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Estimating the Water Retention Shape Parameter from Sand and Clay Content

Faculty of Agriculture, Food & Natural Resources, A05, The Univ. of Sydney, Sydney, NSW 2006, Australia

View larger version (60K): [in this window] [in a new window]   Fig. 1. Distribution of soil texture from the three soil hydraulic databases used in this study.

View larger version (32K): [in this window] [in a new window]   Fig. 2. The relationship between sand content and shape parameter n.

View larger version (21K): [in this window] [in a new window]   Fig. 3. Measured and predicted shape parameter n using a neural network (nnet, sand and clay content) and a physical model (PSD, particle-size distribution function and porosity) for (a) the UNSODA and (b) the GRIZZLY databases.

View larger version (20K): [in this window] [in a new window]   Fig. 4. Response surface of (a) predicted shape parameter n and (b) uncertainty in prediction as a function of sand and clay content.

View larger version (19K): [in this window] [in a new window]   Fig. 5. Response surface of (a) predicted hydraulic conductivity shape parameter and (b) predicted water retention shape index pm as a function of sand and clay content.

View larger version (20K): [in this window] [in a new window]   Fig. 6. Comparison of observed and predicted (a) water retention (water content vs. pressure head h) and (b) hydraulic conductivity (K) curves for sand, sandy loam, and clay. Observed values are represented as thick lines, while the others are lines with numbers next to the curves representing the method of prediction. The three methods for predicting n are: 1, from particle size distribution (PSD) and soil porosity ; 2, from sand and clay content; and 3, soil texture class.