The vegetation along a river reach varies throughout a year. Seasonal vegetation affects the hydrodynamic behaviour of the river system. Accordingly, flood studies should take this temporal variation into account. This also applies to real‐time flood forecasting and control. This paper studies the impact of seasonal vegetation when considering real‐time flood control performance, based on a model predictive control (MPC) scheme. The scheme makes use of a conceptual river model to limit the computational times, as well as a reduced genetic algorithm (RGA) for the optimization of the flood control gates. The impact of seasonal vegetation on the conceptual model accuracy was analysed and a flexible data assimilation approach developed, to adjust the model predictions to different vegetation scenarios. This method can successfully improve the efficiency of a control strategy, by strongly predicting and reducing the impact of seasonal vegetation changes on river conditions.

中文翻译：

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植被季节变化对河流模型预测精度和实时防洪性能的影响

沿河的植被全年变化。季节性植被会影响河流系统的水动力行为。因此，洪水研究应考虑这种时间变化。这也适用于实时洪水预报和控制。本文基于模型预测控制（MPC）方案，研究了在考虑实时防洪性能时季节性植被的影响。该方案利用概念性河流模型来限制计算时间，并使用简化遗传算法（RGA）来优化防洪闸门。分析了季节性植被对概念模型准确性的影响，并开发了一种灵活的数据同化方法，以将模型预测调整为适用于不同植被情景。