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dc.contributor.authorMoghadas, Davood
dc.contributor.authorJadoon, Khan Zaib
dc.contributor.authorMcCabe, Matthew
dc.date.accessioned2017-10-19T07:10:41Z
dc.date.available2017-10-19T07:10:41Z
dc.date.issued2017-10-17
dc.identifier.citationMoghadas D, Jadoon KZ, McCabe MF (2017) Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data. Advances in Water Resources. Available: http://dx.doi.org/10.1016/j.advwatres.2017.10.019.
dc.identifier.issn0309-1708
dc.identifier.doi10.1016/j.advwatres.2017.10.019
dc.identifier.urihttp://hdl.handle.net/10754/625910
dc.description.abstractMonitoring spatiotemporal variations of soil water content (θ) is important across a range of research fields, including agricultural engineering, hydrology, meteorology and climatology. Low frequency electromagnetic induction (EMI) systems have proven to be useful tools in mapping soil apparent electrical conductivity (σa) and soil moisture. However, obtaining depth profile water content is an area that has not been fully explored using EMI. To examine this, we performed time-lapse EMI measurements using a CMD mini-Explorer sensor along a 10 m transect of a maize field over a 6 day period. Reference data were measured at the end of the profile via an excavated pit using 5TE capacitance sensors. In order to derive a time-lapse, depth-specific subsurface image of electrical conductivity (σ), we applied a probabilistic sampling approach, DREAM(ZS), on the measured EMI data. The inversely estimated σ values were subsequently converted to θ using the Rhoades et al. (1976) petrophysical relationship. The uncertainties in measured σa, as well as inaccuracies in the inverted data, introduced some discrepancies between estimated σ and reference values in time and space. Moreover, the disparity between the measurement footprints of the 5TE and CMD Mini-Explorer sensors also led to differences. The obtained θ permitted an accurate monitoring of the spatiotemporal distribution and variation of soil water content due to root water uptake and evaporation. The proposed EMI measurement and modeling technique also allowed for detecting temporal root zone soil moisture variations. The time-lapse θ monitoring approach developed using DREAM(ZS) thus appears to be a useful technique to understand spatiotemporal patterns of soil water content and provide insights into linked soil moisture vegetation processes and the dynamics of soil moisture/infiltration processes.
dc.description.sponsorshipThis work was supported by the Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Saudi Arabia. Three anonymous reviewers helped to improve the manuscript by providing insightful comments which is acknowledged.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0309170817300611
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Advances in Water Resources. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advances in Water Resources, [, , (2017-10-17)] DOI: 10.1016/j.advwatres.2017.10.019 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleSpatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data
dc.typeArticle
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.identifier.journalAdvances in Water Resources
dc.eprint.versionPost-print
dc.contributor.institutionBrandenburg University of Technology, Research Center Landscape Development and Mining Landscapes, D-03046 Cottbus, Germany
dc.contributor.institutionDepartment of the Civil Engineering, International Islamic University, Islamabad 44000, Pakistan
kaust.personMcCabe, Matthew


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