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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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 |
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author | Patton, Nicholas R. Lohse, Kathleen A. Godsey, Sarah E. Crosby, Benjamin T. Seyfried, Mark S. |
author_facet | Patton, Nicholas R. Lohse, Kathleen A. Godsey, Sarah E. Crosby, Benjamin T. Seyfried, Mark S. |
author_sort | Patton, Nicholas R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6102209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-61022092018-08-22 Predicting soil thickness on soil mantled hillslopes Patton, Nicholas R. Lohse, Kathleen A. Godsey, Sarah E. Crosby, Benjamin T. Seyfried, Mark S. Nat Commun Article 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. Nature Publishing Group UK 2018-08-20 /pmc/articles/PMC6102209/ /pubmed/30127337 http://dx.doi.org/10.1038/s41467-018-05743-y Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Patton, Nicholas R. Lohse, Kathleen A. Godsey, Sarah E. Crosby, Benjamin T. Seyfried, Mark S. Predicting soil thickness on soil mantled hillslopes |
title | Predicting soil thickness on soil mantled hillslopes |
title_full | Predicting soil thickness on soil mantled hillslopes |
title_fullStr | Predicting soil thickness on soil mantled hillslopes |
title_full_unstemmed | Predicting soil thickness on soil mantled hillslopes |
title_short | Predicting soil thickness on soil mantled hillslopes |
title_sort | predicting soil thickness on soil mantled hillslopes |
topic | Article |
url | 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 |
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