Open AccessA river flash flood forecasting model coupled with ensemble K alman filter
Author(s) -
Kimura N.,
Hsu M.H.,
Tsai M.Y.,
Tsao M.C.,
Yu S.L.,
Tai A.
Publication year - 2016
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12128
Subject(s) - ensemble kalman filter , flash flood , typhoon , environmental science , data assimilation , streamflow , meteorology , flood forecasting , watershed , hydrology (agriculture) , flood myth , kalman filter , computer science , extended kalman filter , geology , machine learning , drainage basin , geography , cartography , artificial intelligence , geotechnical engineering , archaeology
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.