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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...

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Autores principales: Uhlemann, Sebastian, Dafflon, Baptiste, Wainwright, Haruko Murakami, Williams, Kenneth Hurst, Minsley, Burke, Zamudio, Katrina, Carr, Bradley, Falco, Nicola, Ulrich, Craig, Hubbard, Susan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
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.
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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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