The Surface Hydrology Points (Regional) dataset provides a set of related features classes to be used as the basis of the production of consistent hydrological information. This dataset contains a geometric representation of major hydrographic point elements - both natural and artificial. This dataset is the best available data supplied by Jurisdictions and aggregated by Geoscience Australia it is intended for defining hydrological features.

This record has been created for Sales to be able to invoice data requests that occur from downloading of data from the National Elevation Data Framework (NEDF) Web Portal. The Portal was set up in 2010 and data more than 400MB needs to be downloaded from the holding pen on the NEDF server and copied onto media and sent to the requester. Each data request will come with metadata and the appropriate data licence.

Mean monthly and mean annual rainfall grids. The grids show the rainfall values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1961-1990. Gridded data were generated using the ANU (Australian National University) 3-D Spline (surface fitting algorithm). The resolution of the data is 0.025 degrees ( approximately 2.5km) - as part of the 3-D analysis process a 0.025 degree resolution digital elevation model (DEM) was used. Approximately 6000 stations were used in the analysis over Australia. All input station data underwent a high degree of quality control before analysis, and conform to WMO (World Meteorological Organisation) standards for data quality.

Subtitle: Behind the Scenes of Geofabric Version 3 Pilot & the Future of Geospatial Surface Water Information The Bureau of Meteorology's Australian Hydrological Geospatial Fabric (Geofabric) was established in 2008 as the spatial information database to support water accounting and resource assessment mandated under the Water Act 2007. Foundation layers for Geofabric versions 1 and 2 were developed from 1:250K streamline data and the 9 second resolution national DEM. The uses of the Geofabric data have expanded to new disciplines and have resulted in increased demand for finer national resolution. Version 3 of the Geofabric is now under development in a collaborative project between Geoscience Australia, CSIRO, Australian National University (ANU) and the Bureau of Meteorology. The foundation inputs for Geofabric version 3 are based on the integrated national surface hydrology dataset which uses the best available scale data from the jurisdictions and the 1 second resolution SRTM DEM. This significant enhancement presents both challenges and opportunities. This presentation at the Surveying & Spatial Sciences Institute (SSSI) ACT Region conference on 16 August 2013 aims to show the work being undertaken in the pilot areas of the Namoi and Murrumbidgee River Regions.

The purpose of this paper is to investigate and quantify the accuracy with which hydrological signals in the Murray-Darling Basin, southeast Australia can be estimated from GRACE. We assessed the extent to which the Earth's major geophysical processes contaminate the gravitational signals in the Basin. Eighteen of the world's largest geophysical processes which generate major gravitational signals (e.g. melting of the Greenland icesheet, hydrology in the Amazon Basin) were simulated and the proportion of the simulated signal detected in the Murray - Darling Basin was calculated. The sum of the cumulative effects revealed a maximum of ~4 mm (equivalent water height) of spurious signal was detected within the Murray - Darling Basin; a magnitude smaller than the uncertainty of the basin-scale estimates of changes in total water storage. Thus, GRACE products can be used to monitor broad scale hydrologic trends and variability in the Murray-Darling Basin without the need to account for contamination of the estimates from external geophysical sources.

In many areas of the world, vegetation dynamics in semi-arid floodplain environments have been seriously impacted by increased river regulation and groundwater use. In this study, the condition of two of Australia's iconic riparian and floodplain vegetation elements, River Red Gums (Eucalyptus camaldulensis) and Black Box (E. largiflorens) are examined in relation to differing hydraulic regimes. With increases in regulation along Murray-Darling Basin rivers, flood volume, seasonality and frequency have changed which has in turn affected the condition and distribution of vegetation. Rather than undertaking a field based assessment of tree health in response to current water regimes, this paper documents a remote sensing study that assessed historic response of vegetation to a range of different climatic and hydraulic regimes at a floodplain scale. This methodology innovatively combined high-resolution vegetation structural mapping derived from LiDAR data (Canopy Digital Elevation Model and Foliage Projected Cover) with 23 years of Landsat time-series data. Statistical summaries of Normalised Difference Vegetation Index values were generated for each spatially continuous vegetation structural class (e.g. stand of closed forest) for each Landsat scene. Consequently long-term temporal change in vegetation condition was assessed against different water regimes (drought, local rainfall, river bank full, overbank flow, and lake filling). Results provide insight into vegetation response to different water sources and overall water availability. Additionally, some inferences can be made about lag times associated with vegetation response and the duration of the response once water availability has declined (e.g. after floodwaters recede). This methodology should enable water managers to better assess the adequacy of environmental flows.

Elevation data is a point, line or surface geographically located in the x and y relative to a horizontal datum, that includes a height (z) above or below a known vertical datum. Bathymetry will deal with all offshore elevation data. - Elevation data will include both raw elevation data and digital elevation models (DEM); - Spot Heights, points on the earth's surface, of known elevation. - Contours, lines which represents an imaginary line on the ground joining points of equal elevation. - Horizontal Control Points, points on the ground, the horizontal position of which has been determined by geodetic survey. - Digital Elevation Models (DEM) are interpolated representations of a surface. Elevation points are spaced at a regular interval so as to create a grid or lattice. These grids can be directly observed or, more generally, they are computed from more than one of the above mentioned irregular spaced elevations. - Digital Terrain Models (DTM) are bare earth DEM's representing the terrain They are interpolated using a combination of elevation information and could also be constrained using break lines, such are cliffs, drainage, coast etc. - Digital Surface Models (DSM) are also DEM's, but they include non-surface objects like trees, buildings etc. So, a DSM = DTM + all non surface objects. - Triangulated Irregular Network (TIN). A vector data structure that partitions geographic space into contiguous, nonoverlapping triangles. The vertices of each triangle are sample data points with x, y, and z values. These sample points are connected by lines to form Delaunay triangles. TINs are used to store and display elevation models. - Hydrologically enforced Digital Elevation Models (HDEM) represents DEM with drainage enforcement. The quality of a DEM is a measure of how accurate elevation is at each pixel (absolute accuracy) and how accurately the morphology is represented (relative accuracy). Several factors affect the quality of DEM-derived products: terrain roughness, sampling density (elevation data collection method), grid resolution or pixel size, interpolation algorithm, vertical resolution and terrain analysis algorithm.

The 1 second SRTM derived DEM-H Version 1.0 is a 1 arc second (~30 m) gridded digital elevation model (DEM) that has been hydrologically conditioned and drainage enforced. The DEM-H captures flow paths based on SRTM elevations and mapped stream lines, and supports delineation of catchments and related hydrological attributes. The dataset was derived from the 1 second smoothed Digital Elevation Model (DEM-S; ANZCW0703014016) by enforcing hydrological connectivity with the ANUDEM software, using selected AusHydro V1.6 (February 2010) 1:250,000 scale watercourse lines (ANZCW0503900101) and lines derived from DEM-S to define the watercourses. The drainage enforcement has produced a consistent representation of hydrological connectivity with some elevation artefacts resulting from the drainage enforcement. A full description of the methods is in preparation (Dowling et al., in prep). This product is the last of the Version 1.0 series derived from the 1 second SRTM (DSM, DEM, DEM-S and DEM-H) and provides a DEM suitable for use in hydrological analysis such as catchment definition and flow routing.

We are often faced with uncertainty when making decisions – from trivial decisions such as whether to take an umbrella, or major decisions such as whether to buy that house. Appreciating the uncertainty in future conditions (‘will it rain today?’; ‘will house prices continue to go up?’) is crucial to making good decisions. This is no different for water resource managers, who need to make decisions on flood prevention, climate adaptation or coal resource developments. As scientists, we strive to inform decision-makers about uncertainties in a comprehensive, unbiased and transparent manner. In this paper, we discuss some of the challenges and approaches used to communicate uncertainty during our contributions to the Bioregional Assessments Programme, a federally funded research project to assess the potential impacts of coal resource development on water resources and water-dependent assets in eastern Australia. A first step in analysing potential impacts, is to identify the causal pathways that detail how development activities can possibly affect the groundwater and surface water systems, and how these changes might affect the economic, social and ecological functioning of a region. This conceptual model provides the framework for detailed geological, hydrogeological, hydrological and ecological modelling. Predictions have traditionally been made using a single deterministic model, a calibrated model that best fits the available observations. However, to assess the likelihood of potential impacts, we used a stochastic approach to create an ensemble of possible predictions (hundreds and thousands of possible answers) that are all consistent with the available observations. This results in a range or distribution of predictions. However, communicating the range of model results, as well as all of the complexities and underlying assumptions in a way that is relevant and accessible to decision-makers is very challenging. For bioregional assessments, we have worked with decision makers to improve communication of uncertainty using a consistent, calibrated language, tables, plots of the range of predictions and maps designed to convey probabilistic information in an intuitive manner. Further, model details and assumptions are documented in technical reports, and the data, models and predictions are made publicly available to increase transparency and reproducibility. The amount and technical detail of that information can be challenging for decision-makers to identify what is important and what is not. To support decision-makers, we use a qualitative uncertainty analysis to summarise the rationale for and effect on prediction of each major assumption. This table, in combination with a plain English discussion, allows readers to rapidly appreciate the limitations, as well as opportunities for further data collection or modelling. Bioregional assessments have highlighted the importance of early consultation with target audiences, which has enabled us to tailor the uncertainty communication products to decision-makers, as well as avoid the potential for biased interpretation of results, where decision-makers are drawn to the extremes. <b>Citation:</b> Peeters, L.J.M., Crosbie, R.S., Henderson, B.H., Holland, K., Lewis, S., Post, D.A., Schmidt, R.K., The importance of being uncertain, <i>Water e-Journal</i>, Vol 3, No.2, 2018. ISSN 2206-1991. https//doi.org/10.21139/wej.2018.006

Shows the boundaries of the Australian basins as defined by the Agriculture and Resource Management Council of Australia and New Zealand (ARMCANZ). This data shows boundary and attribute information for 12 divisions, 77 regions and 245 basins. It also contains, for each basin, information relating to its individual basin/region/division name and number. State borders are also included in the data. Data for Division XIII Distant Islands Division is not included. These basins are the primary building block for the collection of national hydrologic data and the assessment of water resources. Data are suitable for GIS applications. Free online download. Available in ArcView Shapefile and MapInfo mid/mif. Product Specifications Coverage: Australia Currency: June 1997 Coordinates: Geographical Datum: AGD66 Format: ArcInfo Export, ArcView Shapefile and MapInfo mid/mif Medium: Free online and CD-ROM (fee applies)