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. |
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| 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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| 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] |
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| ISSN: | 10275606 |
| DOI: | 10.5194/hess-30-4095-2026 |
