HydroTrace: an interpretable attention-based AI for glacio-hydrological modeling Article Swipe

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Cuihui Xia , Lei Yue , Deliang Chen , Yuyang Li , Ancheng Xue , Zhiqiang Li , Qing He , Guoqing Zhang , Dambaru Ballab Kattel , Lei Lei , Ming Zhou ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1088/2515-7620/ae1a41

Hydrological modeling is essential for effective water resource management, especially in complex regions such as the Tibetan Plateau, where conventional models often struggle to capture dynamic glacio-hydrological processes. This study introduces HydroTrace, an algorithm-driven model with custom attention mechanisms that consistently outperforms existing models across hydrological benchmarks at two observational sites on the Tibetan Plateau. HydroTrace achieves high predictive accuracy, with a calibrated Nash-Sutcliffe Efficiency (NSE) of 0.98, enables direct mapping of input–output contributions, identifies key physical drivers, and uses learned attention to guide water-balance regression. The approach reveals a previously overlooked year-round glacier influence on river discharge across the central and southeastern Plateau: in the downstream southeast, glaciers, melt, and baseflow co-vary with the monsoon yet glaciers remain a meaningful contributor outside the ablation peak; at higher central elevations, hydrology remains glacier-dominated throughout the year. By bridging algorithm-driven and physics-based modeling, with attention serving as a data-informed proxy, HydroTrace offers a practical pathway toward high-performing, interpretable, process-aware hydrological AI.

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Type: article
Language: en
Landing Page: https://doi.org/10.1088/2515-7620/ae1a41
OA Status: gold
References: 25
OpenAlex ID: https://openalex.org/W4415753644

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OpenAlex ID: https://openalex.org/W4415753644

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DOI: https://doi.org/10.1088/2515-7620/ae1a41

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Title: HydroTrace: an interpretable attention-based AI for glacio-hydrological modeling

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-10-31

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Authors: Cuihui Xia, Lei Yue, Deliang Chen, Yuyang Li, Ancheng Xue, Zhiqiang Li, Qing He, Guoqing Zhang, Dambaru Ballab Kattel, Lei Lei, Ming Zhou

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Landing page: https://doi.org/10.1088/2515-7620/ae1a41

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OA URL: https://doi.org/10.1088/2515-7620/ae1a41

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Cited by: 0

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References (count): 25

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