The response to emergency situations such as floods and fires demand products in short time frames. If you use remote sensing then the response typically involves detailed examination of imagery in order to determine the spectral bands, ratios and associated thresholds that map the desired features such as flood or burn extent. The trial and error process associated with manual threshold selection is often time consuming and can result in significant errors due to confounding factors such as clouds and shadowed areas. By modelling features such as flood waters or fire scars as Gaussian distributions, allowing for fuzzy thresholds with neighbouring features, the required thresholds can be automatically derived from the imagery and emergency events can have extents determined much more rapidly. Automatic threshold selection minimises trial and error, thereby dramatically reducing processing turn-around time.

The aim of this document is to * outline the information management process for inundation modelling projects using ANUGA * outline the general process adopted by Geoscience Australia in modelling inundation using ANUGA * allow a future user to understand (a) how the input and output data has been stored (b) how the input data has been checked and/or manipulated before use (c) how the model has been checked for appropriateness

User Manual - Australian Flood Studies Database Search

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.

The Swan River is the main river through Perth, the capital city of Western Australia. Direct tangible economic losses to residential dwellings in Perth was based on hydraulic modelling using the one dimensional unsteady flow model HEC-RAS, geographical information systems, a building exposure database and synthetic stage-damage curves. Eight flood scenarios ranging from the 10 year average recurrence interval (ARI) to the 2000 year ARI event were examined. The combined structure and contents flood losses ranged from A$17 million to A$659 million for insured structures and A$14 million to A$583 million for uninsured structures. This equates to an average annual damage of A$9.6 million and A$7.9 million respectively. The results reinforce the need to consider a wide range of varying magnitude flood events when assessing losses due to the temporal and spatial variation between flood scenarios.

<b>This record was retired 29/03/2022 with approval from S.Oliver as it has been superseded by eCat 146091 Geoscience Australia Landsat Water Observation Statistics Collection 3</b> WOfS-STATS (WO_STATS_2.1.5) is a set of statistical summaries of the water observations contained in WOfS (WO_2.1.5). The layers available are: the count of clear observations;the count of wet observations;the percentage of wet observations over time. This product is Water Observations from Space - Statistics (WO-STATS), a set of statistical summaries of the WOfS product that combines the many years of WOfS observations into summary products that help the understanding of surface water across Australia. WO-STATS consists of the following datasets: Clear Count: how many times an area could be clearly seen (ie. not affected by clouds, shadows or other satellite observation problems), Wet Count: how many times water was detected inobservations that were clear, Water Summary: what percentage of clear observations were detected as wet (ie. the ration of wet to clear as a percentage) As no confidence filtering is applied to this product, it is affected by noise where misclassifications have occurred in the WOfS water classifications, and hence can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the WO-Fil-STATS product, which provide a noise-reduced view of the water summary. WO-STATS is available in multiple forms, depending on the length of time over which the statistics are calculated. At present the following are available: WO-STATS: statistics calculated from the full depth of time series (1986 to present) WO-STATS-ANNUAL: statistics calculated from each calendar year (1986 to present) WO-STATS-NOV-MAR: statistics calculated yearly from November to March (1986 to present) WO-STATS-APR-OCT: statistics calculated yearly from April to October (1986 to present)

Replaced by 78873 Record 2014/003 (A Marshall 29/01/14)

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.

Australian Rainfall and Runoff (ARR) is a national guideline document, data and software suite that can be used for the estimation of design flood characteristics in Australia. This is the 4th edition of ARR, after the 1st edition was released by Engineers Australia in 1958. This edition is published and supported by the Commonwealth of Australia. Geoscience Australia supports ARR as part of its role to provide authoritative, independent information and advice to the Australian Government and other stakeholders to support risk mitigation and community resilience. ARR is pivotal to the safety and sustainability of Australian infrastructure, communities and the environment. It is an important component in the provision of reliable and robust estimates of flood risk. Consistent use of ARR ensures that development does not occur in high risk areas and that infrastructure is appropriately designed.

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.