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A quantitative energy model for predicting pedogenic environments and energy flow through soil systems is presented. Energy inputs from precipitation and Net Primary Production (NPP) were calculated based on the temperature of effective precipitation (P_{eff}) and the temperature of the months with Peff, respectively. This energy calculation presents an improvement of previous energy based approaches to modeling soil genesis (e.g., Runge, 1973) in that it allows for quantification of energy input (kJ m^{-2} yr^{-1}) from both P_{eff} (E_{PPT}) and NPP (E_{NPP}). We suggest these parameters may be used to segregate similar pedogenic regimes and may be used to predict specific soil properties. The model was developed using the PRISM climate dataset (at a scale of 1:250,000 or 4 km by 4 km pixels) for the continental United States. Monthly temperature data was used to calculate potential evapotranspiration (ETp) using the Thornthwaite equation and Peff calculated as the difference between monthly precipitation and ETp. Using the specific heat of water, P_{eff} was assumed to have been heated from 0°C to the average temperature of that month, facilitating the conversion from cm of water to E_{PPT}. Likewise, it was assumed that NPP occurred primarily in months of P_{eff} and that NPP was then controlled by the temperature of that month. An empirical equation was used to estimate mass of NPP produced, which was subsequently converted to E_{NPP} assuming a set amount of energy per gram of NPP. The sum of E_{NPP} and E_{PPT} is termed E_{IN}, and represents the total input of energy to the soil system. We also utilized global weather station compiled by the International Atomic Energy Agency (IAEA) to examine the relationship between mean annual precipitation (MAP), mean annual temperature (MAT), and E_{IN}. We were able to fit a 2-dimensional Gaussian model to the MAP, MAT, and E_{IN} data and developed an equation for predicting E_{IN} based on MAP and MAT. We used this equation to calculate E_{IN} in the U.S. and compared the results to E_{IN} estimated from the PRISM dataset (r^{2} = 0.89; P<0.0001). Results suggest the global equation accurately estimates energy input into U.S. soil systems. Using watershed data of Si flux from granitoid watersheds in the continental U.S. (White and Blum, 1995), we observed a significant relationship between E_{IN} and Si flux (r^{2} = 0.71; P<0.001), indicating E_{IN} may provide an estimate of current rates of silicate weathering. We also compared E_{IN} and the percent of E_{IN} derived from E_{NPP} by soil order for the continental U.S. Soil orders in all states showed differences in E_{IN} and the percent of E_{IN} from NPP (%E_{NPP})(e.g., Ultisols E_{IN} =29,915, % E_{NPP} =49%; Mollisols E_{IN} =5,880, % E_{NPP} =90%), suggesting that the model may be used to isolate pedogenic environments at the regional scale. We further tested the model using data from a series of climosequences in the western Sierra Nevada, California. Climosequence data indicates significant relationships between E_{IN} and chemical weathering indices. In addition, E_{IN} provided a better prediction of weathering indices relative to MAP and MAT (r^{2} =0.72 versus r^{2} = 0.18 and 0.49 for MAP and MAT, respectively). Results from multiple scales suggest the potential for using this energy based approach to predict pedogenic regimes, as well as estimate regional and local rates of silicate weathering. The model requires further testing to establish its usefulness in landscape scale applications.

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