The particle size distribution (psd) of rocks and soils is known to control the physico-chemical behavior and has been used as the basis of a variety of property transfer models. Traditional psds often fail to describe the complexity of real sediments, resulting in a discrepancy between measured and predicted properties. In this study we use high-resolution psds, derived from laser diffraction measurements, of size fractions separated on 1-φ intervals, as the basis of property transfer models for complex unconsolidated sediments from the Hanford Site. Size fractions were characterized to determine mean particle density, packing porosity, aspect ratio, hydraulic conductivity, water retention, specific surface area (SA), and cation exchange capacity (CEC). Measurements were repeated on the 12 representative USDA textures derived from mixing the size fractions. A generalized mixing model predicted porosities and saturated hydraulic conductivities from the continuous psds and particle shape that compared well laboratory measurements. Soil water characteristics, derived with the Arya-Paris model with a continuous scaling parameter, α, and constrained by predicted porosities and conductivities also compared well with laboratory measurements.  Geometric SA, assuming spherical particles, was 3 to 30 times higher than measured values. However, the generalized mixing model using the SA of the component size classes predicted values of only 0.84 to 1.2 times the measured values (r²=97%). The generalized mixing model is much more robust and accurate requiring only a one-time characterization of size fractions from a given depositional environment. Scaling of physico-chemical properties requires no additional equations but is accomplished using the scale-mean particle size distribution parameters or textural attributes.