Hierarchical distributed model predictive control for large-scale irrigation canal network under water scarcity condition: A case study of the Zhanghe Irrigation District, China

Irrigation canal networks play a vital role in ensuring water and food security, especially under water scarcity. To address the challenge of accurate water allocation and hydraulic regulation during such periods, this paper proposes a hierarchical distributed model predictive control (HDMPC) strategy. The HDMPC integrates a Decision-making layer, which optimizes daily water distribution based on economic, environmental, and social objectives, and a Control layer, which ensures real-time implementation through model predictive control. A water supply adequacy index is incorporated to improve the execution fidelity of the planned allocations. The method is applied to the Zhanghe Irrigation District in China and evaluated throughout the 96-day growth period of semilate rice under different water scarcity scenarios. Compared to traditional manual control, the HDMPC improves rice yield, economic benefits, and clean energy production by 14.6 %–17.7 %, while maintaining hydraulic stability. This study provides a practical framework for precision irrigation and sustainable canal system management under resource-constrained conditions. • A novel model predictive control architecture is proposed for automation of large-scale irrigation canal systems. • The regulation of water resource allocation and hydraulic response are coordinated on a small temporal scale. • The benefit of water distribution is optimized from the economic, environmental, and social dimensions. • Canal automation can improve the rice yield, economic benefit, and clean energy production.