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

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

doi:https://doi.org/10.5194/hess-23-4323-2019

Articles | Volume 23, issue 10

https://doi.org/10.5194/hess-23-4323-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-23-4323-2019

© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 10

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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Final revised paper (published on 25 Oct 2019)
Supplement to the final revised paper
Preprint (discussion started on 01 Jul 2019)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Equating the NSE and KGE scores', John Ding, 11 Jul 2019
- AC1: 'Reply to John Ding', Wouter Knoben, 06 Aug 2019
RC1: 'Review by HV Gupta', Hoshin Gupta, 23 Jul 2019
- AC3: 'Reply to review by HV Gupta', Wouter Knoben, 07 Aug 2019
RC2: 'comment on technical note by Knoben et al.', Anonymous Referee #2, 04 Aug 2019
- AC2: 'Reply to review', Wouter Knoben, 07 Aug 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (further review by editor) (29 Aug 2019) by Nunzio Romano

AR by Wouter Knoben on behalf of the Authors (06 Sep 2019) Author's response Manuscript

ED: Publish as is (22 Sep 2019) by Nunzio Romano

AR by Wouter Knoben on behalf of the Authors (30 Sep 2019)

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.