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Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5ĝ€¯m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods - auger and cone penetrometer - are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

©2018. The Authors. Permafrost physicochemical parameters play a key role in controlling the response of permafrost carbon to climate change. We studied the physicochemical parameters of permafrost in an Arctic tundra region to evaluate (1) how soil parameters vary with depth and whether and how they are interrelated, (2) whether and how permafrost soil differs from its overlaying active layer, and (3) whether soil property-depth relationships are different across geomorphic features (e.g., low, flat, and high centered polygons). We also explored the possible biogeochemical processes that led to these soil characteristics and how they may affect biogeochemical reactions upon permafrost thaw. We observed (1) consistent relationships between soil property and depth and between major parameters, (2) large contrasts of key soil parameters between active layer and permafrost, indicative of potentially different response of the permafrost carbon to warming when compared to the active layer, and (3) a correlation between soil hydraulic conductivity and topographic features that impacts soil hydrologic processes. Our analysis suggests that the permafrost has a marine-derived chemical signature that differs from the active layer and shapes the physicochemical fingerprints of the different geomorphic features. Specifically, we revealed the unique signatures of the high center polygons, indicative of possible microbial activity at depth (>1 m). Our study suggested consistent key soil parameter-depth correlations while demonstrating complex lateral and vertical variabilities. These results are valuable for identifying approaches to upscale point-based measurements and for improving model parameterization to predict permafrost carbon behavior and feedback under future climate.

© 2016 Elsevier Ltd. In this study, we advance the understanding of three-dimensional (3-D) electrical resistivity tomography (ERT) for monitoring long-term CO2 storage by analyzing two previously published field time-lapse data sets. We address two important aspects of ERT inversion-the issue of resolution decay, a general impediment to the ERT method, and the issue of potentially misleading imaging artifacts due to 2-D model assumptions. The first study analyzes data from a shallow dissolved-CO2 injection experiment near Escatawpa (Mississippi), where ERT data were collected in a 3-D crosswell configuration. We apply a focusing approach designed for crosswell configurations to counteract resolution loss in the inter-wellbore area, with synthetic studies demonstrating its effectiveness. The 3-D field data analysis reveals an initially southwards-trending flow path development and a dispersing plume development in the downgradient inter-well region. The second data set was collected during a deep (over 3 km) injection of supercritical CO2 near Cranfield (Mississippi). Comparative 2-D and 3-D inversions reveal the projection of off-planar anomalies onto the cross-section, a typical artifact introduced by 2-D model assumptions. Conforming 3-D images from two different algorithms support earlier hydrological investigations, indicating a conduit system where flow velocity variations lead to a circumvention of a close observation well and an onset of increased CO2 saturation downgradient from this well. We relate lateral permeability variations indicated by an independently obtained hydrological analysis to this consistently observed pattern in the CO2 spatial plume evolution.