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Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

May 4, 2022

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.

Citation Information

Publication Year 2022
Title Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics
DOI 10.1126/sciadv.abj2479
Authors Sebastian Uhlemann, Baptiste Dafflon, Haruko Murakami Wainwright, Kenneth Hurst Williams, Burke J. Minsley, Katrina D. Zamudio, Bradley Carr, Nicola Falco, Craig Ulrich, Susan S. Hubbard
Publication Type Article
Publication Subtype Journal Article
Series Title Science Advances
Series Number
Index ID 70231257
Record Source USGS Publications Warehouse
USGS Organization Geology, Geophysics, and Geochemistry Science Center