Summary
This technical note addresses a critical misinterpretation in hydrological modelling practice: researchers often apply NSE interpretation frameworks to KGE values, assuming both metrics have equivalent benchmark thresholds. The authors demonstrate that whilst NSE = 0 represents mean flow performance, KGE = 1−√2 ≈ −0.41 represents equivalent performance, meaning models with negative KGE values may still outperform the benchmark. The paper advocates for purpose-dependent evaluation frameworks with explicit benchmarks rather than ad hoc use of aggregated efficiency metrics.
UK applicability
This methodological guidance is relevant to UK-based hydrological and environmental modelling research, particularly in water resource management and flood forecasting studies where NSE and KGE are standard evaluation metrics. UK researchers adopting KGE metrics should recalibrate their interpretation protocols away from NSE-based conventions.
Key measures
Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), mean flow benchmark predictor, coefficient of variation
Outcomes reported
The study compared Nash–Sutcliffe efficiency (NSE) and Kling–Gupta efficiency (KGE) metrics used to evaluate hydrological model performance, demonstrating that these metrics have fundamentally different benchmark reference points and cannot be directly compared.
Topic tags
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.