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.

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.

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.

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.

The development of climate change adaptation policies must be underpinned by a sound understanding of climate change risk. As part of the Hyogo Framework for Action, governments have agreed to incorporate climate change adaptation into the risk reduction process. This paper explores the nature of climate change risk assessment in the context of human assets and the built environment. More specifically, the paper's focus is on the role of spatial data which is fundamental to the analysis. The fundamental link in all of these examples is the National Exposure Information System (NEXIS) which has been developed as a national database of Australia's built infrastructure and associated demographic information. The first illustrations of the use of NEXIS are through post-disaster impact assessments of a recent flood and bushfire. While these specific events can not be said to be the result of climate change, flood and bushfire risks will certainly increase if rainfall or drought become more prevalent, as most climate change models indicate. The second example is from Australia's National Coastal Vulnerability Assessment which is addressing the impact of sea-level rise and increased storms on coastal communities on a national scale. This study required access to or the development of several other spatial databases covering coastal landforms, digital elevation models and tidal/storm surge. Together, these examples serve to illustrate the importance of spatial data to the assessment of climate change risk and, ultimately, to making informed, cost-effective decisions to adapt to climate change.

Wildfires are one of the major natural hazards facing the Australian continent. Chen (2004) rated wildfires as the third largest cause of building damage in Australia during the 20th Century. Most of this damage was due to a few extreme wildfire events. For a vast country like Australia with its sparse network of weather observation sites and short temporal length of records, it is important to employ a range of modelling techniques that involve both observed and modelled data in order to produce fire hazard and risk information/products with utility. This presentation details the use of statistical and deterministic modelling of both observations and synthetic climate model output (downscaled gridded reanalysis information) in the development of extreme fire weather potential maps. Fire danger indices such as the McArthur Fire Forest Danger Index (FFDI) are widely used by fire management agencies to assess fire weather conditions and issue public warnings. FFDI is regularly calculated at weather stations using measurements of weather variables and fuel information. As it has been shown that relatively few extreme events cause most of the impacts, the ability to derive the spatial distribution of the return period of extreme FFDI values contributes important information to the understanding of how potential risk is distributed across the continent. The long-term spatial tendency FFDI has been assessed by calculating the return period of its extreme values from point-based observational data. The frequency and intensity as well as the spatial distribution of FFDI extremes were obtained by applying an advanced spatial interpolation algorithm to the recording stations' measurements. As an illustration maps of 50 and 100-year return-period (RP) of FFDI under current climate conditions are presented (based on both observations and reanalysis climate model output). MODSIM 2013 Conference

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.

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).

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.

Full Version - shows orthographic and fly-through sequence for each of 5 scenarios with a combined max. inundation outline fly-through at end. Description. - Tropical Cyclone Alby passed close to the southwest corner of West Australia on April 4th 1978. Large waves and a storm surge generated by the northerly winds caused substantial coastal erosion along the Lower West coast particularly in the Geographe Bay area. Low-lying areas at Bunbury and Busselton were flooded, forcing the evacuation of many homes including the Bunbury Nursing Home. An approximate 1.1 m storm surge at Busselton caused the tide to peak at 2.5 m about 1 m above the highest astronomical tide. The Busselton Jetty was severely damaged. At Fremantle the surge was about 0.6 m causing a high tide of 1.8 m, about 0.5 m above the highest astronomical tide. [From BOM - http://www.bom.gov.au/weather/wa/cyclone/about/perth/alby.shtml - Retrieved 21/01/2010] This movie displays the results of a number of simulated storm surge events caused by an equivalent storm to Tropical Cyclone Alby on the current built terrain of Mandurah, and projected 2100 coastline with 0.5, 0.8 and 1.1m rises in sea level. Scenario A TC Alby equivalent at current sea level Scenario B Worst case TC Alby equivalent with current sea level Scenario C Worst case TC Alby equivalent in 2100 with 0.5m sea level rise Scenario D Worst case TC Alby equivalent in 2100 with 0.8m sea level rise Scenario E Worst case TC Alby equivalent in 2100 with 1.1m sea level rise