Real-time simulation and scheduling model of a digital twin watershed for water resources preview

Existing water resource system simulation and scheduling models fall short in meeting the demands of "forward and reverse pre-rehearsal" in water resource allocation and management. These demands entail multi-element simulation and forecasting, integrated with the flexible and efficient optimization of complex regulation rules. To address these challenges, this study first reviews the pre-rehearsal requirements for basin-level water resource management. On this basis, it proposes a novel approach that divides the water resource system into core units by twinning key nodes. The study outlines system generalization principles and unit modeling methodologies, and introduces a comprehensive simulation model for basin water resources. This model simulates the dynamic interactions of various elements—available water, inflow, demand, usage, and shortage—across multiple scheduling schemes, capturing the full lifecycle of water resource dynamics within the basin. Further, based on this simulation model, the study develops a method for constructing optimization-based scheduling models adaptable to diverse scheduling goals, thereby providing a framework for efficiently deriving optimal operational rules. The proposed methods are implemented on the digital twin platform developed for the Huanglei River Basin (Shandong Peninsula, China). Validation is carried out through two key case studies: Retrospective Analysis of a Typical Drought Year (2015): The model accurately reproduced historical drought conditions, validating its capability for historical scenario reconstruction ("reverse pre-rehearsal"). Dynamic Assessment of a Real-time Water Shortage Event (May 2023): The platform dynamically evaluated a live water shortage scenario, demonstrating its utility for real-time awareness and forecasting ("forward pre-rehearsal"). The findings confirm that the proposed approach enables: Full-process simulation of multi-element water dynamics throughout the basin; highly efficient optimization of scheduling rules for key infrastructure—including surface reservoirs, sluices/dams, and water diversion works—under various goals. Significance and Support Capabilities: The integrated modeling framework offers robust computational support for pre-rehearsal functions in three key domains: (1) Identifying Issues under Routine Schemes: Facilitates early detection and diagnosis during evaluations of conventional scheduling plans. (2) Pre-rehearsing Decision Impacts during Consultations: Supports assessment of anticipated impacts prior to implementing decisions during coordination meetings. (3) Deriving Optimized Schemes for Specific Targets: Enables generation of scientifically grounded, optimized scheduling strategies for defined management goals (e.g., maximizing supply reliability, minimizing shortages, or preserving ecological flows). In sum, this research delivers a robust, adaptable modeling foundation essential for enhancing the precision and effectiveness of water resource pre-rehearsal within digital twin-based basin management systems.