In the arid landscapes of the Ningxia Yellow River Irrigation District, farmers have long grappled with the challenges of maintaining consistent water supply for their crops. Traditional irrigation methods, reliant on historical data and experience, often fall short due to the natural deterioration of canal systems over time. This is where the innovative work of Li Li, a researcher at the State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi’an University of Technology, comes into play. Li Li’s recent study, published in *Frontiers in Water* (which translates to *Water Frontiers* in English), offers a promising solution to these age-old problems.
Li Li’s research focuses on the critical issue of canal deterioration, which leads to variations in Manning’s roughness coefficient and seepage rates. These changes disrupt irrigation schedules and destabilize downstream water supply, causing farmers to resort to makeshift solutions like sandbag barriers or direct pumping. “The core issue is that irrigation plans based on historical experience no longer meet the accuracy requirements for water levels,” Li Li explains. To address this, Li Li employed the NSGA-II optimization algorithm to simultaneously calibrate Manning’s roughness coefficient and seepage parameters, providing a more precise hydrodynamic model.
The results of Li Li’s study are impressive. By accurately identifying these parameters and combining them with AMR (Adaptive Mesh Refinement) technology, the research team was able to simulate the canal flow process with remarkable precision. This precision allows for timely adjustments to irrigation schedules, resolving conflicts and ensuring a more stable water supply. “Through accurate parameter identification and combined with AMR adaptive mesh refinement technology, the canal flow process is precisely simulated, enabling timely irrigation schedule adjustments to resolve the aforementioned conflicts,” Li Li notes.
The practical implications of this research are significant. By assessing canal section deterioration and prioritizing anti-seepage measures, the study projects an expansion of corn irrigation area by over 3.2 hectares. This not only enhances water resource utilization efficiency but also boosts agricultural productivity, contributing to sustainable development in irrigation districts.
Looking ahead, Li Li suggests that future efforts could integrate water inflow predictions, crop water requirements, and coordinated control of gate groups to establish a digital twin-driven precision irrigation framework for the entire canal system. This vision aligns with the broader trend towards digital agriculture and sustainable farming practices, offering a glimpse into the future of precision irrigation.
For the energy sector, the implications are equally compelling. Efficient water management is crucial for optimizing energy use in irrigation, reducing waste, and enhancing overall system performance. Li Li’s research provides a robust framework for achieving these goals, paving the way for more sustainable and efficient water management practices.
In summary, Li Li’s work represents a significant advancement in the field of irrigation management. By leveraging cutting-edge optimization algorithms and precision modeling techniques, the study offers a practical solution to the challenges faced by farmers in arid regions. As the world continues to grapple with water scarcity and the need for sustainable agriculture, Li Li’s research provides a beacon of hope and a roadmap for the future.