In recent decades, increasing drought frequency, declining renewable water resources, and growing agricultural water demand have intensified the need for sustainable water management, particularly in arid and semi-arid regions such as Iran. The agricultural sector, which accounts for over 90% of total water withdrawals in Iran, suffers from low conveyance efficiency and excessive dependence on groundwater abstraction. Traditional irrigation networks, controlled by manual structures such as Amil regulators, often fail to respond to flow fluctuations and result in significant water losses, high energy consumption, and inequitable water distribution among users. These challenges necessitate the adoption of automated and data-driven operation systems. Model Predictive Control (MPC) has recently emerged as an effective technique for improving the hydraulic performance of open-channel systems. However, MPC alone cannot assess the environmental and energy implications of operational decisions. Therefore, integrating MPC with an analytical sustainability framework such as CLEWs (Climate–Land–Energy–Water Systems) provides a comprehensive understanding of the interlink ages between hydraulic, energy, and environmental dimensions. This research aims to develop a coupled MPC–CLEWs framework to quantify the synergies and trade-offs between operational efficiency, energy use, and carbon emissions in an irrigation network.
Methodology
The study was conducted on the Qazvin irrigation network in northwestern Iran, which covers approximately 59,000 hectares and receives water mainly from the Taleghan Dam. Two operational strategies were modeled: (1) the conventional Amil regulator-based method and (2) a centralized Model Predictive Control (MPC) system. Both methods were simulated under two hydrological scenarios : normal and drought conditions, using a dynamic integrator-delay hydraulic model. Key performance indicators included Water Delivery Adequacy (PA), Energy Consumption (E), and CO₂ Emissions (CE). These indicators were normalized and aggregated into the composite CLEWs Index, defined as:
CLEWs = 0.4·Iw + 0.3·(1–Ie) + 0.3·(1–Im),
Where Iw, Ie, and Im represent the normalized sub-indices of adequacy, energy, and emissions, respectively. Energy consumption was calculated based on pumping volume, head, and pump efficiency according to Howells et al. (2013). The coupled module was implemented in MATLAB R2023b, allowing automatic integration of the operational outputs (from MPC) into the CLEWs analytical structure. Additionally, a multi-objective sensitivity analysis was conducted by varying the weight of each component to identify trade-offs between water supply adequacy and energy use. Pareto front diagrams were then generated to visualize optimal balance points between performance and sustainability under both operational modes.
Results and Discussion
The results revealed significant differences between the two operation strategies under both hydrological conditions. In the normal scenario, the average CLEWs Index improved from 0.75 under the Amil operation to 1.00 under MPC, indicating complete system stability and elimination of groundwater dependency. Downstream reaches (7–10), which previously suffered from inadequate supply (CLEWs < 0.75), achieved full adequacy and zero emissions under MPC. Under the drought scenario, the Amil-based system experienced a sharp decline in performance, with the average CLEWs Index dropping to 0.62, driven by increased groundwater pumping and CO₂ emissions. Conversely, MPC maintained a relatively high CLEWs value of 0.88, demonstrating resilience against reduced inflows. The energy demand in the Amil method increased by more than 40% compared to MPC due to inefficient reallocation of flows. Pareto front analysis highlighted that MPC achieves superior trade-offs, minimizing energy consumption while maximizing adequacy and environmental sustainability. In contrast, the Amil regulator showed steep trade-offs between supply reliability and energy costs, reflecting its vulnerability to hydrological variability. The coupled CLEWs framework effectively captured these multi-dimensional interactions, offering a quantitative link between operational decisions and their sustainability outcomes. Overall, the integrated MPC–CLEWs system enhances not only hydraulic stability but also energy efficiency and carbon reduction potential in irrigation management.
Conclusion
This study introduced a novel coupling between operational control (MPC) and the CLEWs analytical framework to evaluate irrigation sustainability in real time. Results demonstrated that MPC substantially improves water delivery adequacy, reduces energy demand, and minimizes greenhouse gas emissions compared to conventional Amil-based management. The coupled model provides a comprehensive decision-support tool for irrigation managers, enabling the formulation of adaptive strategies under water scarcity. The proposed MPC–CLEWs framework bridges the gap between operational modeling and sustainability assessment, offering a replicable approach for integrated water–energy–environment management.