Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory

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

644 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3fce6852bc7c6e7c08628dc639ea519efecffee2

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Citations in the corpus

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.”
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…”
Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis (2009, methodology): “[6] Earlier BATEA studies focused on the derivation of the posterior distribution given specific CRR and uncertainty models [Kavetski et al., 2002, 2006a, 2006b].”
Review of surrogate modeling in water resources (2012, methodology): “…evolutionary algorithms [e.g., Duan et al., 1992; Wang, 1991], uncertainty-based or Bayesian model calibration studies [e.g., Beven and Freer, 2001; Kavetski et al., 2006; Vrugt et al., 2009], management or design optimization with evolutionary algorithms [e.g., McKinney and Lin, 1994; Savic and…”
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? (2009, methodology): “Y closed by the parameter vector h (Kavetski et al. 2006a).”
Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, background): “…Fenicia et al., 2011; Wagener et al., 2001), the quality of hydrological observations used to drive and evaluate models (Beven & Westerberg, 2011; Kavetski et al., 2002, 2006; McMillan et al., 2012; Renard et al., 2011; Westerberg & Birkel, 2015), and the quality of regionalization procedures…”
Bayesian Updating with Structural Reliability Methods (2015, methodology): “, rainfall and discharge measurements (Kavetski et al. 2006; Beven 2008).”
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).”
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).”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “…Bayesian methods have been used to estimate parameters and quantify uncertainty in land surface and hydrology models (e.g., Beven et al., 2007; Franks & Beven, 1997; Kavetski et al., 2006; Knorr & Kattge, 2005; Raoult et al., 2016; Shi et al., 2015; Smith & Marshall, 2008; Thiemann et al., 2001).”
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.”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “Approaches that do implement formal likelihood measures for inference such as sequential data assimilation methods [59,42], multi-model averaging approaches [1,60] and recent extensions to BATEA (Bayesian total error analysis) [25] have their own weaknesses.”
Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, neutral): “Res., 49, 4335–4345, doi:10.1002/wrcr.20354.”