An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation

By M. Butts, J. Payne, M. Kristensen, Henrik Madsen. 2004

567 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/da9417cfe3706765456684785f0347cd3c817742

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Citations in the corpus

Maximum entropy-Gumbel-Hougaard copula method for simulation of monthly streamflow in Xiangxi river, China (2015, methodology): “Previously, many parametric and nonparametric models have been applied for streamflow simulation (Marengo 1995; Sharma et al. 1997; Tarboton et al. 1998; Butts et al. 2004; Prairie 2006; Salas and Lee 2009; Zhao et al. 2010; Lee and Ouarda 2012; Vano et al. 2013; Ye et al. 2014).”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, background): “…what the terms imply, different applications of fully distributed models span several magnitudes of grid sizes from centimetres to kilometres (e.g. Butts et al., 2004; Kollet and Maxwell, 2006; Zehe et al., 2006; Samaniego et al., 2010; Kumar et al., 2013), and thus do not necessarily describe…”
Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty (2015, unknown): “When trying to adjust numerous parameters with the limited number of observations, the solution of an inverse modelling may not be unique (Beven, 1989 and 2001; Bloschl and Sivapalan, 1995; Senarath et al., 2000; Boyle et al., 2001; Ajami et al., 2004; Butts et al., 2004).”
Model identification for hydrological forecasting under uncertainty (2005, background): “The model structural space is infinite and it is therefore only possible to find a currently best model structure (rather than a ‘true’ structure) or currently acceptable set of model structures by comparing them to all available observations (Butts et al. 2004).”
Pursuing the method of multiple working hypotheses for hydrological modeling (2011, background): “It is not our intention here to debate the relative merits of each approach nor are they necessarily mutually exclusive within a given model application [e.g., see Butts et al., 2004].”
Ensemble evaluation of hydrological model hypotheses (2010, result): “Butts et al. [2004] followed a similar approach in their analysis of an ensemble of structures within a common modeling framework, which they extended to the investigation of parameter and input/output datauncertainties.Clark et al. [2008]tookamodularapproach to combining the conceptual choices of ”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, background): “, 2012] and of the inadequacies of model structure [Butts et al., 2004; Clark et al., 2008; Fenicia et al., 2008].”
Ensemble predictions of runoff in ungauged catchments (2005, background): “Therefore a priority for further work is to integrate the results of a wider range of model types [Neuman, 2003; Georgakakos et al., 2004; Butts et al., 2004].”
Uncertainty in the environmental modelling process - A framework and guidance (2007, background): “For instance, this can be realised by having alternative model codes with different process descriptions (Linkov and Burmistrov, 2003; Butts et al., 2004) or, in the groundwater case, by having different conceptual models based on different geological interpretations (Selroos et al., 2001; Højberg…”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, background): “Butts et al. (2004a) presented an evaluation of model structure on hydrologic modeling uncertainty by selecting different plausible model structures within a general hydrological modeling tool, and emphasize the importance of exploring different model structures as part of the overall modeling…”
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation (2008, background): “…state space filtering [Vrugt et al., 2005; Moradkhani et al., 2005a, 2005b; Slater and Clark, 2006; Vrugt et al., 2006a], multimodel averaging [Butts et al., 2004; Georgakakos et al., 2004; Vrugt et al., 2006b; Marshall et al., 2006; Ajami et al., 2007; Vrugt and Robinson, 2007b] and Bayesian…”
Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, methodology): “…with Markov Chain Monte Carlo simulation 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…”
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): “, 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.”
Assessment of model uncertainty for soil moisture through ensemble verification (2006, background): “The possibility of providing ensembles in the field of hydrology has only recently emerged [Butts et al., 2004; Georgakakos et al., 2004; Carpenter and Georgakakos, 2004; Lee and Anagnostou, 2004; Krzysztofowicz, 2001] and has mostly been explored in the field of ensemble data assimilation for…”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…2005a, b; Slater and Clark, 2006; Reichle, 2008; Salamon and Feyen, 2009; DeChant and Moradkhani, 2012; Vrugt et al., 2013), multimodel averaging (Butts et al., 2004; Georgakakos et al., 2004; Ajami et al., 2007), and various (non)Bayesian approaches to treat individual error sources and assess…”