Abstract
Mathematical descriptions of classical particle size distribution (PSD) data are often used to estimate soil hydraulic properties. Laser diffraction methods (LDM) now provide more detailed PSD measurements, but deriving a function to characterize the entire range of particle sizes is a major challenge. The aim of this study was to compare the performance of eighteen PSD functions for fitting LDM data sets from a wide range of soil textures. These models include five lognormal models, five logistic models, four van Genuchten models, two Fredlund models, a logarithmic model, and an Andersson model. The fits were evaluated using Akaike’s information criterion (AIC), adjusted R2, and root-mean-square error (RMSE). The results indicated that the Fredlund models (FRED3 and FRED4) had the best performance for most of the soils studied, followed by one logistic growth function extension model (MLOG3) and three lognormal models (ONLG3, ORLG3, and SHCA3). The performance of most PSD models was better for soils with higher silt content and poorer for soils with higher clay and sand content. The FRED4 model best described the PSD of clay, silty clay, clay loam, silty clay loam, silty loam, loam, and sandy loam, whereas FRED3, MLOG3, ONLG3, ORLG3, and SHCA3 showed better performance for most soils studied.
Highlights
Modeling the size distribution of soil particles to obtain a continuous particle size distribution (PSD) curves is useful for understanding essential soil properties such as pore distribution, water retention, hydraulic conductivity, and thermal and adsorption properties [1,2,3,4]
(1) The results of model fitting (AIC, adjusted R2, and root-mean-square error (RMSE)) indicated that the FRED4 model provided the best fit for most samples, (2) The performance of the PSD models was obviously affected by soil texture
(3) The FRED4 and FRED3 models are preferable for clay, clay loam, loam, loamy sand, sandy clay loam, sandy loam, and silty clay soils
Summary
Modeling the size distribution of soil particles to obtain a continuous particle size distribution (PSD) curves is useful for understanding essential soil properties such as pore distribution, water retention, hydraulic conductivity, and thermal and adsorption properties [1,2,3,4]. To be able to use these discrete experimental PSD data to estimate other soil properties, many researchers have used parametric functions to extend the limited scope of PSD data. Soil PSD is frequently assumed to follow a lognormal distribution [5, 6], some soils do have a bimodal PSD [7]. Buchan and Hwang compared five lognormal models and certain other types of models in terms of their fit to experimental PSD data [8, 9]. Their results showed that the bimodal model gave a marginally better fit, but incorporates a sub-clay mode, PLOS ONE | DOI:10.1371/journal.pone.0125048. Their results showed that the bimodal model gave a marginally better fit, but incorporates a sub-clay mode, PLOS ONE | DOI:10.1371/journal.pone.0125048 April 30, 2015
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