Towards the characterization of streamflow simulation uncertainty through multimodel ensembles

By K. Georgakakos, Dong-Jun Seo, Hoshin V. Gupta, J. Schaake, M. Butts. 2004

355 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/fbee6a20c1b71e054d65c6742419ca2e53664b55

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In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “The value of a hydrologic prediction to water resources and other relevant decision-making processes is limited if reasonable estimates of the corresponding predictive uncertainty are not provided [e.g., Georgakakos et al., 2004].”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “…combination methods, which may include simple or weighted average of single model predictions [Shamseldin et al., 1997; Xiong et al., 2001; Georgakakos et al., 2004] or using statistical techniques such as multiple linear regression [Krishnamurti et al., 1999], Bayesian model averaging…”
Ensemble predictions of runoff in ungauged catchments (2005, background): “…estimated prediction uncertainty, this seems unlikely to solve the flow peak estimation problem; instead, we may need to integrate input rainfall error and a wider range of model structures, potentially including distributed models [e.g., Carpenter and Georgakakos, 2004; Georgakakos et al., 2004].”
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…”
Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests (2019, positive): “…hydrological model errors (e.g., Clark et al., 2008; Clark et al., 2015; Fenicia et al., 2011; Wagener et al., 2004; Wagener & Montanari, 2011), and in some cases model ensembles have been shown to reduce predictive errors (e.g., Georgakakos et al., 2004; Hsu et al., 2009; Shamseldin et al., 1997).”
Assessing the impact of land use change on hydrology by ensemble modelling(LUCHEM) II: ensemble combinations and predictions (2009, background): “[10], as part of DMIP, assessed predictions from seven distributed models applied to six catchments in the United States.”
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation (2013, background): “…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 predictive…”
An overview of methods to evaluate uncertainty of deterministic models in decision support (2015, neutral): “Ensemblemodelling has mainly been used in twoways: to produce one “best” predictive model (e.g. Kronvang et al., 2009; Grenouillet et al., 2011; Trolle et al., 2014), and to evaluate the predictive uncertainty (e.g. Georgakakos et al., 2004; Gal et al., 2014).”
An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction (2006, background): “The first group [e.g., Shamseldin et al., 1997; Abrahart and See, 2002; Georgakakos et al., 2004; Ajami et al., 2005, 2006] uses a set of deterministic weights to combine multiple model outputs.”
Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments (2010, neutral): “Full is thus not as universal as proposed by Shamseldin et al. (1997) or Georgakakos et al. (2004).”
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.”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, methodology): “The fourth and final approach to quantify model error is to use multi-model ensembles (e.g., Georgakakos et al., 2004).”