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<p>Storm-driven nonpoint-source (NPS) export in steep mountain basins is highly sensitive to runoff peaks, yet nutrient monitoring is typically monthly, limiting event-scale calibration. This study proposes an event-aware SWAT+ calibration framework for the Qingjiang River Basin (China) by coupling Hippopotamus Optimization (HO) with SUFI-2 uncertainty fitting. The objective retains monthly Nash–Sutcliffe efficiency (NSE) for discharge and nutrients and adds an event regularization term computed from daily discharge within objectively delineated runoff events (peak magnitude, time-to-peak, and recession slope). Using 2008–2018 for split-sample calibration/validation and 2020–2024 for an out-of-sample test without parameter retuning, HO-SUFI-2 improves monthly discharge NSE from 0.72 to 0.79 (calibration) and from 0.70 to 0.73 (validation) while tightening uncertainty (<i>P</i>-factor 83%→87%; <i>R</i>-factor 1.21→1.09). Peak constraints reduce median relative peak-magnitude error from 0.104 to 0.069 at Gaobazhou and from 0.114 to 0.069 at Changyang, and tighten peak-timing dispersion (|TPK| IQR 2→1&#xa0;days at Gaobazhou; median |TPK| 1→0&#xa0;day at Changyang), without degrading monthly nutrient skill (TP NSE 0.70→0.73). In 2020–2024, monthly discharge NSE remains 0.62–0.67 and TN/TP NSE ≥ 0.42 at both stations; remaining biases may reflect post-2020 management and reservoir operations not explicitly represented. The framework provides a practical way to constrain storm hydrograph dynamics under data limitations and to report uncertainty-aware diagnostics for storm-period risk screening in mountainous basins.</p>

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Event-aware SWAT+ calibration for stormflows with monthly nutrient data in the Qingjiang River Basin

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Ruining Wang,
  • Xianqi Zhang,
  • Hongyang Zhang,
  • Yanbin Yang,
  • Zelin Tao

摘要

Storm-driven nonpoint-source (NPS) export in steep mountain basins is highly sensitive to runoff peaks, yet nutrient monitoring is typically monthly, limiting event-scale calibration. This study proposes an event-aware SWAT+ calibration framework for the Qingjiang River Basin (China) by coupling Hippopotamus Optimization (HO) with SUFI-2 uncertainty fitting. The objective retains monthly Nash–Sutcliffe efficiency (NSE) for discharge and nutrients and adds an event regularization term computed from daily discharge within objectively delineated runoff events (peak magnitude, time-to-peak, and recession slope). Using 2008–2018 for split-sample calibration/validation and 2020–2024 for an out-of-sample test without parameter retuning, HO-SUFI-2 improves monthly discharge NSE from 0.72 to 0.79 (calibration) and from 0.70 to 0.73 (validation) while tightening uncertainty (P-factor 83%→87%; R-factor 1.21→1.09). Peak constraints reduce median relative peak-magnitude error from 0.104 to 0.069 at Gaobazhou and from 0.114 to 0.069 at Changyang, and tighten peak-timing dispersion (|TPK| IQR 2→1 days at Gaobazhou; median |TPK| 1→0 day at Changyang), without degrading monthly nutrient skill (TP NSE 0.70→0.73). In 2020–2024, monthly discharge NSE remains 0.62–0.67 and TN/TP NSE ≥ 0.42 at both stations; remaining biases may reflect post-2020 management and reservoir operations not explicitly represented. The framework provides a practical way to constrain storm hydrograph dynamics under data limitations and to report uncertainty-aware diagnostics for storm-period risk screening in mountainous basins.