Do Nash values have value?

By B. Schaefli, Hoshin V. Gupta. 2007

684 citations recorded by the source corpus.

Subfields: Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5d2795a515a11ddf8d699e2d0ff2a3f814214e4d

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 Water Science and Technology

Citations in the corpus

Large-sample hydrology: a need to balance depth with breadth (2013, background): “…of routinely conducting such investigations becomes commonplace, it will open the way towards defining benchmarking procedures (Seibert, 2001; Schaefli and Gupta 2007; Parajka et al., 15 2013), and thereby enabling the routine use of reference datasets from a wide variety of catchments for…”
Towards simplification of hydrologic modeling: identification of dominant processes (2016, neutral): “The output produced by DPHMs is difficult to interpret (Schaefli and Gupta 2007; Gupta et al., 2009, 2012; Published by Copernicus Publications on behalf of the European Geosciences Union.”
Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States (2011, methodology): “, 1992; Xu, 2001] and augmented with benchmark measures of model performance [Schaefli and Gupta, 2007].”
Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, methodology): “Suggested baseline models include interannual mean for each calendar day (Schaefli and Gupta 2007), simple models such as the storage and lag (Schaefli and Gupta 2007) or rational formula (Seibert 2001); (2) using other efficiency criteria that are modified to lessen oversensitivity of NSE to peak…”
Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, background): “For such situations, various authors have recommended the use of the seasonal or climatological mean as a baseline model (Garrick et al., 1978; Murphy, 1988; Martinec and Rango, 1989; Legates and McCabe, 1999; Schaefli and Gupta, 2007).”
Estimating the uncertain mathematical structure of a water balance model via Bayesian data assimilation (2009, methodology): “values. The Bias statistic is an indicator of the overall tendency to preserve water balance and the NS statistic is a variance-normalized measure of model accuracy (for a discussion of strengths and weaknesses of the NS measure see Schaefli and Gupta [2007] ).”
Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data (2020, methodology): “…years, numerous authors have evaluated the behavior of NSE (see, e.g., Bardsley, 2013; Gupta & Kling, 2011; Gupta et al., 2009; Jain & Sudheer, 2008; Legates & McCabe, 1999; Liu et al., 2018; Martinec & Rango, 1989; McCuen et al., 2006; Pool et al., 2018; Schaefli & Gupta, 2007, and many others).”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, methodology): “Other performance measures, including modified versions of the NSE, have been proposed to overcome NSE’s emphasis toward various large magnitudes of the model residuals and to allow alternative reference models to be used in the skill score [Croke, 2007; Schaefli and Gupta, 2007; Bulygina et al., 20”
GLUE: 20 years on (2014, background): “The efficiency measure has some important limitations as measure of performance for cases where the residuals can be assumed aleatory (Beran, 1999; Schaefli and Gupta, 2007; Smith et al., 2008; Gupta et al., 2009) but remains a widely used performance measure.”
Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events (2011, neutral): “Along the same lines, Schaefli and Gupta (2007) as well as Jain and Sudheer (2008) found that NSE is a poor metric if the test series show strong seasonality.”
Comparing expert judgement and numerical criteria for hydrograph evaluation (2015, methodology): “Others have advised standardizing evaluation tools to make published studies more consistent in terms of evaluation (Dawson et al. 2007, Moriasi et al. 2007, Schaefli and Gupta 2007, Reusser et al. 2009, Alexandrov et al. 2011, Ritter and Muñoz-Carpena 2013).”
On the use of spatial regularization strategies to improve calibration of distributed watershed models (2010, methodology): “Note that 1.0-NMSE corresponds to the popular Nash Sutcliffe efficiency (NSE) measure [Nash and Sutcliffe, 1970]; see limitations discussed by Schaefli and Gupta [2007] and 1.0-NMSEL corresponds to its counterpart in the log-transformed space (NSEL).”