This paper presents an assessment of the changes in future floods. The ranked area-average heavy daily rainfall amounts simulated by a super-high-resolution (20 km mesh) global climate model output are corrected with consideration of the effects of the topography on heavy rainfall patterns and used as a basis to model design storm hyetographs. The rainfall data are then used as the input for a nearly calibration-free parameter rainfall–runoff model to simulate floods in the future climate (2075–2099) at the Upper Thu Bon River basin in Central Vietnam. The results show that although the future mean annual rainfall will not be considerably different compared to the present-day climate (1979–2003), extreme rainfall is projected to increase vigorously, leading to a similar order of intensification of future floods. It is very likely that the flood peak with a 25-year recurrence will increase approximately 42% relative to the present-day climate. The occurrence of floods with a 10-year recurrence may exceed those with a 25-year recurrence in the present-day climate. The projection results also exhibit insignificant uncertainties caused by an artificial neural network-based bias correction model. Additionally, the presented bias correction model shows advantages over a simple climatology scaling method.

This paper presents the extension of the previous work on the development of short-term flood forecast model using rainfall downscaled from the global NWP outputs. The proposed downscale method has considered physically based corrections to the NWP outputs for optimization of parameters used for calibration phases using artificial neural network (ANN). Downscaled rainfall was then used as inputs to the modified super tank model for runoff forecast. Model uncertainties were quantified against forecast lead-times in order to integrate forecast results into the existing alarm levels for early flood warning. Results showed that flood forecasts based on the downscaled rainfall by ANN outperformed those using multiple linear regression methods. Though it showed larger uncertainties along with the forecast lead-times, the model can provide reliable forecasts up to 18-hour ahead. It has demonstrated an added value in flood forecasting and warning practices for river basins in Central Vietnam.

Abstract:A short-term flood inundation prediction model has been formulated based on the combination of the super-tank model, forced with downscaled rainfall from a global numerical weather prediction model, and a one-dimensional (1D) hydraulic model. Different statistical methods for downscaled rainfall have been explored, taking into account the availability of historical data. It has been found that the full implementation of a statistical downscaling model considering physically-based corrections to the numerical weather prediction model output for rainfall prediction performs better compared with an altitudinal correction method. The integration of the super-tank model into the 1D hydraulic model demonstrates a minimal requirement for the calibration of rainfall-runoff and flood propagation models. Updating the model with antecedent rainfall and regular forecast renewal has enhanced the model's capabilities as a result of the data assimilation processes of the runoff and numerical weather prediction models. The results show that the predicted water levels demonstrate acceptable agreement with those measured by stream gauges and comparable to those reproduced using the actual rainfall. Moreover, the predicted flood inundation depth and extent exhibit reasonably similar tendencies to those observed in the field. However, large uncertainties are observed in the prediction results in lower, flat portions of the river basin where the hydraulic conditions are not properly analysed by the 1D flood propagation model.

Global warming is becoming more serious and causing changes in rainfall pattern and runoff regime in most river basins. Exploration of the changes will help develop appropriate management and adaptation strategies. This study presents an assessment of changes in rainfall and runoff in the upper Thu Bon River basin in central Vietnam in the near-term (2026-2035) climate using direct Coupled Model Intercomparison Project Phase 5 (CMIP5) high-resolution model outputs. A nearly calibration-free parameter rainfall-runoff model was employed to explore the runoff response in the study basin. Most model simulations have detected greater decreases in the near-term runoff in the dry season compared with those of any preceding decades in the baseline (1979-2008) climate, though the rainfall in this period is expected to increase slightly. Meanwhile, monsoonal season flooding has the potential to become more severe, and Japanese models project further increase in the intensity of such extreme weather events. The results also indicate that the treatment of the model physical parameterization schemes tends to contribute more sensitivity to the future projections.

Many reservoirs are operational with single objective like hydropower reservoirs. Its objective function is to maximize water availability for power generation. While objective functions related to vulnerability, for example flood control, are not prioritized or at very limited capability. In many cases, improper reservoir operation may produce extra flood threats downstream because of dam safety issues. This paper presents inflow forecast for real-time flood control based on combination of downscaled precipitation from the global NWP model using local scaling method and a distributed rainfall runoff model. Altitudinal dependence of rainfall is utilized to determine the basin average scaling factor. Results show that inflow prediction based on precipitation forecast using the local scaling factor exhibits significant improvement. It has demonstrated an added value in reservoir inflow prediction for flood control practices, especially in unobserved, remote catchments, that aims to reduce downstream flood.

Global warming has resulted in significant variability of global climate especially with regard to variation in temperature and precipitation. As a result, it is expected that river flow regimes will be accordingly varied. This study presents a preliminary projection of medium-term and long-term runoff variation caused by climate change at a river basin scale. The large scale precipitation projection at the middle and the end of the 21 st century under the A1B scenario simulated by the CGCM model (MRI & JMA, 300 km resolution) is statistically downscaled to a basin scale and then used as input for the super-tank model for runoff analysis at the upper Thu Bon River basin in Central Vietnam. Results show that by the middle and the end of this century annual rainfall will increase slightly; together with a rising temperature, potential evapotranspiration is also projected to increase as well. The total annual runoff, as a result, is found to be not distinctly varied relative to the baseline period 1981 -2000; however, the runoff will decrease in the dry season and increase in the rainy season. The results also indicate the delay tendency of the high river flow period, shifting from Sep-Dec at present to Oct-Jan in the future. The present study demonstrates potential impacts of climate change on streamflow regimes in attempts to propose appropriate adaptation measures and responses at the river basin scales.

Fluvial flood risks are explored at the Vu Gia–Thu Bon River system in Central Vietnam based on a coupled hydrological–hydraulic model combined with design storm hyetographs constructed based on heavy rainfall downscaled from the output of a state‐of‐the‐art super‐high‐resolution (20‐km mesh) global climate model simulated under greenhouse gas emission scenario A1B. The results indicate that intensified rainfall due to future climate changes (2075–2099) will lead to higher flood risks in the fluvial plains, especially in the lower areas of the river system. The potential extent of inundation caused by a 25‐year return period flood in future climate patterns will increase by approximately 150% compared with those produced by the most severe flood in the present‐day climate (1979–2003). Moreover, the flood risks induced by a 10‐year return period flood tend to be slightly more severe than that of the 25‐year return period in the present‐day climate.

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