skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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 themore » 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.« less

Authors:
 [1]; ORCiD logo [2];  [3];  [4];  [3];  [3];  [5]
  1. UNIVERSITY PROGRAMS
  2. BATTELLE (PACIFIC NW LAB)
  3. Tsinghua University
  4. Yellow River Institute of Hydraulic Research, China
  5. Department of Water Environment, China Institute of Water Resources and Hydropower Research, Beijing
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1558415
Report Number(s):
PNNL-SA-124747
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Hydrodynamics
Additional Journal Information:
Journal Volume: 30; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
Channel flood forecasting, probabilistic forecast, particle filter, hydraulic model, data assimilation, inflow uncertainty

Citation Formats

Xu, Xingya, Zhang, Xuesong, Fang, Hongwei, Lai, Ruixun, Zhang, Yuefeng, Huang, Lei, and Liu, Xiaobo. Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter. United States: N. p., 2018. Web. doi:10.1007/s42241-018-0110-x.
Xu, Xingya, Zhang, Xuesong, Fang, Hongwei, Lai, Ruixun, Zhang, Yuefeng, Huang, Lei, & Liu, Xiaobo. Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter. United States. doi:10.1007/s42241-018-0110-x.
Xu, Xingya, Zhang, Xuesong, Fang, Hongwei, Lai, Ruixun, Zhang, Yuefeng, Huang, Lei, and Liu, Xiaobo. Mon . "Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter". United States. doi:10.1007/s42241-018-0110-x.
@article{osti_1558415,
title = {Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter},
author = {Xu, Xingya and Zhang, Xuesong and Fang, Hongwei and Lai, Ruixun and Zhang, Yuefeng and Huang, Lei and Liu, Xiaobo},
abstractNote = {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.},
doi = {10.1007/s42241-018-0110-x},
journal = {Journal of Hydrodynamics},
number = 5,
volume = 30,
place = {United States},
year = {2018},
month = {10}
}