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.

Mean monthly and mean annual areal actual, areal potential and point potential evapotranspiration grids. The grids show the evapotranspiration 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 grid point resolution of the data is 0.1 degrees ( approximately 10km). As part of the 3-D analysis process a 0.1 degree resolution digital elevation model (DEM) was used. Approximately 700 stations were used in the analysis, and all input station data underwent a high degree of quality control before analysis, and conform to WMO (World Meteorological Organisation) standards for data quality. Areal Actual ET is the ET that actually takes place, under the condition of existing water supply, from an area so large that the effects of any upwind boundary transitions are negligible and local variations are integrated to an areal average. Areal Potential ET is the ET that would take place, under the condition of unlimited water supply, from an area so large that the effects of any upwind boundary transitions are negligible and local variations are integrated to an areal average. Point Potential ET is the ET that would take place, under the condition of unlimited water supply, from an area so small that the local ET effects do not alter local airmass properties. It is assumed that latent and sensible heat transfers within the height of measurement are through convection only. The above definitions are based on those given by Morton (1983), but we have used the term areal potential ET for Mortons wet-environment ET and the term point potential ET for Mortons potential ET. Morton, F.I. (1983). Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology. Journal of Hydrology, 66: 1-76.

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.

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.

The AusHydro database provides a seamless surface hydrography layer for Australia at a nominal scale of 1:250,000. It consists of lines, points and polygons representing natural and man-made features such as water courses, lakes, dams and other water bodies. The natural water course layer consists of a linear network with a consistent topology of links and nodes that provide directional flow paths through the network for hydrological analysis. This network was used to produce the National 9 second Digital Elevation Model (DEM) of Australia (http://www.ga.gov.au/nmd/products/digidat/dem_9s.jsp). Surface Hydrology Dataset is an amalgamation of two primary datasets. The first is the hydrographic component of the GEODATA TOPO 250K Series 3 product released by Geoscience Australia in 2006 . The Series 3 dataset contains the following hydrographic features: canal lines, locks, rapid lines, spillways, waterfall points, bores, canal areas, flats, lakes, pondage areas, rapid areas, reservoirs, springs, watercourse areas, waterholes, water points, marine hazard areas, marine hazard points and foreshore flats.It also provides information on naming, hierarchy and perenniality. The dataset also contains Cultural and Transport features that may intersect with hydrography features. These include: Railway Tunnels, Rail Crossings, Railway Bridges, Road Tunnels, Road Bridges, Road Crossings, Water Pipelines. Refer to the GEODATA TOPO 250K Series 3 User Guide http://www.ga.gov.au/image_cache/GA8349.pdf for additonal information The second primary dataset is based on the GEODATA TOPO-250K Series 1 water course lines completed by Geoscience Australia in 1994, which has been supplemented by additional line work captured by the Australian National University during the production of the 9 second DEM to improve the representation of surface water flow. This natural watercourse dataset consists of directional flow paths and provides a direct link to the flow paths derived from the DEM. There are approximately 700,000 more line segments in this version of the data. AusHydro 1.0 uses the natural watercourse geometry from the ANU-enhanced Series 1 data, and the attributes (names, perenniality and hierarchy) associated with Series 3 to produce a fully attributed data set with topologically correct flow paths. The attributes from Series 3 were attached using spatial queries to identify common features between the 2 datasets. Additional semi-automated and manual editing was then undertaken to ensure consistent attribution along the entire network. WatercourseLines includes a unique identifier for each line segment (AusHydro-ID) which will be used to maintain the dataset, and to incorporate higher resolution datasets in the future. The AusHydro-ID will be linked to the ANUDEM-Derived (raster) streams through a common segment identifier, and ultimately to a set of National Catchments and Reporting Units (NCRU). Purpose Surface Hydrology Dataset is the reconciliation of the hydrological features in the two data sets to produce a single authoritative national stream network and water body data set suitable for hydrological analysis at national scales. It uses the natural watercourse geometry from the ANU-enhanced Series 1 data, and the attributes (names, perenniality and hierarchy) associated with Series 3 to produce a fully attributed data set with topologically correct flow paths.

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.

The National Catchment Database is a linked set of spatial layers and associated attribute tables describing key elements of the surface water hydrology of the Australian continent at a map scale of about 1:250,000. It is built upon the representation of surface drainage patterns provided by the GEODATA national 9 second Digital Elevation Model (DEM) Version 3 (ANU Fenner School of Environment and Society and Geoscience Australia, 2008). The stream network and catchment boundaries contained within the database form foundation elements of the Bureau of Meteorology's Australian Hydrological Geospatial Fabric (Geofabric), the spatial framework that underpins the Australian Water Resources Information System (AWRIS) (http://www.bom.gov.au/water/geofabric/index.shtml). This database adds additional environmental attributes not available through the AHGF. The database contains Levels 1 (drainage divisions) and 2 (aggregated river basins group) National Catchment Boundaries (NCB) in raster format including NCB Pfafstetter coding. The Vector format is available from the Bureau's Geobraic website.

Four data formats are available for download, three vector (e00, mif, shp) and one raster (ecw).

This paper presents the application of a neural network methodology to historical time series of GPS data from the global GPS network, based on terrestrial water storage information. Hydrology signals at the global GPS sites are important for including water loading corrections in GPS data processing. However, it is quite common that a correct global water storage model may not be available for this purpose, due to lack of science data. It is therefore mostly assumed that water mass redistribution is one of the potential contributors to the seasonal variations in GPS station position results, particularly, in the vertical direction. Presently, the IERS Special Bureau for Hydrology (SBH) has archived continental water storage data from some of the latest model developments. Examples include the monthly (GRACE, NOAA CPC, NCEP/NCAR CDAS-1) and daily (NCEP/NCAR and ECMWF reanalyses) solutions. It is valuable to study the relationship between these solutions and long-term geodetic results, especially as the water storage models continue to be refined. Using neural networks offers an effective approach to correlate the non-linear input of hydrology signals and output of geodetic results by recognizing the historic patterns between them. In this study, a neural network model is developed to enable the prediction of GPS height residuals based on the input of NOAA CPC hydrology data. The model is applied to eight global GPS sites with satisfactory results.

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.