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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Short summary
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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