The National Hazard Impact Risk Service for Tropical Cyclone Event Impact provides information on the potential impact to residential separate houses due to severe winds. The information is derived from Bureau of Meteorology tropical cyclone forecast tracks, in combination with building location and attributes from the National Exposure Information System and vulnerability models to define the level of impact. Impact data is aggregated to Statistical Area Level 1, categorised into five qualitative levels of impact.

Australian Community Climate and Earth-System (ACCESS) Numerical Weather Prediction (NWP) data is made available by the Bureau of Meteorology for registered subscribers such as GA. ACCESS-C3 (City) model is a forecast-only model performed every 6 hours and consists of grid coordinates covering domains around Sydney, Victoria and Tasmania, Brisbane, Perth, Adelaide and Darwin. ACCESS Impact Modelling (ACCESS-IM) System utilise information from ACCESS-NWP on the forecast wind gust speeds ground surface (single-level) at 10 metres, simulated by the ACCESS-C3 model, for the time period of 0-12, 12-24, 24-36, 0-36.

A presentation delivered at the Australia Reinsurance Pool Corporation / Organisation for Economic Co-operation and Development (ARPC/OECD) Terrorism Risk Insurance Conference held in Canberra from 6-7 October 2016. The presentation focusses on GA's work with the ARPC in developing a capability to estimate insured losses due to blast in Australian cities.

The Risk and Impact Analysis Group (RIAG) at Geoscience Australia (GA) in Canberra is a multidisciplinary research team. Their key role is to develop knowledge on the risk from natural and human-caused hazards for input to policy and operational decision makers for the mitigation of risk to Australian communities. The RIAG achieves this through the development of computational methods, models and decision support tools that assess the hazard, vulnerability and risk posed by hazards. The RIAG also works with other agencies to develop and collect information on natural disasters that is essential for developing valid risk models for forecasting the impact of future hazard events. The Group includes hazard experts, numerical modellers, engineers, economists, and GIS specialists. This paper will discuss the risk analysis process used at GA, with a particular focus on the vulnerability component. Earthquake and tsunami risk examples will highlight technical aspects of the work and step through the risk analysis framework that has been adopted. The method being used to develop vulnerability models for the wind and tsunami hazard is an engineering model approach. The method requires a generalised hazard definition, an engineering model of a particular structure, and a costing module to calculate the real cost of repairs. The initial focus will be Australian residential structures. The engineering model is based on the assumption that connection failure is the primary initiator of structural failure in residential structures (as opposed to say, a beam or wall stud failing in bending). It also assumes that component failures can be aggregated up into overall damage scenarios. The engineering model employs a Monte Carlo simulation approach that allows for the incorporation of variability (in connection strengths, building orientation, opening sizes, and key hazard parameters). The engineering model approach also allows the opportunity to investigate mitigation options through strengthening structural components. The multi-hazard risk approach used at GA is a move towards being able to make informed decisions on how to manage the risk from natural hazards. This paper has presented examples of computational natural hazard risk with a particular focus on the development of engineering vulnerability models. Presented at the International Forum on Engineering Decision Making, 12th to 15th Dec, 2007, Port Stephens, NSW.

The extensive electricity transmission network of Queensland is managed by Powerlink and has a significant exposure to both thunderstorms and tropical cyclones. In coastal North Queensland tropical cyclones (TC) dominate the severe wind hazard environment whereas in South East Queensland both storm types contribute significantly. Some parts of Powerlink’s network have high concentrations of older tower assets such as in the Gladstone region which supply electricity for the smelting and refining of aluminium. Other parts provide vital transmission links with limited redundancy between regions of generation in the south to large communities to the north, such as in far-North Queensland. These assets have been developed over many years, to design standards that have progressively changed, and some have been exposed to corrosive environments that are characteristic of the warm and humid coastal tropics. As a consequence of these factors, some assets within the system are particularly vulnerable to severe wind.

Natural disasters provide an invaluable opportunity to capture data for improving our understanding of risk. Observed damage types and their predominance provide useful insights into the factors contributing to building vulnerability and consequential community risk. They also facilitate the appraisal of mitigation measures directed at reducing that risk where it is found to be high. Survey activities that followed the impact of Tropical Cyclone Larry have highlighted the benefits of a co-ordinated survey response to natural hazard impacts. The response to this event involved liaison with local emergency management and the broad participation of recognised wind engineering experts. Survey techniques were refined to achieve a more efficient and comprehensive approach that ensured consistency, utility and transferability of the data for all collaborators. The refined approach proved very successful and may provide a useful model for similar post-disaster exercises directed at earthquake damage. The sudden nature by which earthquakes inflict damage without warning points to having arrangements already established beforehand for the best survey outcomes. Proposals for advancing such preplanning are presented.

Windstorms cause most of the damage to housing in Australia. Population growth is exposing more people and buildings to risks from these wind hazards. Houses and components are currently designed and built to standards aligned with the Building Code of Australia. Regulatory measures including building inspections are meant to ensure acceptable quality of construction. Inspections and post windstorm damage surveys have consistently shown that contemporary houses (post 1980) perform better than older houses (pre 1980) in cyclone and non cyclone areas. However, errors in design and construction found during recent surveys, reduce the resilience of contemporary housing. Geoscience Australia is developing a software tool for assessing the vulnerability of housing, using empirical models, expert opinion, and engineering methods. These models could be used to assess vulnerability of a range of house types and also recommend adaptation measure to account for increases in the intensity of windstorms in Australia.

Australia has a low to moderate seismicity by world standards. However, the seismic risk is significant due to the legacy of older buildings constructed prior to the national implementation of an earthquake building standard in Australia. The 1989 Newcastle and the 2010 Kalgoorlie earthquakes are the most recent Australian earthquakes to cause significant damage to unreinforced masonry (URM) and light timber frame structures and have provided the best opportunities to examine the earthquake vulnerability of these building types. This paper describes the two above mentioned building types with a differentiation of older legacy buildings constructed prior to 1945 to the relatively newer ones constructed after 1945. Furthermore, the paper presents method to utilise the large damage and loss related data (14,000 insurance claims in Newcastle and 400 surveyed buildings in Kalgoorlie) collected from these events to develop empirical vulnerability functions. The method adopted here followed the GEM Empirical Vulnerability Assessment Guidelines which involves preparing of a loss database, selecting an appropriate intensity measure, selecting and applying a suitable statistical approach to develop vulnerability functions and the identification of optimum functions. The adopted method uses a rigorous statistical approach to quantify uncertainty in vulnerability functions and provides an optimum solution based on goodness-of-fit tests. The analysis shows that the URM structures built before 1945 are the most vulnerable to earthquake with post 1945 URM structures being the next most vulnerable. Timber structures appear to be the least vulnerable, with little difference observed in the vulnerability of timber buildings built before or after 1945. Moreover, the older structures (both URM and timber) depict exhibit more scatter in results reflecting greater variation in building vulnerability and performance during earthquakes. The analysis also highlights the importance of collecting high quality damage and loss data which is, not only a fundamental requirement for developing empirical vulnerability functions, and but is also useful in validating analytically derived vulnerability functions. The vulnerability functions developed herein are the first publically available functions for Australian URM and timber structures. They can be used for seismic risk assessment and to focus the rm a basis for development ofing retrofit strategies to reduce the existing earthquake risk.

Australian Community Climate and Earth-System (ACCESS) Numerical Weather Prediction (NWP) data is made available by the Bureau of Meteorology for registered subscribers such as GA. ACCESS-C3 (City) model is a forecast-only model performed every 6 hours and consists of grid coordinates covering domains around Sydney, Victoria and Tasmania, Brisbane, Perth, Adelaide and Darwin. ACCESS Impact Modelling (ACCESS-IM) System utilise information from ACCESS-NWP on the forecast wind gust speeds ground surface (single-level) at 10 metres, simulated by the ACCESS-C3 model, for the time period of 0-12, 12-24, 24-36, 0-36.

This dynamic dataset is composed of data layers representing the potential damage arising from the impacts of Tropical Cyclone (TC) related winds on residential houses. The impacts are determined using information on the forecast track of the TC issued by the Bureau of Meteorology, nationally consistent exposure (residential building) and vulnerability (likely level of damage) information maintained by Geoscience Australia. The tracks are based on the content of Technical Bulletins issued by the Bureau of Meteorology’s Tropical Cyclone Warning Centres every 6 hours for active TCs in the Australian region. As such, information is generated intermittently, depending on the occurrence of TCs. The tracks are a forecast only, so do not include past position information of the TC. Forecasts may extend up to 120 hours (5 days) ahead of the forecast time. A wind field around each track is simulated using Geoscience Australia’s Tropical Cyclone Risk Model (TCRM, https://pid.geoscience.gov.au/dataset/ga/77484). This provides an estimate of the maximum gust wind speed over open, flat terrain (e.g. airports). Local effects such as topography and land cover changes are incorporated via site wind multipliers (https://pid.geoscience.gov.au/dataset/ga/75299), resulting in a 0.2-second, 10-m above ground level wind speed, with a spatial resolution of approximately 30 metres. The impacts are calculated using Geoscience Australia’s HazImp code (https://pid.geoscience.gov.au/dataset/ga/110501), which utilises the National Exposure Information System building data and a suite of wind vulnerability curves to determine the level of damage sustained by individual buildings (a damage index). The damage index values are aggregated to Australian Bureau of Statistics Statistical Area Level 1 regions, and can be assigned a qualitative damage description based on the mean damage index.