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.

Completion of a pilot study over the Namoi and Murrumbidgee catchments was part of the 2012-13 project schedule between Bureau of Meteorology (Bureau) and Geoscience Australia. The purpose of the pilot was to consolidate four years of research and development of the 1 second SRTM DEM, ANUDEM Streams, and National Catchment Boundaries to enable GA operational capacity to recreate the foundation datasets for Geofabric Phase 3 deliverables. This report is aimed to highlight how successfully the process has worked, issues that have arisen and identify and develop future modifications of the methodology to enable the production of Phase 3 Geofabric products. This professional opinion has been created for the Bureau and the Geofabric Steering Committees for review of Phase 3 of the Geofabric.

The combination of anthropogenic activity and climate variability has resulted in changes to hydrologic regimes across the globe. Changes in water availability impact on vegetation structure and function, particularly in semi-arid landscapes. Riparian and floodplain vegetation communities are sensitive to changes to surface-water and groundwater availability in these water-limited landscapes. Remote-sensing multi-temporal methods can be used to detect changes in vegetation at a regional to local scale. In this study, a `best-available pixel' approach was used to represent dry-season, woody-vegetation-canopy characteristics inferred from Normalised Difference Vegetation Index (NDVI). This paper describes a method in which Landsat 5 TM and Landsat 7 ETM+ data from 1987 to 2011 were processed using object-based image-analysis techniques to generate annual minimum NDVI values for vegetation communities in the Lower-Darling floodplain The changes detected in riparian and floodplain canopies over time can then be integrated with other spatial data to identify water-source dependence and infer a relationship between changes to the hydrologic characteristics of specific water sources and vegetation dynamics.

Floodplain vegetation can be degraded from both too much and too little water due to regulation. Over-regulation and increased use of groundwater in these landscapes can exacerbate the effects related to natural climate variability. Prolonged flooding of woody plants has been found to induce a number of physiological disturbances such as early stomatal closure and inhibition of photosynthesis. However drought conditions can also result in leaf biomass reduction and sapwood area decline. Depending on the species, different inundation and drought tolerances are observed. This paper focuses specifically on differing lake level management practices in order to assess associated environmental impacts. In western NSW, two Eucalyptus species, River Red Gum (E. camaldulensis) and Black Box (E. largiflorens) have well documented tolerances and both are located on the fringes of lakes in the Menindee Lakes Storage Water scheme. Flows to these lakes have been controlled since 1960 and lake levels monitored since 1979. Pre-regulation aerial photos indicate a significant change to the distribution of lake-floor and fringing vegetation in response to increased inundation frequency and duration. In addition, by coupling historic lake water-level data with a Landsat satellite imagery, spatial and temporal vegetation response to different water regimes has been observed. Two flood events specifically investigated are the 2010/11 and 1990 floods. Results from this analysis provide historic examples of vegetation response to lake regulation including whether recorded inundation duration and frequency resulted in positive or negative impacts, the time delay till affects become evident, duration of observed response and general recovery/reversal times. These findings can be used to inform ongoing water management decisions.

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.

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

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 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.

The 9 second DEM derived streams are a a fully connected and directed stream network produced in rastor and vector fomats by Australian National University. This product is the raster format, for the the vector product please refer to the Bureau of Meterology's Geofabric Website (http://www.bom.gov.au/water/geofabric/index.shtml). 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 Environmental Arttibutes Database is a set of lookup tables supplying attributes describing the natural and anthropogenic characteristics of the stream and catchment environment that was developed by the Australian National University (ANU) in 2011. The data is supplied as part of the supplementary Geofabric products which is associated with the 9 second DEM derived streams and the National Catchment Boundaries based on 250k scale stream network. Please consult the spreadsheet below for details of the attributes and their source data.