Data and scripts associated with the manuscript "Estimating soil moisture and salinity response to simulated coastal flooding using time-lapse electrical resistivity and induced polarization monitoring”

Adebayo, Moses; Bailey, Vanessa; Ehosioke, Solomon; Efemena, Emmanuel; Hopple, Anya; Regier, Peter; Rod, Kenton; Megonigal, J.; Chen, Xingyuan; Ward, Nicholas; Doro, Kennedy

doi:10.15485/2298997

Title: Data and scripts associated with the manuscript "Estimating soil moisture and salinity response to simulated coastal flooding using time-lapse electrical resistivity and induced polarization monitoring”

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Abstract

This package contains geophysics datasets generated during the simulated ecosystem-flooding experiment – TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatment). These geophysics data consist of Electrical Resistivity Imaging (ERI) and Induced Polarization (IP) datasets collected to estimate the soil moisture and salinity response to the simulated coastal flooding experiment. In addition to using geophysics datasets to estimate soil moisture and salinity response, petrophysical relationship between the measured resistivity and moisture content and salinity using the multisalinity. This package consists of two folders: “MultiSalinity Experiment” folder and “TEMPEST Experiment and petrophysical conversion” folders. The “MultiSalinity Experiment” folder contains the datasets generated from the multisalinity experiment used to develop the petrophysical reslationship. The “TEMPEST Experiment and petrophysical conversion” folder contains ERI, IP, Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) datasets collected during the TEMPEST experiment. This study shows the ability to used geophysical datasets in estimating moisture content and salinity at scale will be useful in calibrating Earth System Models.

Authors:

;

; Efemena, Emmanuel;

;

; Rod, Kenton;

;

University of Toledo; University of Toledo
Pacific Northwest National Laboratory (PNNL)
University of Toledo
Smithsonian Environmental Research Center

Publication Date:: Sun Dec 31 23:00:00 EST 2023

DOE Contract Number:: AC02-05CH11231

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem; COMPASS-FME

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES; EARTH SCIENCE > AGRICULTURE > SOILS > SOIL MOISTURE/WATER CONTENT; Induced polarization; Multisalinity experiment; Petrophysics; Simulated flooding; Timelapse Resistivity; salinity

OSTI Identifier:: 2298997

DOI:: https://doi.org/10.15485/2298997

Citation Formats


                    Adebayo, Moses, Bailey, Vanessa, Ehosioke, Solomon, Efemena, Emmanuel, Hopple, Anya, Regier, Peter, Rod, Kenton, Megonigal, J., Chen, Xingyuan, Ward, Nicholas, and Doro, Kennedy. Data and scripts associated with the manuscript "Estimating soil moisture and salinity response to simulated coastal flooding using time-lapse electrical resistivity and induced polarization monitoring”.  United States: N. p., 2023. 
        Web.  doi:10.15485/2298997.

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                    Adebayo, Moses, Bailey, Vanessa, Ehosioke, Solomon, Efemena, Emmanuel, Hopple, Anya, Regier, Peter, Rod, Kenton, Megonigal, J., Chen, Xingyuan, Ward, Nicholas, & Doro, Kennedy. Data and scripts associated with the manuscript "Estimating soil moisture and salinity response to simulated coastal flooding using time-lapse electrical resistivity and induced polarization monitoring”.  United States.  doi:https://doi.org/10.15485/2298997

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                    Adebayo, Moses, Bailey, Vanessa, Ehosioke, Solomon, Efemena, Emmanuel, Hopple, Anya, Regier, Peter, Rod, Kenton, Megonigal, J., Chen, Xingyuan, Ward, Nicholas, and Doro, Kennedy. 2023.  
"Data and scripts associated with the manuscript "Estimating soil moisture and salinity response to simulated coastal flooding using time-lapse electrical resistivity and induced polarization monitoring”".  United States.  doi:https://doi.org/10.15485/2298997.  https://www.osti.gov/servlets/purl/2298997. Pub date:Sun Dec 31 23:00:00 EST 2023

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@article{osti_2298997,

  title        = {Data and scripts associated with the manuscript "Estimating soil moisture and salinity response to simulated coastal flooding using time-lapse electrical resistivity and induced polarization monitoring”},

  author       = {Adebayo, Moses and Bailey, Vanessa and Ehosioke, Solomon and Efemena, Emmanuel and Hopple, Anya and Regier, Peter and Rod, Kenton and Megonigal, J. and Chen, Xingyuan and Ward, Nicholas and Doro, Kennedy},

  abstractNote = {This package contains geophysics datasets generated during the simulated ecosystem-flooding experiment – TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatment). These geophysics data consist of Electrical Resistivity Imaging (ERI) and Induced Polarization (IP) datasets collected to estimate the soil moisture and salinity response to the simulated coastal flooding experiment. In addition to using geophysics datasets to estimate soil moisture and salinity response, petrophysical relationship between the measured resistivity and moisture content and salinity using the multisalinity. This package consists of two folders: “MultiSalinity Experiment” folder and “TEMPEST Experiment and petrophysical conversion” folders. The “MultiSalinity Experiment” folder contains the datasets generated from the multisalinity experiment used to develop the petrophysical reslationship. The “TEMPEST Experiment and petrophysical conversion” folder contains ERI, IP, Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) datasets collected during the TEMPEST experiment. This study shows the ability to used geophysical datasets in estimating moisture content and salinity at scale will be useful in calibrating Earth System Models.},

  doi          = {10.15485/2298997},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Dec 31 23:00:00 EST 2023},

  month        = {Sun Dec 31 23:00:00 EST 2023}

}

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DOI: https://doi.org/10.15485/2298997

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