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Complex hydrological processes in watersheds can be carried out by simulation using a hydrological modeling approach. One hydrological model that has been extensively applied and tested is the SWAT model. However, uncertainty in determining sensitive parameters was a problem that needs to be resolved because each watershed has its characteristics according to topography, morphometry, geology, land cover, and hydrology conditions. This research aims to conduct a local and global sensitivity analysis of SWAT parameters in modeling streamflow and compare the results during the manual and automatic calibration processes. The model’s effectiveness was assessed through statistical tests, namely R2 and NSE. The research results showed that the most sensitive parameters were REVAPMN, GWQMN followed by CN2 and ESCO for manual calibration, while for calibration using SUFI-2, the sensitive parameters were ESCO and SOL_AWC, SHALLST, and CN2. Results of the model performance evaluation at the calibration obtained values of R2 = 0.740 and NSE = 0.705, while the validation obtained values of R2 = 0.770 and NSE = 0.616 for validation periods 2014–2021.

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Sensitivity of Swat Model Parameters for Modeling Streamflow at Bajulmati Watershed, Situbondo—East Java, Indonesia

  • 会议论文

  • 发表日期：

    2025年

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  • Aldi Ainun Habibi,
  • Gusfan Halik,
  • Retno Utami Agung Wiyono

摘要

Complex hydrological processes in watersheds can be carried out by simulation using a hydrological modeling approach. One hydrological model that has been extensively applied and tested is the SWAT model. However, uncertainty in determining sensitive parameters was a problem that needs to be resolved because each watershed has its characteristics according to topography, morphometry, geology, land cover, and hydrology conditions. This research aims to conduct a local and global sensitivity analysis of SWAT parameters in modeling streamflow and compare the results during the manual and automatic calibration processes. The model’s effectiveness was assessed through statistical tests, namely R2 and NSE. The research results showed that the most sensitive parameters were REVAPMN, GWQMN followed by CN2 and ESCO for manual calibration, while for calibration using SUFI-2, the sensitive parameters were ESCO and SOL_AWC, SHALLST, and CN2. Results of the model performance evaluation at the calibration obtained values of R2 = 0.740 and NSE = 0.705, while the validation obtained values of R2 = 0.770 and NSE = 0.616 for validation periods 2014–2021.