Hybrid data-model-based mapping of soil thickness in a mountainous watershed

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 examples of hillslopes (south-facing and north-facing, respectively) in the East River Watershed in Colorado that validates the effectiveness of the model. 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 modelling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the north-facing hillslope has a deeper soil layer than the south-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 south-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the north-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the south-facing slopes may influence soil characteristics. With only seven parameters for calibration, this hybrid model can provide a realistic soil thickness map at other study sites by with a relatively small amount of sampling dataset. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction, but integrates the strengths of both statistical approaches and process-based modeling approaches.

Advanced monitoring of soil-vegetation co-dynamics reveals the successive controls of snowmelt on soil moisture and on plant seasonal dynamics in a mountainous watershed

“…The soil present in the region includes shale rock land, Tilton sandy loam, Teoculli loam, Cryaquolls and Histosols (https://websoilsurvey.sc.egov.usda.gov). The hillslope soil textures are generally loam to silt loam, with increasing fraction of fines toward the bottom of the hillslope and silty clay and silty clay loam in the floodplain (Tokunaga et al, 2019;Yan et al, 2021). Soil thickness measurements obtained by identifying the contact layer between soil and weathered bedrock vary between 0.15 and 1.5 m, with a mean value of 0.76 m. The soil thickness shows an increasing trend toward the bottom of the hillslope with largest values in topographic lows and in the floodplain (Yan et al, 2021).…”

“…In this study, we consider only the near-surface soil electrical conductivity as averaged from 0 to 50 cm depth, which is an important zone for plant-soil interactions. It is to note that in this study the ERT data are not used to estimate the soil thickness because of the large electrode spacing relative to the soil thickness measured at the site (Yan et al, 2021) and the limited contrast in electrical conductivity between the soil and the weathered shale bedrock. Temperature correction is critical to analyze near-surface electrical conductivity over time given that temperature variation between first bare-ground date and the summer time can be as high as 16 °C at 50 cm depth, which implies a 30% change in electrical conductivity due to temperature at this depth.…”

“…Besides being valuable indicators for monitoring plant seasonal dynamic due to their large dynamical range in GCC versus data uncertainty, capturing their spatial distribution and dynamics is relevant to reduce the uncertainty in carbon fluxes and storage in such environment. Indeed, studies have shown that locations where water converges and/or is retained and where soil accumulates (Yan et al, 2021), as 3.6 Inferring soil moisture across the hillslope from soil moisture sensors and GCC…”

Impacts of Landscape Evolution on Heterotrophic Carbon Loss in Intensively Managed Landscapes

“…Our assumption here is that the soil depth change is all from the surface soil transport and not from the soil bottom weathering. The main reason is that the bottom of soils is layered with loess and glacial drifts, and the weathering processes are much slower than the surface transport rate, which is very different compared to mountainous areas (Anders et al, 2018;Yan et al, 2021).…”

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

“…There have been significant advances in quantifying the above/ below-ground watershed compartments over space based on a suite of remote sensing datasets, including plant species distributions and traits (e.g., Madritch et al, 2014;Falco et al, 2019;Chadwick et al, 2020;Falco et al, 2020), soil thickness and soil properties (e.g., Patton et al, 2018;Yan et al, 2020), and bedrock variability (e.g., Parsekian et al, 2015;Uhlemann et al, 2022). The co-variability of these compartments has been documented based on analyzing multiple remote sensing and spatial data layers (e.g., Wainwright et al, 2015;Pelletier et al, 2018;Devadoss et al, 2020;Hermes et al, 2020;Enguehard et al, 2022;Wainwright et al, 2022a).…”