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A multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies. [ABSTRACT FROM AUTHOR]

Climate change directly impacts the hydrological cycle via increasing temperatures and seasonal precipitation shifts, which are variable at local scales. The water resources of the Upper Yangtze River Basin (UYRB) account for almost 40% and 15% of all water resources used in the Yangtze Basin and China, respectively. Future climate change and the possible responses of surface runoff in this region are urgent issues for China's water security and sustainable socioeconomic development. This study evaluated the potential impacts of future climate change on the hydrological regimes (high flow (Q5), low flow (Q95), and mean annual runoff (MAR)) of the UYRB using global climate models (GCMs) and a variable infiltration capacity (VIC) model. We used the eight bias-corrected GCM outputs from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) to examine the effects of climate change under two future representative concentration pathways (RCP4.5 and RCP8.5). The direct variance method was adopted to analyze the contributions of precipitation and temperature to future Q5, Q95, and MAR. The results showed that the equidistant cumulative distribution function (EDCDF) can considerably reduce biases in the temperature and precipitation fields of CMIP5 models and that the EDCDF captured the extreme values and spatial pattern of the climate fields. Relative to the baseline period (1961–1990), precipitation is projected to slightly increase in the future, while temperature is projected to considerably increase. Furthermore, Q5, Q95, and MAR are projected to decrease. The projected decreases in the median value of Q95 were 21.08% to 24.88% and 16.05% to 26.70% under RCP4.5 and RCP8.5, respectively; these decreases were larger than those of MAR and Q5. Temperature increases accounted for more than 99% of the projected changes, whereas precipitation had limited projected effects on Q95 and MAR. These results indicate the drought risk over the UYRB will increase considerably in the future. [ABSTRACT FROM AUTHOR]

The upper region of the Yangtze River, as the freshwater source for the middle and lower Yangtze River, is highly vulnerable to climate change. Understanding and projecting the spatiotemporal variability of extreme flows in this region are key to creating appropriate adaptation policies and strategies. In this study, climate change signals were scaled from seven bias-corrected CMIP5 models and were used to force a variable infiltration capacity (VIC) model to quantify and assess the impact of climate change on the hydrology of the region. The respective contribution of precipitation (P) and temperature (T) to evapotranspiration (ET) and hydrological regimes (annual mean runoff, high flow, and low flow) were also quantified using the analysis of variance method (ANOVA). For the scenario period (2031–2060), the projected runoff is projected to increase in vary between 11.33% and 64.08% in winter, while increases in summer range from 8.64% to 40.97%. The changes in ET are different depending on the climate mode used; these increase during winter and decrease in summer under the influence of temperature. In addition, we found that low flows are projected to decrease while the mean annual runoff will increase in the future period. These projected changes demonstrated that temperature effects dominate in the study area for the future period under the representative concentration pathway 8.5 scenario. • We quantify how temperature and precipitation affect evapotranspiration and runoff. • Our method plus VIC model effectively projected climate change impacts on hydrology. • Droughts and floods are projected to increase in the upper region of Yangtze River. [ABSTRACT FROM AUTHOR]

Quantitative evaluation of future climate change impacts on hydrological drought characteristics is one of important measures for implementing sustainable water resources management and effective disaster mitigation in drought-prone regions under the changing environment. In this study, a modeling system for projecting the potential future climate change impacts on hydrological droughts in the Weihe River basin (WRB) in North China is presented. This system consists of a large-scale hydrological model driven by climate outputs from three climate models (CMs) for future streamflow projections, a probabilistic model for univariate drought assessment, and a copula-based bivariate model for joint drought frequency analysis under historical and future climates. With the observed historical climate data as the inputs, the Variable Infiltration Capacity hydrological model projects an overall runoff reduction in the WRB under the Intergovernmental Panel on Climate Change A1B scenario. The univariate drought assessment found that although fewer hydrological drought events would occur under A1B scenario, drought duration and severity tend to increase remarkably. Moreover, the bivariate drought assessment reveals that future droughts in the same return period as the baseline droughts would become more serious. With these trends in the future, the hydrological drought situation in the WRB would be further deteriorated. [ABSTRACT FROM AUTHOR]

Extreme flow projections are of considerable necessity for flood and drought disaster prevention and sustainable water resources management to adapt to future climate change. However, these projections are usually associated with large uncertainties arising from different sources. A numerical modeling system including three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distribution (PD) functions was established in this study to project future extreme flow changes at basin scales. This modeling system was employed in the following three watersheds with different climatic and topographical characteristics in China: The Upper Ganjiang River basin (UGRB) in the humid region, the Laoha River basin (LRB) in the semi-arid region, and the Yellow River source region (YRSR) in the semi-humid region. Subsequently, the analysis of variance approach (ANOVA) was used to quantify the contribution of different uncertainty sources in extreme flow projections. Results show that extreme high flow was projected to increase in all basins. By contrast, extreme low flow would decrease in UGRB, and inconsistent change signals were found in LRB and YRSR. The ANOVA-based assessment reveals that CM is generally the dominant uncertainty source for mean monthly streamflow and extreme low flow projections. ES explains a large proportion of total uncertainty for extreme low flow projection in the 2060s in UGRB because of the large difference in non-precipitation frequency projected under different ESs. SD is the main uncertainty source in extreme high flow projection in UGRB where extreme precipitation events frequently occur. Compared with other basins, HM produces relatively high uncertainty in LRB due to its inferior performance in historical streamflow simulations. PD contributes a low percentage of uncertainty in extreme flow projections. Interactions among uncertainty sources even produce larger uncertainties than most individual sources. Although future extreme flow projections are associated with non-negligible uncertainties, the projected remarkable changes in extreme high and low flows indicate that stakeholders should take certain measures to mitigate flood risks in the three basins and alleviate drought risks in UGRB • Extreme high flow would increase in the three basins with different climate features. • Extreme low flow would decrease in the watershed in humid regions. • Uncertainty of extreme flow projection is generally dominated by climate models. • Uncertainty sources have different impacts associated with watershed characteristics. [ABSTRACT FROM AUTHOR]