Rating curves are derived from the combined measurement of water levels and discharges in rivers. This curve is used to convert observed water levels into flow rates, thereby generating discharge time series. Traditionally, rating curves are computed using exponential regression, which often neglects the underlying hydraulic conditions. Consequently, such curves may provide reasonable estimates of average flow but become unreliable under extreme conditions (e.g., high water levels). This research proposes a strategy for estimating discharge at high water levels using hydraulic modelling to support designers and practitioners in interpreting the upper range of the stage–discharge relationship. The methodology was applied to assess the rating curve for high flows in the Magdalena River at the Magangué reach (Bolívar, Colombia). Daily discharge records from 1974 to 2023 were analysed. The maximum historical discharge recorded was 11,127 m3/s (in 2010), while the mean annual peak discharge was 7904 m3/s. The proposed methodology yielded Manning’s roughness coefficients ranging from 0.046 to 0.052 and achieved satisfactory performance, with a Nash–Sutcliffe Efficiency (NSE) of 0.99. Results demonstrated that the traditional regression-based method tends to underestimate maximum discharges relative to a properly calibrated upper section of the rating curve. The analysis revealed systematic underestimation by the conventional approach, with discrepancies of up to 4.2% in determining maximum discharges. These findings emphasise the importance of incorporating hydraulic modelling to refine rating curves for high-flow conditions, thereby improving the reliability of design discharges.
Florian-Noriega et al. (Sat,) studied this question.