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• The mean groundwater table depth in China dropped by approximately 0.2 m (P < 0.01) during 1979–2018. • The Noah-MP land model forced with reanalysis climate data can reasonably capture terrestrial water and radiation processes across China. • Incorporating the groundwater lateral flow process enhances the seasonality of modeled terrestrial water and radiation processes on a regional scale. Faced with intensified climate change and ever-increasing water demand, the groundwater table depth is vital to sustaining terrestrial environments in a country such as China; however, on a regional scale, insufficient research has been conducted on its variability thus far. Using a land model forced with reanalysis climate data, this paper investigates groundwater table depth changes across China for the 1979–2018 period. The results show a significant overall reduction in the groundwater table depth in China during the past 40 years without explicit consideration of direct human influence. There is an approximately 0.2 m decrease on average, but it is approximately 0.5 m in southern China and 0.8 m to the north of 35°N despite a 1.2 m rise in part of this region due to an increasingly wet climate and a topographic water convergence. The findings provide a first-order pattern of historical groundwater table responses to climate change, highlighting inherent links among subsurface-surface-atmosphere water cycles and worsened terrestrial water availability in China. A model evaluation compared with in situ observations has demonstrated the model's ability to realistically portray terrestrial water and energy balances on regional scales, as well as the enhancement of their seasonality with incorporated lateral groundwater flow processes. This ability gives us reasonable confidence in the simulated trends and spatial variability. Nonetheless, the mean groundwater table still shows large biases relative to observations, and the problems of human influence, two-way interactions between groundwater and surface water, and local topographical complexity have yet to be properly addressed in the present study and deserve further research. [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]