Benthic habitats on the continental shelf are strongly influenced by exposure to the effects of surface ocean waves, and tidal, wind and density driven ocean currents. These processes combine to induce a combined flow bed shear stress upon the seabed which can mobilise sediments or directly influence organisms disturbing the benthic environment. Output from a suite of numerical models predicting these oceanic processes have been utilised to compute the combined flow bed shear stresses over the entire Australian continental shelf for an 8-year period (March 1997- February 2005 inclusive). To quantify the relative influence of extreme or catastrophic combined flow bed shear stress events and more frequent events of smaller magnitude, three methods of classifying the oceanographic levels of exposure are presented: 1. A spectral regionalisation method, 2. A method based on the shape of the probability distribution function, and 3. A method which assesses the balance between the amount of work a stress does on the seabed, and the frequency with which it occurs. Significant relationships occur between the three regionalisation maps indicating seabed exposure to oceanographic processes and physical sediment properties (mean grain size and bulk carbonate content), and water depth, particularly when distinction is made between regions dominated by high-frequency (diurnal or semi-diurnal) events and low-frequency (synoptic or annual) events. It is concluded that both magnitude and frequency of combined-flow bed shear stresses must be considered when characterising the benthic environment. The regionalisation outputs of the Australian continental shelf presented in this study are expected to be of benefit to quantifying exposure of seabed habitats on the continental shelf to oceanographic processes in future habitat classification schemes for marine planning and policy procedures.

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.

The Paterson National Geoscience Agreement project is using a number of tools to better understand the time-space evolution of the northwest Paterson Orogen in Western Australia. One of these tools, 3D Geomodeller, is an emerging technology that constructs three-dimensional (3D) volumetric models based on a range of geological information. The Paterson project is using 3D Geomodeller to build geologically-constrained 3D models for the northwest Paterson Orogen. This report documents the model building capability and benefits of 3D Geomodeller and highlights some of the geological insights gained from the model building exercise. The principal benefit of 3D Geomodeller is that it provides geoscientists with a rapid tool for testing multiple working hypotheses. The Cottesloe Syncline district was selected as the focus for a trial of the 3D Geomodeller software. The 3D model was built by members of the Paterson Project, as well as model building specialists within Geoscience Australia. The resultant Cottesloe Syncline model including two dimensional sections, maps and images was exported from 3D GeoModeller and transformed into a Virtual Reality Modelling Language (VRML), enabling a wide audience to view the model using readily available software.

Ross C Brodie Murray Richardson AEM system target resolvability analysis using a Monte Carlo inversion algorithm A reversible-jump Markov chain Monte Carlo inversion is used to generate an ensemble of millions of models that fit the forward response of a geoelectric target. Statistical properties of the ensemble are then used to assess the resolving power of the AEM system. Key words: Monte Carlo, AEM, inversion, resolvability.

In this study, we conducted a simulation experiment to identify robust spatial interpolation methods using samples of seabed mud content in the Geoscience Australian Marine Samples database. Due to data noise associated with the samples, criteria are developed and applied for data quality control. Five factors that affect the accuracy of spatial interpolation were considered: 1) regions; 2) statistical methods; 3) sample densities; 4) searching neighbourhoods; and 5) sample stratification. Bathymetry, distance-to-coast and slope were used as secondary variables. Ten-fold cross-validation was used to assess the prediction accuracy measured using mean absolute error, root mean square error, relative mean absolute error (RMAE) and relative root mean square error. The effects of these factors on the prediction accuracy were analysed using generalised linear models. The prediction accuracy depends on the methods, sample density, sample stratification, search window size, data variation and the study region. No single method performed always superior in all scenarios. Three sub-methods were more accurate than the control (inverse distance squared) in the north and northeast regions respectively; and 12 sub-methods in the southwest region. A combined method, random forest and ordinary kriging (RKrf), is the most robust method based on the accuracy and the visual examination of prediction maps. This method is novel, with a relative mean absolute error (RMAE) up to 17% less than that of the control. The RMAE of the best method is 15% lower in two regions and 30% lower in the remaining region than that of the best methods in the previously published studies, further highlighting the robustness of the methods developed. The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity prediction. The limitations of this study are discussed. A number of suggestions are provided for further studies.

The Fitzoy Estuary is one of several macrotidal estuaries in tropical northern Australia that face ecological change due to agricultural activities in their catchments. The biochemical functioning of such macrotidal estuaries is not well understood in Australia, and there is a pressing need to identify sediment, nutrient and agrochemical pathways, sinks and accumulation rates in these extremely dynamic environments. This is particularly the case in coastal northern Queensland because the impact of water quality changes in rivers resulting from vegetation clearing, changes in land-use and modern agricultural practices are the single greatest threat to the Great Barrier Reef Marine Park. This report includes: 1 Aims and Research questions 2 Study Area 3 Climate and Hydrology 4 Geology 5 Vegetation and land use 6 Methods 7 Sampling strategy 8 Water column observations and samples 9 Bottom sediment properties 10 Core and bottle incubations 11 Data analysis 12 Results 13 Discussion 14 The roll of Keppel Bay in accumulating and redirecting sediment and nutrients from the catchment 15 Sediment biogeochemistry 16 Links between primary production, biogeochemistry and sediment dynamics: A preliminary zonation for Keppel Bay 17 Conclusions

Tsunami inundation models are computationally intensive and require high resolution elevation data in the nearshore and coastal environment. In general this limits their practical application to scenario assessments at discrete communities. This paper explores the use of moderate resolution (250 m) bathymetry data to support computationally cheaper modelling to assess nearshore tsunami hazard. Comparison with high resolution models using best available elevation data demonstrates that moderate resolution models are valid at depths greater than 10 m in areas of relatively low sloping, uniform shelf environments, however in steeper and more complex shelf environments they are only valid to depths of 20 m or greater. In contrast, arrival times show much less sensitivity to resolution. It is demonstrated that modelling using 250 m resolution data can be useful in assisting emergency managers and planners to prioritise communities for more detailed inundation modelling by reducing uncertainty surrounding the effects of shelf morphology on tsunami propagation. However, it is not valid for modelling tsunami inundation.

A weathering intensity index (WII) over the Australian continent has been developed at 100 m resolution using regression models based on airborne gamma-ray spectrometry imagery and the Shuttle Radar Topography Mission (SRTM) elevation data. Airborne gamma-ray spectrometry measures the concentration of three radioelements - potassium (K), thorium (Th) and uranium (U) at the Earth's surface. The total gamma-ray flux (dose) is also calculated based on the weighted additions of the three radioelements. Regolith accounts for over 85% of the Australian land area and has a major influence in determining the composition of surface materials and in controlling hydrological and geomorphological processes. The weathering intensity prediction is based on the integration of two regression models. The first uses relief over landscapes with low gamma-ray emissions and the second incorporates radioelement distributions and relief. The application of a stepwise forward multiple regression for the second model generated a weathering intensity index equation of: WII = 6.751 + -0.851*K + -1.319* Relief + 2.682 * Th/K + -2.590 * Dose. The WII has been developed for erosional landscapes but also has the potential to inform on deposition processes and materials. The WII correlates well with site based geochemical indices and existing regolith mapping. Interpretation of the WII from regional to local scales and its application in providing more reliable and spatially explicit information on regolith properties is described.

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.

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.