Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics
Name
sciadv.abj2479.pdf
Description
Published version
Size
5.53 MB
Format
Adobe PDF
Checksum (MD5)
3c541cb425e8673f2eae7b73ee713363
Author(s)
Uhlemann, Sebastian
Dafflon, Baptiste
Wainwright, Haruko Murakami
Williams, Kenneth Hurst
Minsley, Burke
Zamudio, Katrina
Carr, Bradley
Falco, Nicola
Ulrich, Craig
Hubbard, Susan
Date Issued
March 25, 2022
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Citation
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).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1126/sciadv.abj2479
