Transport in Porous Media

, Volume 98, Issue 2, pp 485–504 | Cite as

Estimability Analysis and Optimisation of Soil Hydraulic Parameters from Field Lysimeter Data

  • V. V. Ngo
  • M. A. Latifi
  • M.-O. SimonnotEmail author


Modelling the water-flow in the vadose zone requires accurate hydraulic parameters to be obtained at the relevant scale. Weighable lysimeters enable us to monitor hydraulic data at an intermediate scale between lab and field scales and they can be used to optimise these parameters. Parameter optimisation using inverse methods may be limited by the non-uniqueness of the solution. In this contribution, an estimability method has been used to assess the estimability of the van Genuchten–Mualem parameters, to evaluate the information content of the data collected from a bare field lysimeter and to optimise the estimable model parameters. Daily data were monitored from a 2 \(\text{ m }^{3}\) lysimeter, filled with the soil of a former coking plant: pressure heads and water contents were measured at three depths (50, 100, 150 cm), cumulative boundary water fluxes. Water-flow was represented using the one-dimensional single-porosity model implemented in HYDRUS-1D code. The estimability of the 5 van Genuchten–Mualem hydraulic parameters and the information content of different data were evaluated by sequentially calculating a sensitivity coefficient matrix. Optimisation was achieved by the Levenberg-Marquardt algorithm. The estimability analysis revealed that estimability of the soil hydraulic parameters, based on the combination of daily pressure heads and water contents, was higher than those based on these data separately. In case of 2.4 being considered as a cut-off criterion for this study, all the parameters were considered estimable from daily data in the decreasing order: \(\theta _\mathrm{s},\, \textit{n}, K_\mathrm{s},\, \alpha ,\, \theta _\mathrm{r}\). Hydraulic parameters were optimised in four scenarios: \(\theta _\mathrm{s}\) and n were estimated with reliability while \(\alpha ,\, K_\mathrm{s}\) and \(\theta _\mathrm{r}\) were uncertain. However, the narrow variations in measured data restricted parameter optimisation.


Estimability analysis HYDRUS-1D Lysimeter Parameter optimisation Single-porosity model 



The authors thank the French Scientific Interest Group on Industrial Wastelands (, financially supported by the French government, the “Région Lorraine” and the “Conseil Général de Meurthe et Moselle”. They also thank Noële Raoult and Dr. Julien Michel for data collection. The authors highly appreciate the helpful and constructive comments of the four anonymous reviewers.


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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Laboratoire Réactions et Génie des ProcédésUniversité de Lorraine-CNRSNancy CedexFrance
  2. 2.Laboratoire d’Hydrologie et de Géochimie de StrasbourgUniversité de Strasbourg/EOST-CNRSStrasbourg CedexFrance

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