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<p>Estimating suspended sediment yield (SL) in ungauged catchments remains a critical challenge, particularly when gauging stations are distant from the design site. A common approach involves using specific SL values, but this often leads to an inaccurate estimation. Additionally, common sediment rating curve (SRC) methods may underestimate SL during flood events due to data variability and hysteresis effects. In this study, established SRC approaches, including the United States Bureau of Reclamation (USBR) method, the Food and Agriculture Organization (FAO)–USBR method, and the Power-Law SRC Model (standard method), were compared with the proposed Daliri et al. method, which accounts for flood sediment contributions and applies a regression correction factor to reduce data cloud dispersion and improve SRC results. Furthermore, a new empirical suspended sediment supply (DSS) model is introduced for estimating SL in ungauged basins after calibration via the improved SRC. The proposed method achieves an estimation coefficient of 0.89, demonstrating improved reliability for SL assessments in hydrologically complex regions based on Rudbar Lorestan Dam reservoir bathymetric sedimentation data.</p>

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Sediment Yield Estimation in Ungauged Basins with Improved Rating Curves and a New Empirical Model

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Farhad Daliri,
  • Vijay P. Singh,
  • Robert J. Wasson

摘要

Estimating suspended sediment yield (SL) in ungauged catchments remains a critical challenge, particularly when gauging stations are distant from the design site. A common approach involves using specific SL values, but this often leads to an inaccurate estimation. Additionally, common sediment rating curve (SRC) methods may underestimate SL during flood events due to data variability and hysteresis effects. In this study, established SRC approaches, including the United States Bureau of Reclamation (USBR) method, the Food and Agriculture Organization (FAO)–USBR method, and the Power-Law SRC Model (standard method), were compared with the proposed Daliri et al. method, which accounts for flood sediment contributions and applies a regression correction factor to reduce data cloud dispersion and improve SRC results. Furthermore, a new empirical suspended sediment supply (DSS) model is introduced for estimating SL in ungauged basins after calibration via the improved SRC. The proposed method achieves an estimation coefficient of 0.89, demonstrating improved reliability for SL assessments in hydrologically complex regions based on Rudbar Lorestan Dam reservoir bathymetric sedimentation data.