Multi-objective collaborative optimization of water resources in Hebei irrigation areas: maximizing the benefits of the water-energy-grain nexus driven by the NSGA-III algorithm and verified by digital twins

The Hebei irrigation districts, which are crucial for China’s grain security, face severe challenges in the water-energy-food nexus, with 72% of groundwater areas being over-exploited and 60% of total energy consumption attributed to pumping systems. This study develops a digital twin-enabled framework integrating three innovations: an enhanced NSGA-III algorithm achieving 18.5% water conservation and 1285 kWh per hectare energy reduction through dynamic reference points and hybrid constraints, a four-dimensional nexus model capturing nonlinear economic returns with a determination coefficient of 0.913 and groundwater recovery of 0.07 meters during droughts, and a real-time verification platform demonstrating groundwater prediction accuracy of 0.03 meters and decision latency of 186 milliseconds. Empirical validation using multi-year data from 32 meteorological stations and 48 soil sensors confirmed a yield simulation error of 0.48% and robustness under 30% electricity price fluctuations. The scalability of the framework was demonstrated in the Hexi Corridor, achieving 15.3% water savings and 77% decision acceptance through adaptive calibration. Comparative analysis showed a 22.3% higher solution diversity compared to industrial benchmarks, while reducing monthly optimization cycles to 15-minute intervals. This paradigm shift resolves the dilemma between water conservation and energy cost, providing actionable solutions for 89% of China’s water-stressed farmlands. The implementation cost of 485,000 USD is offset within 6.2 years through a 19% reduction in pumping costs, demonstrating the technical and economic viability for global arid regions.