Information content of data for identifying soil hydraulic parameters from outflow experiments

By Jasper A. Vrugt, Willem Bouten, Albrecht H. Weerts. 2001

122 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/69f58bcee2d20e258ee5c1f742fb05cf48f25d97

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In Civil and Structural Engineering

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Estimating Soil Hydraulic Parameters of a Field Drainage Experiment Using Inverse Techniques (2003, methodology): “Vrugt et al. (2001) proposed a parameter identifica-used, four of the five parameters (i.e., Ks, , s, and r) were not identifiable when only h data were used, and tion method based on the localization of information (PIMLI), which uses the variability in time of the modelall the five parameters…”
Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data (2011, methodology): “[20] Previous studies have shown that of the six MVG model parameters, the residual water content r is typically the least sensitive to the calibration data [Inoue et al., 1998; Zurmühl and Durner, 1998; Vrugt et al., 2001; Simůnek et al., 1998; Kelleners et al., 2005; Mertens et al., 2006].”
Validity of First-Order Approximations to Describe Parameter Uncertainty in Soil Hydrologic Models (2002, methodology): “…et al., 1998a; Romano and Santini, 1999) interdependence and model nonlinearity violate the use and from field data (Abbaspour et al., 1999; Musters and of this first-order approximation to obtain exact confiBouten, 2000; Vrugt et al., 2001b,c; among many others). dence intervals of the parameter.”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “Methods to represent model parameter, state and prediction uncertainty include classical Bayesian [30,54,57], pseudo-Bayesian [3,15], set-theoretic [26,28, 52,56], multiple criteria [19,62,7,34,35,58], sequential data assimilation [36,59,42] and multi-model averaging methods [17,1,60].”