Assessment of Changes in Water Balance Components under 1.5 °C and 2.0 °C Global Warming in Transitional Climate Basin by Multi-RCPs and Multi-GCMs Approach

摘要

The global warming of 1.5 °C and 2.0 °C proposed in the Paris Agreement has become the iconic threshold of climate change impact research. This study aims to assess the potential impact of 1.5 °C and 2.0 °C global warming on water balance components (WBC) in a transitional climate basin—Chaobai River Basin (CRB)—which is the main water supply source of Beijing. A semi-distributed hydrological model SWAT (Soil and Water Assessment Tool) was driven by climate projections from five General Circulation Models (GCMs) under three Representative Concentration Pathways (RCPs) to simulate the future WBC in CRB under the 1.5 °C and 2.0 °C global warming, respectively. The impacts on annual, monthly WBC were assessed and the uncertainty associated with GCMs and RCPs were analyzed quantitatively, based on the model results. Finally, spatial variation of WBC change trend and its possible cause were discussed. The analysis results indicate that all the annual WBC and water budget are projected to increase under both warming scenarios. Change trend of WBC shows significant seasonal and spatial inhomogeneity. The frequency of flood will increase in flood season, while the probability of drought in autumn and March is expected to rise. The uneven spatial distribution of change trend might be attributed to topography and land use. The comparison between two warming scenarios indicates that the increment of 0.5 °C could lead to the decrease in annual surface runoff, lateral flow, percolation, and the increase in annual precipitation and evapotranspiration (ET). Uncertainties of surface runoff, lateral flow, and percolation projections are greater than those of other components. The additional 0.5 °C global warming will lead to larger uncertainties of future temperature, precipitation, surface runoff, and ET assessment, but slightly smaller uncertainties of lateral flow and percolation assessment. GCMs are proved to be the main factors that are responsible for the impact uncertainty of the majority assessed components.