Towards a comprehensive assessment of model structural adequacy

By Hoshin V. Gupta, M. Clark, Jasper A. Vrugt, Gab Abramowitz, Ming Ye. 2012

435 citations recorded by the source corpus.

Subfields: Environmental Engineering, Information Systems and Management, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7b769015c40005a5fa93619370417997eea01462

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In Environmental Engineering

In Information Systems and Management

In Water Science and Technology

Citations in the corpus

Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015, neutral): “…the input data, forcing data, initial and boundary conditions, model structure, and parameters due to a lack of data and poor knowledge of hydrological response mechanisms (Ye et al., 2008; Doherty and Welter, 2010; Shi and Zhou, 2010; Zhang et al., 2011; Gupta et al., 2012; Foglia et al., 2013).”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, background): “Multimodel ensemble simulations, (e.g., Neuman, 2003; Clark et al., 2008; Wohling et al., 2008; Gupta et al., 2012), such as Bayesian model averaging (BMA) are a promising approach to quantify these uncertainties; BMA reflects conceptual uncertainty through a weighted average of model-wise ensembles”
Characterising performance of environmental models (2013, background): “Consequently, systems of metrics focussing on several aspects may be needed for a comprehensive evaluation of models, as advocated e.g., by Gupta et al. (2012).”
An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? (2016, methodology): “This paradigm also extends to model structure (Gupta et al., 2012), for example by combining posterior distribu-”
Large-sample hydrology: a need to balance depth with breadth (2013, background): “…a number of " generic " model codes (specific realizations of perceptual-conceptual/mathematical/computational development), or model 10 development frameworks (multi-hypothesis environments), that can be applied to a given catchment study (see discussion in Clark et al., 2011; Gupta et al., 2012).”
Assessment of parametric uncertainty for groundwater reactive transport modeling (2014, methodology): “…caused by propagation of various sources of uncertainty in model structures, parameters, driving forces, and data used in modeling [Neuman, 2003; Wagener and Gupta, 2005; Srinivasan et al., 2007; Meyer et al., 2007; Clark et al., 2011; Refsgaard et al., 2012; Gupta et al., 2012; Tartakovsky, 2013].”
The evolution of process-based hydrologic models: Historical challenges and the collective quest for physical realism. (2017, neutral): “Addressing this challenge requires shifting focus from traditional approaches at short time scales where “properties define processes” [Gupta et al. 2012] towards approaches at longer time scales that focus on predicting how “processes define properties” [Rodríguez ‐ Iturbe et al. 1992; Eagleson…”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, background): “The importance of adequate representations natural heterogeneity is largely undisputed (e.g. Clark et al., 2011; Gupta et al., 2012).”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, methodology): “This characterizes what we define here as model structure inadequacy, which can be resolved by a combination of improved system conceptualization, and/or improved model structural equations [Gupta et al., 2012].”
Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, methodology): “The quantitative measures have been used recently for model structure diagnosis with respect to model complexity and model structure inadequacy (Rosolem et al., 2012; Gupta et al., 2012).”