Multi-period and multi-criteria model conditioning to reduce prediction uncertainty in an application of TOPMODEL within the GLUE framework

By H. Choi, Keith Beven. 2007

184 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/519f1118f130827e5aae7ab11d478dd80df40ac6

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In Environmental Engineering

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In Water Science and Technology

Citations in the corpus

Characterising performance of environmental models (2013, methodology): “As another alternative, Choi and Beven (2007) use a multi-period framework, where a moving window of thirty days is used to classify periods into dry, wetting, wet and drying climate states.”
One decade of multi-objective calibration approaches in hydrological modelling: a review (2010, methodology): “…on finding the “best” model (in either a global- or Pareto-optimal sense, both based on fitting metrics requiring systematic measurements), there has been little consideration of whether this optimal model is actually a consistent simulator according to an expert hydrologist (Choi & Beven, 2007).”
Sensitivity of mean transit time estimates to model conditioning and data availability (2011, methodology): “The likelihood measure used here to weigh the MTT was the Nash–Sutcliffe Efficiency (NSE), which is a common choice ( Freer et al., 1996; Choi and Beven, 2007) as it enables inter-comparability, which is problematic when using the arguably more objective root mean square error.”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “…during periods dominated by interflow or base flow; under the assumption that the catchment response is inherently different during periods driven by rainfall and periods without rainfall) [Boyle et al., 2000; Wagener et al., 2003; Choi and Beven, 2007; Reusser and Zehe, 2011; Zhang et al., 2011].”
Ensemble flood forecasting: a review. (2009, background): “Experiments in other hydrological modeling exercises with respect to sampling size suggest that a far larger number than 50 is needed (Choi and Beven, 2007; Montanari, 2005; Pappenberger and Beven, 2004).”
Facets of uncertainty: epistemic uncertainty, non-stationarity, likelihood, hypothesis testing, and communication (2016, methodology): “…at “blind validation” of the SHE model by Parkin et al. 1996, Bathurst et al. 2004, and the studies of Brazier et al. 2000, Page et al. 2007, Pappenberger et al. 2007, Choi and Beven 2007, Dean et al. 2009, Mitchell et al. 2011, within the GLUE framework using a variety of different models).”
Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods (2016, background): “…Differential Split Sample Testing (DSST) [Kleme s, 1986] show dependence of model parameters on the climate and meteorological conditions dominating the training period and their role in activating different rainfall-runoff processes [Wagener, 2003; Choi and Beven, 2007; Herman et al., 2013].”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, methodology): “…it comes to the GLUE LOA approach, extending the limits (e.g., Blazkova and Beven, 2009; Liu et al., 2009) and using different model realizations for different periods of a hydrological year (e.g., Choi and Beven, 2007) are some of the measures taken to minimize the risk of making type II errors.”
GLUE: 20 years on (2014, background): “…close to some observations to be considered as acceptably behavioural so that all the models tried could be rejected as unacceptable or non-behavioural (e.g. Page et al., 2007; Dean et al., 2009), even in some cases where global performance was actually rather good (Choi and Beven, 2007).”