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Toward Improved Identifiability of Soil Hydraulic Parameters

On the Selection of a Suitable Parametric Model

^a Institute for Biodiversity and Ecosystem Dynamics, Section Physical Geography, University of Amsterdam, Nieuwe Achtergracht 166, Amsterdam, 1018 WV, The Netherlands
^b Department of Hydrology and Water Resources, University of Arizona, Tucson, AZ, 85721, USA
^c Hydrology Program, Department of Land, Air and Water Resources (LAWR), University of California, Davis, CA 95616, USA
^* Corresponding author (j.vrugt{at}science.uva.nl)

Received 5 July 2002.

ABSTRACT

We present a thorough identifiability analysis of the soil hydraulic parameters in the parametric models of Brooks and Corey (BC; Brooks and Corey, 1964), Mualem–van Genuchten (VG; van Genuchten, 1980), and Kosugi (KC; Kosugi, 1996, 1999) using the recently developed Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm (Vrugt et al., 2002b, and unpublished data). Because the SCEM-UA algorithm globally thoroughly exploits the parameter space and therefore explicitly accounts for parameter interdependence and nonlinearity of the employed parametric models, the algorithm is suited to generate a useful description of parameter uncertainty and its antithesis, parameter identifiability. A set of measured water retention characteristics of the UNSODA database (Leij et al., 1996) spanning a wide range of soil textures and three transient laboratory outflow experiments with decreasing flow rates were used to illustrate that a parameter identifiability analysis facilitates the selection of an adequate parametric model structure and provides useful information about the limitations of a model. Moreover, results suggest that one should be especially careful in establishing pedotransfer functions without knowledge of the underlying posterior uncertainty associated with the soil hydraulic parameters using direct estimation methods.

Abbreviations: BC, Brooks and Corey model • KS, Kosugi model • MCMC, Markov Chain Monte Carlo • MH, Metropolis Hastings • RMSE, root mean square error • SCEM-UA, Shuffled Complex Evolution Metropolis algorithm • SLS, simple least square • VG, Mualem–van Genuchten model

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