Snowfall is an important climatic variable with remarkable impacts on vegetation greenness in the alpine and extra-tropical regions. Central Asia (CA) is located in the middle latitude with high mountains, and the ecosystem is vulnerable to climate change in CA. In this region, the vegetation response to winter snowfall and its interactions with growing season climatic factors on vegetation greenness is still unclear. Thus, this study attempted to examine the impact of winter snowfall on vegetation greenness with remote-sensing vegetation index and reanalysis climatic data, and to investigate the interactions between winter snowfall and growing season climatic factors and their influence on vegetation growth via path analysis. The results showed that there is a generally positive correlation between winter snowfall and vegetation greenness from May to September and during the whole growing season (April to September). This positive correlation was significant in 8% of the study area for the whole growing season. However, the increase in winter snowfall was not beneficial to the regional vegetation growth in April. The vegetation response to winter snowfall also relates to land-cover type and elevation. The vegetation greenness in grassland was depicted to be more sensitive to winter snowfall than that in forestland. The response turned from positive to negative when the elevation increased from below 3000 m to above 3000 m. Moreover, the impact of winter snowfall on vegetation greenness was not regulated by temperature and precipitation in the growing season in more than 70% of CA. The impact relates to the interaction with April temperature in Central Kazakhstan, and is regulated by growing season precipitation in North Kazakhstan where annual precipitation mainly occurs in the growing season. The impact of winter snowfall on vegetation greenness is more important than growing season precipitation and temperature in some areas, since annual precipitation does not concentrate in the growing season or the relative increase of winter snowfall is great in these places. The results of the present study improve the understanding of vegetation response to climate change, and provide a scientific reference for environmental protection in CA and similar regions.

Agricultural activities and dry climatic conditions promote the evaporation and salinization of groundwater in arid areas. Long-term irrigation alters the groundwater circulation and environment in arid plains, as well as its hydraulic connection with surface water. A comprehensive assessment of groundwater irrigation suitability and its interaction with surface water is essential for water–ecology–agriculture security in arid areas. This study evaluates the irrigation water quality and groundwater–surface water interaction influenced by agricultural activities in a typical arid plain region using hydrochemical and stable isotopic data from 51 water samples. The results reveal that the area of cultivated land increases by 658.9 km² from 2000 to 2023, predominantly resulting from the conversion of bare land. Groundwater TDS (total dissolved solids) value exhibits significant spatial heterogeneity, ranging from 516 to 2684 mg/L. Cl⁻, SO₄²⁻, and Na⁺ are the dominant ions in groundwater, with a widespread distribution of brackish water. Groundwater δ¹⁸O values range from −9.4‰ to −5.4‰, with the mean value close to surface water. In total, 86% of the surface water samples are good and suitable for agricultural irrigation, while 60% of shallow groundwater samples are marginally suitable or unsuitable for irrigation at present. Groundwater hydrochemistry is largely controlled by intensive evaporation, water–rock interaction, and agricultural activities (e.g., cultivated land expansion, irrigation, groundwater exploitation, and fertilizers). Agricultural activities could cause shallow groundwater salinization, even confined water deterioration, with an intense and frequent exchange between groundwater and surface water. In order to sustainably manage groundwater and maintain ecosystem stability in arid plain regions, controlling cultivated land area and irrigation water amount, enhancing water utilization efficiency, limiting groundwater exploitation, and fully utilizing floodwater resources would be the viable ways. The findings will help to deepen the understanding of the groundwater quality evolution mechanism in arid irrigated regions and also provide a scientific basis for agricultural water management in the context of extreme climatic events and anthropogenic activities.

Soil moisture (SM) in the mountains is critical for agropastoral productivity, and it is subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation and soil. However, how the climate, topography, soil and vegetation factors impact surface SM spatiotemporal dynamics remains elusive in mountainous terrains, due to their complex interactions. Based on multi-source datasets, this study employs the structural equation model to investigate the impact pathways of climate and vegetation factors on annual surface SM dynamics from the year 2000 to 2022 in the Tianshan Mountains of China (TS). We also utilize the factor and interaction detectors of Geographical Detector to explore the individual and interactive effects of climate, topography, soil and vegetation factors on the spatial pattern of the annual surface SM. Moreover, their integrated impacts on the spatiotemporal dynamics of annual surface SM were investigated based on the explanatory power from the factor detector and total effects from structural equation modeling. The results showed that the multi-year average surface SM was 0.21 m³·m⁻³ for the whole region, with greater values in areas with dense vegetation and high elevation. Annual surface SM exhibited significant increasing trends across different land cover classifications and elevation zones, which was directly influenced by vegetation greenness enhancement. Precipitation (PRE) and relative humidity (RH) also significantly influenced the temporal variations in surface SM through their indirect effect on vegetation greenness, while these indirect effects were much lower than the direct effect of vegetation greenness. RH, PRE and surface net solar radiation (SSR) showed strong individual and interactive effects on the spatial distribution of surface SM, particularly the interactive effects of RH and PRE with wind speed (WS). Surface SM was highly sensitive to RH and PRE in the central TS. Overall, vegetation greenness, PRE and RH were the main drivers of surface SM variations across both temporal and spatial scales, while SSR, total evaporation and WS primarily shaped its spatial distribution. These insights enhance our understanding of land–atmosphere interactions in mountainous areas and provide scientific references for sustainable agropastoral water resource management under global warming.