A river flash flood forecasting model coupled with ensemble Kalman filter

Abstract 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 K alman 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 T anshui River watershed in northern T aiwan 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.