Multifractal characterization of saprolite particle-size distributions after topsoil removal

Multifractal analysis is now increasingly used to characterize soil properties as it may provide more information than a single fractal model. During the building of a large reservoir on the Parana River (Brazil), a highly weathered soil profile was excavated to a depth between 5 and 8 m. Excavation resulted in an abandoned area with saprolite materials and, in this area, an experimental field was established to assess the effectiveness of different soil rehabilitation treatments. The experimental design consisted of randomized blocks. The aim of this work was to characterize particle-size distributions of the saprolite material and use the information obtained to assess between-block variability. Particle-size distributions of the experimental plots were characterized by multifractal techniques. Ninety-six soil samples were analyzed routinely for particle-size distribution by laser diffractometry in a range of scales, varying from 0.390 to 2000 μm. Six different textural classes (USDA) were identified with a clay content ranging from 16.9% to 58.4%. Multifractal models described reasonably well the scaling properties of particle-size distributions of the saprolite material. This material exhibits a high entropy dimension, D1. Parameters derived from the left side (q > 0) of the f(α) spectra, D1, the correlation dimension (D2) and the range (α0 − αq+), as well as the total width of the spectra (αmax − αmin), all showed dependence on the clay content. Sand, silt and clay contents were significantly different among treatments as a consequence of soil intrinsic variability. The D1 and the Holder exponent of order zero, α0, were not significantly different between treatments; in contrast, D2 and several fractal attributes describing the width of the f(α) spectra were significantly different between treatments. The only parameter showing significant differences between sampling depths was (α0 − αq+). Scale independent fractal attributes may be useful for characterizing intrinsic particle-size distribution variability.