Advancing hydrological process understanding from long‐term resistivity monitoring systems

Abstract

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)

Design and operation of a long-term monitoring system for spectral electrical impedance tomography (sEIT)

“…The sEIT monitoring system presented in this study is based on a laboratory design aimed at optimal measurement accuracy and precision, but was not designed for ruggedness or large-scale applications. As also mentioned by Slater and Binley (2021), with fixed setups. The adaption of the current sEIT measurements for robust long-term monitoring applications will require improving system resilience to harsh environments.…”

“…The situation of long-term sEIT monitoring is similar to that of long-term resistivity monitoring. Such monitoring applications were recently reviewed by Slater and Binley (2021), who argue that fixed measurement installations have a high potential for a multitude of disciplines, yet remain underused and underdeveloped.…”

Ground and Satellite-Based Methods of Measuring Deformation at a UK Landslide Observatory: Comparison and Integration

“…Ground-based geophysical methods are used to monitor the site, including 4D Electrical Resistivity Tomography (ERT). ERT as a monitoring tool can provide information about changes in the subsurface hydrological conditions [21], and is therefore highly relevant and increasingly used to track precursors of slope failure in moisture-induced landslides [20]. In the spring of 2008, an Automated Time-Lapse Electrical Resistivity Tomography (ALERT) system was permanently installed on the site [22,23].…”

Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects

“…Geolectrical inversion has known issues, typically due to the non-uniqueness of the solution in addition to incomplete or imperfect data due to practical limitations in collecting them. Advantages and drawbacks (structural errors in the hydrological conceptual model, dependency on known petrophysical relationships) of coupled inversions have been discussed by many authors (e.g., Hinnell et al, 2010;Camporese et al, 2015a;Slater and Binley, 2021;Yu et al, 2021). An emerging method for combining model predictions with observations is provided by the DA framework.…”