Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters

By George A. Kuczera, Dmitri Kavetski, S. Franks, Mark A. Thyer. 2006

378 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/65e0d25cb45a30ebc62efb9e5583c2c895557b25

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, methodology): “[33] Early applications of BATEA [Kavetski et al., 2006a] used fixed rainfall error parameters, whileHuard andMailhot [2008] used fixed input/output/structural error parameters.”
On the colour and spin of epistemic error (and what we might do about it) (2011, methodology): “Statistical treatments of errors have been applied quite widely in hydrological modelling, developing from the use of likelihoods based on assumptions about model residuals (e.g.Sorooshian and Dracup, 1980) to the much more Correspondence to:K. Beven (k.beven@lancs.ac.uk) sophisticated hierarchical ”
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation (2008, background): “, 2007; Vrugt and Robinson, 2007b] and Bayesian approaches [Kavetski et al., 2006a, 2006b; Kuczera et al., 2006; P. Reichert and J. Mieleitner, Analyzing input and structural uncertainty of a hydrological model with stochastic, time-dependent parameters, unpublished manuscript, 2008] to explicitly t”
A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010, methodology): “where mean flow Et, hydrologically simulated flow Yh,t, and bias factor mt all vary as a function of time t. The bias factor may be treated as a stochastic variable, similar to the rainfall multiplier approach introduced by Kavetski et al. [2006a, 2006b] to account for model input errors, or the tim”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, background): “Although GLUE can be criticized for not being properly Bayesian, it is not particularly easy to develop a likelihood measure that properly accounts for input, output, parameter and model structural error within a single performance measure [32,29].”
Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models (2008, background): “There is significant uncertainty associated with the correct formulation of these processes underlying the system of interest [Beven and Binley, 1992; Gupta et al., 1998; Kuczera et al., 2006; Vrugt and Robinson, 2007a].”
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “These approaches include consideration of input uncertainty through rain multipliers (Kavetski et al., 2003, 2006a, b; Kuczera et al., 2006), simultaneous optimization and data assimilation (Vrugt et al.”
Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging (2009, methodology): “In the future, incorporating more sources of uncertainty into account (Kuczera et al., 2006) may improve the performance of this method.”
Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, background): “The third conclusion is con sistent with Kuczera et al. (2006) who pointed out that “…poorly determined parameters do not necessarily lead to high predictive uncertainty.””
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “The BATEA framework is an example of this more advanced approach (Kavetski et al., 2006a,b; Kuczera et al., 2006; Renard et al., 2010, 2011), and can be implemented with DREAM as well (Vrugt et al.”
Generic error model for calibration and uncertainty estimation of hydrological models (2008, background): “While one can assume that the errors on successive days are independent for the meteorological variables this assumption does not hold true for the errors due to the inevitable simplifying process descriptions and effective model parameters, qi, [Kuczera et al., 2006; Schaefli et al., 2007].”
GLUE: 20 years on (2014, methodology): “More recent applications of statistical inference to hydrological modelling have often been based on the use of formal likelihood functions but within a Bayesian framework (e.g. Kuczera et al., 2006; Vrugt et al., 2008, 2009a,2009b; Thyer et al., 2009; Renard et al., 2010; Schoups and Vrugt, 2010).”
Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis (2009, methodology): “While BATEA can explicitly incorporate structural error using storm-dependent parameters [Kuczera et al., 2006], this was not attempted because synthetic studies suggest that simultaneous inference of input and structural errors can become ill posed if vague priors are used on both sources of error ”
A critical review of integrated urban water modelling - Urban drainage and beyond (2014, methodology): “Only recently have Kuczera et al. (2006) and Deletic et al. (2012) provided new insights and proposed a new framework for assessing total uncertainties in integrated urban drainage models; the method has been currently tested on integrated rainfall/runoff and pollution generation models (Kleidorfer…”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, methodology): “between the model simulations and observed data; Kuczera et al. [2006] and Reichert and Mieleitner [2009] treat parameters as stochastic variables with possible time or state dependency; Kennedy and O’Hagan [2001] stochastically estimate the model equations themselves when all model inputs, states”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “Studies have also focused on alternative approaches to address structural deficiencies without resorting to model combination [Kuczera et al., 2006; Huard and Mailhot, 2008; Bulygina and Gupta, 2009; Reichert and Mieleitner, 2009].”