Ag, Al, As, Au, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Hg, K, La, Li, Mg, Mn, Mo, Nb, Nd, Ni, P, Pb, Pd, Pr, Pt, Rb, Sb, Sc, Se, Sm, Sn, Sr, Ta, Tb, Te, Th, Ti, Tl, U, V, W, Y, Yb, Zn and Zr) were determined. Maps and quality assessment of these data are presented in reports available from the project website. Preliminary interpretations of the MMI dataset suggest that it potentially has significant value in geological, mineral exploration and agronomic (e.g., bioavailability) applications.

Hydrogeological assessment of the Maryborough Basin, submitted as an abstract for the 2013 IAH Congress.

Geoscience Australia and CO2CRC have constructed a greenhouse gas controlled release reference facility to simulate surface emissions of CO2 (and other GHG gases) from an underground slotted horizontal well into the atmosphere under controlled conditions. The facility is located at an experimental agricultural station maintained by CSIRO Plant Industry at Ginninderra, Canberra. The design of the facility is modelled on the ZERT controlled release facility in Montana. The facility is equipped with a 2.5 tonne liquid CO2 storage vessel, vaporiser and mass flow controller unit with a capacity for 6 individual metered CO2 gas streams (up to 600 kg/d capacity). Injection of CO2 into soil is via a shallow (2m depth) underground 120m horizontally drilled slotted HDPE pipe. This is equipped with a packer system to partition the well into six CO2 injection chambers. The site is characterised by the presence of deep red and yellow podsolic soils with the subsoil containing mainly kaolinite and subdominant illite. Injection is above the water table. The choice of well orientation based upon the effects of various factors such as topography, wind direction, soil properties and ground water depth will be discussed. An above ground release experiment was conducted from July - October 2010 leading to the development of an atmospheric tomography technique for quantifying and locating CO2 emissions1. This technique will be applied to the first sub-surface experiment held in January-March 2012 in addition to soil flux surveys, microbiological surveys, and tracer studies. An overview of monitoring experiments conducted during the subsurface release and preliminary results will be presented. Additional CO2 releases are planned for late 2012 and 2013. Abstract for "11th Annual Conference on Carbon Capture Utilization & Sequestration" April 30 - May 3, 2012, Pittsburgh, Pennsylvania

A seabed mapping survey over a series of carbonate banks, intervening channels and surrounding sediment plains on the Van Diemen Rise in the eastern Joseph Bonaparte Gulf was completed under a Memorandum of Understanding between Geoscience Australia and the Australian Institute of Marine Sciences. The survey obtained detailed geological (sedimentological, geochemical, geophysical) and biological data (macro-benthic and infaunal diversity, community structure) for the banks, channels and plains to establish the late-Quaternary evolution of the region and investigate relationships between the physical environment and associated biota for biodiversity prediction. The survey also permits the biodiversity of benthos of the Van Diemen Rise to be put into a biogeographic context of the Arafura-Timor Sea and wider northern Australian marine region. Four study areas were investigated across the outer to inner shelf. Multibeam sonar data provide 100 per cent coverage of the seabed for each study area and are supplemented with geological and biological samples collected from 63 stations. In a novel approach, geochemical data collected at the stations provide an assessment of sediment and water quality for surrogacy research. Oceanographic data collected at four stations on the Van Diemen Rise will provide an understanding of the wave, tide and ocean currents as well as insights into sediment transport. A total of 1,154 square kilometres of multibeam sonar data and 340 line-km of shallow (<100 mbsf) sub-bottom profiles were collected.

In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of seabed mud content across the Australian Exclusive Economic Zone (AEEZ), which indicated that machine learning methods are generally among the most accurate methods. In this study, we further test the performance of machine learning methods in combination with ordinary kriging (OK) and inverse distance squared (IDS). We aim to identify the most accurate methods for spatial interpolation of seabed mud content in three regions (i.e., N, NE and SW) in AEEZ using samples extracted from Geoscience Australia's Marine Samples Database (MARS). The performance of 18 methods (machine learning methods and their combinations with OK or IDS) is compared using a simulation experiment. The prediction accuracy changes with the methods, inclusion and exclusion of slope, search window size, model averaging and the study region. The combination of RF and OK (RFOK) and the combination of RF and IDS (RFIDS) are, on average, more accurate than the other methods based on the prediction accuracy and visual examination of prediction maps in all three regions when slope is included and when their searching widow size is 12 and 7, respectively. Averaging the predictions of these two most accurate methods could be an alternative for spatial interpolation. The methods identified in this study reduce the prediction error by up to 19% and their predictions depict the transitional zones between geomorphic features in comparison with the control. This study confirmed the effectiveness of combining machine learning methods with OK or IDS and produced an alternative source of methods for spatial interpolation. Procedures employed in this study for selecting the most accurate prediction methods provide guidance for future studies.