Calibration‐constrained Monte Carlo analysis of highly parameterized models using subspace techniques

By M. Tonkin, John Doherty. 2009

197 citations recorded by the source corpus.

Subfields: Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f97239ff2866b5b191e4cc06a69fecec5f85a86a

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In Environmental Engineering

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Citations in the corpus

Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data (2011, methodology): “This development is based on recent advances of the inverse method [e.g., Beven and Freer, 2001; Tang et al., 2006; Gallagher and Doherty, 2007; Vrugt et al., 2008a; Doherty, 2009; Zhang et al., 2009; Tonkin and Doherty, 2009].”
Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality (2010, methodology): “The NSMC methodology implemented by PEST (see Tonkin and Doherty [2009] for a description of this methodology) is used to generate a population of parameter sets, all meeting the criteria F <= F*, where F* is a user‐specified criterion for “calibration adequacy,” given knowledge of measurement noi”
Inverse methods in hydrogeology: Evolution and recent trends (2014, methodology): “…and Doherty, 2005) have been proposed and integrated into this approach, which was subsequently extended357 to account for the uncertainty of estimated parameter within a Monte Carlo framework in the so-called358 subspace Monte Carlo technique (Tonkin and Doherty, 2009; Yoon et al., 2013).”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “Model reduction, however, should be done with caution, as a parameter that seems non-influential under a particular condition might become quite influential under a new condition (e.g., see Tonkin and Doherty, 2009).”
A generalized grid connectivity–based parameterization for subsurface flow model calibration (2011, methodology): “[3] The challenges associated with the data assimilation inverse problem are typically addressed using regularization, which stabilizes the solution by imposing one or more assumptions related to the underlying geologic model [Carrera and Neuman, 1986a, 1986b; Tikhonov and Arsenin, 1977; Tarantola, ”
High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing (2012, background): “[ 62 ] The new results with MT-DREAM (ZS) likely alter some of the previous conclusions of Keating et al. [2010], who compared formal Bayesian inference with DREAM [ Vrugt et al. , 2008] against the more efﬁcient NSMC method of Tonkin and Doherty [2009] that does not maintain detailed balance and th”