Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging

By Jasper A. Vrugt, Bruce A. Robinson. 2007

346 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b5938eac4bb856584285e25dded91221d5c2afa7

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments (2010, neutral): “11 WAGE 8 4 Warmerdam et al. (1997) 12 IHAC 6 3 Jakeman et al.”
Ensemble evaluation of hydrological model hypotheses (2010, background): “Recent efforts, however, have made the specification of output error models more explicit [Kennedy and O’Hagan, 2001; Kavetski et al., 2003; Vrugt et al., 2003, 2005; Beven, 2006; Kavetski et al., 2006b; Huard and Mailhot, 2006; Vrugt and Robinson, 2007; Harmel and Smith, 2007], although error model”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “A MATLAB toolbox, similar to the one presented herein for DREAM, is1204 available for each of these four algorithms, including multi-criteria optimization with AMALGAM (Vrugt and Robinson, 2007b).1206”
Parameter and rating curve uncertainty propagation analysis of the SWAT model for two small Mediterranean catchments (2013, methodology): “These uncertainty methods are based on the concept of Bayesian model averaging (BMA) (Ajami et al. 2007, Vrugt and Robinson 2007) and the multimodel ensemble method (MME) (Butts et al. 2004, Georgekakos et al. 2004, Ajami et al. 2005).”
Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation / Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée (2009, methodology): “…et al., 2001), the SCEM-UA method (Vrugt et al., 2003), the DYNIA method (Wagener et al., 2003), the ensemble method (Butts et al., 2004; Georgakakos et al., 2004; Marshall et al., 2006; Vrugt & Robinson, 2007), and the particle-filtering approach (Moradkhani et al., 2005b; Smith et al., 2008).”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “weather forecast and hydrologic predictions) (Raftery et al., 2005; Ajami et al., 2006; Duan et al., 2007; Vrugt and Robinson, 2007; Montgomery and Nyhan, 2008).”
Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, methodology): “…using DREAM [Vrugt et al., 2008a, 2008b, 2009a; Laloy and Vrugt, 2012], (Bayesian) model averaging [Butts et al., 2004; Ajami et al., 2007; Vrugt and Robinson, 2007], the hypothetico-inductive data-based mechanistic modeling framework of Young [2012], and Bayesian data assimilation…”
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].”
Assessing the impact of land use change on hydrology by ensemble modelling(LUCHEM) II: ensemble combinations and predictions (2009, methodology): “[25], we estimate these parameters by posing an optimization problem in a maximum likelihood context.”