Volume 19 Issue 1
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Yu-fei Yan, Han-xiao Liu, Shu Xu, Qiong-lin Wang, Yu-hui Yang, Qing-qing Chen, Chen-yang Wang, Tian-ling Qin. 2026: Advances in coupling machine learning with hydrological simulation: A review. Water Science and Engineering, 19(1): 1-10. doi: 10.1016/j.wse.2026.01.002
Citation: Yu-fei Yan, Han-xiao Liu, Shu Xu, Qiong-lin Wang, Yu-hui Yang, Qing-qing Chen, Chen-yang Wang, Tian-ling Qin. 2026: Advances in coupling machine learning with hydrological simulation: A review. Water Science and Engineering, 19(1): 1-10. doi: 10.1016/j.wse.2026.01.002
shu

Advances in coupling machine learning with hydrological simulation: A review

doi: 10.1016/j.wse.2026.01.002

  • a. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China;

  • b. State Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing 100038, China;

  • c. School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450000, China;

  • d. Henan Key Laboratory of Yellow Basin Ecological Protection and Restoration, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China;

  • e. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

Funds:

This work was supported by the National Key Research and Development Project (Grant No. 2022YFC3201700), the National Natural Science Foundation of China (Grant No. U2443205), the Anhui Provincial Key Research and Development Project (Grant No. 2408055US002), and the Five Major Excellent Talent Programs of the China Institute of Water Resources and Hydropower Research (IWHR) (Grant No. WR0199A012021).

  • Received Date: 2025-07-29
  • Accepted Date: 2025-12-19
    • Available Online: 2026-03-28
    Abstract
  • Accurate and efficient hydrological simulation is critically important to sustainable water resources management amidst escalating climate change. As an indispensable scientific tool, hydrological modeling employs mathematical frameworks and computational techniques to quantitatively characterize hydrological processes, thereby playing a vital role in water resources assessment, the prediction and management of extreme hydrological events, and climate change impact evaluation. This review article systematically synthesizes recent advances in traditional hydrological models while critically examining their inherent methodological limitations. It further delineates the evolutionary trajectory of machine learning (ML) techniques in hydrological simulation and highlights the comparative advantages of data-driven ML approaches over conventional paradigms. Through a rigorous analysis of contemporary research, this review article establishes that coupling physically-based hydrological models with data-driven ML architectures represents the most promising pathway for overcoming fundamental bottlenecks in hydrological simulation. Furthermore, this review article concludes by identifying persistent challenges within existing coupling frameworks and projecting key future research directions in this rapidly evolving field.

     

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