Observed data Physical geography

Digital elevation model

Information from a digital elevation model (DEM) is needed in the groundwater and surface water models to assess surface topography for representing hydraulic gradients, defining flow directions and contributing areas. The DEM was obtained from the GEODATA 9 second DEM (DEM-9S) (~250 m resolution grid cell) together with the 9 second flow direction grid (D8-9S) covering the whole of Australia (Geoscience Australia, Dataset 1).

Elevation errors in the DEM-9S are closely related to terrain complexity. The errors range from no more than 10 m in low relief areas (about half of Australia), up to around 60 m in highland areas with steep and complex terrain. In such areas there is significant variation in elevation across each 9 second grid cell. Maximum absolute errors are naturally larger than standard errors. These range from around 20 to 40 m in the lower relief half of the continent, up to around 200 to 300 m in complex highland areas (Geoscience Australia, 2008).

The percent slopes were derived from the 1 second smoothed DEM (DEM-S) derived from the Shuttle Radar Topography Mission (STRM) data, then generalised to 3 seconds by taking the mean over 3 x 3 cells. The RMSE of the derived slope varies from place to place due to the nature of source data and the adaptive smoothing. The RMSE is estimated to be between 2% and 5% (J Gallant, 2016, pers. comm.).

Surface watercourses

Surface watercourses were defined using the GeoData Topo 250K Series 3 Topographic Data, which is a vector representation of the major features appearing on 1:250,000 scale NATMAP topographic maps published by Geoscience Australia (2006). Using the hydrology theme from this dataset, major and minor watercourses are identified and both used to describe the surface hydrology of the Namoi subregion. Surface water basins or catchments are defined using the Australian Hydrological Geospatial Fabric (Geofabric), a specialised geographic information system published by the Bureau of Meteorology (2012).


The Australian Water Resources Assessment landscape model (AWRA-L), a rainfall-runoff model, and groundwater model use information derived from vegetation height to differentiate between deep-rooted and shallow-rooted vegetation. The difference in rooting depth is used in the process of scaling potential evapotranspiration (PET) to actual evapotranspiration (AET), by defining the depth to which water can be extracted from the soil via plant roots. Vegetation height was measured using a satellite based light detection and ranging system (LiDAR) between 20 May 2005 and 23 June 2005 using the Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and Land Elevation Satellite (ICESat). Simard et al. (2011) were able to globally model overstorey vegetation height at 1 km spatial resolution with a vertical RMSE of 4.4 m and coefficient of determination (r2) of 0.7 when compared against 59 flux-tower field observations globally.

Fraction of tree cover and leaf area index (LAI) information is also used in the AWRA-L model (Viney et al., 2014). It is based on the Advanced Very High Resolution Radiometer (AVHRR) satellite derived fractions of persistent and recurrent photosynthetically active absorbed radiation (fPAR) (Donohue et al., 2008). Here the persistent vegetation is interpreted to be tree cover (deep-rooted) and recurrent vegetation is interpreted to be grass cover (shallow-rooted). The maximum achievable LAI is derived from a time series of LAI from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. These data form an inherent component of AWRA-L and they are not sourced in the bioregional assessment (BA).

Land use

The Australian Water Resources Assessment river model (AWRA-R) needs details of irrigated areas and crop types along each section of the river in order to distribute non-spatial irrigation diversion data appropriately in the model. Land use data were clipped using reach boundaries to derive irrigated areas and crop types. Namoi subregion land use data were obtained from the Catchment Scale Land Use Management (CLUM) raster surface compiled on November 2012 (Australian Bureau of Agricultural and Resource Economics and Sciences, Dataset 3).

The most current catchment scale land use dataset for Australia uses the nationally agreed land use mapping principles and procedures of the Australian Land Use and Management Program (ALUMP) Classification version 7. The land use datasets have been compiled from vector datasets, part of the state and territory mapping programmes and the Australian Collaborative Land Use and Management Program (ACLUMP). CLUM data, compiled in March 2014, incorporates data from 1997 to 2012 with a mapping scale from 1:25,000 to 1:250,000 to produce a seamless 50 m raster dataset for Australia. The differences in resolution of the primary input datasets (~25 to ~250 m) means that in some areas the boundary between existing land use types may be inaccurate. However, this may have limited contribution to model errors given that modelling is being done at least at a 1 km resolution. Climate

The following variables are required for hydrological modelling of the Namoi subregion: (i) precipitation (P), (ii) maximum and minimum air temperature (Tmax and Tmin) and (iii) net radiation (Rn). National coverage is available at a 0.05 degree (or ~5 km) grid cell resolution and a daily time step. They come from various sources and have different start dates. These input grids are used in the calculation of PET and catchment runoff. A brief description of these climate variables follows.


Daily and monthly precipitation grids generated by the Bureau of Meteorology (Jones et al., 2009) from 1900 onwards are available. These grids are developed using a geostatistics technique, which takes account of ground elevation, to interpolate daily and monthly station P totals between isolated stations (Bureau of Meteorology, Dataset 4). The estimates were cross-validated using seven years of data from 2001 for the whole of Australia (Jones et al., 2009). Between 2001 and 2007, the Australia-wide mean daily P was 1.8 mm/day with a RMSE of 3.1 mm/day (Jones et al., 2009, Table 3b). This represents a relative error of 172% (calculated as RMSE/mean), although absolute differences may be small. For the same period, the Australia-wide mean monthly P was 54.3 mm/month with a RMSE of 21.2 mm/month (Jones et al., 2009, Table 3a). This represents a relative error of 39% (calculated as RMSE/mean). These errors may be large and may reflect the data problem on a continental basis, however errors for the Namoi subregion are much smaller (see Section


Daily Tmax and Tmin grids generated by the Bureau of Meteorology are available from 1900 onwards (Jones et al., 2009). These grids are developed using optimal geostatistics techniques, taking elevation into account (the environmental lapse rate), to interpolate daily extreme air temperatures measured at isolated stations (Bureau of Meteorology, Dataset 4). The mean daily Tmax and mean daily Tmin for Australia between 2001 and 2007 were 24.9 and 12.8 °C with RMSE statistics of 1.2 and 1.7 °C, respectively (Jones et al., 2009, Table 2b). These represent relative errors of 5 and 13%, respectively (calculated as RMSE/mean). The mean monthly Tmax and mean monthly Tmin for all Australia between 2001 and 2007 were 24.9 and 12.7 °C with RMSE statistics of 0.7 and 1.0 °C, respectively (Jones et al., 2009, Table 2a). These represent relative errors of 3 and 8%, respectively.

Solar radiation

Daily solar net radiation (Rn) values are available from 1900 onwards as part of the Bureau of Meteorology gridded climate surfaces for Australia (Bureau of Meteorology, Dataset 4). The dataset comprises two distinct periods: post-1982, daily solar radiation values are based on observations from ground-based and satellite instruments; prior to 1982, the daily values are based on the long-term climatologies from the post-1982 period. This means, for example, that the solar radiation on 1 January is the same for every year from 1900 to 1981 and reflects the average solar radiation on 1 January in the years since 1981. Uncertainties in the solar radiation data arise from the effect of cloud cover (~5%) and water vapour in the atmosphere (~2%). Comparisons with ground-based measurements (made with pyranometers) indicate that satellite methods tend to slightly over estimate the radiant exposure in wet, cloudy conditions and to under estimate in dry conditions (Bureau of Meteorology, 2016).

Last updated:
6 December 2018
Thumbnail of the Namoi subregion

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