Winter irrigation affected the movement of soil moisture, temperature, and salt, which was an effective improvement measure widely used in seasonal freeze–thaw areas. In this paper, we investigated the effects of different salinized cotton fields (mild salinization (S1), 5.15 g·kg⁻¹; moderate salinization (S2), 8.17 g·kg⁻¹; severe salinization (S3), 11.15 g·kg⁻¹) and different winter irrigation rates (W0, 0 m³·hm^-2; W1, 3000 m³·hm^-2; W2, 3600 m³·hm^-2; W3, 4200 m³·hm^-2) on soil moisture, temperature, salinity, and cotton growth in seasonal freeze–thaw areas. The results showed that the winter irrigation affected the temporal and spatial dynamics of soil moisture, temperature, and salinity, and the winter irrigation rate and degree of soil salinization were significantly correlated with soil moisture, temperature, and salinity (p < 0.01). Winter irrigation stabilized the soil temperature and reduced the freeze–thaw index of the soil. The heat conservation effect of winter irrigation increased with increasing winter irrigation rate and salinization degree, with the greatest effect on the freezing index of S2 and on the thawing index of S3. The soil water content and total salt concentration before spring tillage were significantly correlated with winter irrigation rate and degree of soil salinization (p < 0.05), and when the winter irrigation quota of different salinized cotton fields was greater than 3600 m³·hm^-2, the moisture content of soil layer 0–100cm increased by more than 20%, and the desalination reached over 40%, compared with the values before winter irrigation. Winter irrigation improved the emergence rate and yield of cotton, with the soil salinization degree being significantly negatively correlated and winter irrigation rate significantly positively correlated with the emergence rate and yield of cotton fields in the following year (p < 0.01). Compared with the control treatment without winter irrigation, the average increases in cotton yield were W3 (53.32%) > W2 (45.00%) > W1 (29.36%). There was no significant difference in seedling emergence rate or yield between slightly and moderately salinized cotton fields under high winter irrigation rates (W2 and W3) (p > 0.05), although the seedling emergence rate and yield of severely salinized cotton fields increased significantly with increasing winter irrigation rate. In conclusion, winter irrigation proved to be a valuable treatment for severely salinized cotton fields, and the results of this study allowed us to determine the optimal winter irrigation rate for saline alkali cotton fields.

Optimizing irrigation and nitrogen (N) management in saline soils is critical for sustainable cotton production in arid regions that have been subjected to climate change. In this study, a two-year factorial field experiment (3 salinity levels × 3 N rates × 3 irrigation quotas) is integrated with the RZWQM2 model to (1) identify water–N–salinity thresholds for cotton yield and (2) to project climate change impacts under SSP2.4-5 and SSP5.8-5 scenarios (2031–2090) in Xinjiang, China, a global cotton production hub. The results demonstrated that a moderate salinity (6 dS/m) combined with a reduced irrigation (3600 m³/hm²) and N input (210 kg/hm²) achieved a near-maximum yield (6918 kg/hm²), saving 20% more water and 33% more fertilizer compared to conventional practices. The model exhibited a robust performance (NRMSE: 5.94–12.88% for soil–crop variables) and revealed that warming shortened the cotton growing season by 1.2–9.5 days per decade. However, elevated CO₂ (832 ppm by 2090) levels under SSP5.8-5 increased yields by 22.6–42.1%, offsetting heat-induced declines through enhanced water use efficiency (WUE↑27.5%) and biomass accumulation. Critically, high-salinity soils (9 dS/m) required 25% additional irrigation (4500 m³/hm²) and a full N input (315 kg/hm²) to maintain yield stability. These findings provide actionable strategies for farmers to optimize irrigation schedules and nitrogen application, balancing water conservation with yield stability in saline-affected arid agroecosystems that have been subjected to climate change.

Net primary productivity (NPP) reflects the carbon sequestration capacity of terrestrial ecosystems and it is used as an important indicator for measuring ecosystem quality. However, due to the effects of “warming and humidification” and “oasisization”, the spatiotemporal evolution and driving mechanisms of the NPP of vegetation in the northern slope of the Tianshan Mountains (NSTM), a typical arid area in China, are still unclear. Thus, in this study, we used remote sensing data and meteorological data to construct a Carnegie–Ames–Stanford–Approach (CASA) model for estimating the NPP of vegetation in the study area. Trend analysis, partial correlation analysis, and optimal parameter-based geographic detector (OPGD) methods were combined to explore the spatiotemporal evolution and driving mechanisms to changes in the NPP. The results showed that from 2001 to 2020, the annual average NPP on the NSTM exhibited an overall significant upward trend, increasing from 107.33 gC⋅m⁻²⋅yr⁻¹ to 156.77 gC⋅m⁻²⋅yr⁻¹, with an increase of 2.47 gC⋅m⁻² per year and 46.06% year-on-year. Over the past 20 years, climate change and human activities generally positively affected the changes in NPP in the study area. Human activities in the study area are mainly manifested in the large-scale conversion of other land use types into farmland, with a total increase of 16,154 km² in farmland area, resulting in a net increase of 6.01 TgC in NPP. Precipitation has the strongest correlation with NPP in the study area, with a partial correlation coefficient of 0.30, temperature and solar radiation have partial correlation coefficients with NPPs of 0.17 and 0.09, respectively. Therefore, increases in precipitation, temperature, and solar radiation have a promoting effect on the growth of NPP on the NSTM. During the study period, the land use type and soil moisture were the main factors that affected the spatial differentiation of vegetation NPP, and the effects of human interference on natural environmental conditions had significant impacts on vegetation NPP in the area. Therefore, in this study, we accurately determined the spatiotemporal variations in the NPP on the NSTM and comprehensively explored the driving mechanisms to provide a theoretical basis for sustainable development in arid areas and achieving carbon neutrality goals.

Model-based simulation of farmland evapotranspiration and crop growth facilitates precise monitoring of crop and farmland dynamics with high efficiency, real-time responsiveness, and continuity. However, there are still significant limitations in using crop models to simulate the dynamic process of evapotranspiration and cotton growth in mulched drip-irrigated cotton fields under different irrigation gradients. The SWAP crop growth model effectively simulates crop growth. However, the original SWAP model lacks a dedicated module to consider the impact of mulching on cotton field evapotranspiration and cotton dry matter mass. Therefore, in this study, the source codes of the soil moisture, evapotranspiration, and crop growth modules of the SWAP model were improved. The evapotranspiration and cotton growth data of the mulched drip-irrigated cotton fields under three irrigation treatments (W1 = 3360 m³·hm⁻², W2 = 4200 m³·hm⁻², and W3 = 5040 m³·hm⁻²) in 2023 and 2024 at the Xinjiang Modern Water-saving Irrigation Key Experimental Station of the Corps were used to verify the simulation accuracy of the improved SWAP model. Research shows the following: (1) The average relative errors of the simulated evapotranspiration, leaf area index, and dry matter weight of cotton in the improved SWAP crop growth model are all <20% compared with the measured values. The root means square errors of the three treatments (W1, W2, and W3) ranged from 0.85 to 1.38 mm, from 0.03 to 0.18 kg·hm⁻², and 55.01 to 69 kg·hm⁻², respectively. The accuracy of the improved model in simulating evapotranspiration and cotton growth in the mulched cotton field increased by 37.49% and 68.25%, respectively. (2) The evapotranspiration rate of cotton fields is positively correlated with the irrigation water volume and is most influenced by meteorological factors such as temperature and solar radiation. During the flowering stage, evapotranspiration accounted for 62.83%, 62.09%, 61.21%, 26.46%, 40.01%, and 38.8% of the total evapotranspiration. Therefore, the improved SWAP model can effectively simulate the evaporation and transpiration of the mulched drip-irrigated cotton fields in the Manas River Basin. This study provides a scientific basis for the digital simulation of mulched farmland in the arid regions of Northwest China.

The accumulation of heavy metals (HMs) from coal resource exploitation and coal-related industries poses serious ecological and health risks in the hinterland river sediments of Shenfu coal field in Northern China. Using data from 59 samples, the geo-accumulation index and ecological health risks of HMs were assessed probabilistically using Monte Carlo Simulation. Two coupling models were constructed in this study, integrating both the Positive Matrix Factorization and ecological health risk models to identify the risk levels of pollution sources in the Kuye River. The risk of HMs during the wet season was explored due to the lower concentrations and a broader range of pollution sources compared to the dry season. The I_geo value of Hg was greater than 1 in 69% of the samples, suggesting moderately contaminated sediments. According to the source-oriented coupling models (PMF-RI/HRA), coal-mining sources contributed to the overall ecological risk by 48.79%, primarily due to Hg (98.50%). Industrial sources (51.48%) were the largest contributors to carcinogenic risk, with Ni corresponding to the target HM. This indicated that different HMs originating from distinct pollution sources were responsible for ecological or carcinogenic health risks. The probabilistic health risk evaluation results indicated that children faced higher risks than adults, with over 94.07% of carcinogenic risks exceeding thresholds. Traffic sources followed, contributing 34.41% to ecological and 41.09% to carcinogenic risks. The results highlight the priority sources and target HMs based on specific sources in the mixed ‘mining-industrial-traffic’ areas, providing valuable insights for environmental protection and the development of risk prevention strategies in a typical energy industry area.

Riverine–lacustrine ecosystems in river–lake continua face increasing threats, yet conventional vulnerability maps often overlook local degradation drivers. This study presents an advanced satellite-based mapping framework using Deep Attention Networks (DANets) for accurate, interpretable vulnerability assessment. In the Ebinur Lake Basin, a representative dryland river system, we first built a satellite-derived evidence map of ecosystem stress aligned with the IPCC’s vulnerability definition. We then optimized DANets via two nature-inspired algorithms: Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO). The optimized models demonstrated strong predictive capacity, explaining a large share of vulnerability variance (R² = 0.78 for GA-DANets; R² = 0.76 for GWO-DANets). For high/low-vulnerability discrimination, GWO-DANets was most effective and stable, with a mean AUC = 0.960 ± 0.044. Factor importance analysis identified soil organic carbon (SOC; 0.29), precipitation seasonality (0.24), and aridity (0.22) as dominant drivers. Two distinct pathways emerged: chronic degradation in arid plains, driven by low SOC and poor water retention; and acute hydrological stress in wetlands, where carbon-rich soils are sensitive to drying. This insight shifts management from uniform to targeted approaches: soil restoration in plains and water-flow protection in wetlands. By integrating metaheuristically optimized deep learning with multi-sensor satellite data, the framework offers a scalable decision-support tool for safeguarding water-dependent ecosystems. The study confirms that vulnerability in the basin follows two predictable, process-based trajectories, which can be directly linked to measurable soil and hydrological conditions. These clear patterns allow managers to prioritize interventions where they will have the greatest effect under ongoing climate pressure.