Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics
<jats:p>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 covariab...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2023
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| Online Access: | https://hdl.handle.net/1721.1/147621 |
| _version_ | 1826216975539372032 |
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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 |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering 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 | MIT |
| description | <jats:p>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.</jats:p> |
| first_indexed | 2024-09-23T16:56:09Z |
| format | Article |
| id | mit-1721.1/147621 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:56:09Z |
| publishDate | 2023 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | dspace |
| spelling | mit-1721.1/1476212023-01-21T03:01:25Z 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 Massachusetts Institute of Technology. Department of Nuclear Science and Engineering <jats:p>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.</jats:p> 2023-01-20T19:39:44Z 2023-01-20T19:39:44Z 2022-03-25 2023-01-20T19:33:11Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147621 Uhlemann, Sebastian, Dafflon, Baptiste, Wainwright, Haruko Murakami, Williams, Kenneth Hurst, Minsley, Burke et al. 2022. "Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics." Science Advances, 8 (12). en 10.1126/sciadv.abj2479 Science Advances Creative Commons Attribution NonCommercial License 4.0 https://creativecommons.org/licenses/by-nc/4.0/ application/pdf American Association for the Advancement of Science (AAAS) Science Advances |
| spellingShingle | 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 |
| url | https://hdl.handle.net/1721.1/147621 |
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