TY - JOUR
T1 - Surface parameters and bedrock properties covary across a mountainous watershed
T2 - Insights from machine learning and geophysics
AU - Uhlemann, Sebastian
AU - Dafflon, Baptiste
AU - Wainwright, Haruko Murakami
AU - Williams, Kenneth Hurst
AU - Minsley, Burke
AU - Zamudio, Katrina
AU - Carr, Bradley
AU - Falco, Nicola
AU - Ulrich, Craig
AU - Hubbard, Susan
N1 - Publisher Copyright:
Copyright © 2022 The Authors
PY - 2022/3
Y1 - 2022/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85126872540&partnerID=8YFLogxK
U2 - 10.1126/sciadv.abj2479
DO - 10.1126/sciadv.abj2479
M3 - Article
C2 - 35319978
AN - SCOPUS:85126872540
SN - 2375-2548
VL - 8
JO - Science Advances
JF - Science Advances
IS - 12
M1 - eabj2479
ER -