Xinjiang is one of the most sensitive regions in China in terms of its response to climate change. Against the background of global warming, analyses and predictions using different scenarios for Xinjiang should be conducted. The spatial and temporal distribution characteristics and trends of future temperature and precipitation trends should be considered to provide a scientific basis for the government to respond to future climate change. In this paper, using the CN05.1 dataset and seven models from the sixth phase of the Coupled Model Intercomparison Project, the delta downscaling method is used to predict the temperature and precipitation changes in Xinjiang Province from 2021 to 2050 under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The results show that (1) most models of CMIP6 have a good effect on temperature simulation in Xinjiang, and the mean values as well as the trends of the temperatures expressed by the multi-model ensemble averaging are in good agreement with the observed data and have a high degree of confidence. The observed precipitation increase rate is significantly higher than that predicted by the model, and the simulation results of each model overestimate the precipitation. (2) The mean annual temperatures in the Xinjiang region increase at rates of 0.32°C/10 a, 0.46°C/10 a, 0.47°C/10 a and 0.67°C/10 a, respectively, under the four scenarios. The rates of temperature increase in the four seasons exhibit the following pattern: autumn > summer > spring > winter. (3) From 2021 to 2050, the average annual precipitation in Xinjiang will change at rates of 3.95 mm/10 a, 1.90 mm/10 a, 2.50 mm/10 a, and 8.67 mm/10 a, respectively, under the four scenarios. The precipitation amounts predicted under the different scenarios increase at the slowest rates in winter and at faster rates in spring. Spatially, the precipitation in the whole Xinjiang region under the four scenarios shows an increasing trend. Overall, except for the SSP1-2.6 scenario, the rates of increase in precipitation increase gradually in all seasons during the future period as the emission scenarios increase. Overall, the climate of the Xinjiang region will be characterized by warming and humidification from 2021 to 2050.

Water scarcity and ecological degradation in arid zones present significant challenges to regional ecological health. Despite this, integrating the water supply–demand balance and water supply security (SEC) into ecological health assessments—particularly through composite indicators—remains underexplored in arid regions. In this study, we assessed the ecological health changes in Xinjiang by utilizing multivariate remote sensing data, focusing on the balance between water supply and demand, the degree of SEC, and ecosystem resilience (ER). Our results indicate that while water supply and demand remained relatively stable in northern Xinjiang between 2000 and 2020, the conflict between supply and demand intensified in the southern and eastern agricultural regions. SEC evaluations revealed that 73.3% of the region experienced varying degrees of decline over the 20-year period. Additionally, ER assessments showed that 7.12% of the region exhibited a significant decline, with 78.6% experiencing overall reductions in ecological health. The indicators’ response to drought demonstrated that improvements in ecological health during wet conditions were less pronounced than declines during droughts. This study underscores the necessity of prioritizing areas with lower ecological health in future water allocation strategies to optimize water resource utilization.

Agriculture is the largest water user of all sectors. In arid regions in particular, achieving efficient water use in agriculture is an important way to solve water scarcity. However, the difference in water use efficiency between arid and humid regions has long been a focus of academic debate. Many studies consider water use efficiency to be higher in humid areas due to the abundance of water resources. This view is based on the fact that less irrigation in humid areas may lead to higher crop yields and better conditions for agricultural production; however, it ignores the efforts of researchers and agricultural workers in arid zones who have attempted to develop efficient water-saving technologies, as well as the effect of natural conditions on agricultural production. Correctly evaluating the efficiency of agricultural water use in arid zones is important for achieving efficient use of water resources, as well as for water management decisions. This study calculates the yield structure and water productivity of typical crops in both arid and humid regions in China based on the footprint theory and other methodologies. This approach allows for an accurate assessment of irrigation water benefits in various regions, providing a scientific basis for improving agricultural water use efficiency under different climatic conditions. The study results indicate that the average reliance on blue water for wheat and cotton gradually increases from 49.9% to 93.6% as regional aridity intensifies, ranging from the Central China Humid Region to the Northwest China Arid Region. Similarly, the average contribution of blue water to crop yield rises from 31.0% to 100%, while irrigation water productivity increases from 0.27 kg·m⁻³ to 0.53 kg·m⁻³. Finally, this study concludes that, in arid zones with lower precipitation and more hours of sunshine, a higher dependence on blue water for crop growth and development leads to a higher productivity of irrigation water. In addition, in arid zones, the focus should be on optimizing the use of irrigation water and improving irrigation technology and efficiency, while, in humid zones, there should be more use of natural precipitation to efficiently reduce dependence on irrigation water.

The Weigan River Basin, located in the arid region of northwest China, faces severe water scarcity. The complex interactions between surface water and groundwater pose a critical challenge for accurately assessing total water resources. To elucidate the exchange mechanisms and fluxes, this study employs a comprehensive analytical approach integrating hydrochemistry, stable isotopes (δ ¹⁸ O, δD), and Bayesian mixing models (MixSIAR). Hydrochemical analysis reveals a patterned spatial evolution of water chemistry characteristics within the basin. Mountainous surface waters predominantly exhibit a HCO ₃ ·SO ₄ -Ca·Mg type, controlled by the weathering of carbonate and silicate rocks. Groundwater chemistry evolves along the flow path from an HCO ₃ ·Cl-Na·Ca type to an HCO ₃ ·SO ₄ -Na·Ca type, revealing groundwater recharge from surface water rich in SO ₄ ^2- . In the plains, groundwater undergoes further evaporation and concentration, cation exchange adsorption, and human activities, eventually discharging into surface water and causing elevated Na ⁺ levels in rivers. Based on these insights, MixSIAR model quantification reveals a clear and statistically significant spatiotemporal transformation pattern. In mountainous sections (Heizi River, Karasu River, Tairweichuk River, upper reaches of both the Muzhati River and Weigan River), surface water serves as the primary groundwater recharge source (dry period contribution: 59%–70%; wet period contribution: 54%–59%). Conversely, in the plain areas of the lower reaches of both the Muzhati and Weigan Rivers, groundwater replenishes surface water (dry period contribution: 53%–55%; wet period contribution: 56%–63%). Seasonally, surface water contribution during the dry period is on average 7.6% higher than during the wet period. In contrast, groundwater contribution in the plain region is on average 5.5% higher during the wet period than during the dry period. Through a research approach combining geochemical tracing and quantitative modeling, this study not only reveals the water cycle patterns in the Weigan River basin but also provides quantifiable scientific basis for precise simulation and management of water resources in arid inland river basins.

This study explores the water consumption and greenhouse gas (GHG) emissions in the Yarkand River Basin, focusing on their dynamic interactions across industrial sectors. Utilizing environmental input–output analysis (IOA), the CROPWAT model, and life cycle assessment (LCA), we quantified the historical evolution of physical and virtual water cycles in relation to the water–carbon nexus. Our findings reveal that the planting industry, particularly the production of export-oriented, water-intensive crops like cotton, significantly contributes to both blue and green water consumption, exacerbating regional water scarcity. The persistent external market demand drives this over-extraction, further strained by the basin’s limited water retention capabilities. Although advancements have been made in reducing the per-unit water footprint of crops, total water consumption continues to rise due to agricultural expansion, intensifying pressure on blue water resources. Additionally, agricultural GHG emissions have surged, driven by increased electricity consumption, heavy fertilizer use, and escalating soil N₂O emissions. In light of these challenges, our research underscores the critical need for integrated resource management strategies that align with sustainable development goals. By promoting efficient water allocation within the agricultural sector and diversifying crop structures downstream, we can enhance ecosystem resilience and reduce environmental degradation. Furthermore, the advancement of value-added agricultural processing and the implementation of innovative water conservation technologies are essential for fostering economic sustainability. These strategies not only mitigate the environmental impacts associated with agricultural practices but also strengthen the region’s adaptive capacity in the face of climate change and fluctuating market demands. Our findings contribute to the broader discourse on sustainable agricultural practices, emphasizing the interconnectedness of water management, climate resilience, and economic viability in arid regions.

“Invulnerability” of complex network was firstly introduced to virtual water (VW) research, aiming to broaden the scope of studies on water use and management. Beginning with the construction of China's virtual water trade networks (VWTNs) of major grain crops, Node Degree (K) and Betweenness Centrality (B) are employed to evaluate and rank the importance of China's 31 regions. Regions with high values for both indicators are identified as playing pivotal roles in the VWTNs: Jiangsu (ranking 1st for both K and B), Hubei (2nd for K, 3rd for B), Henan (3rd for K, 6th for B), Hebei (4th for K, 4th for B), Hunan (4th for K, 5th for B). Using this ranking to simulate the invulnerability of VWTNs under random and intentional attacks. The results reveal a rapid decrease in both Network Efficiency (E) and Maximum Connectivity (C) under intentional attack. In comparison to seven random attacks, E falls below 0.1 and C drops below 0.5 after only three intentional attacks, and the network completely collapsed after 10 intentional attacks. This highlights the VWTN's vulnerability in maintaining food supply and agricultural water security when key regions are subjected to man‐made destruction, such as military blockades or occupations. Future work should include integrating climate change models, crops yield models, and water resource allocation models to protect the key areas. Furthermore, interdisciplinary approaches are crucial for overcoming the limitations of VW research and these findings will provide valuable insights to enhance the optimal regulation of VWTNs.