Evaluation and development of hydraulic conductivity pedotransfer functions for Australian soil

Abstract

Pedotransfer functions (PTFs) for predicting saturated hydraulic conductivity (K_s) were evaluated using published Australian soil data sets. Eight published PTFs were evaluated. Generally, published PTFs provide a satisfactory estimation of K_s depending on the spatial scale and accuracy of prediction. Several PTFs were developed in this study, including the power function of effective porosity, multiple linear regression, fractal model, and artificial neural networks. Different methods for estimating the fractal dimension of particle-size distributions showed no significant differences in predicting K_s . The simplest model for estimating fractal dimension from the log–log plot of particle-size distribution is therefore recommended. The data set was also stratified into 3 broad classes of texture: sandy, loamy, and clayey. Stratification of PTFs based on textural class showed small improvements in estimation. The published PTF of Dane and Puckett (1994) Proc. Int. Workshop (Univ. of California: Riverside, CA) gives the best prediction for sandy soil; the PTF of Cosby et al. (1984) Water Resources Research 20, 682–90 gives the best production for loamy soil; and the PTF of Schaap et al. (1998) Soil Science Society of America Journal 62, 847–55 gives the best prediction for clayey soil. The data set used comprised different field and laboratory measurements over large areas, and limited predictive variables were available. The PTFs developed here may predict adequately in large areas (residuals = 10–20 mm/h), but for site-specific applications, local calibration is needed.