<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 datasets created to produce the emergency mapping support products which contributed to fulfilling GA's arrangements in supporting the outcomes sought by the Australian Government during disaster events.

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.

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.

Poster showing the 2010 Floods in Queensland fill Lake Eyre

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.

There are a number of factors which influence the direct consequence of flooding. The most important are depth of inundation, velocity, duration of inundation and water quality. Though computer modelling techniques exist that can provide an estimate of these variables, this information is seldom used to estimate the impact of flooding on a community. This work describes the first step to improve this situation using data collected for the Swan River system in Perth, Western Australia. Here, it is shown that residential losses are underestimated when stage-damage functions or the velocity-stage-damage functions are used in isolation. This is because the functions are either limited to assessing partial damage or structural failure resulting from the movement of a house from its foundations. This demonstrates the need to use a combination of techniques to assess the direct economic impact of flooding.

This is a proof of concept web service displaying trial samples of historic flood mapping from satellite. Over the next 2 years this service will be developed into a nationwide portal displaying flooding across Australia as observed by satellite since 1987. The service shows a summary of water observed by the Landsat-5 and MODIS satellites across Australia for periods between 2000 and 2012. The first layer set displays national observed water from MODIS fvrom 2000 to 2012, as derived by Geoscience Australia using an automated flood mapping algorithm. The colouring of the display represents the frequency of observed water in a 500 x 500m grid. The higher the number, the more often water was observed by the satellites over the period. This means that floods have low values, while lakes, dams and other permanent water bodies have high values. The three additional layer sets are study areas demonstrating the water observed in each study area by the Landsat-5 satellite, as derived by Geoscience Australia using an automated flood mapping algorithm. The study areas and the observation periods are: Study Area 1, Condamine River system between Condamine and Chinchilla, Qld, observed between 2006 and 2011 Study Area 2, North-west Victorian rivers between Shepparton and Kerang, observed between 2006 and 2011 Study Area 3, Northern Qld rivers, near Normanton, observed between 2003 and 2011 Each Study Area layer set includes a water summary displaying the frequency of observed water in 25 x 25m grids, plus individual flood extents for specific dates where flooding was observed. Similar to the national, MODIS summary, the higher the value, the more often water was observed by the satellites over the period. Limitations of the Information The automated flood mapping algorithm can confuse cloud shadows and snow with flood water, so some areas shown as water may be incorrect. This is a proof of concept dataset and has not been validated.

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.

11-5519 Metropolitan Manilla (Philippines). Philippine GIS data-sets should arrive from the source on the 15th of July, 2011. GAV will process the data, and produce a short movie. The movie will reveal the 17 town halls of the greater metro Manilla; and outline the fault line, as well as earthquake affected areas, flood affected areas and cyclone affected areas. This movie is for the Philippine Govt. via Ausaide, and will include photographs of Philippine nationals assisting in disaster reduction work. The aquired data-sets will be stored on the GA data store, where access can be gained through communication with Luke Peel - GEMD National Geographic Information Section, Geoscience australia.