Premium
Advancing hydrological process understanding from long‐term resistivity monitoring systems
Author(s) -
Slater Lee,
Binley Andrew
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.413
H-Index - 24
ISSN - 2049-1948
DOI - 10.1002/wat2.1513
Subject(s) - instrumentation (computer programming) , temporal scales , field (mathematics) , data science , computer science , range (aeronautics) , temporal resolution , environmental science , hydrology (agriculture) , geology , engineering , ecology , pure mathematics , biology , aerospace engineering , operating system , physics , mathematics , geotechnical engineering , quantum mechanics
Monitoring subsurface flow and transport processes over a wide range of spatiotemporal scales remains one of the greatest challenges in hydrology. Electrical geophysical techniques have been implemented to noninvasively investigate a broad range of subsurface hydrological processes. Recent advances in instrumentation and interpretational tools highlight the emerging opportunities to adopt long‐term resistivity monitoring (LTRM) to improve understanding of flow and transport processes operating over monthly to decadal timescales that are not adequately captured in short‐term monitoring data sets and are temporally aliased in data sets constructed from occasional reoccupation of a study site. The emergence of LTRM as a robust tool in hydrology represents a paradigm shift in geophysical data acquisition and analysis, with resistivity monitoring now evolving into a hydrological decision support technology. We describe the theoretical basis for adopting LTRM for noninvasive monitoring of hydrological state variables over multiple spatial scales and with higher temporal resolution than achieved from periodic reoccupation of a field site. Instrumentation developments facilitating autonomous data acquisition at off the grid field sites are discussed, along with advances in data processing that enhance the hydrological information content inherent in LTRM data sets. Case studies from a diverse range of hydrology subdisciplines highlight the largely untapped potential for LTRM to provide information beyond the reach of established hydrology tools. Future opportunities and challenges relating to the more widespread adoption of LTRM, including addressing inherent uncertainty in resistivity interpretation, upscaling, computational, and modeling needs are critically discussed. This article is categorized under: Science of Water (WCAA)