The Tropical Cyclone Risk Model (TCRM) is a statistical-parametric model of tropical cyclone behaviour and effects. A statistical model is used to generate synthetic tropical cyclone events. This is then combined with a parametric wind field model to produce estimates of cyclonic wind hazard.

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 NSW State Government in developing inundation models for prioritised locations.

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.

Geoscience Australia's GEOMACS model was utilised to produce hindcast hourly time series of bed shear stress on the Australian continental shelf on a 0.1 degree grid covering the period March 1997 to February 2008 (inclusive). The effective depth range of the model output is approximately 20 - 150 m (see 'Data Quality Attribute Accuracy' below). The hindcast data represents the combined contribution to the bed shear stress by waves, tides, wind and density-driven circulation. The stability of the seabed sediment surface, which is controlled by seabed shear stress, is likely to influence benthic community structure and species diversity. There are 8 grids in the dataset: geomacs_excee, geomacs_gmean, geomacs_qua25, geomacs_qua50, geomacs_qua75, geomacs_range, geomacs_ratio, and geomacs_tmean. Please see the metadata for further information.

This final paper for the session presents the results of the new draft earthquake hazard assessment for Australia and compares them to the previous AS1170.4 hazard values. Draft hazard maps will be presented for several spectral periods (0.0, 0.2 and 1.0 s) at multiple return periods (500, 2500 and 10,000 years). These maps will be compared with both the current earthquake hazard used in AS1170.4 and with other assessments of earthquake hazard in Australia. In general the hazard in the draft map is higher in the western cratonic parts of Australia than it is in the eastern non-cratonic parts of Australia. Where regional source zones are included, peaks in hazard values in the map are generally comparable to those in the current AS1170.4 map. When seismicity 'hotspot zones are included, as described in the previous paper, several of them produce much higher hazard peaks than any in the AS1170.4 map. However, such hotspots do not affect as large an area as many of those in the current AS1170.4 map. Finally, hazard curves for different cities will also be presented and compared to those predicted by the method outlined in AS1170.4.

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 communiteis. This study explores teh use of moderate resolution (250 m) bathymetry data to support computationally cheaper modelling to assess nearshore tsunami hazard. Comparison with high ersolution models using best available elevation data demonstrates that moderate resolution models are valid (errors in waveheight < 20%) at depths greater than 10m in areas of relatively low sloping, uniform shelf environments. However in steeper and more complex shelf environments they are only valid at depths of 20 m or greater. Modelled arrival times show much less sensitivity to data resolution compared with wave heights and current velocities. It is demonstrated that modelling using 250 m resoltuion data can be useful in assisting emergency managers and planners to prioritse communities for more detailed inundation modelling by reducing uncertainty surrounding the effects of shelf morphology on tsunami propagaion. However, it is not valid for modelling tsunami inundation. Further research is needed to define minimum elevation data requirements for modelling inundation and inform decisions to undertake acquisition of high quality elevaiton data collection.

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 2004 Indian Ocean Tsunami raised the importance of tsunami as a significant emergency management issue in Australia. The Australian government responded by initiating a range of measures to help safeguard Australia from tsunami, in particular the Australian Tsunami Warning System (ATWS). In addition it is supporting fundamental research into understanding the tsunami risk to Australian communities. The Risk and Impact Analysis Group (RIAG) of Geoscience Australia achieves this through the development of computational methods, models and decision support tools for use in assessing the impact and risk posed by hazards. Together with support from Emergency Management Australia, it is developing a national tsunami hazard map based on earthquakes generated from the subduction zones surrounding Australia. These studies have highlighted sections of the coastline that appear vulnerable to events of this type. The risk is determined by the likelihood of the event and the resultant impact. Modelling the impacts from tsunami events is a complex task. The computer model ANUGA is used to simulate the propagation of a tsunami toward the coast and estimate the level of damage. A simplification is obtained by taking a hybrid approach where two models are combined: relatively simple and fast models are used to simulate the tsunami event and wave propagation through open water, while the impact from tsunami inundation is simulated with a more complex model. A critical requirement for reliable modelling is an accurate representation of the earth's surface that extends from the open ocean through the inter-tidal zone into the onshore areas. However, elevation data may come from a number of sources and will have a range of reliability.

The major tsunamis of the last few years in the southern hemisphere have raised awareness of the possibility of potentially damaging tsunami to Australia and countries in the Southwest Pacific region. Here we present a probabilistic hazard assessment for Australia and for the SOPAC countries in the Southwest Pacific for tsunami generated by subduction zone earthquakes. To conduct a probabilistic tsunami hazard assessment, we first need to estimate the likelihood of a tsunamigeneic earthquake occurring. Here we will discuss and present our method of estimate the likely return period a major megathrust earthquake on each of the subduction zones surrounding the Pacific. Our method is based on the global rate of occurrence of such events and the rate of convergence and geometry of each particular subduction zone. This allows us to create a synthetic catalogue of possible megathrust earthquakes in the region with associated probabilities for each event. To calculate the resulting tsunami for each event we create a library of "unit source" tsunami for a set of 100km x 50km unit sources along each subduction zone. For each unit source, we calculate the sea floor deformation by modelling the slip along the unit source as a dislocation in a stratified, linear elastic half-space. This sea floor deformation is then fed into a tsunami propagation model to calculate the wave height off the coast for each unit source. Our propagation model uses a staggered grid, finite different scheme to solve the linear, shallow water wave equations for tsunami propagation. The tsunami from any earthquake in the synthetic catalogue can then be quickly calculated by summing the unit source tsunami from all the unit sources that fall within the rupture zone of the earthquake. The results of these calculations can then be combined with our estimate of the probability of the earthquake to produce hazard maps showing (for example) the probability of a tsunami exceeding a given height offshore from a given stretch of coastline. These hazard maps can then be used to guide emergency managers to focus their planning efforts on regions and countries which have the greatest likelihood of producing a catastrophic tsunami.

The quality and type of elevation data used in tsunami inundation models can lead to large variations in the estimated inundation extent and tsunami flow depths and speeds. In order to give confidence to those who use inundation maps, such as emergency managers and spatial planners, standards and guidelines need to be developed and adhered to. However, at present there are no guidelines for the use of different elevation data types in inundation modelling. One reason for this is that there are many types of elevation data that differ in vertical accuracy, spatial resolution, availability and expense; however the differences in output from inundation models using different elevation data types in different environments are largely unknown. This study involved simulating tsunami inundation scenarios for three sites in Indonesia, of which the results for one of these, Padang, is reported here. Models were simulated using several different remotely-sensed elevation data types, including LiDAR, IFSAR, ASTER and SRTM. Model outputs were compared for each data type, including inundation extent, maximum inundation depth and maximum flow speed, as well as computational run-times. While in some cases, inundation extents do not differ greatly, maximum depths can vary substantially, which can lead to vastly different estimates of impact and loss. The results of this study will be critical in informing tsunami scientists and emergency managers of the acceptable resolution and accuracy of elevation data for inundation modelling and subsequently, the development of elevation data standards for inundation modelling in Indonesia.