Evaluation of the Nash-Sutcliffe Efficiency Index

By R. McCuen, Zach Knight, A. Cutter. 2006

735 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/f70ba075b20cc7f9d63e80c87a7ce74386c5f928

How researchers see this paper

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

In Ecology

1. Majid Niazkar
2. Caitline A. Barber
3. Dedi Liu
4. Seied Hosein Afzali
5. Richard M. Vogel
6. J. Lamontagne

In Environmental Engineering

1. Misgana K. Muleta
2. G. Martinez
3. Koray K. Yilmaz
4. H. Kling
5. Hoshin V. Gupta
6. Richard M. Vogel
7. Caitline A. Barber
8. J. Lamontagne

In Water Science and Technology

1. Hoshin V. Gupta
2. Dedi Liu
3. H. Kling
4. G. Martinez
5. Misgana K. Muleta
6. Richard M. Vogel
7. Caitline A. Barber
8. J. Lamontagne

Citations in the corpus

• Model Performance Sensitivity to Objective Function during Automated Calibrations (2012, background): “In spite of its popularity, NSE has many well-documented limitations (ASCE 1993; Legates and McCabe 1999; Seibert 2001; Krause et al. 2005; McCuen et al. 2006; Moriasi et al. 2007; Schaefli and Gupta 2007; Criss and Winston, 2008; Jain and Sudheer 2008; Gupta et al. 2009).”
• A rational performance criterion for hydrological model (2020, positive): “However, the distribution of NSE has not been known exactly (McCuen et al., 2006; Ritter and Muñoz-Carpena, 2013; Liu et al., 2018), and the distribution of KGE seems to be impossible to be derived except by the “bootstrap” method.”
• Assessment of Modified Honey Bee Mating Optimization for Parameter Estimation of Nonlinear Muskingum Models (2015, methodology): “Several evaluation measures were proposed for quantifying the applicability criteria of different methods in nonlinear Muskingum parameters estimation (Yoon and Padmanabhan 1993; Mohan 1997; McCuen et al. 2006; Perumal and Sahoo 2007; Chu 2009; Toprak 2009; Xu et al. 2011; Karahan et al. 2012; Barat”
• Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling (2009, background): “While the NSE criterion may be a convenient and popular (albeit gross) indicator of model skill, there has been a long and vivid discussion about the suitability of NSE (McCuen and Snyder, 1975; Martinec and Rango, 1989; Legates and McCabe, 1999; Krause et al., 2005; McCuen et al., 2006; Schaefli an”
• Monthly runoff forecasting based on LSTM–ALO model (2018, methodology): “Moreover, if the value of R2 and NSEC are close to 1, the correlation between the observed and predicted runoff series is stronger.”
• Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data (2020, background): “…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).”
• Series distance – an intuitive metric to quantify hydrograph similarity in terms of occurrence, amplitude and timing of hydrological events (2011, neutral): “McCuen et al. (2006) investigated the influence of sample size, outliers, magnitude bias and time offsets on the NSE, identifying the adverse effect of time offsets and magnitude bias.”