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  • The flood risk in many urban catchments is poorly understood. Legacy stormwater infrastructure is often substandard and anticipated climate change induced sea level rise and increased rainfall intensity will typically exacerbate present risk. In a Department of Climate Change and Energy Efficiency (DCCEE) funded collaboration between Geoscience Australia (GA) and the City of Sydney (CoS), the impacts on the Alexandra Canal catchment have been studied. This work has built upon detailed flood hazard analyses by Cardno commissioned by the CoS and has entailed the development of exposure and vulnerability information. Significantly, the case study has highlighted the value of robust exposure attributes and vulnerability models in the development of flood risk knowledge. The paper describes how vulnerability knowledge developed following the 2011 Brisbane floods to include key building types found in the inner suburb of Sydney. It also describes the systematic field capture of building exposure information in the catchment area and its categorisation into 19 generic building types. The assessment of ground floor heights using the Field Data Analysis Tool (FiDAT) developed at Geoscience Australia is also presented. The selected hazard scenario was a 100 year ARI event with 20% increased rainfall intensity accompanied by a 0.55m sea level rise in Botany Bay. The impact from the selected scenario was assessed in terms of monetary loss for four combinations of vulnerability model suite (GA and NSW Government) and floor height attribution method (assumed 0.15m uniformly and evaluated from LiDAR and street view imagery). It was observed that the total loss is higher in the case of assumed floor heights compared to FiDAT processed floor heights as the former failed to capture increased floor heights for newer construction. However, the loss is lower when only two vulnerability models developed by NSW Government are applied for the entire building stock in the region as two models could not reliably represent the whole building stock.

  • The Australian Flood Studies Database is available on line by Geoscience Australia. The database provides metadata on Australian flood studies and information on flood risk with a digital version where available. The purpose of the document is to guide new users in data entry and uploading of flood studies to a level acceptable for inclusion in the database.

  • The Australian Flood Studies Database is available on line by Geoscience Australia via the Australian Flood Risk Information Portal. The database provides metadata on Australian flood studies and information on flood risk with a digital version where available. The purpose of the document is to guide new users in data entry and uploading of flood studies to a level acceptable for inclusion in the database.

  • <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)

  • Poster showing the 2010 Floods in Queensland fill Lake Eyre

  • Floods are Australia's most expensive natural hazard with annual average damages estimated at $377 million. Modelling flood hazard and potential flood impact is therefore an important first step in reducing the cost of floods to the community. The availability of a rigorously tested free software modelling tool for flooding would assist in meeting this objective. ANUGA is a collaborative effort of Geoscience Australia and the Australian National University and has gained increasing interest as an open source two-dimensional flood model. The development of ANUGA for flood modelling purposes has been guided and furthered through close consultation with a number of local government and consulting engineers. This paper highlights case studies where ANUGA has been used for both hydrological and hydraulic modelling. This paper also makes two broad recommendations. The first recommendation is for further model validation against historical flood events. Additional model comparison is also needed, particularly against other two-dimensional models. ANUGA should also be validated against a suite of hydraulic tests to provide confidence in ANUGA's ability to be used as a general purpose hydraulic model. The second broad recommendation is that the ANUGA software is further developed to make it comparable with other two-dimensional flood models. Priorities for this development include the ability to model structures (culverts, pipes and bridges), the addition of a kinematic viscosity term and the inclusion of discharge as an inflow boundary condition. The ability to incorporate variable bed elevation in models, account for water storage in buildings and consider spatially and depth varying Manning's friction 'n' are also important. The development of a graphical (geographical information systems) user interface would make ANUGA more accessible.

  • Every year floods cause millions of dollars damage to buildings and infrastructure, as well as to agricultural land and crops. They also disrupt business, and affect the safety and health of communities. The losses due to flooding vary widely from year to year and are dependent on a number of factors such as the severity of a flood and its location. Between 1967 and 2005 the average annual direct cost of floods in Australia has been 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.

  • Background These are the statistics generated from the DEA Water Observations (Water Observations from Space) suite of products, which gives summaries of how often surface water was observed by the Landsat satellites for various periods (per year, per season and for the period from 1986 to the present). Water Observations Statistics (WO-STATS) provides information on how many times the Landsat satellites were able to clearly see an area, how many times those observations were wet, and what that means for the percentage of time that water was observed in the landscape. What this product offers Each dataset in this product consists of the following datasets: - Clear Count: how many times an area could be clearly seen (i.e. 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 (i.e. the ratio 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 input water classifications, and can be difficult to interpret on its own. The confidence layer and filtered summary are contained in the Water Observations Filtered Statistics (WO-FILT-STATS) product, which provides a noise-reduced view of the all-of-time 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)

  • <b>This record was retired 15/09/2022 with approval from S.Oliver as it has been superseded by eCat 146091 DEA Water Observations Statistics (Landsat)</b> In previous versions of WOfS, the basic water classifications, statistical summaries and confidence products were contained within one product with several datasets. As of version 2.1.5, WOfS is split into three products: Water Observation Feature Layers (WO_25_2.1.5), Summary Statistics (WO-STATS_25_2.1.5), and Filtered Summary Statistics (WO-FILT-STATS_25_2.1.5). This product is Water Observations from Space - Filtered Statistics (WO-FILT-STATS), consisting of a Confidence layer that compares the WO-STATS water summary to other national water datasets, and the Filtered Water Summary which uses the Confidence to mask areas of the WO-STATS water summary where Confidence is low. The Filtered Water Summary provides the long term understanding of the recurrence of water in the landscape, with much of the noise due to misclassification filtered out. WO-FILT-STATS consists of the following datasets: Confidence: the degree of agreement between water shown in the Water Summary and other national datasets. The Confidence layer provides understanding of whether the water shown in the Water Summary agrees with where water should exist in the landscape, such as due to sloping land or whether water has been detected in a location by other means. Filtered Water Summary: A simplified version of the Water Summary, showing the frequency of water observations where the Confidence is above a cutoff level. This layer gives a noise-reduced view of surface water across Australia. Even though confidence filtering is applied to the Filtered Water Summary, some cloud and shadow, and sensor noise does persist.

  • 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.