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Applying a Quantitative Pedogenic Energy Model across a Range of Environmental Gradients

^a Soil, Water and Environmental Science Dep., Univ. of Arizona, 1177 E. Fourth St., Shantz Bldg. Rm. 429, Tucson, AZ 85721-0038
^b Dep. of Geological Sciences, 3225 Daniels Rd., Southern Methodist Univ., Dallas, TX 75275-0395

View larger version (24K): [in this window] [in a new window]   Fig. 1. Probability distribution of effective energy and mass transfer (EEMT) for the continental USA. The studied environmental gradients span a range of EEMT that represents >85% of the U.S. continental land mass. The inset shows the spatial distribution of EEMT for the USA derived from the PRISM climate data set (EEMT equivalent to EIN from Rasmussen et al., 2005).

View larger version (31K): [in this window] [in a new window]   Fig. 2. Modeled relationship between mean annual precipitation (MAP), mean annual temperature (MAT), and effective energy and mass transfer (EEMT) derived from the global climate data set of the International Atomic Energy Administration (IAEA). The contour surface represents the best-fit two-dimensional Gaussian function. Symbols indicate the IAEA data used to derive the relationship.

View larger version (12K): [in this window] [in a new window]   Fig. 3. Comparison of effective energy and mass transfer (EEMT) derived from the PRISM data set using the data exhaustive monthly time series (EEMTPRISM) to EEMT calculated using the quantitative two-dimensional Gaussian relationship between mean annual precipitation (MAP), mean annual temperature (MAT), and EEMT derived from the global climate data set of the International Atomic Energy Administration (EEMTIAEA). The black line represents the 1:1 line.

View larger version (12K): [in this window] [in a new window]   Fig. 4. Pedogenic indicators plotted against mean annual precipitation (MAP): (A) pedon depth; (B) total pedon clay content; (C) free Fe oxide to total Fe oxide ratio (Fed/FeT) of the first subsurface genetic horizon; and (D) the chemical index of alteration minus potassium (CIA–K) of the first subsurface genetic horizon. Data derived from pedons sampled from stable landscape positions across four environmental gradients on basalt, andesite, and granite parent materials.

View larger version (15K): [in this window] [in a new window]   Fig. 5. Pedogenic indicators regressed against effective energy and mass transfer (EEMT): (A) pedon depth; (B) total pedon clay content; (C) free Fe oxide to total Fe oxide ratio (Fed/FeT) of the first subsurface genetic horizon; and (D) the chemical index of alteration minus potassium (CIA–K) of the first subsurface genetic horizon. Plotted lines and equations represent the best-fit regression to the data. Data derived from pedons sampled from stable landscape positions across four environmental gradients on basalt, andesite and granite parent materials.

View larger version (12K): [in this window] [in a new window]   Fig. 6. Hypothesized sigmoidal transfer functions relating (A) the free Fe oxide to total Fe oxide ratio (Fed/FeT) of the first subsurface genetic horizon and (B) the chemical index of alteration minus potassium (CIA–K) of the first subsurface genetic horizon to effective energy and mass transfer (EEMT). Prediction equation, parameters, regression coefficient and P value inset in each figure. Data derived from pedons sampled from stable landscape positions across four environmental gradients on basalt, andesite and granite parent materials.