Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo

Handle URI:
http://hdl.handle.net/10754/625971
Title:
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
Authors:
Jadoon, Khan Zaib; Altaf, Muhammad; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Muhammad, Nisar; Moghadas, Davood; Weihermüller, Lutz
Abstract:
A substantial interpretation of electromagnetic induction (EMI) measurements requires quantifying optimal model parameters and uncertainty of a nonlinear inverse problem. For this purpose, an adaptive Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to assess multi-orientation and multi-offset EMI measurements in an agriculture field with non-saline and saline soil. In MCMC the posterior distribution is computed using Bayes' rule. The electromagnetic forward model based on the full solution of Maxwell's equations was used to simulate the apparent electrical conductivity measured with the configurations of EMI instrument, the CMD Mini-Explorer. Uncertainty in the parameters for the three-layered earth model are investigated by using synthetic data. Our results show that in the scenario of non-saline soil, the parameters of layer thickness as compared to layers electrical conductivity are not very informative and are therefore difficult to resolve. Application of the proposed MCMC-based inversion to field measurements in a drip irrigation system demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provides useful insight about parameter uncertainty for the assessment of the model outputs.
KAUST Department:
Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC)
Citation:
Jadoon KZ, Altaf MU, McCabe MF, Hoteit I, Muhammad N, et al. (2017) Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo. Hydrology and Earth System Sciences 21: 5375–5383. Available: http://dx.doi.org/10.5194/hess-21-5375-2017.
Publisher:
Copernicus GmbH
Journal:
Hydrology and Earth System Sciences
Issue Date:
26-Oct-2017
DOI:
10.5194/hess-21-5375-2017
Type:
Article
ISSN:
1607-7938
Sponsors:
This research was funded by the Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
Additional Links:
https://www.hydrol-earth-syst-sci.net/21/5375/2017/
Appears in Collections:
Articles; Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorJadoon, Khan Zaiben
dc.contributor.authorAltaf, Muhammaden
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorMuhammad, Nisaren
dc.contributor.authorMoghadas, Davooden
dc.contributor.authorWeihermüller, Lutzen
dc.date.accessioned2017-10-30T07:55:30Z-
dc.date.available2017-10-30T07:55:30Z-
dc.date.issued2017-10-26en
dc.identifier.citationJadoon KZ, Altaf MU, McCabe MF, Hoteit I, Muhammad N, et al. (2017) Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo. Hydrology and Earth System Sciences 21: 5375–5383. Available: http://dx.doi.org/10.5194/hess-21-5375-2017.en
dc.identifier.issn1607-7938en
dc.identifier.doi10.5194/hess-21-5375-2017en
dc.identifier.urihttp://hdl.handle.net/10754/625971-
dc.description.abstractA substantial interpretation of electromagnetic induction (EMI) measurements requires quantifying optimal model parameters and uncertainty of a nonlinear inverse problem. For this purpose, an adaptive Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to assess multi-orientation and multi-offset EMI measurements in an agriculture field with non-saline and saline soil. In MCMC the posterior distribution is computed using Bayes' rule. The electromagnetic forward model based on the full solution of Maxwell's equations was used to simulate the apparent electrical conductivity measured with the configurations of EMI instrument, the CMD Mini-Explorer. Uncertainty in the parameters for the three-layered earth model are investigated by using synthetic data. Our results show that in the scenario of non-saline soil, the parameters of layer thickness as compared to layers electrical conductivity are not very informative and are therefore difficult to resolve. Application of the proposed MCMC-based inversion to field measurements in a drip irrigation system demonstrates that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provides useful insight about parameter uncertainty for the assessment of the model outputs.en
dc.description.sponsorshipThis research was funded by the Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Saudi Arabia.en
dc.publisherCopernicus GmbHen
dc.relation.urlhttps://www.hydrol-earth-syst-sci.net/21/5375/2017/en
dc.rightsThis work is distributed under the Creative Commons Attribution 3.0 License.en
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/en
dc.titleInferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carloen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.identifier.journalHydrology and Earth System Sciencesen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Civil Engineering, International Islamic University, Islamabad 44000, Pakistanen
dc.contributor.institutionDepartment of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistanen
dc.contributor.institutionBrandenburg University of Technology, Research Center Landscape Development and Mining Landscapes, 03046 Cottbus, Germanyen
dc.contributor.institutionAgrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, GmbH, 52425 Jülich, Germanyen
kaust.authorAltaf, Muhammaden
kaust.authorMcCabe, Matthewen
kaust.authorHoteit, Ibrahimen
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