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  • The service contains the Australian Coastal Geomorphology Landform Type Classifications, used to support a national coastal risk assessment. It describes the location and extent of landform types identifiable at scales between 1:250,000 and 1:25,000. It describes the landform types present in either erosional or dispositional environments.

  • <div>This record links to tarred folders with simulation files used for a study on tsunami hazards in Tongatapu (eCat 146012) - DOI: https://doi.org/10.1093/gji/ggac140. </div><div><br></div><div>Access to this data will only be available by request via datacatalogue@ga.gov.au</div><div><br></div><div>The files were created using code here: </div><div>https://github.com/GeoscienceAustralia/ptha/tree/master/misc/monte_carlo_paper_2021. </div><div><br></div><div>This code should be read to understand the structure and contents of the tar archives. The simulation files are large and for most use cases you won't need them. First check if your needs a met via code and documentation at the link above. If the git repository doesn't include links to what you need, then it may be available in these tar archives. Contents include the datasets used to setup the model and the model outputs for every scenario. While the modelling files and code were developed by GA, at the time of writing, we do not have permission to distribute some of the input datasets outside of GA (including the Tongatapu LIDAR). </div><div><br></div><div>Access to this data will only be available by request via datacatalogue@ga.gov.au</div>

  • Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach. Appeared online in Front. Earth Sci., 29 April 2021

  • On 23 March 2012, at 09:25 GMT, a MW 5.4 earthquake occurred in the eastern Musgrave Ranges region of north-central South Australia, near the community of Ernabella (Pukatja). This was the largest earthquake to be recorded on mainland Australia for the past 15 years and resulted in the formation of a 1.6 km-long surface deformation zone comprising reverse fault scarps with a maximum vertical displacement of over 50 cm, and extensive ground cracking. Numerous small communities in this remote part of central Australia reported the tremor, but there were no reports of injury or significant damage. The maximum ground shaking is estimated to have been in the order of MMI VI. The earthquake occurred in Stable Continental Region (SCR) crust, over 1900 km from the nearest plate boundary. Fewer than fifteen historic earthquakes worldwide are documented to have produced coseismic surface deformation (i.e. faulting or folding) in the SCR setting. The record of surface deformation relating to the Ernabella earthquake therefore provides an important constraint on models relating surface rupture length to earthquake magnitude. Such models may be employed to better interpret Australia's rich prehistoric record of seismicity, thereby improving estimates of seismic hazard.

  • 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.

  • 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.

  • A short film about a scientific project aimed at enhancing risk analysis capacities for flood, severe wind from tropical cyclones and earthquake in the Greater Metropolitan Manila Area. Manila is one of the world's megacities, and the Greater Metro Manila Area is prone to natural disasters. These events may have devastating 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.

  • 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.

  • A multi-hazard and exposure analysis of Asia. A GIS study that incorporates regional data for: landslide, tsunami, earthquake, tropical cyclone, volcanic, drought and flood hazard.

  • <div>The wind hazard climate in South East Queensland is a combination of tropical cyclones, thunderstorms and synoptic storms. This dataset provides estimated average recurrence interval (ARI) or annual exceedance probability (AEP) wind speeds over the region, based on an evaluation of observational (thunderstorms and synoptic winds) and simulated data (tropical cyclones). </div><div><br></div><div>The tropical cyclone wind hazard was evaluated using Geoscience Australia's Tropical Cyclone Risk Model (TCRM), which provides a spatial representation of the AEP wind speeds arising from tropical cyclones. Thunderstorm wind hazard was evaluated from analysis of observed wind gusts across South East Queensland, aggregated into a single 'superstation' to provide a single representative hazard profile for the region.</div><div><br></div><div>The resulting combined wind hazard estimates reflect the dominant source of wind hazard in South East Queensland for the most frequent events (exceedance probabilities greater than 1:50) is thunderstorm-generated wind gusts. For rarer events, with exceedance probabilities less than 1:200, TC are the dominant source of extreme gusts.&nbsp;</div><div><br></div><div>Local effects of topography, land cover and the built environment were incorporated via site exposure multipliers (Arthur & Moghaddam, 2021), which are based on the site exposure multipliers defined in AS/NZS 1170.2 (2021).</div><div><br></div><div>The local wind hazard maps were used to evaluate the financial risk to residential separate houses in South East Queensland.</div><div><br></div><div>Wind speeds are provided for average recurrence intervals ranging from 1 year to 10,000 years. No confidence intervals are provided in the data. </div>