Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency

By S. Pool, M. Vis, Jan Seibert. Hydrological Sciences Journal, 2018

206 citations recorded by the source corpus.

ABSTRACT Goodness-of-fit measures are important for an objective evaluation of runoff model performance. The Kling-Gupta efficiency (RKG), which has been introduced as an improvement of the widely used Nash-Sutcliffe efficiency, considers different types of model errors, namely the error in the mean, the variability, and the dynamics. The calculation of RKG is implicitly based on the assumptions of data linearity, data normality, and the absence of outliers. In this study, we propose a modification of RKG as an efficiency measure comprising non-parametric components, i.e. the Spearman rank correlation and the normalized flow–duration curve. The performances of model simulations for 100 catchments using the new measure were compared to those obtained using RKG based on a number of statistical metrics and hydrological signatures. The new measure resulted overall in better or comparable model performances, and thus it was concluded that efficiency measures with non-parametric components provide a suitable alternative to commonly used measures.

Subfields: Ecology, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/5231d0b5e8e274816951db6a0632f45c19624381

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Citations in the corpus

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores (2019, background): “Note that a weighted KGE version exists that allows specification of the relative importance of the three KGE terms (Gupta et al., 2009), as do a modified KGE (Kling et al., 2012) and a non-parametric KGE (Pool et al., 2018).”
Improved Estimators of Model Performance Efficiency for Skewed Hydrologic Data (2020, methodology): “The primary difference between our treatment in (9) and Gupta et al. (2009), Pool et al. (2018), and others is that they employ sample estimates of the various terms in (9) without referring to their true values.”