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.

Imagine you are an incident controller sitting in front of a computer screen that is showing you where a fire that's just started is likely to head. Not just that, but also what houses and other structures in the fire's path are likely to burn, and even the number and type of people living in the area - children, adults, elderly. In addition imagine that you can quantify the uncertainty in both the fire weather and also the state of the vegetation so as to deliver a range of simulations relating to the expected firespread which allow the incident controller to address 'what if' scenarios. Think of the advantages of such a program in making speedy, accurate decisions about where best to send fire trucks and fire-suppression aircraft; in being able to issue timely, locality-specific warning messages; in judging whether this fire will become so bad that it might warrant recommending not only an early, orderly evacuation of communities in its way, but also identifying the least risky roads for people to get to safety. A computer program that will not only be able to help with all this and more in a fire, but will also be capable of use at any time in identifying what structures, streets and communities would be at risk should a fire occur, enabling those at risk to undertake remedial work around their properties in advance to make them better fire-ready. This will be achieved by building up a library of possible / credible fire impact scenarios based on the knowledge of observed (historical) severe fire weather conditions as well as vegetation information (fuel type/amount/moisture).

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 National Major Dam Walls dataset presents the spatial locations; in point and polygon format, of all known major dam walls within Australia.

The seismicity of the Australian continent is low to moderate by world standards. However, the seismic risk is much higher for some types of Australian infrastructure. The legacy of older unreinforced masonry buildings, in particular, may contribute disproportionately to community risk. At 8:17am on the 20th April a Mw 5.0 earthquake shook Kalgoorlie. The resultant ground motion was found to vary markedly across the town with the older masonry building stock in the suburb of Boulder experiencing a greater shaking intensity than the corresponding vintage of buildings in the Kalgoorlie business district 4km away. The event has provided the best opportunity to examine the earthquake vulnerability of Australian buildings since the Newcastle Earthquake of 1989. This paper describes the event and the staged collaborative survey activity that followed. The initial reconnaissance team of two specialists captured street-view imagery of 12,000 buildings within Kalgoorlie using a vehicle mounted camera array developed by Geoscience Australia. This information subsequently informed a systematic population based building survey using PDA data collection units. The work was performed by a team of nine from the University of Adelaide, the University of Melbourne and Geoscience Australia. This paper describes the preliminary findings of the work and outlines proposed future research.

In order to understand the effects a natural or man-made disaster could have on a community we need to know as much as we can about the people and buildings that occupy that area. This includes information about: People: how many people will be affected and where they live Buildings: the type of construction materials used, the number of storeys, and age all contribute to how a building withstands damage Cost : how much will it cost to rebuild a house or replace contents if damaged This information is used to not only investigate physical impacts of a disaster, but also forms base information that is needed to help inform the socio-economic impacts, such as loss to the business community when impacted by severe cyclonic wind storms. This information is used when calculating the risk from natural and man-made disasters in order to inform policy and operational decision makers of the impact on Australian communities. The National Exposure Information System (NEXIS) aims to capture this information to create up-to-date aggregated exposure data based upon building level for all residential, commercial and industrial building in Australia. Geoscience Australia (GA) embarked on the development of the National Exposure Information System (NEXIS) project in response to the Council of Australian Governments (COAG) reform commitment on Australia's ability to manage natural disasters and other emergencies. The COAG commitment was for the establishment of a nationally consistent system of data collection, research and analysis to ensure a sound knowledge base on natural disasters and disaster mitigation - (DOTARS 2002). NEXIS is also an important component for improving several projects of national interest within Geoscience Australia (GA). These include the Disaster Resilience Advice Information (DRAI), Climate hazards and Risk Section (CHRS) and the Vulnerability, Resilience and Mitigation (VRM) which investigate natural and man-made risks and their impacts on the community. NEXIS information is available at Local Government Are (LGA) & Statistical Area Level 2 (SA2)

Data package relates to tsunami modelling outputs that were used for the Catastrophic Working Group. This data relates is the underlying model development.

Power point presentation given to a meeting of earthquake hazard model stakeholders, in Sydney on July 22nd 2008.

This paper discusses two of the key inputs used to produce the draft National Earthquake Hazard Map for Australia: 1) the earthquake catalogue and 2) the ground-motion prediction equations (GMPEs). The composite catalogue used draws upon information from three key catalogues for Australian and regional earthquakes; a catalogue of Australian earthquakes provided by Gary Gibson, Geoscience Australia's QUAKES, and the International Seismological Centre. A complex logic is then applied to select preferred location and magnitude of earthquakes depending on spatial and temporal criteria. Because disparate local magnitude equations were used throughout Australia, we performed first order magnitude corrections to standardise magnitude estimates to be consistent with the attenuation factors defined by contemporary local magnitude ML formulae. While most earthquake magnitudes do not change significantly, our methodology can result in reductions of up to one magnitude unit in certain cases. Subsequent ML-MW (moment magnitude) corrections were applied. The catalogue was declustered using a magnitude dependent spatio-temporal filter. Previously identified blasts were removed and a time-of-day filter was developed to further deblast the catalogue. Secondly, a suite of candidate GMPEs were systematically tested against 5% damped response spectra recorded from Australian earthquakes in eastern and Western Australia, respectively. Since many GMPEs are developed for earthquakes larger than approximately MW 5.0, much of the data recorded in Australia is below the magnitude threshold prescribed by these equations. Nevertheless, where necessary, we extrapolate these equations to lower magnitudes to test the general applicability of the GMPEs for different source zones across Australia. The relative weights of the GMPEs for the draft national hazard model were initially determined objectively by the authors using these analyses as a basis. Final GMPE weights will be assigned through consultation with key stakeholders through the AEES.

11-5519 Metropolitan Manilla (Philippines). Philippine GIS data-sets should arrive from the source on the 15th of July, 2011. GAV will process the data, and produce a short movie. The movie will reveal the 17 town halls of the greater metro Manilla; and outline the fault line, as well as earthquake affected areas, flood affected areas and cyclone affected areas. This movie is for the Philippine Govt. via Ausaide, and will include photographs of Philippine nationals assisting in disaster reduction work. The aquired data-sets will be stored on the GA data store, where access can be gained through communication with Luke Peel - GEMD National Geographic Information Section, Geoscience australia.