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• The vegetation drought was alleviating during 1982–2020 across China. • Spring drought and summer drought were more likely to occur in South China. • NPI-NAO-AO had the most significant impact on vegetation drought. • Soil moisture was the primary driving factor of vegetation drought. Vegetation drought can reduce the net ecosystem productivity, resulting in disturbances to the sustainable development of humans and nature. In this study, Vegetation Health Index (VHI) was used to identify vegetation drought. The spatial and temporal evolution of vegetation drought was characterized using the Pixel-based Trend Identification Method (PTIM) in China from 1982 to 2020. Moreover, multiple cross-wavelet techniques were used for revealing the correlations between atmospheric circulation factors and their combinations with vegetation drought. The results indicates that: (1) there is a decreasing trend of vegetation drought in China during the study period, while it will show an increasing trend in the future; (2) the South China is prone to spring drought and summer drought, and the Sichuan Basin is prone to autumn drought and winter drought; (3) in spring, the most obvious trend of vegetation drought mitigation appears; (4) a combination of atmospheric circulation factors, North Pacific Index (NPI)-North Atlantic Oscillation (NAO)-Arctic Oscillation (AO), has the most significant impact on vegetation drought across China. [ABSTRACT FROM AUTHOR]

• Created a refined water resource allocation model. • Combining grid idea and remote sensing technology. • It reflects the change of water supply and demand in space and numerical value. • PSO algorithm is introduced to reduce the difficulty in solving the model. • It changes the extensive consideration in water supply nodes and water use nodes. The traditional optimal allocation of water resources mostly focuses on the allocation of different water supply sources to various types of water users in life, production, and ecology, and cannot reflect the differences in time and space of a certain type of water users. Therefore, this paper constructs a refined model for the optimal allocation of water resources, and further explores the optimal allocation of water resources with multiple water sources, multiple users, and multiple objectives. The main steps of the model construction are: (1) Regional gridding. The planting structure map and land use type map of the same resolution in the study area are selected and superimposed on them, the application attributes of each cell can be directly obtained. (2) Water demand gridding. On the basis of gridding in the study area, according to the application attributes of each cell, the quota method is used to predict the water demand of each cell. Then the water demand forecast result for the entire research area is obtained. (3) Water supply gridding. It is possible to reflect the types of water supply sources that a certain or a certain type of cell can accept, as well as the types of water supply sources that can be supplied by a certain type of water supply sources, by determining the types of water supply sources in the research area. The supply ranges that various water supply sources can reach, followed by the completion of the water supply grid. (4) According to the results of water demand gridding and water supply gridding, the water demand of each cell and the corresponding water supply source and water supply topological relationship are established, and the objective function is to minimize water shortage and maximize economic benefits to build a refined model for the optimal allocation of water resources.(5) The particle swarm algorithm is introduced to solve it, and the Pareto optimal solutions is obtained to determine the optimal water distribution for each cell. (6) Taking Guantao County, China as an example, the model was applied. In terms of minimum water shortage, the total water demand in Guantao County is predicted to be 118.8719 million m3 in the planning year (P = 50 %), while the total water supply is 125.2818 million m3. The water supply can meet the demand for water. In terms of financial advantages, the planned level of water Annual economic benefits will amount to 4.0865 million yuan. [ABSTRACT FROM AUTHOR]