Document Type : Research Paper
Authors
Abstract
Saturated hydraulic conductivity is one of the main soil physical properties used in the modeling of water and solute transport and management of irrigation and drainage problems. Laboratory and field methods for direct measurement of this property are time consuming and costly. Thus, indirect methods, such as pedotransfer functions, have been developed to estimate this property. The objective of this study was evaluation of regression-based pedotransfer functions, Rosetta pedotransfer function with artificial neural network approach, and fractal models to estimate saturated hydraulic conductivity. In addition, due to the importance and role of preferential flow of water and chemicals in the soil medium, hydraulic conductivity of large pores was estimated using fractal model. To do so, 31 soil samples with different soil textures and measured saturated hydraulic conductivity by falling head method were selected. Easily measured soil physical properties, such as particle size distribution, bulk density, particle density, and organic matter content were determined in laboratory. Saturated hydraulic conductivity was estimated using the aforementioned models and the measured soil physical properties. For the purpose of comparison and evaluation of pedotransfer functions, fractal model, statistical criteria e.g., deviation time (DT), geometric mean error ratio (GMER) and geometric standard deviation error ratio (GSDER) were calculated for all the models. Results showed that the Wosten et al. and Campbell-Shiozawa models were, respectively, the best and worst estimator of matrix saturated hydraulic conductivity. The statistical criteria indicated that the adjusted fractal model in this study showed the best estimation of macropores saturated hydraulic conductivity.
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