The future of distributed models: model calibration and uncertainty prediction.

By Keith Beven, A. Binley. 1992

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Citations in the corpus

Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method (2010, methodology): “With multiple observations and multiple types of observations, likelihood values for each observation must be combined into an overall value for each candidate parameter set (Beven and Binley, 1992).”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “Input and parameter uncertainties could be addressed using techniques such as Bayesian modeling and generalized approaches for uncertainty estimation [Beven and Binley, 1992; Kuczera and Parent, 1998; Vrugt et al., 2003; Montanari and Brath, 2004] for a given hydrological model.”
Pursuing the method of multiple working hypotheses for hydrological modeling (2011, methodology): “Of these, the GLUE methodology [Beven and Binley, 1992; Beven and Freer, 2001], a broader philosophy that includes model inference, evaluation, and application, has been widely adopted in environmental studies (e.g., Beven [2006a] and references therein; see also Stedinger et al. [2008] for a…”
Efficient nonlinear predictive error variance for highly parameterized models (2006, methodology): “Methods of predictive uncertainty analysis include generalized likelihood uncertainty estimation (GLUE) [Beven and Binley, 1992]; techniques that condition stochastic realizations using property measurements alone [Deutsch and Journel, 1992; Gutjahr et al., 1994]; calibration-constrained Monte…”
Sensitivity analysis for two ground heat flux calculation approaches (2005, methodology): “Sensitivity analysis scheme after Beven and Binley (1992). The procedure is repeated 10,000 times.”
Multi-model ensemble hydrologic prediction using Bayesian model averaging (2007, unknown): “the uncertainty estimation [4,20,41,23].”
Sensitivity analysis and multi‐response, multi‐criteria evaluation of a physically based distributed model (2002, background): “Recognition of the effects of parameter uncertainty and the difficulty of model calibration has led to the development of the generalized likelihood uncertainty estimation (GLUE, Beven and Binley, 1992).”
Improving probabilistic prediction of daily streamflow by identifying Pareto optimal approaches for modeling heteroscedastic residual errors (2017, methodology): “Transformational strategy. In this strategy, observed and predicted responses such as streamflows are first transformed (e.g., using power transformations), before constant-variance error models are applied. As such, the idea is to first simplify the statistical properties of the responses themselve”
Predictive Analysis and Simulation Uncertainty of a Distributed Hydrological Model (2010, methodology): “…in recent years many procedures and program tools such as Parameter ESTimator (PEST), Generalized Likelihood Uncertainty Estimation (GLUE) (Beven and Binley 1992), Parameter Solution (ParaSol) (van Griensven and Meixner 2006), and Sequential Uncertainty FItting (SUFI-2) (Abbaspour et al.…”
Comparison of parameter estimation methods for crop models (2004, methodology): “The GLUE method (generalized likelihood uncertainty estimation) [4] assigns a weight proportional to the likelihood to each parameter set sampled from the prior distribution.”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, unknown): “Regionalized sensitivity analysis (RSA), also called generalized sensitivity analysis, has been originally developed in the context of environmental models by Spear and Hornberger (1980) and further developed by Beven and Binley (1992) in hydrological models.”
Application of temporal streamflow descriptors in hydrologic model parameter estimation (2005, methodology): “Monte Carlo simulation methods, which are gaining popularity in hydrology [Beven and Binley, 1992; Kuczera, 1997; Thiemann et al., 2001; Vrugt et al., 2003], provide a tool to deal with the inherent model complexity and the nonGaussian multivariate distributions of the parameters [Duan et al.,…”
On the colour and spin of epistemic error (and what we might do about it) (2011, methodology): “The main alternative to the formal statistical approach, at least up to now, has been the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, first introduced by Beven and Binley(1992).”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, methodology): “…to justify assumptions about the nature of the errors have led Beven and coworkers to advocate informal statistical approaches using the generalized likelihood uncertainty estimation (GLUE) methodology (Beven and Binley, 1992; Beven, 1993, 2006, 2009; Beven and Freer , 2001; Beven et al., 2008).”
Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model (2006, methodology): “1–3 do not suggest overparametrization (Hooper et al., 1988; Beven, 1989) or equifinality (Beven and Binley, 1992).”