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  • 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.

  • The Regional Tropical Cyclone Hazard for Infrastructure Adaptation to Climate Change project aims to provide improved estimates of tropical cyclone wind hazard in current and future climates, for use in adaptation strategies such as wind speed-based building design criteria. The overarching goal is to make practical recommendations regarding the effect of climate change on tropical cyclones. This is most effectively achieved through evaluating the effect of climate change on extreme return period wind speeds (or severe wind hazard) across tropical Australia. In this manner, the combined effects of changes in frequency, intensity and spatial distribution of tropical cyclone events are integrated into a single quantity. Return period values are used widely in building design standards, and so represent an excellent way of informing adaptation decisions. Preceding components of the project evaluated the performance of existing general circulation models to simulate aspects of the climate important for tropical cyclones. Downscaling methods were applied to these models to create climatological simulations of tropical cyclones for input into Geoscience Australia's statistical-parametric tropical cyclone model. This, in turn, provided new estimates of severe wind hazard in both current and future climates, which may be used to make recommendations for adaptation strategies on a regional basis. Achieving this goal has required a close collaboration between the University of Melbourne, CSIRO Marine and Atmospheric Research (CMAR) and Geoscience Australia. Analysis of the general circulation models and downscaling was undertaken by University of Melbourne. The downscaling was achieved using CMAR's Conformal-Cubic Atmospheric Model (CCAM). This report details the approach used by Geoscience Australia to evaluate severe wind hazard using statistical models, and analyses the effect of climate change on severe wind hazard.

  • This document describes opportunities for supporting the Philippines CSCAND agencies to enhance their capacity to assess the risk and impact from natural hazards based on an assessment of current gaps. The CSCAND agencies include the Mines & Geosciences Bureau, the Philippine Institute of Volcanology and Seismology, Philippine Atmospheric, the Geophysical and Astronomical Services Administration, the National Mapping and Resource Information Agency, and the Office of Civil Defence. It is important to note that efforts to assess natural hazard risk are only one mechanism by which the CSCAND agencies support the reduction of disaster risk in the Philippines and that this paper covers only a part of the disaster risk reduction activity spectrum.

  • A comprehensive earthquake impact assessment requires an exposure database with attributes that describe the distribution and vulnerability of buildings in the region of interest. The compilation of such a detailed database will require years to develop for a moderate-sized city, let alone on a national scale. To hasten this database development in the Philippines, a strategy has been employed to involve as many stakeholders/organizations as possible and equip them with a standardized tool for data collection and management. The best organizations to tap are the local government units (LGUs) since they have better knowledge of their respective area of responsibilities and have a greater interest in the use of the database. Such a tool is being developed by PHIVOLCS-DOST and Geoscience Australia. Since there are about 1,495 towns and cities in the country with varying financial capacities, this tool should involve the use of affordable hardware and software. It should work on ordinary hardware, such as an ordinary light laptop or a netbook that can easily be acquired by these LGUs. The hardware can be connected to a GPS and a digital camera to simultaneously capture images of structures and their location. The system uses an open source database system for encoding the building attributes and parameters. A user-friendly GUI with a simplified drop-down menu, containing building classification schema, developed in consultation with local engineers, is utilised in this system. The resulting national database is integrated by PHIVOLCS-DOST and forms part of the Rapid Earthquake Damage Assessment System (REDAS), a hazard simulation tool that is also made available freely to partner local government units.

  • 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.

  • 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).

  • 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).

  • The impacts of climate change on sea level rise (SLR) will adversely affect infrastructure in a significant number of Australian coastal communities. A first-pass national assessment has identified the extent and value of infrastructure potentially exposed to impacts from future climate by utilizing a number of fundamental national scale datasets. A mid-resolution digital elevation model was used to model a series of SLR projections incorporating 100 year return-period storm-tide estimates where available (maximum tidal range otherwise). The modeled inundation zones were overlaid with a national coastal geomorphology dataset, titled the Smartline, which identified coastal landforms that are potentially unstable 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 coastal recession estimates for the year 2100. In addition, we examined the changes in exposure under a range of future Australian Bureau of Statistics population scenarios. We found that over 270,000 residential buildings are potentially vulnerable to the combined impacts of inundation and recession by 2100 (replacement value of approximately $A72 billion). Nearly 250,000 residential buildings were found to be potentially vulnerable to inundation only ($A64 billion). Queensland and New South Wales have the largest vulnerability considering both value of infrastructure and the number of buildings affected. Nationally, approximately 33,000 km of road and 1,500 km of rail infrastructure are potentially at risk by 2100.

  • 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.

  • Effective disaster risk reduction is founded on knowledge of the underlying risk. While methods and tools for assessing risk from specific hazards or to individual assets are generally well developed, our ability to holistically assess risk to a community across a range of hazards and elements at risk remains limited. Developing a holistic view of risk requires interdisciplinary collaboration amongst a wide range of hazard scientists, engineers and social scientists, as well as engagement of a range of stakeholders. This paper explores these challenges and explores some of the common and contrasting issues sampled from a range of applications addressing earthquake, tsunami, volcano, severe wind, flood, and sea-level rise from projects in Australia, Indonesia and the Philippines. Key issues range from the availability of appropriate risk assessment tools and data, to the ability of communities to implement appropriate risk reduction measures. Quantifying risk requires information on the hazard, the exposure and the vulnerability. Often the knowledge of the hazard is reasonably well constrained, but exposure information (e.g., people and their assets) and measures of vulnerability (i.e., susceptibility to injury or damage) are inconsistent or unavailable. In order to fill these gaps, Geoscience Australia has developed computational models and tools which are open and freely available. As the knowledge gaps become smaller, the need is growing to go beyond the quantification of risk to the provision of tools to aid in selecting the most appropriate risk reduction strategies (e.g., evacuation plans, building retrofits, insurance, or land use) to build community resilience.