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Summary: The hydrological cycle has been substantially influenced by climate change and human activities. It is therefore of utmost importance to analyze the impact of climate change on hydrology, particularly on a regional scale, in order to understand potential future changes of water resources and water-related disaster, and provide support for regional water management. However, during the evaluation of climate change impact on hydrology or water resources, large uncertainty exists. In this paper, the Soil Water Assessment Tool (SWAT) model is used to investigate the potential impact of climate change on hydrology of the upper reaches of Qiantang River Basin, East China, for the future period 2011–2100. The uncertainty is considered by employing upgraded reliability ensemble averaged GCM climate projections under three emission scenarios A1B, A2 and B1 for three different stages of the future period. These projections are downscaled and used in the hydrological model. Impact of climate change on precipitation, potential evapotranspiraton and river runoff is then investigated. The model calibration and validation outcomes show reasonable performance of the SWAT model. The final results suggest that annual river runoff will likely decrease almost under all emission scenarios and time stages of the future period. Particularly, at Jinhua Station, substantial decrease of annual river runoff can be noticed, indicating less water resource possibly available for the region in future. Simulated monthly patterns show that the largest decrease will likely occur in winter while increases will occur in summer, implying possible more water-related disasters in this region. However, it is also noticed that the change signs/amount could be different under different emission scenarios and time stages, indicating large uncertainty involved in the impact analysis. [ABSTRACT FROM AUTHOR]

Abstract: In the North China Plain (NCP), while irrigation using groundwater has maintained a high-level crop productivity of the wheat–maize double cropping systems, it has resulted in rapid depletion of groundwater table. For more efficient and sustainable utilization of the limited water resources, improved understanding of how crop productivity and water balance components respond to climate variations and irrigation is essential. This paper investigates such responses using a modelling approach. The farming systems model APSIM (Agricultural Production Systems Simulator) was first calibrated and validated using 3 years of experimental data. The validated model was then applied to simulate crop yield and field water balance of the wheat–maize rotation in the NCP. Simulated dryland crop yield ranged from 0 to 4.5tha−1 for wheat and 0 to 5.0tha−1 for maize. Increasing irrigation amount led to increased crop yield, but irrigation required to obtain maximum water productivity (WP) was much less than that required to obtain maximum crop yield. To meet crop water demand, a wide range of irrigation water supply would be needed due to the inter-annual climate variations. The range was simulated to be 140–420mm for wheat, and 0–170mm for maize. Such levels of irrigation applications could potentially lead to about 1.5myear−1 decline in groundwater table when other sources of groundwater recharge were not considered. To achieve maximum WP, one, two and three irrigations (i.e., 70, 150 and 200mmseason−1) were recommended for wheat in wet, medium and dry seasons, respectively. For maize, one irrigation and two irrigations (i.e., 60 and 110mmseason−1) were recommended in medium and dry seasons, while no irrigation was needed in wet season. [Copyright &y& Elsevier]

Human activities and climate change are two key factors influencing the variation of the total amount of available surface and groundwater, hereafter termed water resources. Quantitatively separating their impacts remains a challenge. To this end, we used time-varying Budyko-type equations and a hierarchical Bayesian model in this paper to separate their impacts in 31 provincial-level divisions of China. The time-varying Budyko-type equations treat the Budyko equation parameter w as a variable, which depends on human activities (represented by per capita gross regional production) and climate change (represented by temperature and precipitation). The hierarchical model quantifies the uncertainty of parameters and the interrelation between covariates across regions in China. The results show that the time-varying Budyko-type equation can improve the fitting capability for water resources in China. The hierarchical Bayesian model, which considered spatial dependence, reduced the uncertainty of the parameters compared to spatially independent counterparts. For most regions of China, human activities reduce water resources while climate change increases them. Southeastern China is the most influenced area, and its water resources decreased approximately 50 mm because of human activities. This study can provide a basis for water resource management under climate change and human activity constraints in China. [ABSTRACT FROM AUTHOR]