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Xinjiang is home to one of the most serious resource-based water shortages, and at the same time, it is an important main production area of grain, cotton, and high-quality fruits and vegetables in China, placing a heavy burden on water resources. Based on this, this paper determines the basic condition of water resources in regions of Xinjiang using the water footprint method. It then identifies the drivers of water footprint changes using the population scale effect, policy support effect, investment–output effect, economic structure effect, water use efficiency effect, and water use structure effect via the LMDI decomposition model. Finally, this paper illustrates the trajectory of the regional water footprint through individual stochastic convergence. This study found the following: (1) The water footprint of Xinjiang showed a fluctuating upward trend, and the total water footprint varied significantly between regions. From a compositional standpoint, most regions were dominated by the agricultural water footprint, while spatially, the regional water footprint had a high distribution trend in the south and a low distribution in the north. (2) The driving effects of the water footprint, policy support, population scale, and water use structure were incremental, while the effects of water use efficiency, economic structure, and investment output were decremental. (3) Most regions in Xinjiang showed individual stochastic convergence trends, indicating that regions converged to their respective compensating difference equilibrium levels. In this regard, it is necessary to strengthen R&D and the promotion of water use technology, further optimize the industrial structure, and leverage the positive effect of government investment to alleviate the regional water constraint dilemma and promote high-quality regional economic development.

Located in an important biodiversity conservation area in the Yangtze River Delta, the habitats of many species have been severely eroded because of human activities such as tourism development. There is no relevant species conservation plan in place in the region, and scientific guidance on ecosystem change and corridor construction is urgently needed. In this study, we first assess ecosystem service functions based on the InVEST model; then, we assess ecological sensitivity and identify landscape resistance surfaces by constructing ecosystem sensitivity indicators; finally, we construct ecological security patterns by combining landscape resistance surfaces and circuit theory identification. The main results are as follows: (1) The high value area of ecosystem services is located in the southwest, while the northeast part of the study area has lower ecosystem services, and there is a trade-off between the ecosystem services in the study area. (2) There are 38 ecological sources in southern Anhui, with a total area of more than 5742.79 km2, that are the basic guarantees of ecological security, mainly located in the northeast of the study area, and woodland and grassland are the most important components, accounting for 18.4% of the total study area. (3) The ecological security pattern in the study area consists of 63 ecological sources, 37 important corridors, and 26 potential corridors, of which there are 28 pinch point areas and 6 barrier point patches in the study area, mainly located within Huangshan City and Xuancheng City. We recommend that when implementing restoration and rehabilitation measures in the future, policy makers should give priority to pinch points and barrier areas.

Xinjiang is a typical continental arid climate zone and ecologically fragile zone. Drought has caused damage to the local social economy, agricultural production, and the ecological environment. However, the study of drought is more difficult due to the complex topography and the lack of monitoring information. In this paper, based on the meteorological data of 94 meteorological stations in Xinjiang from 1961 to 2020, we used the precipitation and potential evapotranspiration (ET0) to calculate the aridity index (AI); the Mann–Kendall test, Morlet wavelet analysis, and Kriging interpolation were used to identify the trend, period, and spatial distribution. The results showed that (1) the average change rate of the precipitation in Xinjiang was 8.58 mm/10a, the average change rate of the ET0 was −14.84 mm/10a, and the average change rate of the AI was −1.94/10a; (2) the periods of precipitation, ET0, and AI in Xinjiang were 39, 29, and 14 years, respectively, and the abrupt changes occurred in 1986, 1971, and 1987, respectively; (3) The Moran index of precipitation and temperature are 0.41 and 0.33, respectively, indicating that precipitation and temperature in Xinjiang are positively correlated in spatial distribution and have spatial clustering characteristics; and the z-values are both greater than 2.58 (p < 0.01), indicating that the spatial autocorrelation of precipitation and temperature in Xinjiang is significant. This study can provide a reference for the diagnosis of the meteorological drought mechanism and the coping with climate change in Xinjiang.

Exploring the constraint relationship between physical geographic features and urbanization on ecosystem services is important for managing and optimizing regional ecosystem services. Taking Anhui Province as an example, we assessed the spatial and temporal evolution characteristics of five types of ecosystem services (habitat support, water production services, soil conservation, NPP, and carbon fixation) and five types of urbanization levels (population, economic, social, ecological, and spatial) in 2000, 2010, and 2020, and integrated the constraint line method, bivariate spatial autocorrelation model, and spatial regression model to measure the relationship between ecosystem services. The spatial constraints between ecosystem services and urbanization level and natural topography in Anhui Province were measured using the constraint line method, bivariate spatial autocorrelation model and the spatial regression model. The results show that: (1) the spatial distribution of the five types of ecosystem services in Anhui Province is characterized as “low in the north and high in the south”. At the provincial level, the five ecosystem services in southern and central Anhui Province are synergistic, while the five ecosystem services in northern Anhui Province show a trade-off; (2) topography has different effects on the five ecosystem services with “exponential” effects on water production services and NPP, “positive convex” effects on habitat support, and “positive convex” effects on habitat support”; (3) the bivariate global autocorrelation Moran’s I index between ecosystem services and urbanization level in Anhui Province is significant, confirming that ecosystem services and urbanization are spatially related, where the development of population urbanization, spatial urbanization, economic urbanization, and social urbanization leads to the decrease in ecosystem services, and ecological urbanization promotes the increase in ecosystem services. In the spatial regression model, the Spatial Lag Model passed the significance test, indicating that there is a spatial spillover effect between ecosystem services and urbanization. That is, changes in ecosystem services are influenced not only by their own urbanization elements, but also by urbanization elements in neighboring units or more distant units. Exploring the constraints of ecosystem services and identifying their interaction with urbanization can provide a scientific basis for land-use optimization, adjusting management measures and achieving regional sustainability.