Fitting performance of particle-size distribution models on data derived by conventional and laser diffraction techniques
Mathematical description of most classical particle size distribution (PSD) data is often used for estimating soil hydraulic properties. Fast laser diffraction (LD) techniques now provide more detailed PSDs, but deriving a function to characterize the entire range of sizes is a major challenge. The aim of this study was to compare the fitting performance of seven PSD functions with one to four parameters on sieve-pipette and LD data sets of fine-textured soils. The fits were evaluated by the adjusted R2, MSE, and Akaike's information criterion. The fractal and exponential functions performed poorly while the performance of the Gompertz model increased with clay content for the LD data sets. The Fredlund function provided very good fits with sieve-pipette PSDs but not the corresponding LD data sets, probably due to underestimation of the clay fraction in the latter. The two-parameter lognormal function showed better overall performance and provided very good fits with both sieve-pipette and LD data sets.