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DIVISION S-1—SOIL PHYSICS

Models for Estimating Soil Particle-Size Distributions

^a Dep. of Civil and Environmental Engineering, Clarkson Univ., 8 Clarkson Ave., Potsdam, NY 13699-5710
^b D.S. Lee, Graduate School of Environ. Stud., Seoul National Univ., Seoul, 151-742, South Korea

* Corresponding author (sep{at}clarkson.edu)

An accurate mathematical representation of particle-size distributions (PSDs) is required to estimate soil hydraulic properties or to compare texture measurements from different classification systems. The objective of this study was to evaluate the ability of seven models (i.e., five lognormal models, the Gompertz model, and the Fredlund model) to fit PSD data sets from a wide range of soil textures. Special attention was given to the effect of texture on model performance. Several criteria were used to determine the optimum model with the least number of fitting parameters when other conditions are equal. The Fredlund model with four parameters showed the best performance with the majority of soils studied, even when three criteria that impose a penalty for additional fitting parameters were used. Especially, the relative performance of the Fredlund model in regard to other models increased with increase of clay content. Among all soil classes, the lognormal models with two or three parameters showed better fits for silty clay, silty clay loam, and silt loam soils, and worse fit for sandy clay loam soil.

Abbreviations: PSD, particle-size distribution • AIC, Akaike's information criterion • SSE, sum of squared errors • SL, simple lognormal • ORL, offset-renormalized lognormal • ONL, offset-nonrenormalized lognormal • SC, Shiozawa and Campbell

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