Despite growing concerns about potential enhancement of global warming and slope failure by methane produced by gas hydrate dissociation, much uncertainty surrounds estimates of gas hydrate reservoir sizes, as well as methane fluxes and oxidation rates at the sea floor. For cold seep sediments of the eastern Mediterranean Sea, depth-dependent methane concentrations and rates of anaerobic oxidation of methane (AOM) are constrained by modeling the measured pore-water sulfate profile. The calculated dissolved methane distribution and flux are sensitive to the advective flow velocity, which is estimated from the depth distributions of conservative pore-water constituents (Na, B). Near-complete anaerobic oxidation of the upward methane flux is supported by the depth distributions of indicative biomarkers, and the carbon isotopic compositions of organic matter and dissolved inorganic carbon. Pore-water and solid-phase data are consistent with a narrow depth interval of AOM, 14-18 cm below the sediment-water interface. Based on an isotopic mass balance, the biomass of the microbial population carrying out oxidation of methane coupled to sulfate reduction at the given methane flux represents about 20% of the total organic carbon, which is a significant pool of in situ formed organic matter. Model results indicate that the asymptotic methane concentration is reached a few meters below the sediment surface. The predicted asymptotic concentration is close to the in situ saturation value with respect to gas hydrate, suggesting that the rate of shallow gas hydrate formation is controlled by the ascending methane flux. The proposed model approach can be used to predict the formation of gas hydrate, and to quantify methane fluxes plus transformation rates in surface sediments where fluid advection is an important transport mechanism.

The subsurface of the Earth is a complex system, one that we are yet to fully understand and model. It is hence impossible to automate the process of mapping and modelling, and the input of user experience and knowledge ('prior knowledge') is required to produce meaningful and useful outputs. This form of solution does not lend itself to a simple programmatic approach. However, by taking advantage of advances in computer technology and the application of numerical methods for modeling complex environments, we can do much to improve upon past results. Introduction As Australia's national geoscience organisation, Geoscience Australia (GA) plays an important role in the creation and delivery of fundamental geoscientific information. Studies are carried out at a wide range of scales, from a continental perspective to highly detailed local site investigations. In most situations, direct geological observations are supplemented by the inferences that can be made from geophysical measurements. Observations of the Earth's gravity and magnetic fields contain signals from subsurface materials, and extensive holdings of these measurements are commonly used to help create 3D subsurface models. With sparse hard constraints and incomplete, insufficient, noisy observations, knowledge workers or experts continue to play an important role in providing implicit prior constraints on any system to model this volume. The interface to these people becomes an important part of any set of tools for performing geological modeling of gravity and magnetic data. Users constantly demand a better experience and better outcomes when modeling the subsurface. Some of their recurring requests are for: * A simpler, more intuitive user-experience * Higher resolution * Models with larger extents * Faster processing * Inclusion of a greater number of geological and rock property constraints * Estimates of the uncertainty in the outcomes * Improved 3D visualisation * Tracking of input provenance and subsequent processing that is carried out * Organised management of 3D models Integration of the elements is a key consideration when developing solutions, as users are loathe to adopt procedures that become more involved and more difficult to understand and to piece together. Today, developments to produce world-class solutions typically take place across multiple agencies, involving many people, and at locations spread around the globe. This in itself is a challenge! We have focused our efforts on the following: * The management and delivery of rock properties * Spherical and Cartesian coordinate gravity and magnetic modelling software * Use of High Performance Computing (HPC) facilities * Use of a virtual globe application for 3D visualisation

The MODIS (or Moderate Resolution Imaging Spectroradiometer) 250 m EVI dataset provides a valuable ongoing means of characterising and monitoring changes in land use and resource condition. However the multiple factors that influence a time series of greenness data make the data difficult to analyse and interpret. Without prior knowledge, underlying models for time series in a given remote sensing image are often heterogeneous. So while conventional time series analysis methods such as wavelet transform and Fourier analysis may work well for part of the image, these models are either invalid or must be substantially re-parameterised for other parts of the image. To overcome these challenges we propose a new approach to distil information from earth observation time series. The characteristics of a remote sensing time series are represented by a set of statistics (which we call coefficients) selected to correspond to the dynamics of a natural system. To ensure the coefficients are robust and generic, statistics are calculated independently by applying statistical models with less complexity on shorter segments within the time series. An International Standards Organization (ISO) Land Cover classification was generated for cropping regions in the Gwydir and Namoi catchments, in Australia. Areas identified in the classification as irrigated and rain fed cropping were analysed using a tailored time series analysis tool. The crop analysis tool identifies time series features such as the number and duration of fallow periods, crop timing, presence/absence of a crop during a year for a specific growing season. This information is combined with paddock boundaries derived from Landsat imagery to provide detailed year-by-year insight into cropping practices in the Gwydir and Namoi catchments.

A regional scale 3D geological map of the upper crustal sequence in the West Arnhem Land region, Northern Territory, was compiled from surface mapping, limited drilling information, and liberal amounts of geological inference. Modelling of the gravity and magnetic field response of this map was proposed as a means of evaluating the viability of this geological hypothesis. A relatively good supply of mass density and magnetic property data were available to constrain the transformation of 3D geological maps into property models in preparation for potential field modelling. The presence of numerous relatively thin magnetic horizons, dykes, and sills provided many challenges for producing geologically-realistic magnetic property models at a regional scale. Modelling of the gravity field at this scale was far more straight forward and successful. A stochastic procedure was used to derive a large number of geological maps by making small changes to the highly uncertain interpretive parts of the original 3D geological map. A subset of these derived geological maps had associated mass density models that could adequately reproduce the gravity field observations. The common characteristics of the geological models in this subset were isolated using statistical techniques and used to refine our portrayal of the regional scale 3D geological features.

To follow

Nutrients dynamics in estuaries are temporarily variable depending on changing physical-chemical conditions and the response of functional primary producer groups such as phytoplankton, microphytobenthos, seagrass and macroalgae. In order to reveal temporal regime shifts in primary producer groups and associated changes in estuarine nutrient dynamics we developed a box-model coupling the hydrology and nitrogen dynamics in Wilson Inlet, a large, central basin dominated, intermittently closed estuary exposed to Mediterranean climate. The model is calibrated and validated with monitoring data, aquatic plant biomass estimates and biogeochemical rate measurements. Macrophytes and their microalgal epiphytes appear to rapidly assimilate first flush nutrients from the catchment in winter, but this buffer capacity then ceases and a phytoplankton bloom develops in response to subsequent river run-off events in spring. In late spring to autumn high light availability stimulates high primary production by microphytobenthos leading to reduced benthic ammonia fluxes particularly in deep basin areas and contributing about 50% of annual whole-system primary production. Significant amounts of bioavailable nitrogen are flushed out, because phytoplankton predominance occurs concurrently with the opening of the bar.

Modelled groundwater levels from 2010 to 2070 used to estimate the impact of climate change and future groundwater resource development on groundwater levels in the GAB. The modelling considered different scenarios of climate and groundwater development: Scenario A (historical climate and current development); Scenario C (future climate and current development) and Scenario D (future climate and future development). This data set contains spatial data that were created from the outputs from the "A scenario" model and the "Base scenario" model, both of which were based on the GABtran groundwater flow model. The raster grid "A.grd" represents the spatial distribution of predicted hydraulic head for the year 2070 produced by the "A scenario" model. The raster grid "Base.grd" represents the modelled hydraulic head for the year 2010. The raster grid "A-Base.grd" represents the difference in predicted head from 2010 to 2070. 'No data' value is 1e30 Cell size is 5000m x 5000m This data and metadata were produced by CSIRO for the Great Artesian Basin Water Resource Assessment. For more information, please refer to Welsh WD, Moore CR, Turnadge CJ, Smith AJ and Barr TM (2012), "Modelling of climate and groundwater development. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment ". CSIRO Water for a Healthy Country Flagship, Australia. Projection is Albers equal area conic, with central meridian 143 degrees longitude, standard parallels at -21 and -29 degrees latitude and latitude of projection's origin at -25.

Modelled groundwater levels from 2010 to 2070 used to estimate the impact of climate change and future groundwater resource development on groundwater levels in the GAB. The modelling considered different scenarios of climate and groundwater development: Scenario A (historical climate and current development); Scenario C (future climate and current development) and Scenario D (future climate and future development). The future climate scenarios included the wet extreme (wet), the median (mid) and the dry extreme (dry). The raster grids "Cdry.grd"", "Cmid.grd" and "Cwet.grd" show predicted hydraulic head for the year 2070 based on projections of future climate and the continuation of current rates of groundwater extraction The files "Cdry-Base.grd", "Cmid-Base.grd" and ""Cwet-Base.grd" represent predicted differences between the hydraulic heads produced by Scenario C at 2070, and the modelled spatial distributions of hydraulic head for the year 2010 (Base scenario). The files "Cdry-A.grd", "Cmid-A.grd" and "Cwet-A.grd" represent predicted differences between hydraulic heads for 2070 produced by Scenario C and the current climate and development scenario (Scenario A). 'No data' value is 1e30 Cell size is 5000m x 5000m This data and metadata were produced by CSIRO for the Great Artesian Basin Water Resource Assessment. For more information, please refer to Welsh WD, Moore CR, Turnadge CJ, Smith AJ and Barr TM (2012), "Modelling of climate and groundwater development. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment ". CSIRO Water for a Healthy Country Flagship, Australia. Projection is Albers equal area conic, with central meridian 143 degrees longitude, standard parallels at -21 and -29 degrees latitude and latitude of projection's origin at -25.

Modelled groundwater levels from 2010 to 2070 used to estimate the impact of climate change and future groundwater resource development on groundwater levels in the GAB. The modelling considered different scenarios of climate and groundwater development: Scenario A (historical climate and current development); Scenario C (future climate and current development) and Scenario D (future climate and future development). The future climate scenarios included the wet extreme (wet), the median (mid) and the dry extreme (dry). The raster grids "Ddry.grd", "Dmid.grd" and "Dwet.grd" show predicted hydraulic head for the year 2070 based on projections of future climate and future development. The grids "Ddry-Base.grd", "Dmid-Base.grd" and "Dwet-Base.grd" represent predicted differences between the hydraulic heads produced by Scenario D at 2070, and the modelled spatial distributions of hydraulic head for the year 2010 (Base scenario). The grid "Dmid-Cmid.grd" represents the difference between the 2070 spatial distributions of hydraulic head that were produced by Scenario D (mid) and Scenario C (mid) 'No data' value is 1e30 Cell size is 5000m x 5000m This data and metadata were produced by CSIRO for the Great Artesian Basin Water Resource Assessment. For more information, please refer to Welsh WD, Moore CR, Turnadge CJ, Smith AJ and Barr TM (2012) "Modelling of climate and groundwater development. A technical report to the Australian Government from the CSIRO Great Artesian Basin Water Resource Assessment ". CSIRO Water for a Healthy Country Flagship, Australia. Projection is Albers equal area conic, with central meridian 143 degrees longitude, standard parallels at -21 and -29 degrees latitude and latitude of projection's origin at -25.

Conductivity-depth estimates generated using the 1D Geoscience Australia layered earth inversion algorithm (GA-LEI) have been released to the public domain. The GA-LEI has been shown to provide useful mapping of subsurface conductivity features in the Paterson; for example paleovalleys, unconformities and faults. GA-LEI interpretations have been supported by independent borehole conductivity logs, and lithological drill-hole information. The Geoscience Australia Record 2010/12; Geological and energy implications of the Paterson Province airborne electromagnetic (AEM) survey, Western Australia, summarises the AEM processing, inversion, interpretation and implications for mineral exploration using the 1D GA-LEI. There is an inherent assumption in the GA-LEI algorithm that the earth can be represented by a set of 1D layers, which extend to infinite distance in the horizontal plane. This layered earth assumption has some limitations, and has been demonstrated to create artefacts when applied to heterogeneous 3D geological features. 3D inversion methods can potentially overcome some of the limitations of 1D inversion methods, reducing the artefacts of a 1D earth assumption. 3D inversions require much greater computational resources than 1D methods because they have to solve many large systems of equations. In addition, a large sensitivity matrix is computed, which increases memory requirements, and the process must be repeated for multiple iterations. This computational expense has generally limited the application of 3D inversions to AEM datasets, and restricted its practicality as a general mapping tool. The EMVision® inversion generated by TechnoImaging presents a method of running a 3D inversion, with a runtime comparable to 1D inversion methods. The EMVision® algorithm uses a moving footprint to limit the number of data points needed as input to the inversion at any one location. A background conductivity model is chosen to represent the far-field response of the earth, and the data points within the AEM footprint are treated as anomalies with respect to the background. In 2010, Geoscience Australia decided that a comparison of the GA-LEI with the EMVision® inversion would be useful both for geological interpretation and for assessing the benefits of 3D inversion of AEM. A subset of the regional Paterson AEM dataset around the Kintyre uranium deposit was provided to TechnoImaging to create a 3D inversion using EMVision® software. The data subset was a combination of GA data and data owned by Cameco Corporation and the cost of inversion by TechnoImaging was shared by both parties. Under the terms of the agreement between Cameco Corporation and Geoscience Australia there was a moratorium on the data release until 2012.