This service provides Australian surface hydrology, including natural and man-made features such as water courses (including directional flow paths), lakes, dams and other water bodies. The information was derived from the Surface Hydrology database, with a nominal scale of 1:250,000. The National Basins and Catchments are a national topographic representation of drainage areas across the landscape. Each basin is made up of a number of catchments depending on the features of the landscape. This service shows the relationship between catchments and basins. The service contains layer scale dependencies.

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.

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.

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.

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.

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.

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.

This dataset was created for the National Geochemical Survey of Australia (NGSA) to help determine the location of target sites for sampling catchment outlet sediments in the lower reach of defined river catchments. Each polygon represents a surface drainage catchment derived from a national scale 9 second (approximately 250 m) resolution digital elevation model. Catchments were extracted from an unpublished, interim version of a nested catchment framework with an optimal catchment area of 5000 km2. Only catchments from the Australian mainland and Tasmania were included. In order to generate catchments approaching the optimal area, catchments with an area of less than 1000 km2 were excluded from the dataset, while other small catchments were amalgamated, and catchments much larger than 5000 km2 were split.

This document describes a structure for exchanging information to assist discovery and retrieval/transfer of flood information, including GIS flood mapping data. The draft class model represents metadata, data and summary information that supports the goals of the National Flood Risk Information Project (NFRIP) to improve the quality, consistency and accessibility of flood information. This document describes the data model that will be used to create an application schema.

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.