Joint calibration of Manning’s roughness and seepage in canals using NSGA-II for precision hydrodynamic modeling

Based on field investigations in multiple irrigation districts, it was found that farmers frequently employ sandbag barriers or direct pumping to meet irrigation demands, disrupting irrigation schedules and destabilizing downstream water supply. The core issue stems from differential deterioration of canal sections after prolonged operation, causing variations in Manning’s roughness coefficient and seepage rates. Consequently, irrigation plans based on historical experience no longer satisfy water-level accuracy requirements. This study focuses on a typical canal system in the Ningxia Yellow River Irrigation District, utilizing the NSGA-II optimization algorithm to simultaneously calibrate Manning’s roughness coefficient and seepage parameters. The results indicate that Manning’s roughness exhibits significant spatial heterogeneity in canal sections constructed with the same technique after several years of operation; the simulated values at three water level control sections generally align with the measured trends, with absolute water level errors within 0.03 m; 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.; by assessing canal section deterioration and prioritizing anti-seepage measures, corn irrigation area is projected to expand by over 3.2 hectares. This research holds significant practical value for enhancing water resource utilization efficiency, boosting agricultural productivity, and advancing sustainable development in irrigation districts. 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.