Articles | Volume 27, issue 13
https://doi.org/10.5194/hess-27-2397-2023
https://doi.org/10.5194/hess-27-2397-2023
Research article
 | 
03 Jul 2023
Research article |  | 03 Jul 2023

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

Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider

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Cited articles

Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., and Iannone, R.: Rmarkdown: Dynamic documents for r, https://cran.r-project.org/package=rmarkdown (last access: 27 June 2023), 2021. 
Allen, R. G., Pereira, L. S., Raes, D., Smith, M., and FAO (Eds.): Crop evapotranspiration: Guidelines for computing crop water requirements, Food and Agriculture Organization of the United Nations, Rome, https://appgeodb.nancy.inra.fr/biljou/pdf/Allen_FAO1998.pdf (last access: 27 June 2023), 1998. 
Althoff, D. and Rodrigues, L. N.: Goodness-of-fit criteria for hydrological models: Model calibration and performance assessment, J. Hydrol., 600, 126674, https://doi.org/10.1016/j.jhydrol.2021.126674, 2021. 
ARSO: Ministry of the Environment and Spatial Planning, Slovenian Environment Agency, Archive of hydrological data ARSO, http://vode.arso.gov.si/hidarhiv/ (last access: 27 June 2023), 2021a. 
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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, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.