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.

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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.

Exploring potential floods is both essential and critical to making informed decisions for adaptation options at a river basin scale. The present study investigates changes in flood extremes in the future using downscaled CMIP5 (Coupled Model Intercomparison Project—Phase 5) high-resolution ensemble projections of near-term climate for the Upper Thu Bon catchment in Vietnam. Model bias correction techniques are utilized to improve the daily rainfall simulated by the multi-model climate experiments. The corrected rainfall is then used to drive a calibrated supper-tank model for runoff simulations. The flood extremes are analyzed based on the Gumbel extreme value distribution and simulation of design hydrograph methods. Results show that the former method indicates almost no changes in the flood extremes in the future compared to the baseline climate. However, the later method explores increases (approximately 20%) in the peaks of very extreme events in the future climate, especially, the flood peak of a 50-year return period tends to exceed the flood peak of a 100-year return period of the baseline climate. Meanwhile, the peaks of shorter return period floods (e.g., 10-year) are projected with a very slight change. Model physical parameterization schemes and spatial resolution seem to cause larger uncertainties; while different model runs show less sensitivity to the future projections.