HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (15) Citing Articles via Google Scholar Google Scholar Articles by Vrugt, J. A. Articles by Bouten, W. Search for Related Content PubMed Articles by Vrugt, J. A. Articles by Bouten, W. Agricola Articles by Vrugt, J. A. Articles by Bouten, W. Related Collections Soil Models Soil Physics Statistics

DIVISION S-1—SOIL PHYSICS

Validity of First-Order Approximations to Describe Parameter Uncertainty in Soil Hydrologic Models

Institute for Biodiversity and Ecosystem Dynamics, Section Physical Geography, Univ. of Amsterdam, The Netherlands, Nieuwe Achtergracht 166, Amsterdam, 1018 WV

* Corresponding author (j.vrugt{at}science.uva.nl)

Model nonlinearity and parameter interdependence violate the use of a first-order approximation to obtain exact confidence intervals of parameters in soil hydrologic models. In this study, the posterior distribution of parameters in soil water retention and hydraulic conductivity functions is examined using observed water retention data and a laboratory transient multistep outflow experiment. Parameter uncertainties obtained with traditional first-order approximations and uniform grid sampling strategies were compared with those obtained using the Metropolis algorithm, a Markov Chain Monte Carlo (MCMC) sampler. A diagnostic measure, based on multiple sequences generated in parallel, was used to test whether convergence of the Metropolis sampler to the posterior distribution had been achieved. Most significantly, as the Metropolis algorithm can cope with rough response surfaces generated by the objective function used, it not only successfully infers the multivariate posterior probability distribution of the model parameters, but also provides valuable insights in parameter interdependence in the full parameter space.

Abbreviations: CV, coefficient of variation • MCMC, Markov Chain Monte Carlo • RMSE, root mean squared error • SCE, shuffled complex evolution • VG, van Genuchten

This article has been cited by other articles:

				J. A. Vrugt, P. H. Stauffer, Th. Wohling, B. A. Robinson, and V. V. Vesselinov Inverse Modeling of Subsurface Flow and Transport Properties: A Review with New Developments Vadose Zone J., May 27, 2008; 7(2): 843 - 864. [Abstract] [Full Text] [PDF]

				T. Wohling, J. A. Vrugt, and G. F. Barkle Comparison of Three Multiobjective Optimization Algorithms for Inverse Modeling of Vadose Zone Hydraulic Properties Soil Sci. Soc. Am. J., January 25, 2008; 72(2): 305 - 319. [Abstract] [Full Text] [PDF]

				Z. Fan and F. X. M. Casey Estimating Solute Transport Parameters Using Stochastic Ranking Evolutionary Strategy Vadose Zone J., January 23, 2008; 7(1): 124 - 130. [Abstract] [Full Text] [PDF]

				J. A. Vrugt, J. A. Vrugt, W. Bouten, H. V. Gupta, and J. W. Hopmans Toward Improved Identifiability of Soil Hydraulic Parameters: On the Selection of a Suitable Parametric Model Vadose Zone J., February 1, 2003; 2(1): 98 - 113. [Abstract] [Full Text] [PDF]