Articles | Volume 23, issue 10
https://doi.org/10.5194/hess-23-4323-2019
https://doi.org/10.5194/hess-23-4323-2019
Technical note
 | 
25 Oct 2019
Technical note |  | 25 Oct 2019

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

Wouter J. M. Knoben, Jim E. Freer, and Ross A. Woods

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Cited articles

Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017a. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies. version 2.0., UCAR/NCAR, Boulder, CO, USA, https://doi.org/10.5065/D6G73C3Q, 2017b. 
Andersson, J. C. M., Arheimer, B., Traoré, F., Gustafsson, D., and Ali, A.: Process refinements improve a hydrological model concept applied to the Niger River basin, Hydrol. Process., 31, 4540–4554, https://doi.org/10.1002/hyp.11376, 2017. 
Beven, K. J., Younger, P. M., and Freer, J.: Struggling with Epistemic Uncertainties in Environmental Modelling of Natural Hazards, in: Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA), 13–16 July 2014, Liverpool, UK, American Society of Civil Engineers, 13–22, 2014. 
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Short summary
The accuracy of model simulations can be quantified with so-called efficiency metrics. The Nash–Sutcliffe efficiency (NSE) has been often used in hydrology, but recently the Kling–Gupta efficiency (KGE) is gaining in popularity. We show that lessons learned about which NSE scores are acceptable do not necessarily translate well into understanding of the KGE metric.