Tropical Cyclone (TC) Yasi crossed Queensland's Cassowary Coast during the night of the 2nd and 3rd of February, 2011. The cyclone was forecast by BoM (2011) to be a severe storm with wind gusts forecast to exceed the design gust wind speeds for houses set out in AS4055. Following the passage of the cyclone, it was evident that the severe wind and large coastal storm surge had caused significant damage to the region's building stock. Geoscience Australia (GA), together with collaborators from the National Institute of Water and Atmospheric Research, New Zealand (NIWA), Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and Maddocks & Associates, undertook a survey of damage to the region's buildings caused by severe wind and storm surge.

The Flood Study Summary Services support discovery and retrieval of flood hazard information. The services return metadata and data for flood studies and flood inundation maps held in the 'Australian Flood Studies Database'. The same information is available through a user interface at http://www.ga.gov.au/flood-study-web/. A 'flood study' is a comprehensive technical investigation of flood behaviour. It defines the nature and extent flood hazard across the floodplain by providing information on the extent, level and velocity of floodwaters and on the distribution of flood flows. Flood studies are typically commissioned by government, and conducted by experts from specialist engineering firms or government agencies. Key outputs from flood studies include detailed reports, and maps showing inundation, depth, velocity and hazard for events of various likelihoods. The services are deliverables fom the National Flood Risk Information Project. The main aim of the project is to make flood risk information accessible from a central location. Geoscience Australia will facilitate this through the development of the National Flood Risk Information Portal. Over the four years the project will launch a new phase of the portal prior to the commencement of each annual disaster season. Each phase will increase the amount of flood risk information that is publicly accessible and increase stakeholder capability in the production and use of flood risk information. flood-study-search returns summary layers and links to rich metadata about flood maps and the studies that produced them. flood-study-map returns layers for individual flood inundation maps. Typically a single layer shows the flood inundation for a particular likelihood or historical event in a flood study area. To retrieve flood inundation maps from these services, we recommend: 1. querying flood-study-search to obtain flood inundation map URIs, then 2. using the flood inundation map URIs to retrieve maps separately from flood-study-map. The ownership of each flood study remains with the commissioning organisation and/or author as indicated with each study, and users of the database should refer to the reports themselves to determine any constraints in their usage.

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.

The datasets created to produce the emergency mapping support products which contributed to fulfilling GA's arrangements in supporting the outcomes sought by the Australian Government during disaster events.

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.

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 metadata relates to the ANUGA hydrodynamic modelling results for Busselton, south-west Western Australia. The results consist of inundation extent and peak momentum gridded spatial data for each of the ten modelling scenarios. The scenarios are based on Tropical Cyclone (TC) Alby that impacted Western Australia in 1978 and the combination of TC Alby with a track and time shift, sea-level rise and riverine flood scenarios. The inundation extent defines grid cells that were identified as wet within each of the modelling scenarios. The momentum results define the maximum momentum value recorded for each inundated grid cell within each modelling scenario. Refer to the professional opinion (Coastal inundation modelling for Busselton, Western Australia, under current and future climate) for details of the project.

Interactive Maps is a discovery and exploration view of Geoscience Australia's geospatial services. The following scientific and decision support themes have curated content comprised of maps and functions. Each map has queries and functions with linked access to OGC (Open Geospatial Consortium) web services and metadata. This system replaces MapConnect and AMSIS applications.

Tropical cyclone return period wind hazard layers developed using the Tropical Cyclone Risk Model. The hazard layers are derived from a catalogue of synthetic tropical cyclone events representing 10000 years of activity. Annual maxima are evaluated from the catalogue and used to fit a generalised extreme value distribution at each grid point.