An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction

By Newsha K. Ajami, Qingyun Duan, Soroosh Sorooshian. 2006

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Subfields: Global and Planetary Change, Water Science and Technology

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Citations in the corpus

An evaluation of adaptive surrogate modeling based optimization with two benchmark problems (2014, background): “It is also a benchmark problem for evaluation of many parameter estimation methods (Duan et al., 1992, 1994; Yapo et al., 1998; Ajami et al., 2007).”
Combining multiobjective optimization and Bayesian model averaging to calibrate forecast ensembles of soil hydraulic models (2008, background): “, 2006a], streamflow [e.g., Kuczera et al., 2006; Vrugt and Robinson, 2007b; Ajami et al., 2007], and permeability structures in groundwater hydrology [Neuman, 2003; Ye et al.”
Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008, methodology): “…and data assimilation (Vrugt et al., 2005), sequential data assimilation (Moradkhani et al., 2005), integrated Bayesian uncertainty estimation (Ajami et al., 2007), and use of time-dependent parameters for exploring model deficits and considering input and model structure uncertainty…”
Multi‐model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging (2016, methodology): “Thus, an integrated BMA framework that accounts for the parameter and measurement uncertainties is needed to improve the accuracy of long-term global terrestrial ET estimates (Ajami et al., 2007).”
Toward diagnostic model calibration and evaluation: Approximate Bayesian computation (2013, methodology): “, 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 [Bulygina and Gupta, 2011].”
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation (2008, methodology): “To further reduce ambiguity about the inferred record of wholecatchment rainfall, future analysis should include multiple conceptual watershed models using emerging (Bayesian) model averaging approaches in surface water hydrology [Ajami et al., 2007; Vrugt and Robinson, 2007b].”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “…and can be estimated using various methods (Foglia et al., 2013; Lu et al., 2013; Tsai and Elshall, 2013; Ye et al. (2010b); Tsai and Li, 2008; Ye et al. (2008a); Meyer et al., 2007; Ajami et al., 2007; Poeter and Anderson, 2005; Ye et al., 2004; Winter and Nychka, 2010; Wohling and Vrugt, 2008).”
Ensemble evaluation of hydrological model hypotheses (2010, background): “…in a Bayesian framework is an input error term [Kavetski et al., 2003; Vrugt et al., 2005; Kavetski et al., 2006a, 2006b; Huard and Mailhot, 2006; Ajami et al., 2007; Huard and Mailhot, 2008; Vrugt et al., 2008], which, again, may be difficult to estimate in practice [Beven, 2005; Kavetski et…”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, background): “, 2006], and, particularly, for the explicit integration of multiple sources of uncertainty [Kavetski et al., 2006a, 2006b; Ajami et al., 2007; Marshall et al., 2007; Zhang et al., 2009].”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, methodology): “, 2005), multi-model averaging methods (Ajami et al., 2007) and coupled schemes (Blasone at al.”
A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework (2018, background): “may meet the requirements of long-term runoff forecasting (Wood and Rodríguez-Iturbe 1975; Kavetski et al. 2006a; Ajami et al. 2007).”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, background): “…improvements over traditional approaches that lump all uncertainties into a single error term and yields: (1) reduced bias and more consistent parameter estimates; and (2) more reliable estimates of predictive uncertainty [Kavetski et al., 2006a; Renard et al., 2009a; Thyer et al., 2009].”