Improved quantification of the factors controlling soil organic matter (SOM) stabilization at continental to global scales is needed to inform projections of the largest actively cycling terrestrial carbon pool on Earth, and its response to environmental change. Biogeochemical models rely almost exclusively on clay content to modify rates of SOM turnover and fluxes of climate-active CO 2 to the atmosphere. Emerging conceptual understanding, however, suggests other soil physicochemical properties may predict SOM stabilization better than clay content. We addressed this discrepancy by synthesizing data from over 5,500 soil profiles spanning continental scale environmental gradients. Here, we demonstrate that other physicochemical parameters are much stronger predictors of SOM content, with clay content having relatively little explanatory power. We show that exchangeable calcium strongly predicted SOM content in water-limited, alkaline soils, whereas with increasing moisture availability and acidity, iron-and aluminum-oxyhydroxides emerged as better predictors, demonstrating that the relative importance of SOM stabilization mechanisms scales with climate and acidity. These results highlight the urgent need to modify biogeochemical models to

The ''critical zone'' includes the coupled earth surface systems of vegetation, regolith and groundwater that are essential to sustaining life on the planet. The function of this zone is the result of complex interactions among physical, chemical and biological processes and understanding these interactions remains a major challenge to earth system sciences. Here we develop an integrated framework based on thermodynamic theory to characterize the critical zone as a system open to energy and mass fluxes that are forced by radiant, geochemical, and elevational gradients. We derive a statement that demonstrates the relative importance of solar radiation, water, carbon, and physical/chemical denudation mass fluxes to the critical zone energy balance. Within this framework we use rates of effective energy and mass transfer [EEMT; W m -2 ] to quantify the relevant flux-gradient relations. Synthesis of existing data demonstrates that variation in energetics associated with primary production and effective precipitation explains substantial variance in critical zone structure and function. Furthermore, we observe threshold behavior in systems that transition to primary production predominance of the energy flux term. The proposed framework provides a first order approximation of non-linearity in critical zone processes that may be coupled with physical and numerical models to constrain landscape evolution.

Uncertainty about the effects of climate change on terrestrial soil organic C stocks has generated interest in clarifying the processes that underlie soil C dynamics. We investigated the role of soil mineralogy and aggregate stability as key variables controlling soil C dynamics in a California conifer forest. We characterized soils derived from granite (GR) and mixed andesite‐granite (AN) parent materials from similar forest conditions. Granite and AN soils contained similar clay mineral assemblages as determined by x‐ray diffraction (XRD), dominated by vermiculite, hydroxy‐interlayered vermiculite (HIV), kaolinite, and gibbsite. However, AN soils contained significantly more Al in Al‐humus complexes (6.2 vs. 3.3 kg m−2) and more crystalline and short‐range order (SRO) Fe oxyhydroxides (30.6 vs. 16.8 kg m−2) than GR soils. Andesite‐granite pedons contained nearly 50% more C relative to GR soils (22.8 vs. 15.0 kg m−2). Distribution of C within density and aggregate fractions (free, occluded, and mineral associated C) varied significantly between AN and GR soils. In particular, AN soils had at least twice as much mineral associated C relative to GR soils in all horizons. Based on 14C measurements, occluded C mean residence time (MRT) > mineral C > free C in both soil types, suggesting a significant role for aggregate C protection in controlling soil C turnover. We found highly significant, positive correlations between Al‐humus complexes, SRO Al minerals, and total C content. We suggest that a combination of aggregate protection and organomineral association with Al‐humus complexes and SRO Al minerals control the variation in soil C dynamics in these systems.

[1] The hydrologic response of upland watersheds is strongly controlled by soil (regolith) thickness. Despite the need to quantify soil thickness for input into hydrologic models, there is currently no widely used, geomorphically based method for doing so. In this paper we describe and illustrate a new method for predictive mapping of soil thicknesses using high-resolution topographic data, numerical modeling, and field-based calibration. The model framework works directly with input digital elevation model data to predict soil thicknesses assuming a long-term balance between soil production and erosion. Erosion rates in the model are quantified using one of three geomorphically based sediment transport models: nonlinear slope-dependent transport, nonlinear area-and slopedependent transport, and nonlinear depth-and slope-dependent transport. The model balances soil production and erosion locally to predict a family of solutions corresponding to a range of values of two unconstrained model parameters. A small number of fieldbased soil thickness measurements can then be used to calibrate the local value of those unconstrained parameters, thereby constraining which solution is applicable at a particular study site. As an illustration, the model is used to predictively map soil thicknesses in two small, $0.1 km 2 , drainage basins in the Marshall Gulch watershed, a semiarid drainage basin in the Santa Catalina Mountains of Pima County, Arizona. Field observations and calibration data indicate that the nonlinear depth-and slope-dependent sediment transport model is the most appropriate transport model for this site. The resulting framework provides a generally applicable, geomorphically based tool for predictive mapping of soil thickness using high-resolution topographic data sets.

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