Parna is a wind-blown clay, mobilised from inland Australia as the result of a
series of intermittent high wind events during the Quaternary. Parna can be
recognised on the basis of colour, texture, distributional patterns, and
pedology. Parna deposits have been recorded across a wide area of south
eastern Australia and have influenced the local pedology and hydrology. In
some cases parna has increased soil sodicity and the potential for dryland
salinisation. Predicting its spatial distribution is useful when considering
agricultural potential and in assessing the risk and spatial spread of dryland
salinity. Here we present the results of modelling to predict its local
distribution in an area covering 291 km2 in the Young
district of NSW. Two conceptual models of parna deposition and subsequent
redistribution were used to develop a current parna distribution map:
(a) deposition =
f(topography, aspect) after assuming that interactions
of rainfall, vegetation, and wind speed were relatively the same at the local
scale; (b) removal or retention =
f (slope angle, catchment size, slope length) as a
representation of the erosive energy of gravity. Five landscape variables,
elevation, aspect, slope, flow accumulation, and flow length, were derived
from a 20 m digital elevation model (DEM). A training set of parna deposits
was established using air photos and field survey from limited exposures in
the Young district of NSW. These areas were digitised and converted to a grid
of areas of parna and no-parna. This training set for parna and the 5
landscape variable grids were processed in the IDRISI for WINDOWS Geographic
Information System (GIS). Spatial relationships between the parna and no-parna
deposits and the 5 landscape variables were extracted from this training set.
This information was imported into an inductive learning program called
KnowledgeSEEKER. A decision tree was built by recursive partitioning of the
data set using Chi-squares to categorise variables, and an
F test for continuous variables to best replicate the
training data classification of ‘parna’ and
‘no-parna’. The rules derived from this process were applied to
the study area to predict the occurrence of parna in the broader landscape.
Predictions were field checked and the rules adjusted until they best
represented the occurrence of parna in the field. The final model showed
predictions of parna deposits as follows: (i) higher
elevations in the Young landscape were the dominant sites of parna deposits;
(ii) thicker deposits of parna occurred on the windward
south-west and north-west; (iii) thinner deposits
occurred on the leeward side of a central ridge feature;
(iv) because the training set concentrated around the
major central ridge feature, poorer predictions were obtained on gently
undulating country.