This Title All WIREs
How to cite this WIREs title:
WIREs Water
Impact Factor: 4.412

Advancing hydrological process understanding from long‐term resistivity monitoring systems

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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)
Key components of a dedicated long‐term resistivity monitoring (LTRM) system, including field measurement, data transfer/storage, data processing, and interpretation to facilitate near real‐time decision‐support (modified after Holmes et al., 2020)
[ Normal View | Magnified View ]
Changes in resistivity over time (relative to a reference data set on April 23, 2013) for several winter wheat varieties along a 30‐m transect. The variety names are shown below the third image. Date format is dd/mm/yyyy. The position of plants in each plot along the transect are indicated, as well as a fallow plot devoid of plants. After Whalley et al. (2017)
[ Normal View | Magnified View ]
Time series analysis‐based interpretation of a long‐term resistivity monitoring (LTRM) data set examining groundwater‐surface water interaction (a) bulk cond (σb) breakthrough curve behavior compared to water table (b–e) images of parameters derived from time series analysis of the correlation between the electrical conductivity and the water table throughout the image (see the text for explanation). After Wallin et al. (2013)
[ Normal View | Magnified View ]
Top and middle: inverted resistivity dynamics monitored in two virtual boreholes (B14 and B100) using the A‐ERT long‐term resistivity monitoring (LTRM) system for understanding permafrost dynamics on a mountain slope in the Swiss Alps over a 1‐year period. Bottom: temperature recorded in a real borehole at B14 (modified from Hilbich et al., 2011)
[ Normal View | Magnified View ]
Percentage change in gravimetric moisture content (GMC) inferred from long‐term resistivity monitoring (LTRM) data from the Hollin Hill observatory after Uhlemann et al. (2017). Changes are computed from a March 2010 baseline data set. Red colors indicate drying; blue colors indicate wetting. The sequence shows seasonal wetting and drying in addition to localized slope movement events
[ Normal View | Magnified View ]
(a–c) Vertical wetting front dynamics based on resistivity‐derived moisture content changes at three locations (uplslope, midslope, downslope) of a hillslope from a 2‐year long‐term resistivity monitoring (LTRM) data set (modified from Kotikian et al., 2019). (d) Electrical imaging mesh showing slope locations described in (a) along with estimated regolith‐soil boundary based on seismic velocities
[ Normal View | Magnified View ]
Long‐term resistivity monitoring (LTRM) setup on an uninhabited island off the coast of Louisiana (USA), used to monitor natural attenuation of oil in sediments resulting from the BP Deepwater Horizon spill (a,b) photos of LTRM setup (c) example images of ratio resistivity obtained from time‐lapse inversion (1 denotes no change, >1 denotes increases in resistivity, and <1 denotes decreases in resistivity) over a portion (152 days) of the entire monitoring period. Modified from Heenan et al. (2014)
[ Normal View | Magnified View ]

Browse by Topic

Science of Water

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts