A river flash flood forecasting model coupled with ensemble Kalman filter. Issue 2 (8th December 2014)

• Record Type: Journal Article
• Authors: Kimura, N.; Hsu, M.‐H.; Tsai, M.‐Y.; Tsao, M.‐C.; Yu, S.‐L.; Tai, A.
• Is Part Of: Journal of flood risk management. Volume 9:Issue 2(2016:Jun.)
• Page Start: 178
• Page End: 192
• Publication Date: 2014-12-08
• Subjects: Flood forecasting -- ensemble Kalman filter -- artificial neural network -- flash flood routing model; Flood control -- Periodicals; Floods -- Risk assessment -- Periodicals; Flood damage prevention -- Periodicals; Flood warning systems -- Periodicals; 627.405
• DOI: 10.1111/jfr3.12128
• Languages: English
• ISSNs: 1753-318X

Abstract

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 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.