The impacts of climate change, including sea level rise and the increased frequency of storm surge events, will adversely affect infrastructure in a significant number of Australian coastal communities. In order to quantify this risk, Geoscience Australia in collaboration with the Department of Climate Change and Energy Efficiency, have undertaken a first-pass national assessment which has identified the extent and value of infrastructure that are potentially vulnerable to impacts of climate change. We have utilised the best available national scale information to assess the vulnerability of Australia's coastal zone to the impacts of climate change. In addition to assessing coastal vulnerability assuming the current population, we also examined the changes in exposure under a range of future population scenarios provided by the Australian Bureau of Statistics. Continuation of the current trend for significant development in the coastal zone increases the number and value of residential buildings potentially vulnerable by 2100. We found that over 270,000 residential buildings are potentially vulnerable to the combined impacts of inundation and recession by 2100. This equates to a replacement value of approximately AUD$72 billion. Nearly 250,000 residential buildings were found to be potentially vulnerable to inundation only, which equates to AUD$64 billion. Queensland and New South Wales have the largest vulnerability (considering both value and number of buildings affected). Nationally, approximately 33,000 km of road and 1,500 km of rail infrastructure are potentially at risk by 2100. These results are influencing policy and adaptation planning decisions made by federal, state and local government.

Severe wind is one of the major natural hazards in Australia. The main contributors to economic loss in Australia are tropical cyclones, thunderstorms and sub-tropical (synoptic) storms. Geoscience Australia's Risk and Impact Analysis Group (RIAG) is developing mathematical models to study a number of natural hazards including wind hazard. This study examines synoptic wind hazard under current and future climate scenarios using RIAG's synoptic wind hazard model. This model can be used in non-cyclonic regions of Australia (Region A in the Australian-New Zealand Wind Loading Standard; AS/NZS 1170.2:2002) which are dominated by synoptic and thunderstorm winds. The methodology to study synoptic wind hazard involves a combination of three models: - a statistical model (ie. a model based on observed data) to quantify wind hazard using extreme value distributions; - a technique to extract and process wind speeds from a high-resolution regional climate model (RCM), which produces gridded hourly 'maximum time-step mean' wind speed and direction fields; and - a Monte Carlo method to generate gust wind speeds from the RCM mean winds. Gust wind speeds are generated by a numerical convolution of the modelled mean wind speed distribution and a distribution of observed 'regional' gust factor. To illustrate the methodology, wind hazard calculations under current and future climate conditions for the Australian state of Tasmania will be presented. The results show increases in synoptic wind hazard in some parts of the state especially at the end of this century.

We develop globally applicable macroseismic Intensity Prediction Equations (IPEs) for earthquakes of moment magnitude MW 5.0 to 7.9 and intensities of degree II and greater for distances less than Rrup 300km. The IPEs are developed for two distance metrics: closest distance to rupture Rrup, and hypocentral distance, Rhyp. The key objective for developing the model based on hypocentral distance in addition to more rigorous and standard measure Rrup is to provide an IPE which can be used in near real-time earthquake response systems anywhere in the world, where information regarding the rupture dimensions of a fault may not be known in the immediate aftermath of the event. We observe that our models, particularly the model for the Rrup distance metric, generally have low median residuals with magnitude and distance. We provide distance-dependent intra-event uncertainties, in addition inter-event bias uncertainty. In particular, we address whether the direct use of IPEs lead to a reduction in overall uncertainties when compared to methods which use a combination of ground-motion prediction equations (GMPEs) and ground-motion to intensity conversion equations (GMICEs). Finally, we derive intensity-based site amplification factors given the predicted intensity and proxy estimates of near-surface shear-wave velocity. However, we find that these amplification factors lead to little, if any significant reduction of intensity residuals. This is in part due to the observation that the median site condition for intensity observations is approximately near the NEHRP site-class CD.

Geoscience Australia (GA) began the development of the National Exposure Information System (NEXIS) in response to COAG reform Commitment 2 'establish a nationally consistent system of data collection, research and analysis to ensure a sound knowledge base on natural disasters and disaster mitigation' (COAG, 2003). It was also recognised as a priority for the development of better models and tools to allocate investment across prevention, preparedness, response and recovery (PPRR) and also to assess the impact of emergencies on the community in the Emergency Management Information Development Plan (Harper, 2006). The NEXIS underpins various activities of risk assessment modelling, critical infrastructure failures, early warning systems and several national priority initiatives. This system will provide consistent and best available information at a national scale (for example, the number and type of buildings, businesses, people, critical infrastructure, and institutions such as schools and hospitals) to understand hazard exposure, at all locations in Australia.

We describe a weighted-average approach for incorporating various types of data (observed peak ground motions and intensities, and estimates from ground motion prediction equations) into the ShakeMap ground motion and intensity mapping framework. This approach represents a fundamental revision of ShakeMap technique, particularly as it pertains to processing ground motion and intensity data. Combining ground motion and intensity data onto composite ShakeMaps proves invaluable for loss calibration of historical events as well as for loss estimation in near-real time applications. In addition, the increased availability of near-real-time macroseismic intensity data, the development of new relationships between intensity and peak ground motions, and new relationships to directly predict intensity from earthquake source information, have facilitated the inclusion of intensity measurements directly into the ShakeMap computations. Our approach allows for the possible combination of all of the following data sources and estimates: 1) nearby observations (ground motion measurements and reported intensities), 2) converted observations from intensity to ground motion (or vice-versa), and 3) estimated peak ground motions from prediction equations (or numerical estimates).

Geoscience Australia has developed a model to assess severe wind hazard for large-scale numerical model-derived grided data. The severe wind modelling approach integrates two models developed at Geoscience Australia: a) A statistical model based on observations which determines return periods (RP) of severe winds using Extreme Value distributions (EVD), and b) A model which extracts mean wind speeds from high resolution numerical models (climate simulations) and generates wind gust from the mean speeds using Monte Carlo simulation (convolution with empirical gust factors) This methodology is particularly suitable for the study of wind hazard over large regions, and is being developed to provide improved spatial information for the Australia/NZ Wind Loading Standard (AS/NZS 1170.2, 2002). The methodology also allows comparison of current and future wind hazard under changing climate conditions. To illustrate the characteristics and capabilities of the methodology, the determination of severe wind hazard for a high-resolution grid encompassing the state of Tasmania (south of the Australian continent) will be presented and discussed, considering both the current and a range of possible future climate conditions (utilising IPCC B1 & A2 emission scenarios).

This paper is an introduction to the two AJEM Special Issues on risk assessment. The role of risk assessment in emergency management in Australia is firmly established and much progress has been made in utilising risk modelling tools and supporting data to develop new information on risk for some hazards. Significant further work is required to reach an understanding of all hazards risks nationally.

Tropical cyclones, thunderstorms and sub-tropical storms can generate extreme winds that can cause significant economic loss. Severe wind is one of the major natural hazards in Australia. In this study, regional return period wind gust hazard (10 metre height over open terrain) is determined using a new methodology developed by Geoscience Australia over the past 3 years. The methodology developed for severe wind hazard (3-second peak gust) involves a combination of 3 models: - A Statistical Model (ie. data-based model) to quantify wind hazard using extreme value distributions. - A Monte Carlo method to calculate severe wind hazard produced by gust wind speeds using results from the Statistical Model. The method generates synthetic wind gust speeds by doing a numerical convolution of mean wind speeds and gust factors. - A high-resolution regional climate model (RCM) which produces gridded hourly 'maximum time-step mean- wind speed and direction fields. Area-averaged measurements from the RCM are 'corrected' for point measurement exposure by calibration with existing measurements. To assess model accuracy severe wind hazard return period levels (50, 100, 200, 500, 1000 and 2000 years) were determined for a number of locations where a long observation record is available. Comparisons are made between observational and RCM-generated return period of gust speeds; and also with the Australian/New Zealand wind loading standards (AS/NZS 1170.2, 2002).

To be completed

The term "Smartline" refers to a GIS line map format which can allow rapid capture of diverse coastal data into a single consistently classified map, which in turn can be readily analysed for many purposes. This format has been used to create a detailed nationally-consistent coastal geomorphic map of Australia, which is currently being used for the National Coastal Vulnerability Assessment (NCVA) as part of the underpinning information for understanding the vulnerability to sea level rise and other climate change influenced hazards such as storm surge. The utility of the Smartline format results from application of a number of key principles. A hierarchical form- and fabric-based (rather than morpho-dynamic) geomorphic classification is used to classify coastal landforms in shore-parallel tidal zones relating to but not necessarily co-incident with the GIS line itself. Together with the use of broad but geomorphically-meaningful classes, this allows Smartline to readily import coastal data from a diversity of differently-classified prior sources into one consistent map. The resulting map can be as spatially detailed as the available data sources allow, and can be used in at least two key ways: Firstly, Smartline can work as a source of consistently classified information which has been distilled out of a diversity of data sources and presented in a simple format from which required information can be rapidly extracted using queries. Given the practical difficulty many coastal planners and managers face in accessing and using the vast amount of primary coastal data now available in Australia, Smartline can provide the means to assimilate and synthesise all this data into more usable forms.