Modeling Energy Inputs to Predict Pedogenic Environments Using Regional Environmental Databases

Abstract

We present a model for prediction of pedogenic environments and soil properties based on energy input to the soil system. The model estimates rates of precipitation and net primary production (NPP) energy input using the Parameter‐Regression Independent Slope Model (PRISM) climate data, and a parent material index (PMI). Soil order, soil C, and clay data from the State Soil Geographic (STATSGO) database were compared with rates of NPP and precipitation energy input for major geographic regions of the continental USA, including California, Oregon, Washington, Texas, North Dakota, Alabama, Pennsylvania, and New Hampshire. Soil orders in all states show differences in total energy input (Ein, kJ m−2yr−1) and the percentage of Ein from NPP (%Enpp) (e.g., Ultisols Ein = 29915, %Enpp = 49%; Mollisols Ein = 5880, %Enpp = 90%). Using linear regression models, rates of NPP estimated (R2 = 0.82***) trends in soil C content in western states, but failed to estimate soil C in other geographic areas. Parent material index adjusted energy flux estimated soil clay content for the majority (99.5%) of igneous parent materials in California and Oregon (R2 = 0.67**), the only states with digital geologic data. The model underestimated clay content in steeply sloping Inceptisols and Andisols (0.5% of igneous land area). Results suggest that rates of NPP may be used to estimate soil C for climate regimes with steep environmental gradients. Landscape age and stability components might improve clay prediction in young and erosive landscapes. Modeled energy input provides a tool for estimating pedogenic environments, soil order, and soil properties. Energy input parameters may aid efforts to pre‐map broad landscape units for soil survey.

Thermodynamic constraints on effective energy and mass transfer and catchment function

“…In a series of papers, Rasmussen et al presented a coupled energy and mass transfer term referred to as "effective energy and mass transfer" (EEMT) that couples energy and mass flux to the subsurface in the form of effective precipitation and net primary production in a common energy unit [W m −2 ] (Rasmussen et al, 2005(Rasmussen et al, , 2011bRasmussen and Tabor, 2007). The EEMT parameter exhibits strong and significant correlation to a variety of measures of critical zone structure and function, including chemical weathering rates, soil depth, classification and geochemistry, and ecosystem respiration.…”

Technical Note: A comparison of model and empirical measures of catchment-scale effective energy and mass transfer

“…The linear regressions indicated that EEMT MOPEX-MODIS , E PPT-MOPEX and E BIO-MODIS were, on average, 0.57 to 0.48 to 0.67 times less than EEMT MODEL , E PPT-MODEL and E BIO-MODEL . The strong linear correlations and relatively low RMSE indicate that while the magnitude of EEMT MODEL in previous work relating EEMT to critical zone properties and process may be overestimated Pelletier and Rasmussen, 2009a;Rasmussen, 2012;Rasmussen et al, 2005Rasmussen et al, , 2011Rasmussen and Tabor, 2007;Sheldon and Tabor, 2009), the overall trends in critical zone properties relative to EEMT remain valid.…”

“…The EEMT framework was developed based on a rich history in the soil science literature from the initial conceptualization and semi-quantitative approaches used to describe and define soil forming factors (Dokuchaev, 1967;Jenny, 1941;Runge, 1973;Smeck et al, 1983) to later work that formalized these factors into quantitative energy terms (Volobuyev, 1964), and revisited in more recent work (Minasny et al, 2008;Phillips, 2009;Rasmussen, 2012;Rasmussen et al, 2005Rasmussen et al, , 2011Rasmussen and Tabor, 2007). This framework quantifies the drivers of critical zone evolution as the summation of energy and mass fluxes associated with soil and critical zone development, wherein development refers to chemical alteration, structure formation, and the layering, zonation, and organization of the weathered regolith.…”

“…Previous applications of the EEMT framework Pelletier and Rasmussen, 2009a;Rasmussen, 2012;Rasmussen et al, 2005Rasmussen et al, , 2011Rasmussen and Tabor, 2007;Sheldon and Tabor, 2009) have largely focused on using relatively easy to obtain annual and monthly precipitation and temperature data to calculate local water and energy balance to derive the components needed for E PPT and E BIO . Specifically, for E PPT the F term is approximated using effective precipitation:…”

“…This term represents the energy and mass transferred to the critical zone in the form of water in excess of evapotranspiration and biological production; therefore EEMT provides a measure of the energy available to perform work on the subsurface. Previous work demonstrates strong correlation of EEMT to measures of critical zone structure and function including regolith depth, chemical depletion and denudation rates, soil development and taxonomic classification, and ecosystem respiration (Pelletier and Rasmussen, 2009a;Rasmussen et al, 2005Rasmussen and Tabor, 2007), which has been used as a predictive parameter in numerical models of Published by Copernicus Publications on behalf of the European Geosciences Union.…”