Spatial interpolation methods for generating spatially continuous data from point locations of environmental variables are essential for ecosystem management and biodiversity conservation. They can be classified into three groups (Li and Heap 2008): 1) non-geostatistical methods (e.g., inverse distance weighting), 2) geostatistical methods (e.g., ordinary kriging: OK) and 3) combined methods (e.g. regression kriging). Machine learning methods, like random forest (RF) and support vector machine (SVM), have shown their robustness in data mining fields. However, they have not been applied to the spatial prediction of environmental variables (Li and Heap 2008). Given that none of the existing spatial interpolation methods is superior to the others, several questions remain, namely: 1) could machine learning methods be applied to the spatial prediction of environmental variables; 2) how reliable are their predictions; 3) could the combination of these methods with the existing interpolation methods improve the predictions; and 4) what contributes to their accuracy? To address these questions, we conducted a simulation experiment to compare the predictions of several methods for mud content on the southwest Australian marine margin. In this study, we discuss results derived from this experiment, visually examine the spatial predictions, and compare the results with the findings in previous publications. The outcomes of this study have both practical and theoretical importance and can be applied to the spatial prediction of a range of environmental variables for informed decision making in environmental management. This study reveals a new direction in and provides alternative methods for spatial interpolation in environmental sciences.

Geoscience Australia is supporting the exploration and development of offshore oil and gas resources and establishment of Australia's national representative system of marine protected areas through provision of spatial information about the physical and biological character of the seabed. Central to this approach is prediction of Australia's seabed biodiversity from spatially continuous data of physical seabed properties. However, information for these properties is usually collected at sparsely-distributed discrete locations, particularly in the deep ocean. Thus, methods for generating spatially continuous information from point samples become essential tools. Such methods are, however, often data- or even variable- specific and it is difficult to select an appropriate method for any given dataset. Improving the accuracy of these physical data for biodiversity prediction, by searching for the most robust spatial interpolation methods to predict physical seabed properties, is essential to better inform resource management practises. In this regard, we conducted a simulation experiment to compare the performance of statistical and mathematical methods for spatial interpolation using samples of seabed mud content across the Australian margin. Five factors that affect the accuracy of spatial interpolation were considered: 1) region; 2) statistical method; 3) sample density; 4) searching neighbourhood; and 5) sample stratification by geomorphic provinces. Bathymetry, distance-to-coast and slope were used as secondary variables. In this study, we only report the results of the comparison of 14 methods (37 sub-methods) using samples of seabed mud content with five levels of sample density across the southwest Australian margin. The results of the simulation experiment can be applied to spatial data modelling of various physical parameters in different disciplines and have application to a variety of resource management applications for Australia's marine region.

The aim of this document is to provide the Fire and Emergency Services Authority of Western Australia (FESA WA) with a preliminary assessment of tsunami impact to Mandurah. This follows preliminary assessments to six South West (SW) Western Australian (WA) communities previously modelled (Carnarvon, Geraldton, Fremantle, Rockingham, Bunbury and Busselton) to underpin evidence-based tsunami planning and preparation activities. This also follows the preliminary assessment of tsunami impact for six North West Shelf (NW Shelf) communities that used deep water tsunami hazard information from the probabilistic tsunami hazard assessment for WA. This hazard assessment has now been updated and completed at a national level. The current impact assessment draws tsunami hazard information from the more recent national tsunami hazard assessment that describes the probability of a given tsunami wave amplitude at the 100 m contour being exceeded.

The aim of this project is to equip ANUGA with a storm surge capability in partnership with the Department of Planning Western Australia (DoP), take steps to validate the methodology and provide a case study to DoP in the form of a storm surge scenario for Bunbury. The developed capability will provide a mechanism whereby DoP can investigate mitigation options for a range of hydrodynamic hazards.

The information within this document and associated DVD is intended to assist emergency managers in tsunami planning and preparation activities. The Attorney General's Department (AGD) has supported Geoscience Australia (GA) in developing a range of products to support the understanding of tsunami hazard through the Australian Tsunami Warning System Project. The work reported here is intended to further build the capacity of the Tasmanian State Government in developing inundation models for prioritised locations.

The information within this document and associated DVD is intended to assist emergency managers in tsunami planning and preparation activities. The Attorney General's Department (AGD) has supported Geoscience Australia (GA) in developing a range of products to support the understanding of tsunami hazard through the Australian Tsunami Warning System Project. The work reported here is intended to further build the capacity of the QLD State Government in developing inundation models for prioritised locations. Internally stored data /nas/cds/internal/hazard_events/sudden_onset_hazards/tsunami_inundation/gold_coast/gold_coast_tsunami_scenario_2009

The short historical record of tropical cyclone activity in the Australian region is insufficient for estimating return period wind speeds at long return periods (greater than 100 years). Utilising the auto-correlated nature of tropical cyclone behaviour (forward speed and direction, intensity and size), Geoscience Australia has developed a statistical-parametric model of tropical cyclone behaviour to generate synthetic event sets that are statistically similar to the historical record. The track model is auto-regressive, with lag-1 auto-regression used for forward speed and bearing, and lag-2 auto-regression applied to the intensity and size characteristics. Applying a parametric wind field and a linear boundary layer model to the synthetic tropical cyclone tracks allows users to generate synthetic wind swaths, and in turn fit extreme value distributions to evaluate return period wind speeds spatially. The model has been applied to evaluate severe wind hazard across Australia and neighbouring regions. In conjunction with statistical models of synoptic (mid-latitude storms) and thunderstorm wind hazard, we have been able to generate a national assessment of severe wind hazard, which is comparable to existing wind loading design standards. Using tropical cyclone-like vortex tracks directly detected from regional climate models, it is also possible to project cyclonic wind hazard into future climate conditions, accounting for both changes in frequency and intensity of tropical cyclones.

Robust methods for generating spatially continuous data from point locations of physical seabed properties are essential for accurate biodiversity prediction. For many national-scale applications, spatially continuous seabed sediment data are typically derived from sparsely and unevenly distributed point locations, particularly in the deep ocean due to the expense and practical limitations of acquiring samples. Methods for deriving spatially continuous data are usually data- and variable-specific making it difficult to select an appropriate method for any given physical seabed property. To improve the spatial modelling of physical seabed properties, this study compared the results of a variety of methods for deriving spatially continuous mud content data for the southwest margin of Australia (523,400 km2) based on 177 sparsely and unevenly distributed point samples. For some methods, secondary variables were also used in the analysis, including: bathymetry, distance-to-coast, seabed slope, and geomorphic province (i.e., shelf, slope, etc.). Effects of sample density were also investigated. The predictive performance of the methods was assessed using a 10-fold cross validation and visual examination. A combined method (random forest and ordinary kriging: RFrf) proved the most accurate method, with an RMAE up to 17% less than the control. No threshold sample density was detected; as sample density increased so did the accuracy of the method. The RMAE of the most accurate method is about 30% lower than that of the best methods in previous publications, further highlighting the robustness of the method developed in this study. The results of this study show that significant improvements in the accuracy of the spatially continuous seabed properties can be achieved through the application of an appropriate interpolation method. The outcomes of this study can be applied to the modelling of a wide range of physical properties for improved marine biodiversity prediction.

Geoscience Australia (GA) has an active research interest in using multibeam bathymetry, backscatter data and their derivatives together with geophysical data, sediment samples, biological specimens and underwater video/still footage to create seabed habitat maps. This allows GA to provide spatial information about the physical and biological character of the seabed to support management of the marine estate. The main advantage of using multibeam systems over other techniques is that they provide spatially continuous maps that can be used to relate to physical samples and video observations. Here we present results of a study that aims to reliably and repeatedly delineate hard and soft seabed substrates using bathymetry, backscatter and their derivatives. Two independent approaches were employed: (1) un-supervised classification-based, using a two stage clustering method; and (2) supervised prediction-based, using the Random Forest method. Data for the analysis were collected by Geoscience Australia and the Australian Institute of Marine Science (AIMS) over two consecutive surveys in 2009 & 2010 in eastern Joseph Bonaparte Gulf using the AIMS Research Vessel Solander. The results indicate that the approaches developed are robust and reliable because of their overall classification accuracies (78% and 87% respectively). They are consistent with the current state of knowledge on geoacoustics i.e. the most 'acoustically hard' substrates are dominated by hard-grounds and relatively coarse seabed sediments, and the most 'acoustically soft' substrates are associated with finer sediments. Patterns associated with geomorphic facies and biological categories are also observed. For instance, 'hard' classes are mostly found on banks and are always associated with mixed sponge and coral gardens whereas 'soft' classes mainly occur in valleys where fine-grained sediments are concentrated. These results demonstrate the utility of acoustic data to broadly and objectively characterise the seabed substrate and thereby inform our understanding of the distribution of key habitat types.

This folder contains WindRiskTech data used in preliminary stages of the National Wind Risk Assessment. The data are synthetic TC event sets, generated by a statistical-dynamical model of TCs that can be applied to general circulation models to provide projections of TC activity. Output from two GCMs is available here - the NCAR CCSM3 and the GFDL CM2.1 model. For each, there are a number of scenarios (based on the SRES scenarios from AR4 and previous IPCC reports) and time periods (the time periods are not the same for the A1B scenario). For each mode, scenario and time period, the data are a set of 1000 TC track files in tab-delimited format contained in the huur.zip files in each sub-folder. The output folder contains the output of running TCRM (pre-2011 version) on each of the datasets.