Bayesian analysis of input uncertainty in hydrological modeling: 2. Application

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

467 citations recorded by the source corpus.

The Bayesian total error analysis (BATEA) methodology directly addresses both input and output errors in hydrological modeling, requiring the modeler to make explicit, rather than implicit, assumptions about the likely extent of data uncertainty. This study considers a BATEA assessment of two North American catchments: (1) French Broad River and (2) Potomac basins. It assesses the performance of the conceptual Variable Infiltration Capacity (VIC) model with and without accounting for input (precipitation) uncertainty. The results show the considerable effects of precipitation errors on the predicted hydrographs (especially the prediction limits) and on the calibrated parameters. In addition, the performance of BATEA in the presence of severe model errors is analyzed. While BATEA allows a very direct treatment of input uncertainty and yields some limited insight into model errors, it requires the specification of valid error models, which are currently poorly understood and require further work. Moreover, it leads to computationally challenging highly dimensional problems. For some types of models, including the VIC implemented using robust numerical methods, the computational cost of BATEA can be reduced using Newton‐type methods.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/c3bb600e3ba52744b9128fb427c964a6b4e34ae3

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Citations in the corpus

Bayesian Updating with Structural Reliability Methods (2015, methodology): “In hydrology, Bayesian analysis has been frequently applied for model calibration with measurements, e.g. of rainfall and discharge measurements (Kavetski et al. 2006; Beven 2008).”
Ensemble evaluation of hydrological model hypotheses (2010, methodology): “…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 models have rarely been justified…”
Ancient numerical daemons of conceptual hydrological modeling: 2. Impact of time stepping schemes on model analysis and prediction (2010, methodology): “[10] The broader evaluation of the impact of numerical model errors on sensitivity analysis, parameter optimization, uncertainty assessment, and model interpretation significantly advances previous work in this direction [Kavetski et al., 2003b, 2006a, 2006b].”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “Scenario uncertainty here is similar to the input uncertainty used in surface hydrology (e.g., Kavetski et al., 2006; Vrugt et al., 2008; Renard et al., 2010), but with focus on future states and conditions.”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, methodology): “Uncertainty in hydrologic predictions can originate from several major sources, including errors in the model structure and model parameters, as well as model initial conditions and hydrometeorologic forcing (e.g., Ajami et al., 2007; Kavetski et al., 2006a, b; Salamon and Feyen, 2010).”
Parameter and modeling uncertainty simulated by GLUE and a formal Bayesian method for a conceptual hydrological model (2010, methodology): “Many other examples of the methods dealing with model and data uncertainty are available in the hydrological literature (e.g., Georgakakos et al., 2004; Carpenter and Georgakakos, 2004; Kavetski et al., 2006a,b).”
Correcting the mathematical structure of a hydrological model via Bayesian data assimilation (2011, background): “…mathematical equations are also considered to be correct, one can make inferences about the joint distribution of the inputs and model parameters [Kavetski et al., 2006; Renard et al., 2010] or one can adjust the model states to compensate for input, initial conditions, and model structure…”
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.”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “…used to augment the parameter space and handle forcing data and model structural error ex-86 plicitly through extension of the likelihood function (Kavetski et al., 2006; Vrugt et al., 2008a).88 Nevertheless, the very construction of the likelihood function - as a summary variable of the (usually…”
Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments (2010, background): “…areas of input data uncertainty and model structural uncertainty, a wealth of recent research from the University of Newcastle research group [e.g., Kavetski et al., 2006; Renard et al., 2010; Thyer et al., 2009] has indicated the importance of addressing these components for improved…”