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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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...
