Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores
By Wout Knoben, Jim Freer, Ross A. Woods. Hydrology and Earth System Sciences, 2019
1,148 citations recorded by the source corpus.
Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.
Subfields: Ocean Engineering, Soil Science, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/39e86c6c92e5c41881045532f66424b797075b3b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- I. Marchesini
- C. Massari
- L. Brocca
- L. Ciabatta
- S. Camici
Citations in the corpus
- Which rainfall score is more informative about the performance in river discharge simulation? A comprehensive assessment on 1318 basins over Europe (2020, methodology): “In particular, for KGE, values in the range −0.41 KGE = 1 indicate that satellite rainfall data outperform the mean of the E-OBS observations (Knoben et al., 2019).”