A hybrid data–model approach to map soil thickness in mountain hillslopes

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.

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

“…Note that the blended area in the top panel indicates the area that is not shown by the geological cross section. is in agreement with other studies that showed that steeper slopes are usually related to higher runoff and less groundwater infiltration [e.g., (23)], while increased soil erosion leads to a thinner soil cover (80), which also likely contributes to a higher aggregated resistivity response.…”

Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions

“…However, there is a difference in two grassland-dominated zones, Zone 5 and Zone 1, with the high-elevation grassland less sensitive to droughts, possibly owing to increased water availability at higher elevation. In addition, in a separate study by Yan et al (2021), the soil thickness and associated parameters such as soil diffusion coefficients are found to be distinctly different between the two zones in this domain (Zone 1 and Zone 2).…”

“…This hierarchical representation provides a tractable framework for watershed characterization. Furthermore, Yan et al (2021) developed two separate parameterizations for the soil evolution model in the two hillslopes that belonged to Zones 1 and 2 and then simulated the variable soil thickness within those hillslopes. In this way, our method can improve model parameterization in large-scale hydrological models by honoring distinct boundaries and water/element export contributions and provide a new comprehensive way of linking above-and below-ground properties to watershed functions critical to maintaining water resources.…”

Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico

“…Research has made much progress in addressing these knowledge gaps. For example, many physically based and empirical soil-depth models are available (Roering, 2008;Pelletier and Rasmussen, 2009;Ho et al, 2012;Catani et al, 2010;Nicótina et al, 2011;Gomes et al, 2016;Patton et al, 2018;Yan et al, 2021;Xiao et al, 2023), making it possible to estimate the field distribution of soil depth in landslide prone areas (Godt et al, 2008a;Segoni et al, 2009;Ho et al, 2012). Some studies have combined field or laboratory measured properties with mapped lithologic characteristics and statis-R. L. Baum et al: Assessing locations susceptible to shallow landslide initiation tical analysis to describe the spatial distribution of soil properties (Godt et al, 2008b;Tofani et al, 2017).…”