A community Safety Capbility Flyer was produced to showcase the work undertaken in the Community Safety Value Stream. The flyer includes an introduction to the Community Safety Value Stream, case studies of the work Geoscience Australia does in this space and information on how to engage with Geoscience Australia via the products, tools, models and applications that are produced. This flyer is intended for use a conferences and where promotional material would beneficial to showcase the work undertaken at Geoscience Australia such as the Floodplain Management Association Conference on 19-22 May 2015.

This paper presents a model to assess bushfire hazard in south-eastern Australia. The model utilises climate model simulations instead of observational data. Bushfire hazard is assessed by calculating return periods of the McArthur Forest Fires Danger Index (FFDI). The return periods of the FFDI are calculated by fitting an extreme value distribution to the tail of the FFDI data. The results have been compared against a spatial distribution of bushfire hazard obtained by interpolation of FFDI calculated at a number of recording stations in Australia. The results show that climate simulations produce a similar pattern of bushfire hazard than the interpolated observations but the simulated values tend to be up to 60% lower than the observations. This study shows that the major source of error in the simulations is the values of wind speed. Observational wind speed is recorded at a point-based station whilst climate simulated wind speed is averaged over a grid cell. On the other hand FFDI calculation is very sensitive to wind speed and hence to improve the calculation of FFDI using climate simulations it is necessary to correct the bias observed in the simulations. A statistically-based procedure to correct the simulation bias has been developed in this project. Bias-corrected calculation of FFDI shows that the major bushfire hazard in south-eastern Australia is in the western parts of SA and NSW; and in south-western Tasmania.

Stochastic finite-fault ground-motion prediction equations (GMPEs) are developed for the stable continental region of southeastern Australia (SEA). The models are based on reinterpreted source and attenuation parameters for small-to-moderate magnitude local earthquakes and a dataset augmented with ground-motion records from recent significant earthquakes. The models are applicable to horizontal-component ground-motions for earthquakes 4.0 <= MW <= 7.5 and at distances less than 400 km. The models are calibrated with updated source and attenuation parameters derived from SEA ground-motion data. Careful analysis of well-constrained earthquake stress parameters indicates a dependence on hypocentral depth. It is speculated that this is the effect of an increasing crustal stress profile with depth. However, rather than a continuous increase, the change in stress parameter appears to indicate a discrete step near 10 km depth. Average stress parameters for SEA earthquakes shallower and deeper than 10 km are estimated to be 23 MPa and 50 MPa, respectively. These stress parameters are consequently input into the stochastic ground-motion simulations for the development of two discrete GMPEs for shallow and deep events. The GMPEs developed estimate response spectral accelerations comparable to the Atkinson and Boore (2006) GMPE for eastern North America (ENA) at short rupture distances (less than approximately 100 km). However, owing to higher attenuation observed in the SEA crust (Allen and Atkinson, 2007), the SEA GMPEs estimate lower ground-motions than ENA models at larger distances. A correlation between measured VS30 and ?0 was developed from the limited data available to determine the average site condition to which the GMPEs are applicable. Assuming the correlation holds, a VS30 of approximately 820 m/s is obtained assuming an average path-independent diminution term ?0 of 0.006 s from SEA seismic stations. Consequently, the GMPE presented herein can be assumed to be appropriate for rock sites of B to BC site class in the National Earthquake Hazards Reduction Program (NEHRP, 2003) site classification scheme. The response spectral models are validated against moderate-magnitude (4.0 <= MW <= 5.3) earthquakes from eastern Australia. Overall the SEA GMPEs show low median residuals across the full range of period and distance. In contrast, ENA models tend to overestimate response spectra at larger distances. Because of these differences, the present analysis justifies the need to develop Australian-specific GMPEs where ground-motion hazard from a distant seismic source may become important.

This document is intended to provide a record of the participants, program, and discussions held at the Fire Weather and Risk Workshop, held at Peppers Craigieburn in Bowral, from 1st -4th September 2011. The workshop was attended by 77 delegates and was sponsored by the ACT Emergency Services Agency, Geoscience Australia, the Bureau of Meteorology, and the Federal Attorney Generals Department. These proceedings include the: - workshop program - executive summary by the workshop organizers - presentation abstracts (optional) - summaries of presentations and discussions (compiled at the workshop by the session chairs and scribes) - survey of participants- expectations of the workshop (received prior to the workshop) - results of a post-workshop evaluation - list of participants. This document also includes an invited journalistic-styled article by science journalist, Nick Goldie (Senior Deputy Captain, Colinton Rural Fire Brigade, NSW RFS) which provided an independent view on the activities that occurred over the three days.

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.

This document describes a structure for exchanging information to assist discovery and retrieval/transfer of flood information, including GIS flood mapping data. The draft class model represents metadata, data and summary information that supports the goals of the National Flood Risk Information Project (NFRIP) to improve the quality, consistency and accessibility of flood information. This document describes the data model that will be used to create an application schema.

The Philippine Institute of Volcanology and Seismology (PHIVOLCS) and Geoscience Australia (GA) have developed a long-term partnership in order to better understand and reduce the risks associated with earthquake hazards in the Philippines. The Project discussed herein was supported by the Australian Agency for International Development (AusAID). Specifically, this partnership was designed to enhance the exposure and damage estimation capabilities of the Rapid Earthquake Damage Assessment System (REDAS), which has been designed and built by PHIVOLCS. Prior to the commencement of this Project, REDAS had the capability to model a range of potential earthquake hazards including ground shaking, tsunami inundation, liquefaction and landslides, as well as providing information about elements at risk (e.g., schools, bridges, etc.) from the aforementioned hazards. The current Project enhances the exposure and vulnerability modules in REDAS and enable it to estimate building damage and fatalities resulting from scenario earthquakes, and to provide critical information to first-responders on the likely impacts of an earthquake in near real-time. To investigate this emergent capability within PHIVOLCS, we have chosen the pilot community of Iloilo City, Western Visayas. A large component of this project has been the compilation of datasets to develop building exposure models, and subsequently, developing methodologies to make these datasets useful for natural hazard impact assessments. Collection of the exposure data was undertaken at two levels: national and local. The national exposure dataset was gathered from the Philippines National Statistics Office (NSO) and comprises basic information on wall type, roof type, and floor area for residential buildings. The NSO census dataset also comprises crucial information on the population distribution throughout the Philippines. The local exposure dataset gathered from the Iloilo City Assessors Office includes slightly more detailed information on the building type for all buildings (residential, commercial, government, etc.) and appears to provide more accurate information on the floor area. However, the local Iloilo City dataset does not provide any information on the number of people that occupy these buildings. Consequently, in order for the local data to be useful for our purposes, we must merge the population data from the NSO with the local Assessors Office data. Subsequent validation if the Iloilo City exposure database has been conducted through targeted foot-based building inventory surveys and has allowed us to generate statistical models to approximate the distribution of engineering structural systems aggregated at a barangay level using simple wall and roof-type information from the NSO census data. We present a comparison of the national and local exposure data and discuss how information assembled from the Iloilo City pilot study - and future study areas where detailed exposure assessments are conducted - could be extended to describe the distribution of building stock in other regions of the Philippines using only the first-order national-scale NSO data. We present exposure information gathered for Iloilo City at barangay level in a format that can be readily imported to REDAS for estimating earthquake impact.

This document presents a new set of earthquake hazard maps for consideration in the next revision of the earthquake loading code AS1170.4 "Structural design actions: Part 4 Earthquake actions in Australia". The earthquake catalogue used here includes events up until 2011. It is a combined version of several catalogues provided by external agencies. This represents the most complete catalogue of earthquakes compiled for Australia. The catalogue is more consistent through conversion of various magnitude measurements into a 'pseudo ML' scale. A systematic logic is used to select preferred magnitude types. Aftershocks, foreshocks and mine blasts have been identified and the declustered catalogue used here is cleaner than any previous Australian catalogue. Earthquake source zones applied in the hazard map use a unique combination of three different layers, which capture seismic characteristics at sub-national, regional and high-activity point scales. The map is one of the first in the world to apply a semi-quantitative measure of Mmax for majority of the source zones in the map. We apply recently developed ground motion prediction equations based on modern methods and data. These equations were used to calculate the ground motion at a range of response spectral accelerations, rather than just calculating the hazard for peak ground acceleration (PGA). A suite of maps is calculated using GA's Earthquake Risk Model (EQRM). The EQRM is open-source, allowing the results to be tested or modified independently. The final 2012 Australian earthquake hazard maps for a range of return periods and response spectral periods are presented herein.

A compilation of short animations, describing the key processes involved in tsunami generation.

In 2012 Geoscience Australia produced a National Seismic Hazard Map (NSHM) of Australia using the Probabilistic Seismic Hazard Assessment (PSHA) methodology. The primary product of the project was single 500 year return period Peak Ground Acceleration (PGA) map GA record 2012/71. For this assessment the hazard has been calculated for 14 return periods (100 - 100,000 years) and 21 SA periods (0.0 - 5.0s), giving 294 hazard layers (maps) for 48000 sites across Australia. We show five of the possible 294 hazard maps and 34 of the tens of thousands of possible hazard curves and spectra. These were selected to cover the main types of additional maps that have been requested since the NSHM was released and to cover a reasonable range of return periods, SA periods and locations. In this record, the probability factor (Kp) curve given in AS1170.4 is also compared to the curves calculated for the eight capital cities. Finally, the hazard spectra for the capital cities and some selected locations is compared to the spectra for site class Be given in AS1170.4.