15 Nov 2022
15 Nov 2022
Status: this preprint is currently under review for the journal HESS.

When best is the enemy of good – critical evaluation of performance criteria in hydrological models

Guillaume Cinkus1, Naomi Mazzilli2, Hervé Jourde1, Andreas Wunsch3, Tanja Liesch3, Nataša Ravbar4, Zhao Chen5, and Nico Goldscheider3 Guillaume Cinkus et al.
  • 1HydroSciences Montpellier (HSM), Univ. Montpellier, CNRS, IRD, 34090 Montpellier, France
  • 2UMR 1114 EMMAH (AU-INRAE), Université d’Avignon, 84000 Avignon, France
  • 3Karlsruhe Institute of Technology (KIT), Institute of Applied Geosciences, Kaiserstr. 12, 76131 Karlsruhe, Germany
  • 4ZRC SAZU, Karst Research Institute, Titov trg 2, 6230 Postojna, Slovenia
  • 5Institute of Groundwater Management, Technical University of Dresden, 01062 Dresden, Germany

Abstract. Performance criteria play a key role in the calibration and evaluation of hydrological models and have been extensively developed and studied, but some of the most used criteria still have unknown pitfalls. This study set out to examine counterbalancing errors, which are inherent to the Kling-Gupta Efficiency (KGE) and its variants. A total of nine performance criteria – including the KGE and its variants, as well as the Nash-Sutcliffe Efficiency (NSE) and the refined version of the Willmott’s index of agreement (dr) – were analysed using synthetic time series and a real case study. Results showed that, assessing a simulation, the score of the KGE and some of its variants can be increased by concurrent over- and underestimation of discharge. These counterbalancing errors may favour bias and variability parameters, therefore preserving an overall high score of the performance criteria. As bias and variability parameters generally account for 2/3 of the weight in the equation of performance criteria such as the KGE, this can lead to an overall higher criterion score without being associated to an increase in model relevance. We recommend using (i) performance criteria that are not or less prone to counterbalancing errors (NSE, dr, modified KGE, non-parametric KGE, Diagnostic Efficiency) in a multi-criteria framework, and/or (ii) scaling factors in the equation to reduce the influence of relative parameters.

Guillaume Cinkus et al.

Status: open (until 10 Jan 2023)

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Guillaume Cinkus et al.

Guillaume Cinkus et al.


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Short summary
The Kling-Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without being associated to an increase in model relevance. We suggest to carefully choose performance criteria and to use scaling factors.