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/10754/625870
Title:
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo
Authors:
Jadoon, Khan; Altaf, Muhammad; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Muhammad, Nisar; 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 the MCMC simulations, posterior distribution was 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. The model parameters and uncertainty 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 are not well estimated as compared to layers electrical conductivity because layer thicknesses in the model exhibits a low sensitivity to the EMI measurements, and is hence difficult to resolve. Application of the proposed MCMC based inversion to the field measurements in a drip irrigation system demonstrate that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provide useful insight about parameter uncertainty for the assessment of the model outputs.
KAUST Department:
Water Desalination and Reuse Research Center (WDRC); Earth Science and Engineering Program
Citation:
Jadoon KZ, Altaf MU, McCabe MF, Hoteit I, Muhammad N, et al. (2016) Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo. Hydrology and Earth System Sciences Discussions: 1–18. Available: http://dx.doi.org/10.5194/hess-2016-299.
Publisher:
Copernicus GmbH
Journal:
Hydrology and Earth System Sciences Discussions
Issue Date:
8-Aug-2016
DOI:
10.5194/hess-2016-299
Type:
Article
ISSN:
1812-2116
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-discuss.net/hess-2016-299/
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, Khanen
dc.contributor.authorAltaf, Muhammaden
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorMuhammad, Nisaren
dc.contributor.authorWeihermüller, Lutzen
dc.date.accessioned2017-10-17T08:48:35Z-
dc.date.available2017-10-17T08:48:35Z-
dc.date.issued2016-08-08en
dc.identifier.citationJadoon KZ, Altaf MU, McCabe MF, Hoteit I, Muhammad N, et al. (2016) Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo. Hydrology and Earth System Sciences Discussions: 1–18. Available: http://dx.doi.org/10.5194/hess-2016-299.en
dc.identifier.issn1812-2116en
dc.identifier.doi10.5194/hess-2016-299en
dc.identifier.urihttp://hdl.handle.net/10754/625870-
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 the MCMC simulations, posterior distribution was 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. The model parameters and uncertainty 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 are not well estimated as compared to layers electrical conductivity because layer thicknesses in the model exhibits a low sensitivity to the EMI measurements, and is hence difficult to resolve. Application of the proposed MCMC based inversion to the field measurements in a drip irrigation system demonstrate that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provide 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 Arabiaen
dc.publisherCopernicus GmbHen
dc.relation.urlhttps://www.hydrol-earth-syst-sci-discuss.net/hess-2016-299/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.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.contributor.departmentEarth Science and Engineering Programen
dc.identifier.journalHydrology and Earth System Sciences Discussionsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistanen
dc.contributor.institutionAgrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Juelich, GmbH, 52425 Juelich, Germanyen
kaust.authorJadoon, Khanen
kaust.authorAltaf, Muhammaden
kaust.authorMcCabe, Matthewen
kaust.authorHoteit, Ibrahimen
kaust.authorMuhammad, Nisaren
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