Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo
Type
ArticleAuthors
Jadoon, KhanAltaf, Muhammad
McCabe, Matthew

Hoteit, Ibrahim

Muhammad, Nisar
Weihermüller, Lutz
KAUST Department
Biological and Environmental Sciences and Engineering (BESE) DivisionEarth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Earth System Observation and Modelling
Environmental Science and Engineering Program
Physical Science and Engineering (PSE) Division
Water Desalination and Reuse Research Center (WDRC)
Date
2016-08-08Permanent link to this record
http://hdl.handle.net/10754/625870
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Show full item recordAbstract
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.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.Sponsors
This research was funded by the Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Saudi ArabiaPublisher
Copernicus GmbHAdditional Links
https://www.hydrol-earth-syst-sci-discuss.net/hess-2016-299/ae974a485f413a2113503eed53cd6c53
10.5194/hess-2016-299
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Articles; Biological and Environmental Sciences and Engineering (BESE) Division; Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program; Environmental Science and Engineering Program; Physical Science and Engineering (PSE) Division; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Water Desalination and Reuse Research Center (WDRC)
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