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Predicting soil thickness on soil mantled hillslopes

Soil thickness is a fundamental variable in many earth science disciplines due to its critical role in many hydrological and ecological processes, but it is difficult to predict. Here we show a strong linear relationship (r(2) = 0.87, RMSE = 0.19 m) between soil thickness and hillslope curvature acr...

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Detalles Bibliográficos
Autores principales: Patton, Nicholas R., Lohse, Kathleen A., Godsey, Sarah E., Crosby, Benjamin T., Seyfried, Mark S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6102209/
https://www.ncbi.nlm.nih.gov/pubmed/30127337
http://dx.doi.org/10.1038/s41467-018-05743-y
Descripción
Sumario:Soil thickness is a fundamental variable in many earth science disciplines due to its critical role in many hydrological and ecological processes, but it is difficult to predict. Here we show a strong linear relationship (r(2) = 0.87, RMSE = 0.19 m) between soil thickness and hillslope curvature across both convergent and divergent parts of the landscape at a field site in Idaho. We find similar linear relationships across diverse landscapes (n = 6) with the slopes of these relationships varying as a function of the standard deviation in catchment curvatures. This soil thickness-curvature approach is significantly more efficient and just as accurate as kriging-based methods, but requires only high-resolution elevation data and as few as one soil profile. Efficiently attained, spatially continuous soil thickness datasets enable improved models for soil carbon, hydrology, weathering, and landscape evolution.