Home > Research > Publications & Outputs > Advancing hydrological process understanding fr...

Associated organisational unit

Electronic data

  • Slater & Binley WATER accepted (author copy)

    Rights statement: 12m

    Accepted author manuscript, 2.31 MB, PDF document

    Embargo ends: 1/01/50

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License


Text available via DOI:

View graph of relations

Advancing hydrological process understanding from long term resistivity monitoring (LTRM) systems

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Article numbere1513
<mark>Journal publication date</mark>2/04/2021
<mark>Journal</mark>WIREs WATER
Issue number3
Number of pages26
Publication StatusPublished
Early online date8/02/21
<mark>Original language</mark>English


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 non-invasively 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 datasets and are temporally aliased in datasets 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 non-invasive 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 datasets. 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 modelling needs are critically discussed.