Development and application of a flood forecasting model based on the Shule River digital-twin basin platform
Received date: 2025-04-08
Revised date: 2025-08-11
Online published: 2025-12-13
Intensifying climate change has led to a surge in extreme precipitation events, posing unprecedented challenges to basin-wide flood prevention and mitigation. The unique combination of meteorological, topographical, hydrological and vegetation factors in the Shule River Basin makes it highly prone to flooding, yet existing forecasting models in the region exhibit low predictive skill. To address the flood control needs of the Shule River Basin，a digital twin platform was developed, and its precision in prediction and simulation was enhanced，this study establishes a coupled flood-prediction framework that centers on an improved SCS rainfall-runoff model and a Snowmelt Runoff Model (SRM), integrated with the Muskingum channel-routing scheme. In data-scarce areas, the SCS-CN loss method is employed to compute net rainfall, and a triangular unit hydrograph is adopted for efficient flow concentration. For alpine snowmelt-driven floods, the SRM is introduced and refined from a daily to a sub-daily time step. The upper Shule River is discretized into 121 sub-basins, and a digital-twin architecture comprising a “data foundation-model platform-knowledge platform” is built, enabling model registration, parameter calibration, real-time forcing and rolling forecasts. Model parameters were calibrated against the July 11, 2023 flood event; the simulated peak discharge showed an error of 6.8% and a timing lag of 2 h relative to observations. During the operational forecast for the July 15, 2024 flood, the platform issued an alert 72 h in advance, predicting a peak discharge of 438 m³·s-1 arriving at 12:00 on July 15, while the observed values were 491 m3·s-1 and 14:00, respectively. All errors meet the tolerance criteria specified in the Chinese national standard SL250-2000 for hydrological forecasting. The system provided timely support for reservoir pre-release and downstream evacuation, effectively reducing flood losses. The study advances flood-forecast accuracy and offers an intelligent solution for smart water management and resilient basin development.
AN Jianmin , ZHANG Pengju , ZHANG Jianxin , SHI Yongjie . Development and application of a flood forecasting model based on the Shule River digital-twin basin platform[J]. Arid Zone Research, 2025 , 42(11) : 2018 -2030 . DOI: 10.13866/j.azr.2025.11.06
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