Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 17
Articles | Volume 30, issue 17
https://doi.org/10.5194/hess-30-5521-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-5521-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 17
Research article
 | 
01 Sep 2026
Research article |  | 01 Sep 2026

Integrating Physical-Based Xinanjiang Model and Deep Learning for Interpretable Streamflow Simulation: A Multi-Source Data Fusion Approach across Diverse Chinese Basins

Zhaocai Wang, Nannan Xu, Wei Song, Xingxing Zhang, Junhao Wu, and Xi Chen

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Short summary
This study integrates Xinanjiang (XAJ) and Temporal Convolutional Network - Gated Recurrent Unit (TCN-GRU) via Random Forest (RF) for streamflow simulation. It combines XAJ’s physical modeling with TCN-GRU’s temporal analysis. Validated in four hydrologically diverse basins, the model achieves Nash-Sutcliffe Efficiency (NSE) 0.971–0.991, outperforming traditional models. Robust in flood/interval simulations, analysis identifies dew point temperature and evaporation as key factors through three interpretable methods.
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