Hydrological thresholds of soil surface properties identified using conditional inference tree analysis


M. Tighe, Agronomy and Soil Science, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.

E-mail: mtighe2@une.edu.au


There has been limited success in determining critical thresholds of ground cover or soil characteristics that relate to significant changes in runoff or sediment production at the microscale (<1 m2), particularly in semi-arid systems where management of ground cover is critical. Despite this lack of quantified thresholds, there is an increasing research focus on the two-phase mosaic of vegetation patches and inter-patches in semi-arid systems. In order to quantify ground cover and soil related thresholds for runoff and sediment production, we used a data mining technique known as conditional inference tree analysis to determine statistically significant values of a range of measured variables that predicted average runoff, peak runoff, sediment concentration and sediment production at the microscale. On Chromic Luvisols across a range of vegetation states in semi-arid south-eastern Australia, large changes in runoff and sediment production were related to a hierarchy of different variables and thresholds, but the percentage of bare soil played a primary role in predicting runoff and sediment production in most instances. The identified thresholds match well with previous thresholds found in semi-arid and temperate regions (including the approximate values of 30%, 50% and 70% total ground cover). The analysis presented here identified the critical role of soil surface roughness, particularly where total ground cover is sparse. The analysis also provided evidence that a two-phase mosaic of patches and inter-patches identified via rapid visual assessment could be further delineated into distinct groups of hydrological response, or a multi-phase rather than a two-phase system. The approach used here may aid in assessing scale-dependent responses and address data non-linearity in studies of semi-arid hydrology. Copyright © 2012 John Wiley & Sons, Ltd.