Evidence based disaster management enables decision makers to manage more effectively because it yields a better informed understanding of the situation. When based on evidence, the decision making process delivers more rational, credible and objective disaster management decisions, rather than those influenced by panic. The translation of fundamental data into information and knowledge is critical for decision makers to act and implement the decisions. The evidence from appropriate information helps both tactical and strategic responses to minimise impacts on community and promote recovery. The information requirements of such a system are quite comprehensive in order to estimate the direct and indirect losses; the short and long term social and economic resilience. Disasters may be of rapid onset in nature like earthquakes, tsunamis and blast. Others are slow onset such those associated with gradual climate change. Climate change has become a real challenge for all nations and the early adaptors will reduce risk from threats such as increased strength of tropical cyclones, storm surge inundations, floods and the spread of disease vectors. The Australian Government has recognised the threats and prioritised adaptation as an opportunity to enhance the nation's existing infrastructure and thereby reduce risk. A thorough understanding of the exposure under current and future climate projections is fundamental to this process of future capacity building. The nation's exposure to these increased natural hazards includes all sectors from communities to businesses, services, lifeline utilities and infrastructure. The development of a 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 multi-hazard impacts.

Note: A more recent version of this product is available. This dataset contains the high voltage electricity transmission lines that make up the electricity transmission network in Australia. For government use only. Access through negotiation with Geoscience Australia

Note: A more recent version of this product is available. This point dataset contains the major power stations in Australia including all those that feed into the electricity transmission network.

Disaster management is most effective when it is based on evidence. Evidence-based disaster management means that decision makers are better informed, and the decision making process delivers more rational, credible and objective disaster management outcomes. To achieve this, fundamental data needs to be translated into information and knowledge, before it can be put to use by the decision makers as policy, planning and implementation. Disaster can come in all forms: rapid and destructive like earthquakes and tsunamis, or gradual and destructive like drought and climate change. Tactical and strategic responses need to be based on the appropriate information to minimise impacts on the community and promote subsequent recovery. This implies a comprehensive supply of information, in order to establish the direct and indirect losses, and to establish short and long term social and economic resilience. The development of the National Exposure Information System (NEXIS) is a significant national project being undertaken by Geoscience Australia (GA). NEXIS collects, collates, manages and provides the information required to assess multi-hazard impacts. Exposure information may be defined as a suite of information relevant to all those involved in a natural disaster, including the victims, the emergency services, and the policy and planning instrumentalities.

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.

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.

Manila is one of the world's megacities, and the Greater Metro Manila Area is prone to natural disasters. These events may have devestating consequences for individuals, communities, buildings, infrastructure and economic development. Understanding the risk is essential for implementing Disaster Risk Reduction programs. In partnership with AusAID, Geoscience Australia is providing technical leadership for risk analysis projects in the Asia-Pacific Region. In the Philippines, Geoscience Australia is engaging with Government of the Philippines agencies to deliver the "Enhancing Risk Analysis Capacities for Flood, Tropical Cyclone Severe Wind and Earthquake in the Greater Metro Manila Area" Project.

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).