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

Sebastian Uhlemann, Baptiste Dafflon, Haruko Murakami Wainwright, Kenneth Hurst Williams, Burke Minsley, Katrina Zamudio, Bradley Carr, Nicola Falco, Craig Ulrich, Susan Hubbard

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish
Article numbereabj2479
JournalScience Advances
Volume8
Issue number12
DOIs
StatePublished - Mar 2022
Externally publishedYes

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