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 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.

This point dataset contains the major desalination plants in Australia.

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.

This is a short and informative 3.3 minute movie for the Engineering, Economics and Exposure Project - NEXIS Development for DCCEE - late 2010. It is a promotional movie that demonstrates NEXIS capabilities, and explains how NEXIS will be benefitial to the NEXIS stakeholder. This movie may also go onto the web, where it's purpose is to convince the public that NEXIS is a worthwhile investment in Australia's future.

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).

Crucial elements for assessing earthquake risk are exposure and vulnerability. In assessing earthquake risk to the Australian built environment we need to know what is exposed to earthquake ground motion and also how vulnerable the exposed infrastructure is to the severity of shaking. While central business district (CBD) buildings make up a relatively small proportion of Australia's built environment their function and the business activity they support is vital to Australia's economy. This paper describes an ongoing effort by the Australian Government to undertake engineering and architectural surveys of buildings within state capital CBDs. With funding from the Attorney-General's Department Geoscience Australia has recently completed a survey of the Melbourne CBD and will complete surveys of the Sydney, Adelaide and Brisbane CBDs this financial year. Survey teams comprise a structural engineer and a GIS operator who populates survey fields on a handheld computer. Approximately 90 survey data fields are incorporated in the template to enable capture of the variety in building features. The fields cover building characteristics that are understood to influence earthquake vulnerability. A summary of the survey activity undertaken to date is presented here along with some examples of the type of data that is being collected.

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.

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.

These datasets contain both fundamental and also final outputs in the form of files, rasters and vectors. These datasets are utilised to provide a measure of Tasmanian severe wind risk for both current climate and two climate change scenarios. To provide a measure of Tasmanian severe wind risk for both current climate and two climate change scenarios, this study has developed: (1) an understanding of severe wind hazard for two climate change scenarios (at 2060 & 2100) separately considering thunderstorm downbursts and synoptic winds and then combining the elements to construct hazard with regards to likelihood and intensity for the region. The outputs of general circulation climate models were forced by two increasing greenhouse gas trajectories (A2 & B1 scenarios) to give representative wind hazard for the respective possible future greenhouse gas concentrations scenarios. (2) an understanding of how residential building exposure may change for the case study regions (2060 & 2100) utilising the Australian Bureau of Statistics population projections (A, B & C series) and the National Exposure Information System (NEXIS) to project current trends in occupancy statistics. (3) a preliminary understanding of annualised loss due to wind exposure for urban areas within 42 Tasmanian regions (considering 10 year to 2000 year return period hazard). Regions have been ranked on the severity of loss, and key contributing factors driving the risk in these high wind risk regions are considered.