A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach. (February 2017) - Xu, Xingya; Zhang, Xuesong; Fang, Hongwei; Lai, Ruixun; Zhang, Yuefeng; Huang, Lei; Liu, Xiaobo
Abstract: Reliable real-time probabilistic flood forecasting is critical for effective water management and flood protection all over the world. In this study, we develop a real-time probabilistic channel flood-forecasting model by combining a channel hydraulic model with the Bayesian particle filter approach. The new model is tested in the upstream river reach of Three Gorges Dam (TGD) on the Yangtze River, China. Stage observations at seven hydrological stations are used simultaneously to adjust the Manning's roughness coefficients and to update discharges and stages along the river reach to attain reliable probabilistic flood forecasting. The synthetic experiments are applied to demonstrate the new model's correction and forecasting performances. The real-world experiments show that the new model can make accurate flood forecasting as well as derive reliable intervals for different confidence levels. The new probabilistic flood forecasting model not only outperforms the existing deterministic channel flood-forecasting models in accuracy, but also provides a more robust tool with which to incorporate uncertainty into flood-control efforts. Highlights: A real-time flood-forecasting model is proposed by assimilating real-time stage observations into a hydraulic model. Particle filter is adopted as the data assimilation method to update/correct stage, discharge, and roughness coefficient. Synthetic experiments are employed to explore model settings and evaluate model performance. Model performance is compared with previous studies using Kalman Filter based methods. Probabilistic predictions provided by the model are more accurate and reliable.