<div>The A1 poster incorporates 4 images of Australia taken from space by Earth observing satellites. The accompanying text briefly introduces sensors and the bands within the electromagnetic spectrum. The images include examples of both true and false colour and the diverse range of applications of satellite images such as tracking visible changes to the Earth’s surface like crop growth, bushfires, coastal changes and floods. Scientists, land and emergency managers use satellite images to analyse vegetation, surface water or human activities as well as evaluate natural&nbsp;hazards.</div>

The map shows the spatial distribution of short-duration rapid-onset floods and long-duration slow-rise floods. The Great Dividing Range in eastern Australia provides a natural separation of slower, wider rivers flowing west from faster, narrower coastal rivers flowing east.

With the average annual cost of floods estimated at $377 million, floods are Australia's most expensive natural hazard. As a result, considerable expenditure is made by government and industry to define flood areas in an effort to reduce the impacts of floods. This work typically involves the creation of reports describing the methodology used, data sources and results of hydrological and hydraulic modelling and damage assessments. While numerous reports are developed each year, there was no centralised record of what studies had been undertaken in Australia at a state/territory or national level until the development of the Australian Flood Studies Database in 2004. In 2009 Geoscience Australia reviewed the Australian Floods Studies Database via an online questionnaire. Opinion of the database was sought in three key areas including database functionality and content, and updating the database. The respondents confirmed the usefulness of the existing database content including hydrology and hydraulic scenarios, historical flood events used in the calibration, terrain and floor level surveys, damage assessments, inundation and hazard scenarios, information on what has occurred since a study's completion and related studies. Recurring themes highlighted by the survey respondents include the ability to be able to access the flood study reports and GIS flood layers via the database and be able to input data. Over 170 people completed the survey; 90% of whom were from local government. While only 20% of respondents had used the database, 72% of all respondents to the survey indicated that they would use the database in the future, whether or not they had used the database in the past. Three main recommendations can be concluded from the survey responses. The first recommendation is that the Australian Flood Studies Database is updated and that the lead agency for floodplain management in each State/Territory be responsible for that update on at least an annual basis. The second recommendation is that the database's existing functionality and content is maintained and further enhanced. The final recommendation is that the database is further publicised.

Floods are Australia's most expensive natural hazard with the average annual cost of floods estimated at AUD$377 million (BITRE 2008). This figure is likely to have risen following the widespread and devastating floods across eastern Australia that occurred over the summer of 2010-11. The development of tools to support the identification and analysis of flood risk is an important first step in reducing the cost of floods in the community. The Australian Government through Geoscience Australia (GA) has been leading the development of tools which assist in flood intelligence, modelling and damage assessment. An overview of three of these tools will be provided in this presentation. Note: Rest of abstract is too long for space provided.

The increasing availability of high-resolution digital elevation models (DEMs) is leading to improvements in flood analysis and predictions of surface-groundwater interaction in floodplain landscapes. To produce accurate predictions of flood inundation and calculations of flood volume, a 1m resolution LiDAR DEM was initially levelled to the Darling River floodplain by subtracting interpolated floodplain elevation trend surface from the DEM. This produces a de-trended flood-plain surface. Secondly, the levelled DEM surface was adjusted to the water-level reading at the Darling-River gauging station (Site 425012) at the time when the LiDAR was acquired. Flood extents were derived by elevation slicing of the adjusted levelled DEM up to any chosen river level. River-level readings from historical and current events utilised NSW Office of Water real-time river data. The flood-depth dataset is an inverted version of the flood-extent grid. Predicted flood depth and extent were classified by depth/elevation slice ranges of the adjusted de-trended DEM with 25 and 50 cm increments. In summary, the extent and depth of water inundation across the Darling floodplain have been predicted under different flooding scenarios, and validated using satellite data from historical (1990) and recent (2010/11) flood events. In all cases imagery and photo validation proved that predicted extents are accurate. The flood-risk predictions were then applied to a number of river-level scenarios. The flood risk predictions maps have been used as an input into developing recharge potential maps, and are being employed in flood-hazard assessments and infrastructure planning.

A Flood Study is a comprehensive technical investigation of flood behaviour. It defines the nature and extent of flooding across the landscape by providing information such as the extent, level and velocity of floodwaters and the distribution of flood flows. The Australian Flood Studies Database provides metadata on Australian flood studies and information on flood risk. It contains general information for all studies, including the study location, date, commissioning organisation and lead consultant. Additional information reflects the scope of each flood study. The types of additional information which may be included fall into nine categories, including hydrological estimates, hydraulic modelling, damage assessments, terrain survey, survey of building floor levels, maps of inundation extent, maps of flood hazard, flood mitigation strategies and post flood information. Where available, attachments of the flood studies and associated reports are included. 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. The database uses boundary datasets that are published by other agencies: - Catchment Area Data Source: <a href="http://www.bom.gov.au/water/geofabric/about.shtml">Geofabric 2011 (V2.0.1)</a> data © Commonwealth of Australia (Bureau of Meteorology) 2011 http://creativecommons.org/licenses/by/3.0/au/deed.en - Local Government Area Data Source: <a href="http://abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/1270.0.55.003July%202011?OpenDocument">Australian Bureau of Statistics 2011, Local Government Areas ASGS Non ABS Structures Ed 2011</a> Digital Boundaries in ESRI Shapefile Format, cat. no. 1270.0.55.003, retrieved 18 October 2012

In this paper a new benchmark for tsunami model validation is pro- posed. The benchmark is based upon the 2004 Indian Ocean tsunami, which provides a uniquely large amount of observational data for model comparison. Unlike the small number of existing benchmarks, the pro- posed test validates all three stages of tsunami evolution - generation, propagation and inundation. Specifically we use geodetic measurements of the Sumatra{Andaman earthquake to validate the tsunami source, al- timetry data from the jason satellite to test open ocean propagation, eye-witness accounts to assess near shore propagation and a detailed inundation survey of Patong Bay, Thailand to compare model and observed inundation. Furthermore we utilise this benchmark to further validate the hydrodynamic modelling tool anuga which is used to simulate the tsunami inundation. Important buildings and other structures were incorporated into the underlying computational mesh and shown to have a large inuence of inundation extent. Sensitivity analysis also showed that the model predictions are comparatively insensitive to large changes in friction and small perturbations in wave weight at the 100 m depth contour.

ACRES acquired SPOT 2 satellite images over the Namoi River, between the towns of Walgett and Wee Waa in December 1997 and November 2000. The November 2000 image consists of 12 scenes in which floodwaters, peaking at 8 metres, inundating the region are visible as green and light blue. Extensive flooding is evident. The December 1997 image shows the area of the Namoi River without floodwaters. The Namoi River catchment area is more than 350 kilometres long and stretches from Walcha in the east to Walgett in the west. Other river systems in the region include the Gwydir, Castlereagh, Hunter, Macquarie, Macleay, Manning, Culgoa and Condamine. You can find these rivers on Geoscience Australia's interactive Map of Australia.

The Risk Research Group at Geoscience Australia (GA) in Canberra is a multidisciplinary team engaged in the development of risk models for a range of natural hazards that are applicable to Australian urban areas. The Group includes hazard experts, numerical modellers, engineers, economists, and a specialist researching social vulnerability. The risk posed by riverine flooding to residential buildings is an important component of the work undertaken by the Group and is the focus of this paper. In 1975 researcher Richard Black published a report titled Flood Proofing Rural Residences as part of a multidisciplinary investigation of flood risk management in the USA. Black's research produced a number of curves describing combinations of water depth and velocity theoretically required to move a flooded house from its foundations. These so-called 'Black's Curves' have been referenced by numerous researchers worldwide since their publication. The houses used in Black's study are small by modern standards, and construction materials used in Australia can differ from those used in Black's research.

In this study, various hydrochemical approaches were used to understand recharge processes in shallow (<120m) unconsolidated alluvial sediments in a 7,500 km2 area of the Darling River floodplain. Pore fluids were extracted from sediments from 60 sonic-cored bores, and together with surface and groundwater samples, provided a hydrochemical dataset with over 1600 samples and 25 analytes. Major ion chemistry highlights a mixing signature between river waters, the shallow unconfined aquifer and the underlying semi-confined Calivil Formation aquifer. These represent the fresh groundwater resources near the river and are Na-(Ca-Mg)-HCO3-Cl waters. Away from the influence of river leakage, the regional groundwater is more saline and sodic with an evolved Na-Cl-SO4 watertype. The mixing associated with river leakage is also supported by age dating. Stable isotope data show that recharge is episodic and linked to high-flow flood events rather than continuous river leakage, as demonstrated by hydrographic monitoring. The combination of surface water and groundwater sampling, the pore fluid analyses and fuzzy-k means (FCM) cluster analysis, provides a novel, relatively simple but powerful tool to assist with interpretation of groundwater processes. The FCM cluster analysis used analytes that were present in at least 60% of samples and resulted in samples being classified into eight classes (or hydrochemical facies). Pore fluids and groundwater with the greatest affinity to the surface water samples were easily identified. In this way, sites with significant active recharge, principally by river leakage, were mapped. Downhole plots of the pore fluid FCM classes provided additional insights into groundwater processes. Comparing the FCM classification of pore fluids within the target (semi)confined aquifer with those from the overlying clay aquitard and shallow aquifer allowed the assessment of vertical inter-aquifer leakage.