2.1.1 Geography


This section covers data characteristics, including accuracy, for all datasets used in the Geography section of the companion product 1.1 for the Gloucester subregion (McVicar et al., 2014).

For physical geography brief assessments are provided from the relevant literature for the: (i) digital elevation model (DEM) data, (ii) surface watercourses and basin/catchment boundary data, (iii) physiographic classes, (iv) soil classes, (v) pre-European vegetation, (vi) current vegetation, (vii) land cover dynamics and (viii) vegetation height.

For human geography concise descriptions are provided of the: (i) population density and (ii) land use management.

For retrospective climate analysis brief descriptions are provided for: (i) precipitation (P), (ii) maximum and minimum air temperature (Tmax and Tmin, respectively), (iii) vapour pressure (VP), (iv) net radiation (Rn), (v) wind speed and (vi) potential evapotranspiration (PET).

For prospective climate analysis some commentary, a brief outline of the approach used, and the utility of the approach are provided. Finally, subregion-specific characterisation of errors of the input climate data for the long-term (from January 1980 to December 2009) monthly relative error values were calculated. This was performed as the root mean squared error (RMSE) grid divided by the mean grid for each P, Tmax and Tmin, in turn. The final relative error was expressed as a percentage. For P a relative error of 46% is calculated, due, in part to P being a highly spatially variable process (it has low spatial autocorrelation) especially in areas such as those proximal to the Gloucester subregion that have high amounts of relative relief. Additionally, in the relative error grid the influence of spatial density of meteorological stations is clearly seen. For Tmax the relative error is approximately 2% and for Tmin the relative error is 6%. Air temperature variation is more spatially homogenous than precipitation variation, hence the relative errors are lower. Maximum air temperatures, which are not influenced by cold air drainage like minimum temperatures are, are modelled with greater accuracy.

All geographic data specific to the bioregional assessment (BA) of the Gloucester subregion were obtained from state or national datasets. No statistical analyses or interpolations were undertaken within the Assessment to generate any of these datasets. Spatial datasets were clipped to the Gloucester subregion boundary such that subregion characteristics could be identified and simple statistics calculated (e.g. areas, maximum and minimum elevations). Details of the source data and/or methods are provided in Section about observed data.

Spatial analyses specific to the Gloucester subregion were undertaken on some of the meteorological datasets to characterise the errors for water balance modelling. These methods are presented in Section about statistical analysis and interpolation.

Last updated:
26 October 2018
Thumbnail of the Gloucester subregion

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