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. A traditional metric used in hydrology to summarize modelperformance is the Nash–Sutcliffe efficiency (NSE). Increasingly analternative metric, the Kling–Gupta efficiency (KGE), is used instead. WhenNSE is used, NSE  =  0 corresponds to using the mean flow as a benchmarkpredictor. The same reasoning is applied in various studies that use KGE asa metric: negative KGE values are viewed as bad model performance, and onlypositive values are seen as good model performance. Here we show that usingthe 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 themodel's KGE value is negative. NSE and KGE values cannot be directlycompared, because their relationship is non-unique and depends in part onthe coefficient of variation of the observed time series. Therefore,modellers who use the KGE metric should not let their understanding of NSEvalues guide them in interpreting KGE values and instead develop newunderstanding based on the constitutive parts of the KGE metric and theexplicit use of benchmark values to compare KGE scores against. Moregenerally, a strong case can be made for moving away from ad hoc use ofaggregated efficiency metrics and towards a framework based onpurpose-dependent evaluation metrics and benchmarks that allows for morerobust model adequacy assessment.

Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores