Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2016-299
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
08 Aug 2016
Review status
A revision of this discussion paper is under review for the journal Hydrology and Earth System Sciences (HESS).
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo
Khan Zaib Jadoon1,2, Muhammad Umer Altaf2,3, Matthew Francis McCabe2, Ibrahim Hoteit3, Nisar Muhammad2, and Lutz Weihermüller4 1Department of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan
2Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
3Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
4Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Juelich, GmbH, 52425 Juelich, Germany
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.

Citation: Jadoon, K. Z., Altaf, M. U., McCabe, M. F., Hoteit, I., Muhammad, N., and Weihermüller, L.: Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-299, in review, 2016.
Khan Zaib Jadoon et al.
Khan Zaib Jadoon et al.
Khan Zaib Jadoon et al.

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Short summary
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity in an agriculture field irrigated with drip irrigation system. Electromagnetic model parameters and uncertainty were estimated using adaptive Bayesian Markov chain Monte Carlo (MCMC). Application of the MCMC based inversion to the synthetic and field measurements demonstrate that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil.
In this study electromagnetic induction (EMI) measurements were used to estimate soil salinity...
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