Assessment of Near-Term Runoff Response at a River Basin Scale in Central Vietnam Using Direct CMIP5 High-Resolution Model Outputs

Abstract

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.

Assessment of Flood Extremes Using Downscaled CMIP5 High-Resolution Ensemble Projections of Near-Term Climate for the Upper Thu Bon Catchment in Vietnam

“…The model concept is based on a hydrological process represented by linear cascade tanks [37]; however, it focuses on more physical-based features and takes advantages of spatial available datasets (e.g., digital elevation model, land use, and soil type). As a result, the model has successfully shown its robustness in flood forecasting and climate change impact assessment across a wide range of spatial and temporal scales, especially for catchments with limited data (e.g., [20,38,39]). The details of model structure can be found in the literature [39].…”

“…Practices to assess climate change's impact on runoff responses in general and flood extremes in particular used direct precipitation estimated by general circulation models (GCMs) to drive hydrological models for runoff simulations (e.g., [3,6,19,20]). However, a large number of studies have demonstrated that a major source of uncertainties in runoff simulation is precipitation estimation, especially the estimation of extreme rainfall by GCMs (e.g., [2,6,21]).…”

“…Because GCMs are generally formulated for diagnosing synoptic weather patterns and offering climate data of coarse spatiotemporal resolution, they tend to underestimate extreme rainfall events [22,23], which are the main driver of flooding disasters. Though an increase in the resolution of GCMs would improve the estimations of extreme rainfall (e.g., [15,[24][25][26][27]), applying model bias correction to the projected rainfall to obtain a better simulated runoff is still needed, especially over areas of complex topography (e.g., [20,28,29]).…”

“…As an extension of the previous work on assessing interseasonal variability of runoff using direct CMIP5 outputs [20], this paper will further explore changes in future flood extremes based on downscaled CMIP5 high-resolution ensemble projections of near-term climate. The study employs a quantile-based model to correct for the heavy rainfall projected by CMIP5 high-resolution multi-model projections, while a scaling bias-correction method is used for the remaining.…”

Uncertainty assessment of streamflow projection under the impact of climate change in the Lower Mekong Basin: a case study of the Srepok River Basin, Vietnam

“…At present, there are many studies on the climate change impacts on water resources conducted in Vietnam (e.g. Raghavan et al, ; Le and Sharif, ; Vo et al, ; Huyen et al, ; Nam et al, ). However, only one study carried out by Khoi and Suetsugi () investigated uncertainties in streamflow projections with a wide range of climate scenarios developed from different GCMs, emission scenarios, global warming in mean air temperature in the Be River Catchment.…”