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Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics
Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8942364/ https://www.ncbi.nlm.nih.gov/pubmed/35319978 http://dx.doi.org/10.1126/sciadv.abj2479 |
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author | Uhlemann, Sebastian Dafflon, Baptiste Wainwright, Haruko Murakami Williams, Kenneth Hurst Minsley, Burke Zamudio, Katrina Carr, Bradley Falco, Nicola Ulrich, Craig Hubbard, Susan |
author_facet | Uhlemann, Sebastian Dafflon, Baptiste Wainwright, Haruko Murakami Williams, Kenneth Hurst Minsley, Burke Zamudio, Katrina Carr, Bradley Falco, Nicola Ulrich, Craig Hubbard, Susan |
author_sort | Uhlemann, Sebastian |
collection | PubMed |
description | Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems. |
format | Online Article Text |
id | pubmed-8942364 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-89423642022-04-04 Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics Uhlemann, Sebastian Dafflon, Baptiste Wainwright, Haruko Murakami Williams, Kenneth Hurst Minsley, Burke Zamudio, Katrina Carr, Bradley Falco, Nicola Ulrich, Craig Hubbard, Susan Sci Adv Earth, Environmental, Ecological, and Space Sciences Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems. American Association for the Advancement of Science 2022-03-23 /pmc/articles/PMC8942364/ /pubmed/35319978 http://dx.doi.org/10.1126/sciadv.abj2479 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Earth, Environmental, Ecological, and Space Sciences Uhlemann, Sebastian Dafflon, Baptiste Wainwright, Haruko Murakami Williams, Kenneth Hurst Minsley, Burke Zamudio, Katrina Carr, Bradley Falco, Nicola Ulrich, Craig Hubbard, Susan Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics |
title | Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics |
title_full | Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics |
title_fullStr | Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics |
title_full_unstemmed | Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics |
title_short | Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics |
title_sort | surface parameters and bedrock properties covary across a mountainous watershed: insights from machine learning and geophysics |
topic | Earth, Environmental, Ecological, and Space Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8942364/ https://www.ncbi.nlm.nih.gov/pubmed/35319978 http://dx.doi.org/10.1126/sciadv.abj2479 |
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