Preprints
https://doi.org/10.5194/esurf-2020-110
https://doi.org/10.5194/esurf-2020-110

  18 Jan 2021

18 Jan 2021

Review status: a revised version of this preprint was accepted for the journal ESurf and is expected to appear here in due course.

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

Qina Yan1, Haruko Wainwright1, Baptiste Dafflon1, Sebastian Uhlemann1, Carl I. Steefel1, Nicola Falco1, Jeffrey Kwang2, and Susan S. Hubbard1 Qina Yan et al.
  • 1Earth and Environmental Science Area, Lawrence Berkeley National Laboratory, Berkeley, CA., USA
  • 2Department of Geosciences, University of Massachusetts Amherst, Amherst, MA, USA

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.

Qina Yan et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2020-110', Nicholas Patton, 28 Feb 2021
    • AC1: 'Reply on RC1', Qina Yan, 31 Mar 2021
  • RC2: 'Comment on esurf-2020-110', Jon Pelletier , 01 Mar 2021
    • AC2: 'Reply on RC2', Qina Yan, 31 Mar 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on esurf-2020-110', Nicholas Patton, 28 Feb 2021
    • AC1: 'Reply on RC1', Qina Yan, 31 Mar 2021
  • RC2: 'Comment on esurf-2020-110', Jon Pelletier , 01 Mar 2021
    • AC2: 'Reply on RC2', Qina Yan, 31 Mar 2021

Qina Yan et al.

Data sets

Soil thickness estimation Qina Yan https://doi.org/10.5281/zenodo.4445383

Model code and software

Soil thickness estimation Qina Yan https://doi.org/10.5281/zenodo.4445383

Qina Yan et al.

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Short summary
We develop a hybrid model to estimate the spatial distribution of the thickness of the soil layer, which also provides estimations of soil transport and soil production rates. We apply this model to two examples of hillslopes in the East River Watershed in Colorado and validate the model. The results show that the north-facing (NF) hillslope has a deeper soil layer than the south-facing (SF) hillslope, and the hybrid model provides better accuracy than a Machine Learning model.