ARCHIVED CONTENT: In December 2020, the CZO program was succeeded by the Critical Zone Collaborative Network (CZ Net) ×

Shepard et al., 2015


Probabilistic modeling of soil development variability with time

Shepard C., Schaap M., Rasmussen C. (2015)
Abstract EP31B-1009 presented at 2015 Fall Meeting, AGU, San Francisco, CA, 14-18 Dec.  


Soils develop as the result of a complex suite of biogeochemical and physical processes; however, effective modeling of soil development over pedogenic time scales and the resultant soil property variability is limited to individual chronosequence studies or overly broad generalizations. Soil chronosequence studies are used to understand soil development across a landscape with time, but traditional soil chronosequence studies do not account for uncertainty in soil development, and the results of these studies are site dependent. Here we develop a probabilistic approach to quantify the distribution of probable soil property values based on a review of soil chronosequence studies. Specifically, we examined the changes in the distributions of soil texture and solum thickness with increasing time and influx of pedogenic energy from climatic and biological forcings. We found the greatest variability in maximum measured clay content occurred between 103 to 105 years, with convergence of clay contents in soils older than 106 years. Conversely, we found that the variability in maximum sand content increased with increasing time, with the greatest variability in soils between 105 to 106 years old; we did not find distributional changes in maximum silt content with time. Bivariate normal probability distributions were parameterized using the chronosequence data, from which conditional univariate distributions based on the total pedogenic energy (age x rate of energy flux) were calculated, allowing determination of a probable range of soil properties for a given age and bioclimatic environment. The bivariate distribution was capable of effectively representing the measured maximum clay content values with an r2 of 0.53 (p < 0.0001, RMSE = 14.36%). By taking a distributional approach to quantifying soil development and variability, we can quantitatively probabilistically represent the full state factor model, while explicitly quantifying the uncertainty in soil development.



Shepard C., Schaap M., Rasmussen C. (2015): Probabilistic modeling of soil development variability with time. Abstract EP31B-1009 presented at 2015 Fall Meeting, AGU, San Francisco, CA, 14-18 Dec..

This Paper/Book acknowledges NSF CZO grant support.