On typical range, sensitivity, and normalization of Mean Squared Error and Nash‐Sutcliffe Efficiency type metrics

By Hoshin V. Gupta, H. Kling. 2011

259 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3c06c13bba6427475b7036e8e75bea16dbc52f61

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In Global and Planetary Change

In Statistics and Probability

In Water Science and Technology

Citations in the corpus

Comparing expert judgement and numerical criteria for hydrograph evaluation (2015, background): “Besides, despite their apparent simplicity, the behaviours of numerical criteria remain difficult to fully understand and can exhibit unexpected properties (see e.g. Gupta et al. 2009, Berthet et al. 2010a, 2010b, Gupta and Kling 2011, Pushpalatha et al. 2012).”
Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data (2020, background): “This is also a different and more general interpretation of the conditions under which E ¼ ρ2 than is given by either Gupta et al. (2009) or Gupta and Kling (2011).”
Use of an entropy‐based metric in multiobjective calibration to improve model performance (2014, background): “…in hydrology are the mean squared error (MSE) and its related normalization, the Nash-Sutcliffe efficiency (NSE) [Nash and Sutcliffe, 1970]; however, their suitability as measures of fit has been questioned [Krause et al., 2005; Schaefli and Gupta, 2007; Smith et al., 2008; Gupta and Kling, 2011].”
Ordinary kriging as a tool to estimate historical daily streamflow records (2016, methodology): “Previous work (Pebesma et al., 2005; Gupta et al., 2009; Gupta and Kling, 2011) has shown the dependencies between Pearson correlation, root mean squared errors and the Nash– Sutcliffe model efficiency.”
Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach (2013, background): “Of course, all of these metrics are interrelated—the NSE is approximately equal to 2 [see Gupta et al., 2009; Gupta and Kling, 2011], and the MI I Qsim ; Qobsð Þ can be interpreted as a generalized correlation coefficient (that provides a more accurate assessment of relationship strength when the…”