Soils form as the result of a complex suite of biogeochemical and physical processes; however, effective modeling of soil property change and variability is still limited and does not yield widely applicable results. We suggest that predicting a distribution of probable values based upon the soil-forming state factors is more effective and applicable than predicting discrete values. Here we present a probabilistic approach for quantifying soil property variability through integrating energy and mass inputs over time. We analyzed changes in the distributions of soil texture and solum thickness as a function of increasing time and pedogenic energy (effective energy and mass transfer, EEMT) using soil chronosequence data compiled from the literature. Bivariate normal probability distributions of soil properties were parameterized using the chronosequence data; from the bivariate distributions, conditional univariate distributions based on the age and flux of matter and energy into the soil were calculated and probable ranges of each soil property determined. We tested the ability of this approach to predict the soil properties of the original soil chronosequence database and soil properties in complex terrain at several Critical Zone Observatories in the US. The presented probabilistic framework has the potential to greatly inform our understanding of soil evolution over geologic timescales. Considering soils probabilistically captures soil variability across multiple scales and explicitly quantifies uncertainty in soil property change with time.
ASJC Scopus subject areas
- Soil Science