A river flash flood forecasting model coupled with ensemble Kalman filter

N. Kimura, M. H. Hsu, M. Y. Tsai, M. C. Tsao, S. L. Yu, A. Tai

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A flash flood forecasting model including a state-of-the-art data assimilation method was developed to provide a precise water stage forecast for flood emergency response. The model integrates a flash flood routing model (FFRM) coupled with an ensemble Kalman filter (EnKF) and an artificial neural network (ANN) submodel. In the model, the ANN forecasts river water stages at gauge stations first. Then, these are used as the initial and boundary conditions of the FFRM. The water stages, simulated from the FFRM, are then corrected by the EnKF for lead time. The model was applied to the Tanshui River watershed in northern Taiwan during past typhoons. The model forecasts almost covered the data observed during a typhoon period to within 95% confidence intervals. Compared with the use of FFRM without EnKF, the forecast water stages from the EnKF improved the accuracy at the conjunctions between upstream and downstream channels and the steep slope location.

Original languageEnglish
Pages (from-to)178-192
Number of pages15
JournalJournal of Flood Risk Management
Issue number2
Publication statusPublished - Jun 1 2016

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Geography, Planning and Development
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology


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