Comment on Liu (2020): A rational performance criterion for hydrological model

Choi, Hyun Il

doi:10.1016/j.jhydrol.2021.126927

Cited by 5 publications

(2 citation statements)

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“…The LME criterion has a very distinctive envelope, for which the maximum score of 1 is reached for a lot of BB models, even when both 𝜔 1 and 𝜔 2 are different from 1. This can be explained by the interaction between 𝑟 and 𝛼 that leads to an infinite number of solutions (Choi, 2022). The KGENP and the DE' (FDC-based criteria) both shows similar envelopes with a break point near the maximum transformation score in both ways around 𝜔 1 = 1.…”

Section: Exploring Counterbalancing Errors With Synthetic Transformat...mentioning

confidence: 97%

“…This applies to all the KGE variants except the KGENP where the error on 𝑟 𝑠 is significant, resulting in a better score for the ANN model. The LME score is extremely high (0.99) for the reservoir model, which is probably due to the compensation of 𝑟 and 𝛼 identified by Choi (2022). Also, using 𝛾 instead of 𝛼 for assessing the variability seems to lower counterbalancing errors.…”

Section: Impact Of Counterbalancing Errors On Model Evaluationmentioning

confidence: 97%

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

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Mazzilli

Jourde

et al. 2022

Preprint

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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.

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Section: Exploring Counterbalancing Errors With Synthetic Transformat...mentioning

confidence: 97%

Section: Impact Of Counterbalancing Errors On Model Evaluationmentioning

confidence: 97%