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Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter

引用

Journal of Hydrodynamics 2018年第5期30卷 828-840页

作者： Xing-ya Xu Xuesong Zhang Hong-wei Fang Rui-xun Lai Yue-feng Zhang Lei Huang Xiao-bo Liu Changjiang Institute of Survey PlanningDesign and ResearchWuhan 430010China State Key Laboratory of Hydroscience and Engineering Department of Hydraulic EngineeringTsinghua UniversityBeijing 100084China Joint Global Change Research Institute Pacific Northwest National LaboratoryCollege ParkMDUSA Great Lakes Bioenergy Research Center Michigan State UniversityEast LansingMichiganUSA Yellow River Institute of Hydraulic Research Zhengzhou 450003China Development Research Center of the Ministry of Water Resources of People＇s Republic of China Beo＇ing 100038China Department of Water Environment China Institute of Water Resources and Hydropower ResearchBeo＇ing 100038China

An accurate and reliable real-time flood forecast is crucial for mitigating flood disasters. The errors associated with the inflow boundary forcing data are considered as an important source of uncertainties in hydraulic model. In this paper, a real-time probabilistic channel flood forecasting model is developed with a novel function to incorporate the uncertainty of the forcing inflow. This new approach couples a hydraulic model with the particle filter（PF） data assimilation algorithm, a sequential Bayesian Monte Carlo method. The stage observations at hydrological stations are assimilated at each time step to update the model states in order to improve the next time step＇s forecasting. This new approach is tested against a real flood event occurred in the upper Yangtze River. As compared with the open loop simulations, the evaluations of model performance with several deterministic and probabilistic metrics indicate that the accuracy of the ensemble mean prediction and the reliability of the uncertainty quantification are improved pronouncedly as a result of the PF assimilation. Further assessment of the prediction results at different lead times shows that the improvement of model performance deteriorates with the increase of the lead time due to the gradual diminishing of the updating effect for the initial conditions. Based on the analyses of the number of particles and the assimilation frequency, we find that the optimal number of particles can be determined by balancing the model performance and the computation cost, while a high assimilation frequency is preferred to incorporate the emerging observations to update the model states to match the real conditions.

关键词： Channel flood forecasting probabilistic forecast particle filter hydraulic model data assimilation inflow uncertainty

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