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Identifying dominant parameters in SWAT across subbasin and HRU scales using a two-step deep learning-assisted spatial sensitivity analysis.

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Title: Identifying dominant parameters in SWAT across subbasin and HRU scales using a two-step deep learning-assisted spatial sensitivity analysis.
Authors: Yang, Jing1,2 (AUTHOR), Zhang, Jiangjiang3,4 (AUTHOR), Jiao, Tian1 (AUTHOR) tianjiao@nwu.edu.cn, Zhao, Yonghua2 (AUTHOR) yonghuaz@chd.edu.cn, Luo, Manya2 (AUTHOR), Wu, Lei5 (AUTHOR), Ye, Ming6 (AUTHOR), Song, Jinxi1 (AUTHOR)
Source: Hydrology & Earth System Sciences. Jun2026, Vol. 30 Issue 12, p4095-4116. 22p.
Subject Terms: *Sensitivity analysis, *Deep learning, *Hydrologic models, *Spatial variation, *Watersheds
Abstract: Distributed hydrological models require sensitivity analyses that explicitly account for spatial heterogeneity, yet such analyses are often constrained by high computational demands. This study presents a two-step, deep learning-assisted spatial sensitivity analysis (SSA) framework to identify dominant parameters across multiple spatial scales. Using the Soil and Water Assessment Tool (SWAT) in the Jinghe River Basin as a case study, the Morris method was first applied with a spatially lumped strategy to screen influential parameters. Subsequently, SSA was conducted using the Sobol' method with multilayer perceptron (MLP) surrogates to evaluate parameter sensitivities under both subbasin- and hydrologic response unit (HRU)-scale parameterizations. The surrogate models emulated SWAT with high accuracy for 195 subbasin-scale and 2559 HRU-scale parameters, enabling efficient estimation of Sobol' sensitivity indices. Results reveal pronounced spatial heterogeneity in parameter sensitivities. Sensitivity hotspots are consistently concentrated in gauge-proximal subbasins, while HRU-scale analysis further resolves localized controls. Robustness analyses demonstrate that the identified sensitivity patterns remain stable across different performance metrics, NSE-constrained posterior parameter distributions, alternative observation configurations, and varying numbers of screened parameters. These findings confirm the reliability of the proposed two-step framework and highlight its capability to efficiently diagnose dominant hydrological controls in high-dimensional distributed models. The methodology provides a transferable and computationally feasible approach for sensitivity assessment, supporting scale-aware calibration and improving the interpretability and predictive reliability of distributed hydrological simulations. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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An: 195100200
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  Label: Title
  Group: Ti
  Data: Identifying dominant parameters in SWAT across subbasin and HRU scales using a two-step deep learning-assisted spatial sensitivity analysis.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Jing%22">Yang, Jing</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Jiangjiang%22">Zhang, Jiangjiang</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiao%2C+Tian%22">Jiao, Tian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tianjiao@nwu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yonghua%22">Zhao, Yonghua</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yonghuaz@chd.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Manya%22">Luo, Manya</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Lei%22">Wu, Lei</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ye%2C+Ming%22">Ye, Ming</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Jinxi%22">Song, Jinxi</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Hydrology+%26+Earth+System+Sciences%22">Hydrology & Earth System Sciences</searchLink>. Jun2026, Vol. 30 Issue 12, p4095-4116. 22p.
– Name: Subject
  Label: Subject Terms
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  Data: *<searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Hydrologic+models%22">Hydrologic models</searchLink><br />*<searchLink fieldCode="DE" term="%22Spatial+variation%22">Spatial variation</searchLink><br />*<searchLink fieldCode="DE" term="%22Watersheds%22">Watersheds</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Distributed hydrological models require sensitivity analyses that explicitly account for spatial heterogeneity, yet such analyses are often constrained by high computational demands. This study presents a two-step, deep learning-assisted spatial sensitivity analysis (SSA) framework to identify dominant parameters across multiple spatial scales. Using the Soil and Water Assessment Tool (SWAT) in the Jinghe River Basin as a case study, the Morris method was first applied with a spatially lumped strategy to screen influential parameters. Subsequently, SSA was conducted using the Sobol' method with multilayer perceptron (MLP) surrogates to evaluate parameter sensitivities under both subbasin- and hydrologic response unit (HRU)-scale parameterizations. The surrogate models emulated SWAT with high accuracy for 195 subbasin-scale and 2559 HRU-scale parameters, enabling efficient estimation of Sobol' sensitivity indices. Results reveal pronounced spatial heterogeneity in parameter sensitivities. Sensitivity hotspots are consistently concentrated in gauge-proximal subbasins, while HRU-scale analysis further resolves localized controls. Robustness analyses demonstrate that the identified sensitivity patterns remain stable across different performance metrics, NSE-constrained posterior parameter distributions, alternative observation configurations, and varying numbers of screened parameters. These findings confirm the reliability of the proposed two-step framework and highlight its capability to efficiently diagnose dominant hydrological controls in high-dimensional distributed models. The methodology provides a transferable and computationally feasible approach for sensitivity assessment, supporting scale-aware calibration and improving the interpretability and predictive reliability of distributed hydrological simulations. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.5194/hess-30-4095-2026
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      – Code: eng
        Text: English
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        PageCount: 22
        StartPage: 4095
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      – SubjectFull: Sensitivity analysis
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Hydrologic models
        Type: general
      – SubjectFull: Spatial variation
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      – SubjectFull: Watersheds
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      – TitleFull: Identifying dominant parameters in SWAT across subbasin and HRU scales using a two-step deep learning-assisted spatial sensitivity analysis.
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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