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Data and code for hydrologic data-constrained inversion of time-lapse electrical resistivity measurements [Dataset]

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Created: Dec 03, 2023 at 10:56 p.m.
Last updated: Feb 22, 2024 at 5:02 p.m.
DOI: 10.4211/hs.42ee6fcbf9f64bf882c579df948176e2
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Sharing Status: Published
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This dataset contains the codes and data used in the manuscript "Temporal hydrologic constraints on time-lapse electrical resistivity inversion in hydrogeophysics".In the first folder, Hydrology-constrain includes the three inversion codes and processed field ERT data for the inversion. In the second folder, Hydrologic modeling includes the codes for hydrologic modeling and generating synthetic datasets. Abstract for the manuscript: Time-lapse electrical resistivity method has been frequently used in hydrology to monitor dynamic water flows and storage changes in the subsurface. To construct temporal resistivity images for hydrologic interpretations, measured apparent resistivity datasets at different times need to be inverted. Traditionally, this time-lapse resistivity inversion either assumes no links between adjacent resistivity models (individual inversion) or enforces the maximum smoothness condition in the time domain (temporal smoothness-constrained inversion). While the former method applies no temporal constraint to the resistivity changes, the latter minimizes the temporal resistivity changes. Both inversions could introduce biases to the reconstructed resistivity models, especially in hillslopes where the subsurface moisture (and thus ground resistivity) is neither independent nor unchanged during a typical monitoring period (e.g., a water year). In this study, we propose to construct realistic temporal resistivity constraint from subsurface water storage data. To test this new method, we combined integrated hydrologic modeling and resistivity forward modeling to design a synthetic case. Comparing the inversion results to ground truth shows that the hydrologic data-constrained inversion captures the dynamic water flows and storage changes in the subsurface. Application of this new method to a field dataset was also performed. Compared to existing inversion methods, the new method better reveals the abrupt resistivity changes associated with some intense hydrologic events such as rainfall/snowmelt infiltration and soil drying in the summer. This study thus provides a useful tool for processing time-lapse resistivity data collected at many dynamic hydrologic systems such as hillslopes, drylands, agriculture fields, and forest land.

Subject Keywords



Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees



Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
National Science Foundation RAPID: Monitoring subsurface water storage dynamics associated with the 2023 extreme snowfall events in precipitation-limited systems EAR#2330004


People or Organizations that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Hang Chen Boise State University Idaho, US
Qifei Niu Boise State University Idaho, US

How to Cite

Chen, H., Q. Niu (2024). Data and code for hydrologic data-constrained inversion of time-lapse electrical resistivity measurements [Dataset], HydroShare,

This resource is shared under the Creative Commons Attribution CC BY.


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