Summary
This study challenges the conventional practice of using squared-error metrics like NSE for calibrating hydrologic models, demonstrating that such calibrations substantially underestimate annual peak flows critical for flood frequency estimation. Across 492 United States basins, the authors show that Kling–Gupta efficiency (KGE) produces superior high-flow estimates compared to NSE, with an alternative application-specific metric (APFB) performing best for annual peaks but at the cost of poorer performance on other high-flow metrics. The findings suggest that metric selection during model calibration fundamentally shapes hydrologic predictions relevant to flood risk management.
UK applicability
The methodological findings regarding calibration metric selection are internationally applicable to UK hydrologic modelling practice, particularly for flood forecasting and water resource management. However, the study's calibration was conducted on United States basins with distinct hydroclimatic characteristics, so direct quantitative conclusions may require validation using UK streamflow data.
Key measures
Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and variants, annual peak flow bias (APFB), annual peak flow estimates, flow time series metrics (mean and variance)
Outcomes reported
The study evaluated how different calibration metrics (NSE, KGE, and APFB) affect hydrologic model performance in simulating high-flow events across 492 basins in the contiguous United States. Results showed that NSE-based calibrations produced annual peak flow estimates more than 20% worse than KGE-based calibrations, primarily due to NSE's tendency to underestimate flow variability.
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