Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China

Researchers often examine hydro-climatological processes via Global Circulation Model (GCM) and hydrological model, which have been shown to benefit water resources management and prediction, especially at the basin scale. In this study, the Soil and Water Assessment Tool (SWAT) and Statistical Downscaling Method (SDSM) were integrated and applied to estimate streamflows in the Xin River Basin, China, based on climate change scenarios downscaled from different GCMs (BCC-CSM1.1, CanESM2, and NorESM1-M) under three Representative Concentration Pathways (RCPs). Results confirmed that the calibrated SWAT model accurately depicts hydrological processes features at daily, monthly, and yearly scales. Three GCMs based on the calibrated SDSM showed that temperature is continually increasing in the region, however, future precipitation is highly complex and uncertain; there were significant differences among various GCM RCP scenarios. The average of the precipitation in three models showed slight and steady increase trends under RCP2.6 and RCP4.5, but a significant increase under the RCP8.5 scenario. The ensemble average of streamflow in GCMs demonstrated that many RCPs significantly decrease from May to June but increase from August to September relative to the baseline period. The ensemble mean of the multi-GCM indicated that future streamflows under RCP2.6 and RCP4.5 scenarios will be closer to the current streamflow volume. Many RCPs also revealed a significant increase in monthly streamflow dispersion coefficient in October, reflecting a tendency for drought and flood events in that month. The BCC-CSM1.1 and NorESM1-M models showed that streamflows are higher than the baseline with median probability in the future. The low monthly streamflow (10th percentile) processes for each GCM were altogether similar to the baseline, whereas the high monthly streamflows (90th percentile) showed various levels of disparity compared to the baseline. (C) 2016 Elsevier B.V. All rights reserved.