Knowledge of the nature of buildings within CBD areas is fundamental to a broad range of decision making processes, including planning, emergency management and the mitigation of the impact of natural hazards. To support these activities, Geoscience Australia has developed a building information system called the National Exposure Information System (NEXIS) which provides information on buildings across Australia. Most of the building level information in NEXIS is statistically derived, but efforts are being made to include more detailed information on the nature of individual buildings, particularly in CBD areas. This is being achieved in Hobart through field survey work.

The impact of seismic surveys on the catchability of marine fish is a contentious issue, with claims that seismic surveys may negatively affect catch rates. However little empirical evidence exists to quantify the impacts or determine potential causes. In this study, we used a 2-D seismic survey in the Gippsland Basin, Bass Strait, Australia in April 2015 as an opportunity to quantify fish behaviour (field-based) and commercial fisheries catch (desktop study) across the region before and after airgun operations. Three species found in abundance (gummy shark, swell shark, tiger flathead) were acoustically tagged and released within one of two acoustic arrays (experimental and control zone) and monitored before, during and after the seismic survey. In the field study, only 35% of the gummy sharks and 30% of the swell sharks were subsequently detected two days after release, although various individuals returned sporadically over the period of monitoring including during the seismic survey operations. Behaviour consistent with a possible response to the seismic survey operations was restricted to flathead which increased their swimming speed during the seismic survey period and changed their diel movement patterns after the survey. We also investigated the potential impacts of the seismic survey on catch rates using Commonwealth fisheries logbook data from Jan 2012 – Oct 2015. Fifteen species and two gear types (Danish seine, gillnet) were modelled to examine differences in catch rates before and after the seismic survey. The catch rates in the six months following the seismic survey were different than predicted in nine out of the 15 species examined, with six species (tiger flathead, goatfish, elephantfish, boarfish, broadnose shark and school shark) showing increases in catch following to the seismic survey, and three species (gummy shark, red gurnard, sawshark) showing reductions. Overall, we found little evidence consistent with behavioural or catch rate changes induced by the seismic survey in the targeted species, although behavioural data were limited because many sharks left the acoustic receiver array prior to the commencement of the seismic survey. <b>Citation:</b> Barry Bruce, Russ Bradford, Scott Foster, Kate Lee, Matt Lansdell, Scott Cooper, Rachel Przeslawski, Quantifying fish behaviour and commercial catch rates in relation to a marine seismic survey,<i> Marine Environmental Research</i>, Volume 140, 2018, Pages 18-30, ISSN 0141-1136. https://doi.org/10.1016/j.marenvres.2018.05.005.

Severe TC Vance was one of the most intense cyclones to impact mainland Australia. The observed damage to buildings could be explained in terms of structural performance of those buildings. Combining the structural vulnerability of housing with an estimate of the maximum wind gusts, we can explore the possible impacts that a repeat of Vance would cause in Exmouth, and compare the outcomes with what occurred in 1999. The analysis of the impacts of TC Vance on present-day Exmouth shows that very few houses would be completely destroyed. Not surprisingly, older houses (pre-1980’s construction era, excluding the US Navy block houses) would dominate those destroyed, and most likely the timber-framed style houses, many of which were substantially damaged in TC Vance. Published in the Australian Journal of Emergency Management July 2019 edition

The Geological and Bioregional Assessments (GBA) Program is a series of independent scientific studies undertaken by Geoscience Australia and the CSIRO, supported by the Bureau of Meteorology, and managed by the Department of Agriculture, Water and the Environment. The Program consists of three stages across three regions with potential to deliver gas to the East Coast Gas Market. Stage 1 was a rapid regional prioritisation conducted by Geoscience Australia, to identify those sedimentary basins with the greatest potential to deliver shale and/or tight gas to the East Coast Gas Market within the next five to ten years. This prioritisation process assessed 27 onshore eastern and northern Australian basins with shale and/or tight gas potential. Further screening reduced this to a shortlist of nine basins where exploration was underway. The shortlisted basins were ranked on a number of criteria. The Cooper Basin, the Beetaloo Sub-basin and the Isa Superbasin were selected for more detailed assessment. Stage 2 of the program involved establishing a baseline understanding of the identified regions. Geoscience Australia produced regional geological evaluations and conceptualisations that inform the assessment of shale and/or tight gas prospectivity, ground- and surface-water impacts, and hydraulic fracturing models. Geoscience Australia’s relative prospectivity assessments provide an indication of where viable petroleum plays are most likely to be present. These data indicate areal and stratigraphic constraints that support the program’s further work in Stage 3, on understanding likely development scenarios, impact assessments, and causal pathways. <b>Citation:</b> Hall Lisa S., Orr Meredith L., Lech Megan E., Lewis Steven, Bailey Adam H. E., Owens Ryan, Bradshaw Barry E., Bernardel George (2021) Geological and Bioregional Assessments: assessing the prospectivity for tight, shale and deep-coal resources in the Cooper Basin, Beetaloo Subbasin and Isa Superbasin. <i>The APPEA Journal</i><b> 61</b>, 477-484. https://doi.org/10.1071/AJ20035

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

The Severe Wind Hazard Assessment project aims to provide DFES with intelligence on the scale of impacts that could arise from major tropical cyclone events in communities along the northwest and western coast of WA. We simulated category 3 and 5 scenarios in the northwest, and category 1 and 3 scenarios down the west coast. Simulations included translating the local-scale wind fields into the level of damage to residential housing, through the application of vulnerability models applied to residential buildings which had been categorised on the basis of attributes such as construction era, roof type, wall type and location. Some scenarios produce impacts that are comparable to past events (e.g. the category 5 scenario for Exmouth is similar to TC Vance). Other scenarios are catastrophic, such as the category 3 scenario for Geraldton, where nearly all residential buildings in the city are extensively or completely damaged. The different outcomes for communities arises because of the different profiles of residential buildings in each community. Geraldton lies outside the cyclonic regions defined in AS/NZS 1170.2, so houses are not explicitly designed cope with to the extreme winds that can arise in TCs, hence major impacts were found there in our analysis. DFES used these scenarios to guide planning and preparations for events, such as TC Veronica in March 2019, guiding decisions on preparations and recovery options, which are explored in a companion paper. Abstract presented at the 2020 Australian Meteorological and Oceanographic Society 2020 National Conference (http://amos-2020.w.amos.currinda.com/)

The National Hazard Impact Risk Service for Tropical Cyclone Event Impact provides information on the potential impact to residential separate houses due to severe winds. The information is derived from Bureau of Meteorology tropical cyclone forecast tracks, in combination with building location and attributes from the National Exposure Information System and vulnerability models to define the level of impact. Impact data is aggregated to Statistical Area Level 1, categorised into five qualitative levels of impact.

Knowledge of the nature of buildings within CBD areas is fundamental to a broad range of decision making processes, including planning, emergency management and the mitigation of the impact of natural hazards. To support these activities, Geoscience Australia has developed a building information system called the National Exposure Information System (NEXIS) which provides information on buildings across Australia. Most of the building level information in NEXIS is statistically derived, but efforts are being made to include more detailed information on the nature of individual buildings, particularly in CBD areas. This is being achieved in Brisbane through field survey work.

<div>To set out how Geoscience Australia is meeting its vision for the Exploring for the Future program, we have summarised the ways our scientific activities, outputs and intended outcomes and impacts are linked, using the Impact Pathway diagram. This updated brochure includes program impact infographics.</div>

<div>Ask a Queenslander where tropical cyclones (TCs) occur, and the inevitable response will be North Queensland. Whilst most of the tropical cyclones have made landfall north of Bundaberg, the cascading and concurrent effects are felt much further afield. The major flooding following TC Yasi in 2011 and TC Debbie in 2017, are just two examples where impacts were felt across the State, and of course, the wind impacts to the banana plantation following TC Larry (2006) was felt nationally.&nbsp;</div><div> &nbsp;</div><div>South East Queensland has not been forgotten when it comes to tropical cyclone impact with an event crossing Coolangatta in 1954. There was also the more recent TC Gabrielle which tracked offshore on its path southwards to New Zealand.&nbsp;&nbsp;</div><div>&nbsp;</div><div>Acknowledging that climate is influencing the intensity and frequency of more intense severe weather hazards, understanding how tropical cyclone hazard varies under future climate conditions is critical to risk-based planning in Queensland. With this climate influence, along with increasing population and more vulnerable building design in South East Queensland (relative to northern Queensland), there is an urgent need to assess the wind risk and set in place plans to reduce the impacts of a potential tropical cyclone impact in South East Queensland. <b>Citation:</b> Sexton, J., Tait, M., Turner, H., Arthur, C., Henderson, D., Edwards, M; Preparing for the expected: tropical cyclones in South East Queensland.<i> AJEM</i> 38:4, October 2023, pages 33-39.