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  • In June 2012 Geoscience Australia was commissioned by Commonwealth Scientific and Industrial Research Organisation (CSIRO) to undertake detailed wind hazard assessments for 14 Pacific Island countries and East Timor as part of the Pacific-Australia Climate Change Science and Adaptation Planning (PACCSAP) program. PACCSAP program follows on from work Geoscience Australia did for the Pacific Climate Change Science Program (PCCSP) looking at CMIP3 generation of climate models. The objective of this study is to improve scientific knowledge by examining past climate trends and variability to provide regional and national climate projections. This document presents results from current and future climate projections of severe wind hazard from tropical cyclones for the 15 PACCSAP partner countries describing the data and methods used for the analysis. The severe wind hazard was estimated for current (1981 to 2000) and future (2081 to 2100) climate scenarios. Tropical-cyclone like vortices from climate simulations conducted by CSIRO using six Coupled Model Intercomparison Project phase 5 (CMIP5) models (BCC-CSM1.1, NorESM1-M, CSIRO-Mk3.6, IPSL-CM5A, MRI-CGM3 and GFDL-ESM2M) as well as the International Best Track Archive for Climate Stewardship were used as input to the Geoscience Australia's Tropical Cyclone Risk Model to generate return period wind speeds for the 15 PACCSAP partner countries. The Tropical Cyclone Risk Model is a statistical-parametric model of tropical cyclone behaviour, enabling users to generate synthetic records of tropical cyclones representing many thousands of years of activity. The 500-year return period wind speed is analysed and discussed into more details in this report, since it is used as a benchmark for the design loads on residential buildings. Results indicate that there is not a consistent spatial trend for the changes in 500-year cyclonic wind speed return period when CMIP5 models are compared individually. BCC-CSM1M and IPSL-CM5A presented an increase in the annual TC frequency for East Timor, northern hemisphere and southern hemisphere. On the other hand, NorESM1M showed a decrease in the annual TC frequency for the same areas. The other three models showed a mixed of increase and decrease in their annual TC frequency. When CMIP5 models were analysed by partner county capitals for the 500-year cyclonic wind speed return period, IPSL-CM5A and GFDL-ESM2M models presented an increase in the cyclonic wind speed intensity for almost all capitals analysed with exception of Funafuti (GFDL-ESM2M), which presented a decrease of 0.7% and Honiara (IPSL-CM5A) with a decrease of 1.6%. The tropical cyclone annual frequency ensemble mean indicates an increase in the tropical cyclone frequency within all three regions considered in this study. When looking at individual capitals, a slight increase in the 500-year return period cyclonic wind speed ensemble mean varying between 0.8% (Port Vila) to 9.1% (Majuro) is noticed. A decline around 2.4% on average in the 500-year return period cyclonic wind speed ensemble mean is observed in Dili, Suva, Nukualofa and Ngerulmud. The ensemble spatial relative change did not show any particular consistency for the 500-year cyclonic wind speed. Areas where Marshall Islands and Niue are located presented an increase in the 500-year cyclonic wind speed while a decrease is observed in areas around South of Vanuatu, East of Solomon Islands, South of Fiji and some areas in Tonga. The information from the evaluation of severe wind hazard from tropical cyclones, together with other PACCSAP program outputs, will be used to build partner country capacity to effectively adapt and plan for the future and overcome challenges from climate change.

  • 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).

  • The Collaborative Australian Protected Areas Database (CAPAD) 2012 provides both spatial and text information about government, Indigenous and privately protected areas for continental and marine Australia. State and Territory conservation agencies supplied data, current to 31 December 2012, to Australian Government Department of the Environment.

  • 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 report provides background information about the Ginninderra controlled release Experiment 1 including a description of the environment and weather during the experiment, the groundwater conditions and a brief description of all the monitoring techniques that were trialled during the experiment. Release of CO2 began 28 March 2012 at 10:30 AM and stopped 30 May 2012 4:15 PM. The total CO2 release rate during Experiment 1 was 144 kg/d CO2. Krypton gas was also released as a tracer gas at a rate of 10 mL/min Kr in one section of the release well only. The aim of the Ginninderra Experiment 1 controlled release was to artificially simulate the leakage of CO2 along a line source, to represent leakage along a fault. Multiple methods and techniques were then trialled in order to assess their abilities to: - detect that a leak was present - pinpoint the location of the leak - identify the strength of the leak - monitor how the CO2 behaves in the sub-surface - assess the effects it may have on soil ecology Several monitoring and assessment techniques were trialled for their effectiveness to quantify and qualify the CO2 that was release. The methods are described in this report and include: - soil gas - CO2 carbo-cap (GMP343) - eddy covariance - groundwater levels and chemistry - soil microbial samples - soil flux - krypton in air - electromagnetic (EM-31) - meteorology - CO2 isotopes in tank This report is a reference guide to describe the Ginninderra Experiment 1 details. Only methods are described in this report with the results of the study published in conference papers and future journal articles.

  • 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.

  • Since 2012, Geoscience Australia has been providing spatial support and advice to the Crisis Coordination Centre (CCC) within Emergency Management Australia (EMA) as part of our collaboration with the Attorney-General's Department. Geoscience Australia designed the Exposure Report to quickly provide exposure information for timely emergency response and recovery decision-making. This document describes the datasets and processes that create the Exposure Report

  • 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 resource contains sediment data for the Oceanic Shoals Commonwealth Marine Reserve (CMR) in the Timor Sea collected by Geoscience Australia during September and October 2012 on RV Solander (survey GA0339/SOL5650). Seabed sediment samples were collected from four survey areas by either a Smith McIntyre grab or box corer at 62 stations, divided between Area 1 (n=22), Area 2 (n=17), Area 3 (n=21) and Area 4 (n=2). The Oceanic Shoals Commonwealth Marine Reserve survey was undertaken as an activity within the Australian Government's National Environmental Research Program Marine Biodiversity Hub and was the key component of Research Theme 4 - Regional Biodiversity Discovery to Support Marine Bioregional Plans. Hub partners involved in the survey included the Australian Institute of Marine Science, Geoscience Australia, the University of Western Australia, Museum Victoria and the Museum and Art Gallery of the Northern Territory. Data acquired during the survey included: multibeam sonar bathymetry and acoustic backscatter; sub-bottom acoustic profiles; physical samples of seabed sediments, infauna and epibenthic biota; towed underwater video and still camera observations of seabed habitats; baited video observations of demersal and pelagic fish, and; oceanographic measurements of the water column from CTD (conductivity, temperature, depth) casts and from deployment of sea surface drifters. Further information on the survey is available in the post-survey report published as Geoscience Australia Record 2013/38 (Nichol et al. 2013).