Geoscience Australia defines a sample as a feature observed, measured or collected in the field. A specimen is a physical individual sample collected during the field work. This data set represents a subset of all Sampling data held by Geoscience Australia that have been collected as part of drilling activities (ie relate to Australian Boreholes). The data will be utilised by other data domains by providing Sampling context to various Observation & Measurement data.

Monitoring is a regulatory requirement for all carbon dioxide capture and geological storage (CCS) projects to verify containment of injected carbon dioxide (CO2) within a licensed geological storage complex. Carbon markets require CO2 storage to be verified. The public wants assurances CCS projects will not cause any harm to themselves, the environment or other natural resources. In the unlikely event that CO2 leaks from a storage complex, and into groundwater, to the surface, atmosphere or ocean, then monitoring methods will be required to locate, assess and quantify the leak, and to inform the community about the risks and impacts on health, safety and the environment. This paper considers strategies to improve the efficiency of monitoring the large surface area overlying onshore storage complexes. We provide a synthesis of findings from monitoring for CO2 leakage at geological storage sites both natural and engineered, and from monitoring controlled releases of CO2 at four shallow release facilities - ZERT (USA), Ginninderra (Australia), Ressacada (Brazil) and CO2 field lab (Norway).

The DMCii Mosaic presents a sample of imagery acquired by Geoscience Australia under CC-BY Creative Commons Attribution 3.0 Australia licence. This imagery was captured by UK2-DMC satellite between December 2011 to April 2012 and has spatial resolution of 22 metres. Spectral bands are: Band 1 NIR; Band 2 Red; Band 3 Green. The DMCii Mosaic is displayed as a Pseudo Natural Colour Image.

Geoscience Australia has completed a re-development of Sentinel, from the infrastructure that supports the system through to the spatial technology and user-interface. These changes will allow Geoscience Australia to more easily integrate data from different platforms and sources as well as provide additional products through the Sentinel interface. The new Sentinel system was developed in consultation with stakeholders to ensure a close alignment between end-users needs and the services provided by Sentinel. This paper presents the key features of the new Sentinel.

We have developed a Building Fire Impact Model to evaluate the probability that a building located in a peri-urban region of a community is affected/destroyed by a forest fire. The methodology is based on a well-known mathematical technique called Event Tree (ET) modeling, which is a useful graphical way of representing the dependency of events. The tree nodes are the event itself, and the branches are formed with the probability of the event happening. If the event can be represented by a discrete random variable, the number of possible realisations of the event and their corresponding probability of occurring, conditional on the realisations of the previous event, is given by the branches. As the probability of each event is displayed conditional on the occurrence of events that precede it in the tree, the joint probability of the simultaneous occurrence of events that constitute a path is found by multiplication (Hasofer et al., 2007). BFIM contains a basic implementation of the main elements of bushfire characteristics, house vulnerability and human intervention. In the first pass of the BFIM model, the characteristics of the bushfire in the neighboring region to the house is considered as well as the characteristics of the house and the occupants of the house. In the second pass, the number of embers impacting on the house is adjusted for human intervention and wind damage. In the third pass, the model examines house by house conditions to determine what houses have been burnt and their impact on neighboring houses. To illustrate the model application, a community involved in the 2009 Victorian bushfires has been studied and the event post-disaster impact assessment is utilized to validate the model outcomes. MODSIM 2013 Conference

Wind multipliers are factors that transform regional wind speeds into local wind speeds, accounting for the local effects which include topographical, terrain and shielding influences. Wind multipliers have been successfully utilized in various wind related activities such as wind hazard assessment (engineering building code applications), event-based wind impact assessments (tropical cyclones), and also national scale wind risk assessment. The work of McArthur in developing the Forest Fire Danger Index (FFDI: Luke and McArthur, 1978) indicates that the contribution of wind speed to the FFDI is about 45% of the magnitude, indicating the importance of determining an accurate local wind speed in bushfire hazard and spread calculations. For bushfire spread modeling, local site variation (@ 100 metre and also 25 metre horizontal resolution) have been considered through the use of wind multipliers, and this has resulted in a significant difference to the currently utilized regional '10 metre height' wind speed (and further to the impact analysis). A series of wind multipliers have been developed for three historic bushfire case study areas; the 2009 Victorian fires (Kilmore fire), the 2005 Wangary fire (Eyre Peninsula), and the 2001 Warragamba - Mt. Hall fire (Western Sydney). This paper describes the development of wind multiplier computation methodology and the application of wind multipliers to bushfire hazard and impact analysis. The efficacy of using wind multipliers within a bushfire spread hazard model is evaluated by considering case study comparisons of fire extent, shape and impact against post-disaster impact assessments. The analysis has determined that it is important to consider wind multipliers for local wind speed determination in order to achieve reliable fire spread and impact results. From AMSA 2013 conference

Abstract: Land Surface Temperature (Ts) is an important boundary condition in many land surface modelling schemes. It is also important in other application areas such as, hydrology, urban environmental monitoring, agriculture, ecological and bushfire monitoring. Many studies have shown that it is possible to retrieve Ts on a global scale using thermal infrared data from satellites. Development of standard methodologies that generate Ts products routinely would be of broad benefit to the application of remote sensing data in areas such as hydrology and urban monitoring. AVHRR and MODIS datasets are routinely used to deliver Ts products. However, these data have 1km spatial resolution, which is too coarse to detect the detailed variation of land surface change of concern in many applications, especially in heterogeneous areas. Higher resolution thermal data from Landsat is a possible option in such cases. To derive Ts, two scientific problems need to be resolved: to remove the atmospheric effects and derive surface brightness temperature (TB) and to separate the emissivity and Ts effects in the surface brightness temperature (TB). To derive TB, for single thermal band sensors such as, Landsat 5, 7 and (due to a faulty dual-band thermal instrument) on Landsat-8, the split window methods, such as those used for NOAAAVHRR data (Becker & Li, 1990), and the day/night pairs of thermal infrared data in several bands, as used for MODIS (Wan et al., 2002) are not available for correcting atmospheric effects. The retrieval of surface brightness temperature TB from Landsat data therefore needs more care, as the accuracy of the TB retrieval depends critically on the ancillary data, such as atmospheric water vapour data (precipitable water). In this paper, a feasible operational method to remove the atmospheric effects and retrieve surface brightness temperature from Landsat data is presented. The method uses the MODTRAN 5 radiative transfer model and global atmospheric profile data sets, such as NASA MERRA (The Modern Era Retrospective-Analysis for Research and Applications) atmospheric profiles, NOAA NCEP (National Center for Environmental Prediction) reanalysis product and ECMWF (The European Centre for Medium-Range Weather Forecasts) to correct for the atmospheric effects. The results derived from the global atmospheric profiles are assessed against the TB product estimated by using (accurate) ground based radiosonde data (balloon data). The results from this study have found: The global data sets NCEP1, NCEP2, MERRA and ECMWF can all generally give satisfactory TB products and can meet the levels of accuracy demanded by many practitioners, such as 1º K. Among global data sets, ECMWF data set performs best. The root mean square difference (RMSD) for the 9 days and 3 test sites are all within 0.4º K when compared with the TB products estimated using ground radiosonde measurements.

The dry-tropics of central Queensland has an annual bushfire threat season that generally extends from September to November. Fire weather hazard is quantified using either the Forest Fire Danger Index (FFDI) or the Grassland Fire Danger Index (GFDI) (Luke and McArthur, 1978). Weather observations (temperature, relative humidity and wind speed) are combined with an estimate of the fuel state to predict likely fire behaviour if an ignition eventuates. A high resolution numerical weather model (dynamic downscaling) was utilised to provide spatial texture over the Rockhampton region for a range of historical days where bushfire hazard (as measured at the Rockhampton Airport meteorological station) was known to be severe to extreme. From the temperature, relative humidity and wind speeds generated by the model, the maximum FFDI for each simulated day was calculated using a maximum drought factor. Each of these FFDI maps was then normalised to the value of the FFDI at the grid point corresponding to Rockhampton Airport (ensemble produced). The annual recurrance interval (ARI) of FFDI at Rockhampton Airport for the current climate was calculated from observations by fitting Generalised Extreme Value (GEV) distributions. For future climate, we considered three downscaled General Circulation Models (GCM's) forced by the A2 emission scenario for atmospheric greenhouse gas emissions. The spatial pattern of the 50 and 100 year ARI fire danger rating for the Rockhampton region (current and future climate) was determined. In general, a small spatial increase in the fire danger rating is reflected in the ensemble model average for the 2090 climate. This is reflected throughout the Rockhampton region in both magnitude and extent through 2050 to 2090. Cluster areas of higher (future climate) bushfire hazard were mapped for planning applications. Handbook MODSIM2013 Conference

<b>IMPORTANT NOTICE:</b> This web service has been deprecated. The Hydrochemistry Service OGC service at https://services.ga.gov.au/gis/hydrogeochemistry/ows should now be used for accessing Geoscience Australia hydrochemistry analyses data. This is an Open Geospatial Consortium (OGC) web service providing access to hydrochemistry data (groundwater analyses) obtained from water samples collected from Australian water bores.

<b>IMPORTANT NOTICE: </b>This web service has been deprecated. The Australian Onshore and Offshore Boreholes OGC service at https://services.ga.gov.au/gis/boreholes/ows should now be used for accessing Geoscience Australia borehole data. This is an Open Geospatial Consortium (OGC) web service providing access to a subset of Australian geoscience samples data held by Geoscience Australia. The subset currently relates specifically to Australian Boreholes.