Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 12
Articles | Volume 30, issue 12
https://doi.org/10.5194/hess-30-4095-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-4095-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 12
Research article
 | 
30 Jun 2026
Research article |  | 30 Jun 2026

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

Jing Yang, Jiangjiang Zhang, Tian Jiao, Yonghua Zhao, Manya Luo, Lei Wu, Ming Ye, and Jinxi Song

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Cited articles

Acero Triana, J. S., Ajami, H., and Razavi, S.: The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales, J. Hydrol., 650, 132482, https://doi.org/10.1016/j.jhydrol.2024.132482, 2025. 
Aloui, S., Mazzoni, A., Elomri, A., Aouissi, J., Boufekane, A., and Zghibi, A.: A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions, J. Environ. Manage., 326, 116799, https://doi.org/10.1016/j.jenvman.2022.116799, 2023. 
Arnold, J., Moriasi, D., Gassman, P., Abbaspour, K., White, M., Srinivasan, R., Santhi, C., Harmel, R., van Griensven, A., Van Liew, M., Kannan, N., and Jha, M.: SWAT: Model Use, Calibration, and Validation, T. ASABE, 55, 1491–1508, https://doi.org/10.13031/2013.42256, 2012. 
Bai, J., Zhou, Z., Li, J., Liu, T., Zhu, Q., and Zheng, T.: Predicting soil conservation service in the Jinghe River Basin under climate change, J. Hydrol., 615, 128646, https://doi.org/10.1016/j.jhydrol.2022.128646, 2022. 
Bai, T., Liu, X., Liu, D., and Zhou, Y.: Water allocation and irrigation scheme considering runoff forecasting of Ulungur River, Ecol. Indic., 173, 113381, https://doi.org/10.1016/j.ecolind.2025.113381, 2025. 
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Understanding how rainfall becomes river flow is essential for effective water management, yet complex computer models are often difficult to interpret. This study developed an efficient approach, supported by artificial intelligence, to identify where and how key model parameters influence river flow across different scales. The results reveal clear spatial differences and highlight critical areas controlling runoff, improving model reliability and supporting better water management decisions.
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