Articles | Volume 9, issue 5
https://doi.org/10.5194/esurf-9-1347-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-9-1347-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Oct 2021
Research article |
| 11 Oct 2021
| 11 Oct 2021
A hybrid data–model approach to map soil thickness in mountain hillslopes
Qina Yan
CORRESPONDING AUTHOR
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Haruko Wainwright
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Baptiste Dafflon
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Sebastian Uhlemann
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Carl I. Steefel
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Nicola Falco
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
Jeffrey Kwang
Department of Geosciences, University of Massachusetts Amherst,
Amherst, MA, USA
Susan S. Hubbard
Earth and Environmental Science Area, Lawrence Berkeley National
Laboratory, Berkeley, CA, USA
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Cited
11 citations as recorded by crossref.
- Soil thickness prediction models: Types, accuracy and influencing factors Q. Zhu et al. 10.1016/j.catena.2025.109282
- Physically-based modelling of shallow slides susceptibility at the basin scale using proxy soil thickness and geotechnical data R. Melo et al. 10.1016/j.catena.2025.108788
- Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al. 10.5194/nhess-24-1579-2024
- Prediction of overburden layer thickness based on spatial heterogeneity analysis and machine learning models in hillslope regions Z. Chang et al. 10.1016/j.gsf.2025.102109
- A comparative study of empirical and machine learning approaches for soil thickness mapping in the Joshimath region (India) K. Gupta et al. 10.1016/j.catena.2024.108024
- Soil depth and catchment geomorphology: A field, vegetation and GIS based assessment I. Senanayake et al. 10.1016/j.geodrs.2024.e00824
- Predicting the thickness of alpine meadow soil on headwater hillslopes of the Qinghai-Tibet Plateau X. Han et al. 10.1016/j.geoderma.2025.117271
- Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics S. Uhlemann et al. 10.1126/sciadv.abj2479
- Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions H. Wainwright et al. 10.5194/hess-26-429-2022
- Impacts of Landscape Evolution on Heterotrophic Carbon Loss in Intensively Managed Landscapes Q. Yan & P. Kumar 10.3389/frwa.2021.666278
- 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 B. Dafflon et al. 10.3389/feart.2023.976227
9 citations as recorded by crossref.
- Soil thickness prediction models: Types, accuracy and influencing factors Q. Zhu et al. 10.1016/j.catena.2025.109282
- Physically-based modelling of shallow slides susceptibility at the basin scale using proxy soil thickness and geotechnical data R. Melo et al. 10.1016/j.catena.2025.108788
- Assessing locations susceptible to shallow landslide initiation during prolonged intense rainfall in the Lares, Utuado, and Naranjito municipalities of Puerto Rico R. Baum et al. 10.5194/nhess-24-1579-2024
- Prediction of overburden layer thickness based on spatial heterogeneity analysis and machine learning models in hillslope regions Z. Chang et al. 10.1016/j.gsf.2025.102109
- A comparative study of empirical and machine learning approaches for soil thickness mapping in the Joshimath region (India) K. Gupta et al. 10.1016/j.catena.2024.108024
- Soil depth and catchment geomorphology: A field, vegetation and GIS based assessment I. Senanayake et al. 10.1016/j.geodrs.2024.e00824
- Predicting the thickness of alpine meadow soil on headwater hillslopes of the Qinghai-Tibet Plateau X. Han et al. 10.1016/j.geoderma.2025.117271
- Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics S. Uhlemann et al. 10.1126/sciadv.abj2479
- Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions H. Wainwright et al. 10.5194/hess-26-429-2022
2 citations as recorded by crossref.
- Impacts of Landscape Evolution on Heterotrophic Carbon Loss in Intensively Managed Landscapes Q. Yan & P. Kumar 10.3389/frwa.2021.666278
- 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 B. Dafflon et al. 10.3389/feart.2023.976227
Latest update: 26 Jul 2025
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 that the hybrid model provides better accuracy than a machine-learning model.
We develop a hybrid model to estimate the spatial distribution of the thickness of the soil...
