Tropical cyclone Gita impacted the Kingdom of Tonga in February 2018, causing significant damage across the main island of Tongatapu. This dataset is a best estimate of the maximum local gust wind speed across Tongatapu, based on the best-available track information, elevation and land cover data. The data represents the maximum 0.2 second, 10-metre above ground level wind speed at (approximately) 25 metre horizontal resolution. The wind field was generated using: Geoscience Australia's Tropical Cyclone Risk Model - https://github.com/GeoscienceAustralia/tcrm Wind Multipliers code - https://github.com/GeoscienceAusralia/Wind_Multipliers TC Gita track was sourced from the Joint Typhoon Warning Center (http://www.metoc.navy.mil/jtwc/jtwc.html)

Tropical cyclone scenario prepared for Tonga National Emergency Management Office (NEMO) as part of the PacSAFE Project (2016-2018)

Australian Community Climate and Earth-System (ACCESS) Numerical Weather Prediction (NWP) data is made available by the Bureau of Meteorology for registered subscribers such as GA. ACCESS-C3 (City) model is a forecast-only model performed every 6 hours and consists of grid coordinates covering domains around Sydney, Victoria and Tasmania, Brisbane, Perth, Adelaide and Darwin. ACCESS Impact Modelling (ACCESS-IM) System utilise information from ACCESS-NWP on the forecast wind gust speeds ground surface (single-level) at 10 metres, simulated by the ACCESS-C3 model, for the time period of 0-12, 12-24, 24-36, 0-36.

Natural hazard data supports the nation to respond effectively to emergencies, reduce the threat natural hazards pose to Australia¿s national interests and address issues relating to community safety, urban development, building design, climate change and insurance. A baseline understanding of hazards, impacts and risk can help to enhance community resilience to extreme events and a changing environment. Probabilistic hazard and risk information provides planners and designers opportunity to investigate the cost and benefit of policy options to mitigate natural hazard impacts. Modelled disaster scenario information can enable disaggregation of probabilistic hazard to identify the most probable event contributing to hazard. Tropical cyclone return period wind hazard maps developed using the Tropical Cyclone Risk Model. The hazard maps are derived from a catalogue of synthetic tropical cyclone events representing 10,000 years of activity. Annual maxima are evaluated from the catalogue and used to fit a generalised extreme value distribution at each grid point. Wind multipliers are factors that transform regional wind speed to local wind speed, mathematically describing the influences of terrain, shielding and topographic effects. Local wind speeds are critical to wind-related activities that include hazard and risk assessment. The complete dataset is comprised of: - Stochastic tracks, wind fields and impact data; - Probabilistic wind speed data (hazard); - Site-exposure wind multipliers.

The local wind multiplier data for Tongatapu is used to generate local wind speeds over the island of Tongatapu, Tonga.

Global solar exposure is the total amount of solar energy falling on a horizontal surface. The daily global solar exposure is the total solar energy for a day. Typical values for daily global exposure range from 1 to 35 MJ/m2 (megajoules per square metre). For mid-latitudes, the values are usually highest in clear sun conditions during the summer, and lowest during winter or very cloudy days. The monthly means are derived from the daily global solar exposure. See metadata statement for more information.

The Bushfire Attack Level Toolbox provides access to ArcGIS geoprocessing scripts that calculate the Bushfire Attack Level (BAL) as per Method 1 in AS-3959 (2009). BAL is a measure of the severity of a building's potential exposure to ember attack, radiant heat and direct flame contact in the event of a bushfire. It serves as a basis for establishing the requirements for construction to improve protection of building elements from attack by bushfire. The BAL Maps and Exposure report provide maps of three communities in Western Australia, with indicative BAL levels, and the aggregate inventory of assets and population exposed to the different levels of BAL.

Tropical cyclone scenario prepared for Tonga National Emergency Management Office (NEMO) as part of the PacSAFE Project (2016-2018)

The world's first satellite-derived mineral maps of a continent, namely Australia, are now publicly available as digital, web-accessible products. The value of this spatially comprehensive mineral information is readily being captured by explorers at terrane to prospect scales. However, potentially even greater benefits can ensue for environmental applications, especially for the Earth's extensive drylands which generate nearly 50% of the world's agricultural production but are most at risk to climate change and poor land management. Here we show how these satellite mineral maps can be used to: characterise soil types; define the extent of deserts; fingerprint sources of dust; measure the REDOX of iron minerals as a potential marine input; and monitor the process of desertification. We propose a 'Mineral Desertification Index' that can be applied to all Earth's drylands where the agriculturally productive clay mineral component is being lost by erosion. Mineral information is fundamental to understanding geology and is important for resource applications1. Minerals are also a fundamental component of soils2 as well as dust eroded from the land surface, which can potentially impact on human health3, the marine environment4 and climate5. Importantly, minerals are well exposed in the world's 'drylands', which account for nearly 50% of Earth's land area6. Here, vegetation cover is sparse to non-existent as a result of low rainfall (P) and high evaporation (E) rates (P/E<0.65). However, drylands support 50% of the world's livestock production and almost half of all cultivated systems6. In Australia, drylands cover 85% of the continent and account for 50% of its beef, 80% of its sheep and 93% of its grain production7. Like other parts of the world, Australia is facing serious desertification of its drylands6. Wind, overgrazing and overstocking are major factors in the desertification process8. That is, the agriculturally productive clay-size fraction of soils (often includes organic carbon) is lost largely through wind erosion, which is acerbated by the loss of any vegetative groundcover (typically dry plant materials). Once clay (and carbon) loss begins, then the related break down of the soil structure and loss of its water holding capacity increases the rate of the degeneration process with the final end products being either exposed rock or quartz sands that often concentrate in deserts.

Limited data from emergency services for the April 2015 East Coast Low (ECL) event initially investigated. SES call-out data provides spatial coverage, but does not capture detail of the damage to buildings. EICU data has detailed information, including indicative damage state, but limited spatial coverage. Neither dataset consistently links the damage to the hazard that caused it. Showing that the impact forecasting process adds value beyond the underlying hazard forecasts in this situation is challenging. EICU data can help to calibrate the vulnerability functions applied to model-based hazard forecast data. The SES callout data can help evaluate whether the indicative damage rates for an area are reasonable, through use of a service demand metric. Service demand is the number of callouts compared to the number of buildings for a statistical area (e.g. mesh block, SA1 or local government area). We use the total building count in each area, as the SES callout data does not differentiate between residential and non-residential buildings. It also includes callouts for downed trees or power lines that may not have directly caused structural damage to buildings. Service demand is compared to mesh block-based impact forecast data for the 2015 ECL, using existing heuristic vulnerability functions for severe wind. We recognise these functions are not calibrated against forecast model data, but provide a starting point from which we can establish the workflow while working towards refined vulnerability functions in parallel. The project has sourced EICU and SES post-event survey data, and high-resolution model (reanalysis) data for two additional severe wind and rain events to improve the calibration of the vulnerability functions. Poster presentation at the 2019 AFAC Conference