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Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. Future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human land-use change can affect the climate system through bio-geophysical and bio-geochemical effects. To investigate these interrelationships, we developed MIROC-INTEG1 (MIROC INTEGrated terrestrial model version 1), an integrated model that combines the global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land use models. In this paper, we introduce the details and interconnections of the sub-models of MIROC-INTEG1, compare historical simulations with observations, and identify the various interactions between sub-models. MIROC-INTEG1 makes it possible to quantitatively evaluate the feedback processes or nexus between climate, water resources, crop production, land use, and ecosystem, and to assess the risks, trade-offs and co-benefits associated with future climate change and prospective mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

As a typical alpine marsh wetland on the Qinghai-Tibet Plateau, the Zoige Wetland plays an important role in the water source recharge of Yellow River. Meanwhile, it is more sensitive to climate change. Therefore, it is necessary to study the hydrological cycle of Zoige Wetland and the impact of climate change, which will also provide important support and reference for the study of hydrological process, and protection of wetland water resources in Alpine areas. Based on the distributed hydrological model, this paper analyzed the runoff process in the Zoige wetland basin, and explored the trend of runoff variation in the Zoige wetland basin under different climate change scenarios in the next 30 years from 2020 to 2050. The impact of climate change on wetland runoff were also discussed. Results showed that the runoff in the Zoige Wetland basin decreased under the future climate change scenario. The rate of runoff reduction was the largest in Machu station, followed by Zoige station, and finally Tangke station. The reduction in non-flooding runoff is significantly higher than that in the flood season. The runoff during non-flood season in the Zoige Wetland basin from 2020 to 2050 will decrease by more than 25% in the future climate change scenario. The sharp decline in non-flooded runoff may have an impact on the protection of the Zoige Wetland and the management of water resources in the middle reaches of the Yellow River and downstream areas. This poses more stringent requirements for the protection of the Zoige Wetland in response to future climate change. Under future climate change scenarios, how to strengthen the management and protection of wetland water resources and prevent the degradation and atrophy of wetlands is the issue we need to further discuss and consider. [ABSTRACT FROM AUTHOR]

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]