Automated procedure of real-time flood forecasting in Vu Gia – Thu Bon river basin, Vietnam by integrating SWAT and HEC-RAS models

The precise and reliable simulation of hydrologic and hydraulic processes is important for efficient flood forecasting and warning. The study proposes a real-time flood forecasting system which integrates a coupled hydrological-hydraulic modeling system, weather station network, and stream gauges in...

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Bibliographic Details
Published in:Journal of water and climate change 2019-09, Vol.10 (3), p.535-545
Main Authors: Loi, Nguyen Kim, Liem, Nguyen Duy, Tu, Le Hoang, Hong, Nguyen Thi, Truong, Cao Duy, Tram, Vo Ngoc Quynh, Nhat, Tran Thong, Anh, Tran Ngoc, Jeong, Jaehak
Format: Article
Language:English
Subjects:
Climate change
Climate models
Computational fluid dynamics
Computer applications
Computer simulation
Correlation analysis
Flood control
Flood forecasting
Flood predictions
Floods
Fluid flow
Gauges
Hydraulic models
Hydraulics
Hydrologic models
Hydrologic processes
Hydrology
Rain
Rainfall runoff
Rainfall-runoff relationships
Real time
River basins
River discharge
Rivers
Runoff
Statistical correlation
Storms
Stream discharge
Stream flow
Watersheds
Weather forecasting
Weather stations
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Summary:The precise and reliable simulation of hydrologic and hydraulic processes is important for efficient flood forecasting and warning. The study proposes a real-time flood forecasting system which integrates a coupled hydrological-hydraulic modeling system, weather station network, and stream gauges in a web-based visualization environment. An automated procedure was developed for linking dynamically terrestrial rainfall-runoff processes and river hydraulics by coupling the SWAT hydrological model and the HEC-RAS hydraulic model. The flood forecasting system was trialed in the Vu Gia – Thu Bon river basin, Quang Nam province, Vietnam. The results showed good statistical correlation between predicted and measured stream flow for a 10-year calibration period (R² = 0.95, NSI = 0.95, PBIAS = −1.54) and during the following 10-year validation period as well (R² = 0.93, NSI = 0.93, PBIAS = 6.18). A close-up analysis of individual storm events indicated that the magnitude and timing of peak floods were accurately predicted in 2015 (R² = 0.88, NSI = 0.69, PBIAS = 4.50) and 2016 (R² = 0.80, NSI = 0.93, PBIAS = 6.18). In addition, the automated procedure was demonstrated to be reliable with dependable computational efficiency of less than 5 minutes' processing time.
ISSN:2040-2244
2408-9354
DOI:10.2166/wcc.2018.015