Abstract
Abstract. 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 (0.5 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.
Highlights
The soil layer is an element of the critical zone where water, carbon, nitrogen, and other elements exchange between air and plants and the subsurface
We first investigate the scaling issues from digital elevation model (DEM) resolution with curvature and discuss the sensitivity of all the parameters used in this hybrid model
Soil thickness plays a central role in the feedbacks among surface–subsurface water flow, vegetation, soil production, drainage density, and topography, and these in turn control soil thickness
Summary
The soil layer is an element of the critical zone where water, carbon, nitrogen, and other elements exchange between air and plants and the subsurface. The thickness of a soil layer regulates the hydrologic response, including surface and base flow runoff, water partitioning, evapotranspiration, plant-available water, and water and nutrient residence time (Fan et al, 2019). It determines hillslope stability (or landslide potential), channel initiation, drainage density, and other geomorphic processes (Dietrich et al, 1995). An accurate soil thickness map can improve the estimation of water, carbon, nitrogen, and other elements dynamics for hydrologic and biogeochemical modeling (Carvalhais et al, 2014; Fan et al, 2019; Li et al, 2020; Patton et al, 2019; Pelletier et al, 2016). Mapping soil thickness remains one of the key uncertainties in land sur-
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