Wildfires are one of the major natural hazards facing the Australian continent. Chen (2004) rated wildfires as the third largest cause of building damage in Australia during the 20th Century. Most of this damage was due to a few extreme wildfire events. For a vast country like Australia with its sparse network of weather observation sites and short temporal length of records, it is important to employ a range of modelling techniques that involve both observed and modelled data in order to produce fire hazard and risk information/products with utility. This presentation details the use of statistical and deterministic modelling of both observations and synthetic climate model output (downscaled gridded reanalysis information) in the development of extreme fire weather potential maps. Fire danger indices such as the McArthur Fire Forest Danger Index (FFDI) are widely used by fire management agencies to assess fire weather conditions and issue public warnings. FFDI is regularly calculated at weather stations using measurements of weather variables and fuel information. As it has been shown that relatively few extreme events cause most of the impacts, the ability to derive the spatial distribution of the return period of extreme FFDI values contributes important information to the understanding of how potential risk is distributed across the continent. The long-term spatial tendency FFDI has been assessed by calculating the return period of its extreme values from point-based observational data. The frequency and intensity as well as the spatial distribution of FFDI extremes were obtained by applying an advanced spatial interpolation algorithm to the recording stations' measurements. As an illustration maps of 50 and 100-year return-period (RP) of FFDI under current climate conditions are presented (based on both observations and reanalysis climate model output). MODSIM 2013 Conference

In this study, we aim to identify the most accurate methods for spatial prediction of seabed gravel content in the northwest Australian Exclusive Economic Zone. We experimentally examined: 1) whether input secondary variables affect the performance of RFOK and RFIDW, 2) whether the performances of RF, SIMs and their hybrid methods are data-specific, and 3) whether model averaging improves predictive accuracy of these methods in the study region. For RF and the hybrid methods, up to 21 variables were used as predictors. The predictive accuracy was assessed in terms of relative mean absolute error and relative root mean squared error based on the average of 100 iterations of 10-fold cross validation. In this study, the following important findings were achieved: - the predictive errors fluctuate with the input secondary variables; - the existence of correlated variables can alter the results of model selection, leading to different models; - the set of initial input variables affects the model selected; - the most accurate model can be missed out during the model selection; - RF, RFOK and RFIDW prove to be the most accurate methods in this study, with RFOK preferred; and these methods are not data-specific, but their models are, so best model needs to be identified; and - Model averaging is clearly data-specific. In conclusion, model selection is essential for RF and the hybrid methods. RF and the hybrid methods are not data-specific, but their models are. RFOK is the most accurate method. Model averaging is also data-specific. Hence best model needs to be identified for individual studies and application of model averaging should also be examined accordingly. RF and the hybrid methods have displayed substantial potentials for predicting environmental properties and are recommended for further test for spatial predictions in environmental sciences and other relevant disciplines in the future. This study provides suggestions and guidelines for improving the spatial predictions of biophysical variables in both marine and terrestrial environments.

A video for the launch of new Great Barrier Reef bathymetry data on 30 November 2017.

Abstract for a Poster for the CO2CRC Symposium 2013: Atmospheric tomography is a CO2 quantification and localisation technique that uses an array of sampling points and a Bayesian inversion method to solve for the location and magnitude of a CO2 leak. Knowledge of a normalized three-dimensional dispersion plume is required in order to accurately model a leak using many meteorological parameters. A previous small scale (~20 m) study using a high precision Fourier Transform Infrared found that the emission rate was determined to within 3% of the actual release rate and the localisation within 1 m of the correct position. The technique was applied during the CO2CRC Otway Stage 2B residual saturation and dissolution test in August-October 2011. A network of eight independent CO2 sensors (Vaisala GMP343 CO2 probes) were positioned at distances ranging from 154 to 473 m from the well. A 3D sonic anemometer within the measurement area collected wind turbulence data. The results of the study indicate that, through careful data processing, measurements from the reasonably inexpensive (but lower accuracy and lower precision) CO2 sensor array can provide useful data for the application of atmospheric tomography. Results have found that the low precision of the sensors over time becomes a problem due to sensor drift. A reference measurement of CO2 helps to resolve this problem and improves the perturbation signal during data processing. Preliminary inversion modeling results will be shown to show the best estimation of locating a CO2 leakage source for the Otway Stage 2B residual saturation and dissolution test. CO2CRC Symposium 2013, Hobart

The potential for using a single high precision atmospheric station for detecting CO2 leaks has been investigated using a variety of statistical approaches. Geoscience Australia and CSIRO Marine and Atmospheric Research installed an atmospheric monitoring station, Arcturus, in the Bowen Basin, Australia, in 2010 and have collected over 3 years' worth of atmospheric concentration measurements. The facility is designed as a prototype remote baseline monitoring station that could be deployed in areas targeted for commercial scale geological storage of carbon dioxide. Two Picarro gas analysers are deployed in the station to continuously monitor CO2, CH4 and CO2 isotopes. An automated weather station and an eddy covariance flux tower have also been installed at the site. Atmospheric CO2 perturbations, from simulated leaks, have been modelled to determine the minimum statistically significant emissions that can be detected above background concentrations at Arcturus. CO2 leakage was simulated from January to December (2011) using a 3D-coupled prognostic meteorological and pollutant dispersion model (TAPM). Simulations were conducted for various locations, emission rates and distances (1-10 km) from the station. The simulated leaks were simulated using an area source (100 m x 100 m) and a point source located in the optimum wind direction (SSE), which showed the largest perturbation. To better understand the observed CO2 signal, a statistical model combining both a regression and time series model was constructed. The regression model is a time dependent generalised additive model relating the CO2 to other observed atmospheric variables (e.g. wind speed, temperature, humidity). It accounts for seasonal trends through the inclusion of dummy variables. The time series model is based on a seasonal auto-regressive integrated moving average (ARIMA) model, but with the additional complexity of allowing auto-regressive relationships to depend on the time of day. A non-parametric goodness of fit approach using the Kolmogorov-Smirnoff (KS) test was then used to test whether simulated perturbations can be detected against the modelled expected value of the background for certain hours of the day and for particular seasons. The developed regression model allows us to pre-whiten the CO2 time series. Pre-whitening reduces both the variance and skew of the marginal distribution of the signal. This improves the power of the Kolmogorov-Smirnoff (KS) test when attempting to detect simulated perturbations against the background signal. The KS test calculates the probability that the modelled leak perturbation could be caused by natural variation in the background. For hours between 10am and 2pm in the winter of 2011, minimum detectable leaks located 1km from the measurement station improve from 44 to 22 tpd for an area source and 33 to 14 tpd for a point source at a p-value of 0.05. These are very large leaks located only 1 km from the station. Additionally, this approach results in a high false alarm rate of 56%. An alternative p-value could be chosen to reduce the false alarm rate but the overall conclusion is the same. A long term, single measurement station monitoring program that is unconstrained by prior information on possible leaks, and based on detection of perturbations of CO2 alone due to leakage above a (noisy) background signal, is likely to take one or more years to detect leaks of the order of 10kt p.a.

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).

This report provides the first comprehensive assessment of geomorphological and geological features of the Great Barrier Reef (GBR) whose intrinsic characteristics represent elements of the Outstanding Universal Value (OUV) of the Great Barrier Reef World Heritage Area (GBRWHA). Specific examples of these features are described and an initial assessment made of the environmental pressures that they currently or in the future may experience. Importantly, the information compiled in this report improves our knowledge of an important set of physical and biophysical features in the GBRWHA with key natural heritage values and thereby has the potential to better inform the conservation and management of this unique region.

This dataset provides the spatially continuous data of seabed gravel (sediment fraction >2000 µm), mud (sediment fraction < 63 µm) and sand content (sediment fraction 63-2000 µm) expressed as a weight percentage ranging from 0 to 100%, presented in 0.0025 decimal degree (dd) resolution raster grids format and ascii text file. The dataset covers the Petrel sub-basin in the Australian continental EEZ. This dataset supersedes previous predictions of sediment gravel, mud and sand content for the basin with demonstrated improvements in accuracy. Accuracy of predictions varies based on density of underlying data and level of seabed complexity. Artefacts occur in this dataset as a result of insufficient samples in relevant regions. This dataset is intended for use at the basin scale. The dataset may not be appropriate for use at smaller scales in areas where sample density is insufficient to detect local variation in sediment properties. To obtain the most accurate interpretation of sediment distribution in these areas, it is recommended that additional samples be collected and interpolations updated.

This dataset contains seascape classification layer derived from bathymetry and backscatter, and their derivative from seabed mapping surveys in Darwin Harbour. The survey was undertaken during the period 24 June to 20 August 2011 by iXSurvey Australia Pty Ltd for the Department of Natural Resources, Environment, The Arts and Sport (NRETAS) in collaboration with Geoscience Australia (GA), the Darwin Port Corporation (DPC) and the Australian Institute of Marine Science (AIMS) using GA's Kongsberg EM3002D multibeam sonar system and DPC's vessel Matthew Flinders. The survey obtained detailed bathymetric map of Darwin Harbour. Refer to the GA record ' Mapping and Classification of Darwin Harbour Seabed' for further information on processing techniques applied (GeoCat: 79212; GA Record: 2015/xx)

Phase two of the China Australia Geological Storage of CO2 (CAGS2) project aimed to build on the success of the previous CAGS project and promote capacity building, training opportunities and share expertise on the geological storage of CO2. The project was led by Geoscience Australia (GA) and China's Ministry of Science and Technology (MOST) through the Administrative Centre for China's Agenda 21 (ACCA21). CAGS2 has successfully completed all planned activities including three workshops, two carbon capture and storage (CCS) training schools, five research projects focusing on different aspects of the geological storage of CO2, and ten researcher exchanges to China and Australia. The project received favourable feedback from project partners and participants in CAGS activities and there is a strong desire from the Chinese government and Chinese researchers to continue the collaboration. The project can be considered a highly successful demonstration of bi-lateral cooperation between the Australian and Chinese governments. Through the technical workshops, training schools, exchange programs, and research projects, CAGS2 has facilitated and supported on-going collaboration between many research institutions and industry in Australia and China. More than 150 experts, young researchers and college students, from over 30 organisations, participated in CAGS2. The opportunity to interact with Australian and international experts at CAGS hosted workshops and schools was appreciated by the participants, many of whom do not get the opportunity to attend international conferences. Feedback from a CAGS impact survey found that the workshops and schools inspired many researchers and students to pursue geological storage research. The scientific exchanges proved effective and often fostered further engagement between Chinese and Australian researchers and their host organisations. The research projects often acted as a catalyst for attracting additional CCS funding (at least A$700,000), including two projects funded under the China Clean Development Mechanism Fund. CAGS sponsored research led to reports, international conference presentations, and Chinese and international journal papers. CAGS has established a network of key CCS/CCUS (carbon capture, utilisation and storage) researchers in China and Australia. This is exemplified by the fact that 4 of the 6 experts that provided input on the 'storage section of the 12th Five-Year plan for Scientific and Technological Development of Carbon Capture, Utilization and Storage, which laid out the technical policy priorities for R&D and demonstration of CCUS technology in China, were CAGS affiliated researchers. The contributions of CAGS to China's capacity building and policy CCUS has been acknowledged by the Chinese Government. CAGS support of young Chinese researchers is particularly noted and well regarded. Letters have been sent to the Secretary of the Department of Industry and Science and to the Deputy CEO of Geoscience Australia, expressing China's gratitude for the Australian Government's support and GA's cooperation in the CAGS project.