Reconciling theory with observations: elements of a diagnostic approach to model evaluation

By Hoshin V. Gupta, Thorsten Wagener, Yuqiong Liu. 2008

638 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/64790e20d095ee421ca3528dd7a492be4182b678

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Ocean Engineering

In Water Science and Technology

Citations in the corpus

The stationarity paradigm revisited: Hypothesis testing using diagnostics, summary metrics, and DREAM(ABC) (2015, background): “Such likelihood based metrics have little correspondence with the underlying hydrologic processes that control watershed behavior [Gupta et al., 2008; Vrugt and Sadegh, 2013; Sadegh and Vrugt, 2014].”
Pursuing the method of multiple working hypotheses for hydrological modeling (2011, methodology): “In addition to tying together themes raised as key challenges in previous commentaries [e.g., Beven, 2006a, 2008; Blöschl, 2006; Dunn et al., 2008; Gupta et al., 2008; Kirchner, 2006; McDonnell et al., 2007; Savenije, 2001, 2009; Sivapalan, 2009; Soulsby et al., 2008; Tetzlaff et al., 2008; Troch et”
The future of hydrology: An evolving science for a changing world (2010, background): “Instead, what is required is a greater consistency between model and real‐world system, including new strategies to demonstrate this consistency [Gupta et al., 2008].”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “…evaluation approach that aims to: (1) characterize the information contained in the data and in the model, (2) examine the extent to which a model can be reconciled with observations, and (3) point toward the aspects of the model that need improvement [Gupta et al., 2008; Schaefli et al., 2011].”
Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States (2011, background): “Note that the final outcome correlates with the consistency filters in different ways for each model/location; this illustrates the dangers inherent in oversimplifying the evaluation and also supports the usefulness of a diagnostic multiple criteria approach [Gupta et al., 2008].”
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, methodology): “This severely92 diminishes our chances to detect epistemic errors (Gupta et al., 2008; Vrugt and Sadegh, 2013) and refine existing theory as integral part of the model94 hypothesis.”
Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events (2011, neutral): “Recently, Gupta et al. (2008) proposed a step beyond multi-objective evaluation towards diagnostic, behavioural evaluation of catchment/process signature indices.”
Time‐varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior (2013, methodology): “There remains a need for diagnostic methods to link model formulation to its consequent impacts on process-level behavior to inform model selection, calibration, and interpretation [Gupta et al., 2008].”
Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration (2013, methodology): “…to extract more information from available data, for example through the development of signatures representing different aspects of the data (e.g., Gupta et al., 1998, 2008; Boyle et al., 2000, 2001; Madsen, 2000; Fenicia et al., 2006; Rouhani et al., 2007; Khu et al., 2008; Winsemius et al.,…”
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities (2012, methodology): “The inference of the different sources of uncertainty can therefore be improved through more intelligent use of the available data (e.g., Gupta et al., 1998, 2008), for example, by separately examining amplitude and phase errors (e.g., Liu et al., 2011).”
The role of expert opinion in environmental modelling (2012, background): “The choice of a valid test of model assumptions can itself be controversial (e.g. Jakeman et al., 2006; Gupta et al., 2008; Bellocchi et al., 2009) and hence a matter of opinion.”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, background): “In recent years, much progress has been made in the development of process-based model evaluation methods that much better extract information from the available data (Gupta et al., 2008; Vrugt and Sadegh, 2013).”