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.

The Srepok watershed in the Central Highland of Vietnam plays an important role in the economic development of the region. Any harmful effects of climate change on natural resources may cause difficulties for social and economic development in this area. The present study aims to predict and evaluate changes of water resources in the Srepok watershed under the impact of climate change scenarios by using the soil and water assessment tool (SWAT) model. The study used observed weather data from 1990 to 2010 for the first period and climate change scenarios A1B and A2 from 2011 to 2039 for the second period and from 2040 to 2069 for the third period. According to the climate change scenarios of the studied watershed, future minimum and maximum daily average temperature will rise in all climate change scenarios and the amount of annual precipitation will fall in scenario A1B and go up in scenario A2. Based on the simulation results, the annual water discharge in scenario A1B decreased by 11.1% and 1.2% during the second and third periods, respectively, compared with the first. In scenario A2, annual water discharge increased by 2.4% during the second period but decreased by 1.8% during the third period.

Future projections of anthropogenic climate change play a pivotal role in devising viable countermeasures to address climate-related risks. This study strove to construct future daily rainfall and maximum and minimum temperature scenarios in Vu Gia Thu Bon river basin by employing the Statistical DownScaling Model (SDSM). The model performance was evaluated by utilizing a Taylor diagram with dimensioned and dimensionless statistics. During validation, all model-performance measures show good ability in simulating extreme temperatures and reasonable ability for rainfall. Subsequently, a set of predictors derived from HadCM3 and CanESM2 was selected to generate ensembles of each climatic variables up to the end of 21st century. The generated outcomes exhibit a consistent increase in both extreme temperatures under all emission scenarios. The greatest changes in maximum and minimum temperature were predicted to increase by 2.67–3.9 °C and 1.24–1.96 °C between the 2080s and reference period for the worst-case scenarios. Conversely, there are several discrepancies in the projections of rainfall under different emission scenarios as well as among considered stations. The predicted outcomes indicate a significant decrease in rainfall by approximately 11.57%–17.68% at most stations by 2099. Moreover, all ensemble means were subjected to the overall and partial trend analysis by applying the Innovative-Şen trend analysis method. The results exhibit similar trend patterns, thereby indicating high stability and applicability of the SDSM. Generally, it is expected that these findings will contribute numerous valuable foundations to establish a framework for the assessment of climate change impacts at the river basin scale.

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