Large research initiatives such as the Global Earthquake Model (GEM) or the Seismic HAzard haRmonization in Europe (SHARE) projects concentrate a great collaborative effort for defining a global standard for seismic hazard estimations. In this context, there is an increasing need for identifying ground motion prediction equations (GMPEs) that can be applied at both global and regional scale. With increasing amounts of strong motion records which are now available worldwide, observational data can provide a valuable resource to tackle this question. Using the global dataset of Allen and Wald (2009), we evaluate the ability of fifteen GMPEs for active shallow crustal regions to predict ground-motion in California, Japan, Europe and Middle East, Italy and Turkey. Adopting the approach of Scherbaum et al. (2009), we rank these GMPEs according to their likelihood of having generated the data. In particular, we estimate how strongly data support or reject the models with respect to the state of non-informativeness defined by a uniform weighting. Such rankings derived from this particular global dataset enable us to determine conditions in terms of magnitudes and distances under which a model could be applied in its main region of derivation but also in other regions. In the ranking process, we particularly focus on the influence of the distribution of the testing dataset compared to the GMPE's native dataset. One of the results of this study is that some non-indigenous models present a high degree of consistency with the data from a target region. Two models in particular demonstrated a strong power of geographically wide applicability in different geographic regions with respect to the testing dataset: the models of Akkar and Bommer (2010) and Chiou et al. (2010).

An increase in the frequency and intensity of storms, coastal flooding, and spread of disease as a result of projected climate change and sea-level rise is likely to damage built environments and adversely affect a significant proportion of Australia's population. Understanding the assets at risk from climate change hazards is critical to the formulation of adaptation responses and early action is likely to be the most cost effective approach to managing the risk. Understanding the level of exposure of assets, such as buildings, lifeline utilities and infrastructure, under current and future climate projections is fundamental to this process. The National Exposure Information System (NEXIS) is a significant national capacity building task being undertaken by Geoscience Australia (GA). NEXIS is collecting, collating, managing and providing the exposure information required to assess climate change impacts. It provides residential, business and infrastructure exposure information derived from several fundamental datasets. NEXIS is also expanding to include institutions (such educational, health, emergency, government and community buildings) and lifeline support infrastructure exposure. It provides spatial exposure data in GIS format at a building level and is often provided to clients for an area of interest. It is also designed to predict future exposure for climate change impact analysis. NEXIS is currently sourcing more specific datasets from various data custodians including state and local governments along with private data providers. NEXIS has been utilised in various climate change impact projects undertaken by CSIRO, the Department of Climate Change (DCC), the Department of Environment, Water, Heritage and the Arts (DEWHA), and several universities. Examples of these projects will be outlined during the presentation.

Geoscience Australia (GA) is currently undertaking a process of revising the Australian National Earthquake Hazard Map using modern methods and an updated catalogue of Australian earthquakes. This map is a key component of Australia's earthquake loading standard, AS1170.4. Here we present an overview of work being undertaken within the GA Earthquake Hazard Project towards delivery of the next generation earthquake hazard map. Knowledge of the recurrence and magnitude (including maximum magnitude) of historic and pre-historic earthquakes is fundamental to any Probabilistic Seismic Hazard Assessment (PSHA). Palaeoseismological investigation of neotectonic features observed in the Australian landscape has contributed to the development of a Neotectonic Domains model which describes the variation in large intraplate earthquake recurrence behaviour across the country. Analysis of fault data from each domain suggests that maximum magnitude earthquakes of MW 7.0-7.5±0.2 can occur anywhere across the continent. In addition to gathering information on the pre-historic record, more rigorous statistical analyses of the spatial distribution of the historic catalogue are also being undertaken. Earthquake magnitudes in Australian catalogues were determined using disparate magnitude formulae, with many local magnitudes determined using Richter attenuation coefficients prior to about 1990. Consequently, efforts are underway to standardise magnitudes for specific regions and temporal periods, and to convert all earthquakes in the catalogue to moment magnitude. Finally, we will review the general procedure for updating the national earthquake hazard map, including consideration of Australian-specific ground-motion prediction equations. We will also examine the sensitivity of hazard estimates to the assumptions of certain model components in the hazard assessment.

As part of the Climate Futures for Tasmania project (CFT) Geoscience Australia's Risk and Impact Analysis Group (RIAG) is conducting a severe wind hazard assessment for Tasmania under current climate conditions as well as two future climate scenarios. The assessment uses climate-simulated data generated by a high resolution regional model. A poster presented to this workshop shows the main results of the project [1]; a brief description of the methodology developed for the project is discussed in a paper also presented to this workshop [2]. In this paper three possible sources of error in the calculation of the severe wind hazard (using the methodology discussed in [2]) will be examined and recommendations on ways to improve the model results will be provided.

Exposure refers to the elements at risk which may be subjected to the impact of severe hazards within a defined geographic area or region. These elements include the built environment, i.e buildings, infrastructure services and utilities, and also population and business activity. Geoscience Australia (GA) is developing the National Exposure Information System (NEXIS) as a national capability to provide an exposure profile to underpin analysis of natural hazards; potential disaster footprints, risk assessments and climate change adaptation research. The NEXIS capability enables modelling to gain a greater understanding of the impact and risk exposure to these events. The information is used to inform evidence based decision making and future planning to aid in the prevention, preparedness, response and recovery to severe hazard events and climate change adaptation. The current NEXIS database provides exposure profile on building type, building construction materials (roof and wall), number of floors, floor area, year built and population demographics, business activity (turnover) and employee numbers. NEXIS is a demonstrated capability used in response to Tropical Cyclone Yasi, Victoria Bushfires, Queensland Floods and other recent national disaster events. The database also provides input data for use with the Earthquake Risk Model (EQRM) and Tropical Cyclone Risk Model (TCRM) to estimate direct and indirect losses to the built environment and possible population casualities. Further development of the database is planned to incorporate infrastructure and facilities data to enhance the capability and availability of nationally consistent data and exposure information.

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 and develop suitable adaptation strategies, the Department of Climate Change and Energy Efficiency (DCCEE) commissioned the National Coastal Vulnerability Assessment (NCVA). With contributions from Geoscience Australia (GA) and the University of Tasmania, this first-pass national assessment has identified the extent and value of infrastructure that are potentially vulnerable to impacts of climate change. A number of fundamental national scale datasets underpinned the NCVA. A mid-resolution digital elevation model was used to model a series of sea level rise projections incorporating 1 in 100 year storm-tide estimates where available. The model outputs were overlain with a national coastal geomorphology dataset, titled the Smartline. The Smartline identified coastal landforms that are potentially unstable and may recede under the influence of rising sea level. These datasets were then overlain with Geoscience Australia's National Exposure Information System (NEXIS) to quantify the number and value of infrastructure elements (including residential and commercial buildings, roads and rail) potentially vulnerable to a range of sea-level rise and recession estimates for the year 2100.

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 and develop suitable adaptation strategies, the Department of Climate Change and Energy Efficiency (DCCEE) commissioned the National Coastal Vulnerability Assessment (NCVA). With contributions from Geoscience Australia (GA) and the University of Tasmania, this first-pass national assessment has identified the extent and value of infrastructure that is potentially vulnerable to impacts of climate change. In addition, the NCVA examined the changes in exposure under a range of future population scenarios. The NCVA was underpinned by a number of fundamental national scale datasets; a mid-resolution digital elevation model (DEM) used to model a series of sea level rise projections incorporating 1 in 100 year storm-tide estimates where available; the 'Smartline' (nationa; coastal geomorphology dataset) identified coastal landforms that are potentially unstable and may recede with the influence of rising sea level. The inundation outputs were then overlain with GA's National Exposure Information System to quantify the number and value of infrastructure elements (including residential and commercial buildings, roads and rail) potentially vulnerable to a range of sea-level rise and recession estimates for the year 2100.

The sensitivity of the Jaiswal and Wald (Earthq. Spectra, 2010) empirical earthquake fatality model is evaluated relative to the model space for a suite of macroseismic intensity prediction methods. The relative difference between intensity prediction methods is shown through the use of self-organizing maps to visualize high-dimensional ground shaking data in a two-dimensional space. Among all the macroseismic intensity prediction methods evaluated, there is significant variability in the resulting loss estimates for an earthquake of given source parameters with losses being most sensitive to those intensity models that predict high near-source ground shaking. Because the empirical fatality models evaluated herein are based on a consistent suite of ground-motion model inputs, application of the fatality models with other intensity prediction methods may result in undesirable outcomes. Consequently, it is recommend that empirical loss models be calibrated directly with hazard inputs used in the proposed loss assessment methodology.

Climate change is expected to exacerbate a range of natural hazards in Australia leading to more severe community impacts in the future. There is a need to adapt to a changing hazard environment and increasing community exposure in regions most likely influenced by climate change. Through this paper GA develops a methodology for projecting Australian communities in a spatial sense into the future. The application of this methodology is demonstrated in a case study. In order to address the fact that the impacts of climate change are expected to be more evident in the second half of this century, this model was to extend beyond the 30 year limitation of finer scale population projections, dwelling projections and development plans.

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.