The spatial modelling of DUL and LL15 is done at six standard depth intervals conforming to the GlobalSoilMap Specifications ie 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm and 100-200cm. To facilitate modelling at these standard depths the observed data set depths were harmonised to these depths using a mass preserving spline method. A total of 20545 soil profiles were splined in this way and used as inputs to the spatial modelling.
We utilised the publicly available Terrestrial Ecosystem Research Network (TERN) raster covariate stack. It is comprised of 154 individual raster data layers. The covariate stack was used as the independent variable data for the predictions across all grid cells and at each depth. Environmental correlation using the Cubist machine learning algorithm was used to develop relationships between the soil property value estimates derived from the pedotransfer functions and the values of the covariates.
Fifty bootstrapped model realisations, using the Cubist machine learning algorithm (Quinlan, 1992), were generated, and used to predicted DUL and L15 values (mean of the bootstrap realisations) and estimate upper and lower confidence intervals (5% and 95%) across the entire continent
The Available Water Capacity values were calculated by subtracting the L15 values of each layer from the DUL values of each layer and the upper and lower confidence intervals were estimated by combining the variances of the upper and lower confidence intervals of L15 and DUL.
To estimate the Total Available Volumetric Water Capacity (mm) to 1 and 2 metres we summed all the AWC layer values converted to mm of water to the estimated soil depth (Australia-wide 3D digital soil property maps - Depth of Soil (3 arc second resolution - Version 2) or the designated depth of the product - which ever was shallowest.
Maps of LL15, DUL and AWC at the six depth intervals were generated and are presented below, along with maps of Total Available Water Capacity in mm of water for 0 to 100 cm and 0-200.