Abstract
The objective of this study is to assess Sejong University River Forecast (SURF) model which consists of a continuous rainfall-runoff model and measured streamflow assimilation using ensemble Kalman filter technique for streamflow forecast on Nakdong river basin. The study area is divided into 43 subbasins. The forecasted streamflows are evaluated at 12 measurement sites during flood season from 2006 to 2007. The forecasted ones are improved due to the impact of the measured streamflows assimilation. In effectiveness indices corresponding to 1~5 h forecast lead times, the accuracy of the forecasted streamflows with the assimilation approach is improved by 46.2~30.1% compared with that using only the rainfall-runoff model. The mean normalized absolute error of forecasted peak flow without and with data assimilation approach in entering 50% of the measured rainfall, respectively, the accuracy of the latter is improved about 40% than that of the former. From these results, SURF model is able to be used as a real-time river forecast model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
