<div>The Abbot Point to Hydrographers Passage bathymetry survey was acquired for the Australian Hydrographic Office (AHO) onboard the RV Escape during the period 6 Oct 2020 – 16 Mar 2021. This was a contracted survey conducted for the Australian Hydrographic Office by iXblue Pty Ltd as part of the Hydroscheme Industry Partnership Program. The survey area encompases a section of Two-Way Route from Abbot Point through Hydrographers Passage QLD. Bathymetry data was acquired using a Kongsberg EM 2040, and processed using QPS QINSy. The dataset was then exported as a 30m resolution, 32 bit floating point GeoTIFF grid of the survey area.</div><div>This dataset is not to be used for navigational purposes.</div>

This is a subset of Geoscience Australia's Marine Connectivity Database (<a href="https://pid.geoscience.gov.au/dataset/ga/82692">here</a>), covering the North-west marine planning region for initial releases taking place in the interval January-March 2010. The subset is intended for use in development and testing as part of the GovHack 2016 competition.

The Intertidal Extents Model (ITEM v1.0) product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range. The current version utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1987 and 2015 (inclusive). The Relative Extents Model (ITEM_REL_mosaic_1987_2015.tif) utilises the tidal information attributed to each tile observation to indicate the spatial extent of intertidal substratum exposed at each 10% percentile of the observed tidal range for the cell. Attributes: Single Band Integer Raster: 0 - Always water 1 - Exposed at lowest 0-10% of the observed tidal range 2 - Exposed at 10-20% of the observed tidal range 3 - Exposed at 20-30% of the observed tidal range 4 - Exposed at 30-40% of the observed tidal range 5 - Exposed at 40-50% of the observed tidal range 6 - Exposed at 50-60% of the observed tidal range 7 - Exposed at 60-70% of the observed tidal range 8 - Exposed at 70-80% of the observed tidal range 9 - Exposed at highest 80-100% of the observed tidal range (land) -999 - No Data

The Intertidal Extents Model (ITEM v1.0) product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range. The current version utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1987 and 2015 (inclusive). The Intertidal Extents Model (ITEM v1.0) consists of three datasets derived from the Landsat NBAR data managed in the Australian Geoscience Data Cube (AGDC) for the period 1987 to 2015. The Confidence Layer (ITEM_CL_mosaic_1987_2015.tif) reflects the confidence level of the Relative Extents Model, based on the distribution of classification metrics within each of the 10% intervals of the tidal range. The layer should be used to filter region/pixels in the model where the derived spatial extents may be adversely affected by data and modelling errors. Note: The confidence layer should be utilised on a cell-by-cell basis. Standard deviation values within the confidence layer for a particular cell are not comparable to other cells within the model. Attributes: Single Band Integer Raster: 0 - Model is invalid. Indicates pixels where data quality and/or number of observations have resulted in no available observations in one or more of the percentile interval subsets. -32767 - No Data All other values - The pixel-based average of the NDWI standard deviations calculated independently for each 10% percentile interval of the observed tidal range (x 1000).

A mini-poster on GA's capability in tsunami hazard modelling.

<p>The Intertidal Extents Model (ITEM v1.0) product is a national scale gridded dataset characterising the spatial extents of the exposed intertidal zone, at intervals of the observed tidal range. The current version utilises all Landsat observations (5, 7, and 8) for Australian coastal regions (excluding off-shore Territories) between 1987 and 2015 (inclusive). <p>The Intertidal Extents Model (ITEM v1.0) consists of three datasets derived from the Landsat NBAR data managed in the Australian Geoscience Data Cube (AGDC) for the period 1987 to 2015. <p>The Coastal Cells shapefile (ITEM_CoastalCells.shp) identifies the location and extents of the 221 one degree by one degree AGDC cells used in the product, covering the mainland and Tasmanian coastline of the continent (Figure 1). The shapefile also includes information on the lowest (LOT) and highest (HOT) observed tides for the cell, and hence the observed tidal range (HOT-LOT), based on tidal modelling for the time of acquisition of each of the corresponding Landsat tile observations in the cell. <p>Attributes: <p>AGDC Cell Identifier <p>Lowest Observed Tide (LOT) - The lowest modelled tidal offset based on the acquisition times of all observations in the cell. Relative to Mean Sea Level (MSL) (m) x 1000. <p>Highest Observed Tide (HOT) - The highest modelled tidal offset based on the acquisition times of all observations in the cell. Relative to Mean Sea Level (MSL) (m) x 1000.

Offshore Probabilistic Tsunami Hazard Assessments (offshore PTHAs) provide large-scale analyses of earthquake-tsunami frequencies and uncertainties in the deep ocean, but do not provide high-resolution onshore tsunami hazard information as required for many risk-management applications. To understand the implications of an onshore PTHA for the onshore hazard at any site, in principle the tsunami inundation should be simulated locally for every scenario in the offshore PTHA. In practice this is rarely feasible due to the computational expense of inundation models, and the large number of scenarios in offshore PTHAs. Monte-Carlo methods offer a practical and rigorous alternative for approximating the onshore hazard, using a random subset of scenarios. The resulting Monte-Carlo errors can be quantified and controlled, enabling high-resolution onshore PTHAs to be implemented at a fraction of the computational cost. This study develops novel Monte-Carlo sampling approaches for offshore-to-onshore PTHA. Modelled offshore PTHA wave heights are used to preferentially sample scenarios that have large offshore waves near an onshore site of interest. By appropriately weighting the scenarios, the Monte-Carlo errors are reduced without introducing any bias. The techniques are applied to a high-resolution onshore PTHA for the island of Tongatapu in Tonga. In this region, the new approaches lead to efficiency improvements equivalent to using 4-18 times more random scenarios, as compared with stratified-sampling by magnitude, which is commonly used for onshore PTHA. The greatest efficiency improvements are for rare, large tsunamis, and for calculations that represent epistemic uncertainties in the tsunami hazard. To facilitate the control of Monte-Carlo errors in practical applications, this study also provides analytical techniques for estimating the errors both before and after inundation simulations are conducted. Before inundation simulation, this enables a proposed Monte-Carlo sampling scheme to be checked, and potentially improved, at minimal computational cost. After inundation simulation, it enables the remaining Monte-Carlo errors to be quantified at onshore sites, without additional inundation simulations. In combination these techniques enable offshore PTHAs to be rigorously transformed into onshore PTHAs, with full characterisation of epistemic uncertainties, while controlling Monte-Carlo errors. Appeared online in Geophysical Journal International 11 April 2022.

The Wallaby-Zenith Fracture Zone Survey was acquired by the Minderoo-UWA Deep-Sea Research Centre at the University of Western Australia during the expedition “Indomitable” onboard the RV DSSV Pressure Drop from the 8th March to the 2nd June 2021 led by Dr. Alan Jamieson, using a Kongsberg EM124. The expedition was funded by a joint mission between Caladan Oceanic LLC (US) and the Minderoo Foundation (Australia). This dataset contains a 64m-resolution and a 128mm-resolution 32-bit floating point GeoTIFF files of the bathymetry in the study area, derived from the processed EM124 bathymetry data, using QPS Qimera v.2.5 software. This dataset is not to be used for navigational purposes. This dataset is published with the permission of the CEO, Geoscience Australia.

A new methodology is proposed to estimate storm demand and dune recession by clustered and non-clustered events, to determine if the morphological response to storm clusters results in greater beach erosion than that from individual storms that have the same average recurrence interval (ARI) or return period. The method is tested using a numerical morphodynamic model that combines both cross-shore and longshore beach profile evolution processes, forced by a 2D wave transformation model, and is applied as an example within a 20 km long coastal cell at an erosion hotspot at Old Bar, NSW mid-north coast, Australia. Wave and water level data hindcast in previous modelling (Davies et al., 2017) were used to provide two thousand different synthetic wave and tide records of 100 years duration for input to a nested nearshore 2D SWAN model that provides wave conditions at the 12 m depth contour. An open-source shoreline evolution model was used with these wave conditions to model cross-shore and longshore beach profile evolution, and was calibrated and verified against long-term dune recession observations. After a 50 year model spin up, 50 years of storm demand (change in sub-aerial beach volume) and dune toe position were simulated and ranked to form natural estimators for the 50, 25, 16, 12.5 and 10 year return period of individual events, together with confidence limits. The storm demand analysis was then repeated to find the return period of clustered and non-clustered morphological events. Morphological clusters are defined here by considering the response of the beach, rather than the forcing, with a sensitivity analysis of the influence of different recovery thresholds between storms also investigated. The new analysis approach provides storm demand versus return period curves for the combined population of clustered and non-clustered events, as well as a curve for the total population of individual events. In this approach, non-clustered events can be interpreted as the response to isolated storms. For clustered and non-clustered morphological events the expected storm demand for a 50-year return period is approximately 25% greater than that for individual events. Alternatively, for clustered and non-clustered events the magnitude of the storm demand that occurs at a return period of 17 years is the same as that which occurs at a return period of 50 years for individual events. However, further analysis shows that for a 50-year return period, the expected storm demand for the population of non-clustered events is similar to that of the clustered events, although the size of the population of the latter is much greater. Hence, isolated storms can generate the same storm demand as storm clusters, but there is a much higher probability that a given storm demand is generated by a morphologically clustered event. Appeared online in Coastal Engineering Volume 168, September 2021.

<div>The Shark Bay Bathymetry was derived form a time series of multispectral satellite data from the Copernicus Sentinel-2 satellite sensor, acquired between January 2017 and December 2020. This dataset was produced by the University of Western Australia to support student research projects. The dataset encompasses the Shark Bay in Western Australia. These critical geospatial data layers provide the essential environmental baseline information for the long-term monitoring and management. Mapping the shallow water zone is of importance both from an environmental and socioeconomic perspective. Bathymetry data was processed following the workflow of Lebrec et al. (2021) [https://doi.org/10.5194/essd-13-5191-2021]. This dataset is not to be used for navigational purposes. This dataset is published with the permission of the CEO, Geoscience Australia.</div>