Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis

By Thorsten Wagener, N. McIntyre, M. Lees, Howard S. Wheater, Hoshin V. Gupta. 2003

615 citations recorded by the source corpus.

Subfields: Environmental Engineering, Soil Science, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/085015aaef28e06af8fd78358eae5c1b8550b6e1

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Citations in the corpus

Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods (2009, background): “The combination with recursive estimation techniques (Vrugt et al., 2002; Wagener et al., 2003) or the extension to multiple objectives (Bastidas et al., 1999) can provide an interesting insight into the behaviour of hydrological models.”
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (2005, methodology): “This supports the findings of other studies [e.g., Thiemann et al., 2001; Wagener et al., 2003; Gupta et al., 2003a; Vrugt et al., 2003; Moradkhani et al., 2005] that the role of other sources of uncertainty (e.g., model structural error and input measurement errors) must also be considered to…”
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].”
Towards simplification of hydrologic modeling: identification of dominant processes (2016, background): “The mod- eling problem becomes less complex to the modeler when hydrological processes not relevant to the modeled domain or watershed are removed from consideration (Wagener et al., 2003; Reusser et al., 2011; Guse et al., 2014; Bock et al., 2016).”
Sensitivity-guided reduction of parametric dimensionality for multi-objective calibration of watershed models (2009, background): “In the last two decades, advances in global optimization have proven very useful for hydrological model identification [12,14,21,25,30,32,34,38,39,42,44].”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “…(SCEM) [Vrugt et al., 2003a], the multiobjective extension of SCEM [Vrugt et al., 2003b], the dynamic identifiability analysis framework (DYNIA) [Wagener et al., 2003], the maximum likelihood Bayesian averagingmethod (MLBMA) [Neuman, 2003], the dual state-parameter estimation methods…”
Model identification for hydrological forecasting under uncertainty (2005, background): “Necessary conditions for a RR model to be ‘‘well-calibrated’’ are that it has (at least) the following three characteristics (Gupta et al. 2005; Wagener et al. 2003b): 1.”
Predictions of rainfall-runoff response and soil moisture dynamics in a microscale catchment using the CREW model (2006, background): “Models that are composed of such typical REWs could then be expected to have a lower structural uncertainty/ be more compatible with the landscape in the sense of Wagener et al. (2003) and yield much certain predictions.”
Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops (2006, background): “The question of how to estimate model parameters has been receiving increasing attention from the hydrology and land surface modeling community (Franks and Beven, 1997; Bastidas et al., 1999; Gupta et al., 1999; Duan et al., 2001; Duan et al., 2003; Jackson et al., 2003; Wagener et al., 2003 ).”
Nonparametric direct mapping of rainfall‐runoff relationships: An alternative approach to data analysis and modeling? (2004, methodology): “Interference has received less attention in the hydrological literature, although recent research [e.g., Gupta et al., 1998; Wagener et al., 2003] has questioned the use of a ‘‘global’’ performance measure on the grounds that it entails a loss of information through the aggregation of model…”
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches (2018, background): “During calibration, equifinality arises when different parameter sets give equally good results in terms of predefined efficiency criteria (Beven, 1993; Savenije, 2001; Wagener et al., 2003).”
The German drought monitor (2015, neutral): “These uncertainties stem from the input data, the model structure and the model parameters (e.g., Wagener et al 2003).”
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, neutral): “…sensitivities at predefined intervals throughout the model simulation, thereby determining the dynamics of model controls in changing conditions (Wagener et al., 2003; van Werkhoven et al., 2008a; Reusser and Zehe, 2011; Reusser et al., 2011; Garambois et al., 2013; Herman et al., 2013a; Guse…”
A Matlab toolbox for Global Sensitivity Analysis (2015, methodology): “…Analysis (VBSA, or Sobol' method, e.g. Saltelli et al. (2008)), the Fourier Amplitude Sensitivity Test (FAST by Cukier et al. (1973)), Dynamic identi ﬁ - ability analysis (DYNIA by Wagener et al. (2003)) and a novel density-based sensitivity method (PAWN by Pianosi and Wagener (2015) ) .”
Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops (2006, background): “…solving this dilemma is to develop a priori relations between land surface characteristics and model parameters for basins where the model is appropriately calibrated (Abdulla et al., 1996; Duan et al., 1996; Merz and Bloschl, 2004; Lamb and Kay, 2004; McIntyre et al., 2004; Wagener et al., 2004).”
Sensitivity analysis of environmental models: A systematic review with practical workflow (2014, background): “Sensitivity indices can be computed at different temporal resolutions, therefore obtaining their temporal evolution over the simulation horizon (Wagener and Harvey,1997; Wagener et al., 2003; Cloke et al., 2008; Reusser and Zehe, 2011; Kelleher et al., 2013; Guse et al., 2014).”