The Tianshan Mountains of Central Asia, highly sensitive to climate change, has been comprehensively assessed for its ecosystem vulnerability across multiple aspects. However, studies on the region's main river systems and hydropower resources remain limited. Thus, examining the impact of climate change on the runoff and gross hydropower potential (GHP) of this region is essential for promoting sustainable development and effective management of water and hydropower resources. This study focused on the Kaidu River Basin that is situated above the Dashankou Hydropower Station on the southern slope of the Tianshan Mountains, China. By utilizing an ensemble of bias-corrected global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) and the Variable Infiltration Capacity (VIC) model coupled with a glacier module (VIC-Glacier), we examined the variations in future runoff and GHP during 2017-2070 under four shared socio-economic pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) compared to the baseline period (1985-2016). The findings indicated that precipitation and temperature in the Kaidu River Basin exhibit a general upward trend under the four SSP scenarios, with the fastest rate of increase in precipitation under the SSP2-4.5 scenario and the most significant changes in mean, maximum, and minimum temperatures under the SSP5-8.5 scenario, compared to the baseline period (1980-2016). Future runoff in the basin is projected to decrease, with rates of decline under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios being 3.09, 3.42, 7.04, and 7.20 m³/s per decade, respectively. The trends in GHP are consistent with runoff, with rates of decline in GHP under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios at 507.74, 563.33, 1158.44, and 1184.52 MW/10a, respectively. Compared to the baseline period (1985-2016), the rates of change in GHP under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios are -20.66%, -20.93%, -18.91%, and -17.49%, respectively. The Kaidu River Basin will face significant challenges in water and hydropower resources in the future, underscoring the need to adjust water resource management and hydropower planning within the basin.

Ecological security patterns are paramount to the advancement of an ecological civilization in China, aiming to enhance the stability and service functions of ecosystems to achieve sustainable regional development. However, current regional ecological protection efforts have not been effectively integrated into the regional development planning of ecological security pattern. This study systematically assessed the effectiveness of ecological protection projects in Yanchi County, Ningxia Hui Autonomous Region, China, through the evaluation of landscape ecological quality. Based on the evaluation results of landscape ecological quality, this study used morphological pattern analysis (MSPA), minimum cumulative resistance (MCR) model, and gravity model together to construct the ecological security pattern of Yanchi County. The findings revealed that from 1990 to 2020, with the implementation of ecological protection projects started from 2000, the landscape stability of Yanchi County first decreased and then increased, and the intensity of landscape disturbance first intensified but then decreased, indicating an improvement in the landscape ecological quality and a significant enhancement of the ecological environment in Yanchi County. The ecological security pattern of Yanchi County consisted of 10 ecological sources, 10 ecological source points, 23 ecological corridors, and 27 ecological nodes. The ecological security pattern of Yanchi County exhibited distinct spatial variations, with stronger ecological security observed in the southern part than in northern part of the county. The ecological sources were denser in the southern part than in the northern part of the county, and accordingly, the length of ecological corridors was shorter and denser in the southern than that in the northern part of the county. Based on the spatial distribution of landscape ecological quality and the characteristics of ecological security pattern of Yanchi County in 2020, we suggested Yanchi County to build four zones to optimize the ecological security pattern construction: the Haba Lake ecological conservation zone, the urban ecological planning zone, the ecological environment restoration zone, and the ecological security improvement zone. This study can provide essential guidance for the construction of ecological security pattern in farming-pastoral areas both in China and worldwide.

Land use/cover change (LUCC) constitutes the spatial and temporal patterns of ecological security, and the construction of ecological networks is an effective way to ensure ecological security. Exploring the spatial and temporal change characteristics of ecological network and analyzing the integrated relationship between LUCC and ecological security are crucial for ensuring regional ecological security. Gansu is one of the provinces with fragile ecological environment in China, and rapid changes in land use patterns in recent decades have threatened ecological security. Therefore, taking Gansu Province as the study area, this study simulated its land use pattern in 2050 using patch-generating land use simulation (PLUS) model based on the LUCC trend from 2000 to 2020 and integrated the LUCC into morphological spatial pattern analysis (MSPA) to identify ecological sources and extract the ecological corridors to construct ecological network using circuit theory. The results revealed that, according to the prediction results in 2050, the areas of cultivated land, forest land, grassland, water body, construction land, and unused land would be 63,447.52, 39,510.80, 148,115.18, 4605.21, 8368.89, and 161,752.40 km², respectively. The number of ecological sources in Gansu Province would increase to 80, with a total area of 99,927.18 km². The number of ecological corridors would increase to 191, with an estimated total length of 6120.66 km. Both ecological sources and ecological corridors showed a sparse distribution in the northwest and dense distribution in the southeast of the province at the spatial scale. The number of ecological pinch points would reach 312 and the total area would expect to increase to 842.84 km², with the most pronounced increase in the Longdong region. Compared with 2020, the number and area of ecological barriers in 2050 would decrease significantly by 63 and 370.71 km², respectively. In general, based on the prediction results, the connectivity of ecological network of Gansu Province would increase in 2050. To achieve the predicted ecological network in 2050, emphasis should be placed on the protection of cultivated land and ecological land, the establishment of ecological sources in desert areas, the reinforcement of the protection for existing ecological sources, and the construction of ecological corridors to enhance the stability of ecological network. This study provides valuable theoretical support and references for the future construction of ecological networks and regional land resource management decision-making.

Due to irrational human activities and extreme climate, the Qinghai-Xizang Plateau, China, faces a serious threat of desertification. Desertification has a detrimental effect on the ecological environment and socioeconomic development. In this study, the desertification sensitivity index (DSI) model was established by integrating the spatial distance model and environmentally sensitive area index evaluation method, and then the model was used to quantitatively analyze the spatial and temporal characteristics of desertification sensitivity of the Qinghai-Xizang Plateau from 1990 to 2020. The results revealed that: (1) a general increasing tendency from southeast to northwest was identified in the spatial distribution of desertification sensitivity. The low-sensitivity areas were mostly concentrated in the Hengduan and Nyaingqêntanglha mountains and surrounding forest and meadow areas. The high-sensitivity areas were located mainly in the Kunlun and Altun mountains and surrounding decertified areas. The center of gravity of all types of desertification-sensitive areas moved to the northwest, and the desertification sensitivity showed a decreasing trend as a whole; (2) the area of highly sensitive desertification areas decreased by 8.37%, with extreme sensitivity being the largest change among the sensitivity types. The desertification sensitivity transfer was characterized by a greater shift to lower sensitivity levels (24.56%) than to higher levels (2.03%), which demonstrated a declining trend; (3) since 1990, the change in desertification sensitivity has been dominated by the stabilizing type I (29.30%), with the area of continuously increasing desertification sensitivity accounting for only 1.10%, indicating that the management of desertification has achieved positive results in recent years; and (4) natural factors have had a more significant impact on desertification sensitivity on the Xizang Plateau, whereas socioeconomic factors affected only localized areas. The main factors influencing desertification sensitivity were vegetation drought tolerance and aridity index. Studying spatiotemporal variations in desertification sensitivity and its influencing factors can provide a scientific foundation for developing strategies to control desertification on the Qinghai-Xizang Plateau. Dividing different desertification-sensitive areas on the basis of these patterns of change can facilitate the formulation of more targeted management and protection measures, contributing to ecological construction and sustainable economic development in the area.

Studying the spatiotemporal variation and driving mechanisms of vegetation net primary productivity (NPP) in the Guanzhong Plain Urban Agglomeration (GPUA) of China is highly important for regional green and low-carbon development. This study used the Theil-Sen trend analysis, Mann-Kendall trend test, coefficient of variation, Hurst index, and machine learning method (eXtreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP)) to analyze the spatiotemporal variation of NPP in the GPUA from 2001 to 2020 and reveal its response to climate change and human activities. The results found that during 2001-2020, the averageNPP in the GPUA showed a significant upward trend, with an annual growth rate of 10.84 g C/(m²•a). The multi-year average NPP in the GPUA was 484.83 g C/(m²•a), with higher values in the southwestern Qinling Mountains and lower values in the central and northeastern cropland and built-up areas. The average coefficient of variation of NPP in the GPUA was 0.14, indicating a relatively stable state overall, but 72.72% of the study area showed weak anti-persistence, suggesting that NPP in most areas may have declined in the short term. According to XGBoost-SHAP analyses, elevation, land use type and precipitation were identified as the main driving factors of NPP. Appropriate precipitation and higher temperatures promote NPP growth, whereas extreme climates, high population density, and nighttime lighting inhibit NPP. This study has important theoretical and practical significance for achieving regional sustainable development, offers a scientific basis for formulating effective ecological protection and restoration strategies, and promotes green, coordinated, and sustainable development in the GPUA.

As one of the main characteristics of atmospheric pollutants, PM_2.5 severely affects human health and has received widespread attention in recent years. How to predict the variations of PM_2.5 concentrations with high accuracy is an important topic. The PM_2.5 monitoring stations in Xinjiang Uygur Autonomous Region, China, are unevenly distributed, which makes it challenging to conduct comprehensive analyses and predictions. Therefore, this study primarily addresses the limitations mentioned above and the poor generalization ability of PM_2.5 concentration prediction models across different monitoring stations. We chose the northern slope of the Tianshan Mountains as the study area and took the January-December in 2019 as the research period. On the basis of data from 21 PM_2.5 monitoring stations as well as meteorological data (temperature, instantaneous wind speed, and pressure), we developed an improved model, namely GCN-TCN-AR (where GCN is the graph convolution network, TCN is the temporal convolutional network, and AR is the autoregression), for predicting PM_2.5 concentrations on the northern slope of the Tianshan Mountains. The GCN-TCN-AR model is composed of an improved GCN model, a TCN model, and an AR model. The results revealed that the R² values predicted by the GCN-TCN-AR model at the four monitoring stations (Urumqi, Wujiaqu, Shihezi, and Changji) were 0.93, 0.91, 0.93, and 0.92, respectively, and the RMSE (root mean square error) values were 6.85, 7.52, 7.01, and 7.28 μg/m³, respectively. The performance of the GCN-TCN-AR model was also compared with the currently neural network models, including the GCN-TCN, GCN, TCN, Support Vector Regression (SVR), and AR. The GCN-TCN-AR outperformed the other current neural network models, with high prediction accuracy and good stability, making it especially suitable for the predictions of PM_2.5 concentrations. This study revealed the significant spatiotemporal variations of PM_2.5 concentrations. First, the PM_2.5 concentrations exhibited clear seasonal fluctuations, with higher levels typically observed in winter and differences presented between months. Second, the spatial distribution analysis revealed that cities such as Urumqi and Wujiaqu have high PM_2.5 concentrations, with a noticeable geographical clustering of pollutions. Understanding the variations in PM_2.5 concentrations is highly important for the sustainable development of ecological environment in arid areas.

Tenebrionid beetles represent a crucial arthropod taxon in the Gobi desert ecosystems owing to their species richness and high biomass, both of which are essential for maintaining ecosystem health and stability. However, the spatiotemporal variations of tenebrionid beetle assemblages in the Gobi desert remain poorly understood. In this study, the monthly dynamics of tenebrionid beetles in the central part of the Hexi Corridor, Northwest China, a representative area of the Gobi desert ecosystems, were monitored using pitfall trapping during 2015-2020. The following results were showed: (1) monthly activity of tenebrionid beetles was observed from March to October, with monthly activity peaking in spring and summer, and monthly activity periods and peak of tenebrionid beetle species exhibited interspecific differences that varied from year to year; (2) spatial distribution of tenebrionid beetle community was influenced by structural factors. Specifically, at a spatial scale of 24.00 m, tenebrionid beetle community was strongly and positively correlated with the dominant species, with distinct spatial distribution patterns observed for Blaps gobiensis and Microdera kraatzi alashanica; (3) abundance of tenebrionid beetles was positively correlated with monthly mean precipitation and monthly mean temperature, whereas monthly abundance of B. gobiensis and M. kraatzi alashanica was positively correlated with monthly mean precipitation; and (4) the cover of Reaumuria soongarica (Pall.) Maxim. and Nitraria sphaerocarpa Maxim. had a positive influence on the number of tenebrionid beetles captured. In conclusion, monthly variation in precipitation significantly influences the community dynamic of tenebrionid beetles, with precipitation and shrub cover jointly determining the spatial distribution pattern of these beetles in the Gobi desert ecosystems.

Biological soil crusts (BSCs) play crucial roles in improving soil fertility and promoting plants settlement and reproduction in arid areas. However, the specific effects of BSCs on growth status and nutrient accumulation of plants are still unclear in different arid areas. This study analyzed the effects of three different BSCs treatments (without crust (WC), intact crust (IC), and broken crust (BC)) on the growth, inorganic nutrient absorption, and organic solute synthesis of three typical desert plants (Grubovia dasyphylla (Fisch. & C. A. Mey.) Freitag & G. Kadereit, Nitraria tangutorum Bobrov, and Caragana koraiensis Kom.) in the Minqin desert-oasis ecotone of Northwest China. Results showed that the effects of three BSCs treatments on seed emergence and survival of three plants varied with seed types. The IC treatment significantly hindered the emergence and survival of seeds, while the BC treatment was more conducive to seed emergence and survival of plants. BSCs significantly promoted the growth of three plants, but their effects on plant growth varied at different stages of the growth. Briefly, the growth of G. dasyphylla was affected by BSCs in early stage, but the effects on the growth of G. dasyphylla significantly weakened in the middle and late stages. However, the growth of N. tangutorum and C. koraiensis only showed differences at the middle and late stages, with a significant enhancement in growth. Analysis of variance showed that BSCs, plant species, growth period, and their interactions had significant effects on the biomass and root: shoot ratio of three plants. BSC significantly affected the nutrients absorption and organic solute synthesis in plants. Specifically, BSCs significantly promoted nitrogen (N) absorption in plants and increased plant adaptability in N poor desert ecosystems, but had no significant effects on phosphorus (P) absorption. The effects of BSCs on inorganic nutrient absorption and organic solute synthesis in plants varied significantly among different plant species. The results suggest that BSCs have significant effects on the growth and nutrient accumulation of desert plants, which will provide theoretical basis for exploring the effects of BSCs on desert plant diversity, biodiversity conservation, and ecosystem management measures in arid and semi-arid areas.