Earth Surface Dynamics
Earth Surface Dynamics
ESurf
Articles | Volume 9, issue 5
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.
https://doi.org/10.5194/esurf-9-1347-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 5
Research article
 | 
11 Oct 2021
Research article |  | 11 Oct 2021

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

Qina Yan, Haruko Wainwright, Baptiste Dafflon, Sebastian Uhlemann, Carl I. Steefel, Nicola Falco, Jeffrey Kwang, and Susan S. Hubbard

Supplement

https://doi.org/10.5194/esurf-9-1347-2021-supplement

Data sets

Qinayan/Soil-thickness: Soil thickness estimation (v1.0.0) Q. Yan https://doi.org/10.5281/zenodo.4445383

Custom NEON AOP reflectance mosaics and maps of shade masks, canopy water content, Watershed Function SFA P. Brodrick, T. Goulden, and K. D. Chadwick https://doi.org/10.15485/1618131

NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters, Watershed Function SFA T. Goulden, B. Hass, E. Brodie, K. D. Chadwick, N. Falco, K. Maher, H. Wainwright, and K. Williams https://doi.org/10.15485/1617203

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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.
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