Wildfires are one of the major natural hazards facing the Australian continent. Chen (2004) rated wildfires as the third largest cause of building damage in Australia during the 20th Century. Most of this damage was due to a few extreme wildfire events. For a vast country like Australia with its sparse network of weather observation sites and short temporal length of records, it is important to employ a range of modelling techniques that involve both observed and modelled data in order to produce fire hazard and risk information/products with utility. This presentation details the use of statistical and deterministic modelling of both observations and synthetic climate model output (downscaled gridded reanalysis information) in the development of extreme fire weather potential maps. Fire danger indices such as the McArthur Fire Forest Danger Index (FFDI) are widely used by fire management agencies to assess fire weather conditions and issue public warnings. FFDI is regularly calculated at weather stations using measurements of weather variables and fuel information. As it has been shown that relatively few extreme events cause most of the impacts, the ability to derive the spatial distribution of the return period of extreme FFDI values contributes important information to the understanding of how potential risk is distributed across the continent. The long-term spatial tendency FFDI has been assessed by calculating the return period of its extreme values from point-based observational data. The frequency and intensity as well as the spatial distribution of FFDI extremes were obtained by applying an advanced spatial interpolation algorithm to the recording stations' measurements. As an illustration maps of 50 and 100-year return-period (RP) of FFDI under current climate conditions are presented (based on both observations and reanalysis climate model output). MODSIM 2013 Conference

The dry-tropics of central Queensland has an annual bushfire threat season that generally extends from September to November. Fire weather hazard is quantified using either the Forest Fire Danger Index (FFDI) or the Grassland Fire Danger Index (GFDI) (Luke and McArthur, 1978). Weather observations (temperature, relative humidity and wind speed) are combined with an estimate of the fuel state to predict likely fire behaviour if an ignition eventuates. A high resolution numerical weather model (dynamic downscaling) was utilised to provide spatial texture over the Rockhampton region for a range of historical days where bushfire hazard (as measured at the Rockhampton Airport meteorological station) was known to be severe to extreme. From the temperature, relative humidity and wind speeds generated by the model, the maximum FFDI for each simulated day was calculated using a maximum drought factor. Each of these FFDI maps was then normalised to the value of the FFDI at the grid point corresponding to Rockhampton Airport (ensemble produced). The annual recurrance interval (ARI) of FFDI at Rockhampton Airport for the current climate was calculated from observations by fitting Generalised Extreme Value (GEV) distributions. For future climate, we considered three downscaled General Circulation Models (GCM's) forced by the A2 emission scenario for atmospheric greenhouse gas emissions. The spatial pattern of the 50 and 100 year ARI fire danger rating for the Rockhampton region (current and future climate) was determined. In general, a small spatial increase in the fire danger rating is reflected in the ensemble model average for the 2090 climate. This is reflected throughout the Rockhampton region in both magnitude and extent through 2050 to 2090. Cluster areas of higher (future climate) bushfire hazard were mapped for planning applications. Handbook MODSIM2013 Conference

We have developed a Building Fire Impact Model to evaluate the probability that a building located in a peri-urban region of a community is affected/destroyed by a forest fire. The methodology is based on a well-known mathematical technique called Event Tree (ET) modeling, which is a useful graphical way of representing the dependency of events. The tree nodes are the event itself, and the branches are formed with the probability of the event happening. If the event can be represented by a discrete random variable, the number of possible realisations of the event and their corresponding probability of occurring, conditional on the realisations of the previous event, is given by the branches. As the probability of each event is displayed conditional on the occurrence of events that precede it in the tree, the joint probability of the simultaneous occurrence of events that constitute a path is found by multiplication (Hasofer et al., 2007). BFIM contains a basic implementation of the main elements of bushfire characteristics, house vulnerability and human intervention. In the first pass of the BFIM model, the characteristics of the bushfire in the neighboring region to the house is considered as well as the characteristics of the house and the occupants of the house. In the second pass, the number of embers impacting on the house is adjusted for human intervention and wind damage. In the third pass, the model examines house by house conditions to determine what houses have been burnt and their impact on neighboring houses. To illustrate the model application, a community involved in the 2009 Victorian bushfires has been studied and the event post-disaster impact assessment is utilized to validate the model outcomes. MODSIM 2013 Conference

Abstract: Land Surface Temperature (Ts) is an important boundary condition in many land surface modelling schemes. It is also important in other application areas such as, hydrology, urban environmental monitoring, agriculture, ecological and bushfire monitoring. Many studies have shown that it is possible to retrieve Ts on a global scale using thermal infrared data from satellites. Development of standard methodologies that generate Ts products routinely would be of broad benefit to the application of remote sensing data in areas such as hydrology and urban monitoring. AVHRR and MODIS datasets are routinely used to deliver Ts products. However, these data have 1km spatial resolution, which is too coarse to detect the detailed variation of land surface change of concern in many applications, especially in heterogeneous areas. Higher resolution thermal data from Landsat is a possible option in such cases. To derive Ts, two scientific problems need to be resolved: to remove the atmospheric effects and derive surface brightness temperature (TB) and to separate the emissivity and Ts effects in the surface brightness temperature (TB). To derive TB, for single thermal band sensors such as, Landsat 5, 7 and (due to a faulty dual-band thermal instrument) on Landsat-8, the split window methods, such as those used for NOAAAVHRR data (Becker & Li, 1990), and the day/night pairs of thermal infrared data in several bands, as used for MODIS (Wan et al., 2002) are not available for correcting atmospheric effects. The retrieval of surface brightness temperature TB from Landsat data therefore needs more care, as the accuracy of the TB retrieval depends critically on the ancillary data, such as atmospheric water vapour data (precipitable water). In this paper, a feasible operational method to remove the atmospheric effects and retrieve surface brightness temperature from Landsat data is presented. The method uses the MODTRAN 5 radiative transfer model and global atmospheric profile data sets, such as NASA MERRA (The Modern Era Retrospective-Analysis for Research and Applications) atmospheric profiles, NOAA NCEP (National Center for Environmental Prediction) reanalysis product and ECMWF (The European Centre for Medium-Range Weather Forecasts) to correct for the atmospheric effects. The results derived from the global atmospheric profiles are assessed against the TB product estimated by using (accurate) ground based radiosonde data (balloon data). The results from this study have found: The global data sets NCEP1, NCEP2, MERRA and ECMWF can all generally give satisfactory TB products and can meet the levels of accuracy demanded by many practitioners, such as 1º K. Among global data sets, ECMWF data set performs best. The root mean square difference (RMSD) for the 9 days and 3 test sites are all within 0.4º K when compared with the TB products estimated using ground radiosonde measurements.

Imagine you are an incident controller viewing a computer screen which depicts the likely spread of a bushfire that's just started. The display shows houses and other structures in the fire's path, and even the demographics of the people living in the area, such as the number of people, their age spread, whether households have independent transport, and whether English is their second language. In addition, imagine that you can quantify and display the uncertainty in both the fire weather and also the type and state of the vegetation, visualising the sensitivity of the expected fire spread and impact to these uncertainties. It will be possible to consider 'what if' scenarios as the event unfolds, and reject those scenarios that are no longer plausible. The advantages of such a simulation system in making speedy, well-informed decisions has been considered by a group of Bushfire CRC researchers who have collaborated to produce a 'proof of concept' for such a system, demonstrated initially on three case studies. The 'proof of concept' system has the working name FireDST (Fire Impact and Risk Evaluation Decision Support Tool). FireDST links various databases and models, including the Phoenix RapidFire fire prediction model and building vulnerability assessment models, as well as infrastructure and demographic databases. The information is assembled into an integrated simulation framework through a geographical information system (GIS) interface. Pre-processed information, such as factors that determine the local and regional wind, and also the typical response of buildings to fire, are linked through a database, along with census-derived social and economic information. This presentation provides an overview of the FireDST simulation 'proof of concept' tool and walks through a sample probabilistic simulation constructed using the tool. Handbook MODSIM2013 Conference

Monitoring is a regulatory requirement for all carbon dioxide capture and geological storage (CCS) projects to verify containment of injected carbon dioxide (CO2) within a licensed geological storage complex. Carbon markets require CO2 storage to be verified. The public wants assurances CCS projects will not cause any harm to themselves, the environment or other natural resources. In the unlikely event that CO2 leaks from a storage complex, and into groundwater, to the surface, atmosphere or ocean, then monitoring methods will be required to locate, assess and quantify the leak, and to inform the community about the risks and impacts on health, safety and the environment. This paper considers strategies to improve the efficiency of monitoring the large surface area overlying onshore storage complexes. We provide a synthesis of findings from monitoring for CO2 leakage at geological storage sites both natural and engineered, and from monitoring controlled releases of CO2 at four shallow release facilities - ZERT (USA), Ginninderra (Australia), Ressacada (Brazil) and CO2 field lab (Norway).

Geoscience Australia undertook a marine survey of the Vlaming Sub-basin in March and April 2012 to provide seabed and shallow geological information to support an assessment of the CO2 storage potential of this sedimentary basin. The survey was undertaken under the Australian Government's National CO2 Infrastructure Plan (NCIP) to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. The Vlaming Sub-basin is located offshore from Perth, Western Australia, and was previously identified by the Carbon Storage Taskforce (2009) as potentially highly suitable for CO2 storage. The principal aim of the Vlaming Sub-basin marine survey (GA survey number GA334) was to look for evidence of any past or current gas or fluid seepage at the seabed, and to determine whether these features are related to structures (e.g. faults) in the Vlaming Sub-basin that may extend up to the seabed. The survey also mapped seabed habitats and biota in the areas of interest to provide information on communities and biophysical features that may be associated with seepage. This research addresses key questions on the potential for containment of CO2 in the Early Cretaceous Gage Sandstone (the basin's proposed CO2 storage unit) and the regional integrity of the South Perth Shale (the seal unit that overlies the Gage Sandstone). This dataset comprises sidescan grids.

Geoscience Australia undertook a marine survey of the Vlaming Sub-basin in March and April 2012 to provide seabed and shallow geological information to support an assessment of the CO2 storage potential of this sedimentary basin. The survey was undertaken under the Australian Government's National CO2 Infrastructure Plan (NCIP) to help identify sites suitable for the long term storage of CO2 within reasonable distances of major sources of CO2 emissions. The Vlaming Sub-basin is located offshore from Perth, Western Australia, and was previously identified by the Carbon Storage Taskforce (2009) as potentially highly suitable for CO2 storage. The principal aim of the Vlaming Sub-basin marine survey (GA survey number GA334) was to look for evidence of any past or current gas or fluid seepage at the seabed, and to determine whether these features are related to structures (e.g. faults) in the Vlaming Sub-basin that may extend up to the seabed. The survey also mapped seabed habitats and biota in the areas of interest to provide information on communities and biophysical features that may be associated with seepage. This research addresses key questions on the potential for containment of CO2 in the Early Cretaceous Gage Sandstone (the basin's proposed CO2 storage unit) and the regional integrity of the South Perth Shale (the seal unit that overlies the Gage Sandstone). This dataset comprises high resolution backscatter grids.

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