Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

By Jasper A. Vrugt, C. T. ter Braak, M. Clark, James M. Hyman, Bruce A. Robinson. 2008

845 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b7fb8992d86712a04274b75c09142fa17d08c808

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, methodology): “The value of ̂ can be specified a priori based on knowledge of the measurements errors, or alternatively its value can be inferred simultaneously with the values of [Vrugt et al., 2008b; Laloy and Vrugt, 2012].”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, neutral): “|D) is often highly dimensional and analytically intractable, and Monte Carlo simulation methods are required to approximate the target (Vrugt et al., 2008, 2009a,b; Laloy and Vrugt, 2012).”
Differential Evolution Markov Chain with snooker updater and fewer chains (2008, background): “More generally, the parameters to be updated jointly can be selected randomly with some probability CR, the crossover rate (Price et al. 2005; Vrugt et al. 2008a, 2008b).”
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010, background): “[44] Results for both case studies confirm previous work [e.g., Kuczera, 1983; Vrugt et al., 2008a; Thyer et al., 2009] that accurate estimation of parameter and prediction uncertainty depends on an adequate statistical representation of the residual errors.”
Ensemble evaluation of hydrological model hypotheses (2010, background): “…framework is an input error term [Kavetski et al., 2003; Vrugt et al., 2005; Kavetski et al., 2006a, 2006b; Huard and Mailhot, 2006; Ajami et al., 2007; Huard and Mailhot, 2008; Vrugt et al., 2008], which, again, may be difficult to estimate in practice [Beven, 2005; Kavetski et al., 2006a].”
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013, methodology): “For the first step, the Markov Chain Monte Carlo (MCMC) method has proven to be a powerful approach to generate parameter samples from the PPDF [9,15,21,31,32].”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, background): “…[Vrugt et al., 2005; Ewen et al., 2006], leading to explorations of input uncertainties due to errors in the forcing data [Kavetski et al., 2006a, 2006b; Vrugt et al., 2008; Li et al., 2012] and of the inadequacies of model structure [Butts et al., 2004; Clark et al., 2008; Fenicia et al., 2008].”
High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing (2012, methodology): “To benchmark the results of MT-DREAM (ZS) , we include comparison against the DREAM [ Vrugt et al. , 2008, 2009], DREAM (ZS) (Vrugt et al., manuscript in preparation, 2012), and RWMN algorithms using standard settings of the algorithmic variables reported in the literature.”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, methodology): “[39] On the basis of work by other authors [Boyle et al., 2000; Vrugt et al., 2008; Wagener et al., 2001], we adopt the HyMod daily time step deterministic catchment model having five state variables (Figure 1) as a suitable hypothesis regarding the conceptual structure and prior assumptions…”
Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation (2011, methodology): “A detailed algorithmic description of DREAM appears in Vrugt et al. (2008) and Vrugt et al. (2009) and so will not be repeated here.”