Search Results (243)

Central Asia, as a chronically water-stressed region marked by extreme aridity, faces significant environmental challenges from intensifying desertification and deteriorating ecological stability. The region’s vulnerability to shifting precipitation regimes and extreme hydrometeorological events has been magnified under anthropogenic climate forcing. Although global climate models (GCMs) remain essential tools for climate projections, their utility in Central Asia’s complex terrain is constrained by inherent limitations: coarse spatial resolution (~100–250 km) and imperfect parameterization of orographic precipitation mechanisms. This investigation advances precipitation modeling through deep learning-enhanced statistical downscaling, employing convolutional neural networks (CNNs) to generate high-resolution precipitation data at approximately 10 km resolution. Our results show that the deep learning models successfully simulate the high center of precipitation and extreme precipitation near the Tianshan Mountains, exhibiting high spatial applicability. Under intermediate (SSP-245) and high-emission (SSP-585) future scenarios, the increase in extreme precipitation over the next century is significantly more pronounced compared to mean precipitation. By the end of the 21st century, the interannual variability of mean precipitation and extreme precipitation will become even larger under SSP-585, indicating an increased risk of extreme droughts/floods in Central Asia under high greenhouse gas emissions. Our findings provide technical support for climate change impact assessments in the region and highlight the potential of CNN-based downscaling for future climate change studies. Full article

Awulale Mountain is one of the most important Fe-Cu concentration areas situated in the eastern part of Western Tianshan. The Cu deposits in the belt are genetically associated with the Permian intermediate and felsic intrusions. However, the precise age and magma source of the causative intrusions are currently not confirmed, constraining our understanding of regional mineralization. The Cahanwusu porphyry Cu deposit is located in the western part of Awulale Mountain. Field investigations have shown that the mineralization in the deposit is genetically associated with granitic porphyry and diorite porphyry. In this paper, we provide detailed zircon U-Pb ages and in-situ Hf isotopic compositions of the granitic porphyry and diorite porphyry. The granitic porphyry and diorite porphyry have zircon U-Pb ages of 328.6 ± 2.6 Ma (MSWD = 0.52; n = 23) and 331 ± 2.8 Ma (MSWD = 0.95; n = 21), respectively. This indicates that the Cahanwusu deposit was formed in the Carboniferous in a subduction setting. This is distinguishable from other porphyry Cu deposits in the belt, which were generally formed in the Permian in the post-collision extensional setting. The granitic porphyry and diorite porphyry exhibit positive ε_Hf(t) values varying from +2.8 to +5.4 (average of +4.1) and +2.0 to +5.1 (average of +4.1), respectively. The magmas of these causative intrusions were interpreted to be derived from the partial melting of the juvenile lower crust which originated from cooling of mantle-derived magmas related to the subduction process. Our new results highlight that the Cahanwusu deposit represents a new episode of Cu mineralization in the belt and the Carboniferous granitoids in Awulale Mountain are potential candidates for Cu exploration. Full article

Within the context of global desertification trends in arid regions, advancing afforestation and sand stabilization efforts are not only vital for human survival but are also key considerations in addressing the challenges of climate change and achieving sustainable development. This study, set against the backdrop of China’s new round of Grain-for-Green compensation policies implemented in 2014, investigates farmers’ behavior in planting economically valuable forests and grasslands driven by compensation incentives. Grounded in the principles of behavioral economics and assuming farmers as rational “economic agents”, this study focuses on farmers residing on the northern and southern slopes of the Tianshan Mountains in Xinjiang. Employing the fuzzy-set qualitative comparative analysis (fsQCA) approach, it examines the intricate causal mechanisms that shape farmers’ involvement or lack thereof in economic forest and grassland activities. These mechanisms are analyzed through the lenses of resource endowment, psychological perception, and external environmental factors. The results indicate that perceived benefits and neighbor imitation serve as essential conditions for non-participation in planting economic forests and grasslands. Three configurational pathways account for participation: farmers motivated by perceived benefits, those guided by the combined influence of “psychological perception and external environment”, and individuals driven by ecological aspirations alongside neighbor imitation. Additionally, four configurational pathways explain non-participation, with two types of farmers identified: those facing a dual deficiency of psychological perception and external environment, and non-high income traditional farmers dependent on agricultural irrigation water. Full article

Coal mining disrupts soil structure and causes water loss, thereby affecting the ecological environment of mining areas. Rapid, accurate, and non-destructive detection of surface soil moisture is crucial for advancing ecological restoration in these regions. This study focuses on the mined and unmined areas of the Yushuquan coal mine, located on the southern slope of the Tianshan Mountains in Xinjiang, China. The soil volumetric water content (SVWC) was measured using time-domain reflectometry (TDR), while the shallow subsurface soil was investigated using ground-penetrating radar (GPR). Various features were extracted from GPR signals in both the time- and frequency-domains, and their relationships with SVWC were analyzed. Multiple features were selected and optimized to determine the optimal feature combination for building a multi-feature backpropagation neural network model for soil volumetric water content prediction (Muti-BP-SVWC). The performance of this model was compared with two single-feature-based methods for SVWC prediction: the average envelope amplitude (AEA) method and the frequency shift method. The application results of the Muti-BP-SVWC model in different regions demonstrated significant improvements in accuracy and stability compared to the AEA method and the frequency shift method. In the mined area validation set, the model achieved an determination coefficient (R²) of 0.77 and the root mean square error (RMSE) of 0.0091 cm³/cm³, while in the unmined area validation set, the R² of 0.84 and an RMSE of 0.0059 cm³/cm³. These results indicate that incorporating multiple features into the BP neural network can better capture the complex relationship between GPR signals and SVWC. This approach effectively inverts the shallow subsurface soil moisture in mining areas and provides valuable guidance for ecological restoration in these regions. Full article

This paper addresses the dual challenges of food security and sustainable development by examining the balance between arable land quality and economic development. Coordinating and optimizing development models is essential for achieving sustainable agricultural and economic progress. The North Slope Economic Belt of Tianshan Mountain (UANST), a semi-arid agriculturalpastoral transition zone in northwest China, exemplifies a coupled human environment system where global sustainability targets confront regional development imperatives. Focusing on seven cities and counties within the UANST, this study employs information sensitivity indicators to quantitatively select evaluation metrics. It provides a comprehensive analysis of the current state of the coupling and coordination degree (CCD) between arable land quality and economic development in the region. Using a system dynamics model (SDM), four scenario models were developed to predict and analyze the interaction between cultivated land quality and economic development on the North Slope of Tianshan. The study proposes a model to improve coordination between cultivated land quality and economic development. The key findings are as follows: (1) “preliminary screening + information sensitivity analysis” method identified 12 arable land quality evaluation indicators and 11 economic development evaluation indicators for the North Slope Economic Belt of Tianshan. (2) The coupling coordination between arable land quality and economic development in the seven counties and cities improved from 0.469 to 0.663, reflecting a transition from “marginal imbalance” to “primary coordination”. By 2021, all regions had reached the initial stage of coordinated development. (3) Among the development models analyzed, the coordinated development model achieved the highest coupling coordination score (0.9136). This model also demonstrated lower carbon dioxide emissions and reduced water resource consumption, alleviating environmental pressures and offering an optimal solution for regional coordinated development. Full article

Landslides frequently occur in the Xinjiang Uygur Autonomous Region of China due to its complex geological environment, posing serious risks to human safety and economic stability. Existing studies widely use machine learning models for landslide susceptibility prediction. However, they often fail to capture the threshold and interaction effects among environmental factors, limiting their ability to accurately identify high-risk zones. To address this gap, this study employed a gradient boosting decision tree (GBDT) model to identify critical thresholds and interaction effects among disaster-causing factors, while mapping the spatial distribution of landslide susceptibility based on 20 covariates. The performance of this model was compared with that of a support vector machine and deep neural network models. Results showed that the GBDT model achieved superior performance, with the highest AUC and recall values among the tested models. After applying clustering algorithms for non-landslide sample selection, the GBDT model maintained a high recall value of 0.963, demonstrating its robustness against imbalanced datasets. The GBDT model identified that 8.86% of Xinjiang’s total area exhibits extremely high or high landslide susceptibility, mainly concentrated in the Tianshan and Altai mountain ranges. Lithology, precipitation, profile curvature, the Modified Normalized Difference Water Index (MNDWI), and vertical deformation were identified as the primary contributing factors. Threshold effects were observed in the relationships between these factors and landslide susceptibility. The probability of landslide occurrence increased sharply when precipitation exceeded 2500 mm, vertical deformation was greater than 0 mm a⁻¹, or the MNDWI values were extreme (<−0.4, >0.2). Additionally, this study confirmed bivariate interaction effects. Most interactions between factors exhibited positive effects, suggesting that combining two factors enhances classification performance compared with using each factor independently. This finding highlights the intricate and interdependent nature of these factors in landslide susceptibility. These findings emphasize the necessity of incorporating threshold and interaction effects in landslide susceptibility assessments, offering practical insights for disaster prevention and mitigation. Full article

The northern slope of the Tianshan Mountains city cluster (NSTM), as a key urban agglomeration for the development of western China, has experienced rapid regional economic development and high population concentration since the twenty-first century. Accompanied by the increase in human activities in the NSTM, it has significantly altered the land use structure, leading to varying levels of habitat disturbance and degradation. In this paper, based on the land use and land cover (LULC) of NSTM from 2000 to 2020. The InVEST model was employed to assess habitat quality, revealing notable spatial and temporal variations. A geoprobe was further employed to explore the key drivers of the spatially distributed pattern of habitat quality in the research region. The results show that (1) from 2000 to 2020, the NSTM was largely characterized by grassland, unused land, and cropland in terms of land use, with a notable expansion of cropland and construction land; (2) the overall habitat quality in the study area is poor, with a clear spatial distribution pattern of high in the south and low in the north, with a predominance of low grades, and a trend of decreasing and then increasing is shown in the temporal direction; (3) under the influence of rapid urbanization in the region, the degradation degree of habitat quality on the NSTM shows a distinct radial structure, with high degradation in the middle and low degradation at the edges, and shows the trend of “increase-decrease-increase” over time; and (4) the results of the geodetector show that altitude and land use type have the greatest influence on habitat quality on the NSTM, indicating that the habitat quality of the research region is primarily influenced by the type of land use. Full article

The Tianshan Mountains are located in the hinterland of the Eurasian continent, spanning east to west across China, Kazakhstan, Kyrgyzstan, and Uzbekistan. As the primary water source for Central Asia’s arid regions, the Tianshan mountain system is pivotal for regional water security and is highly sensitive to the nuances of climate change. Utilizing ERA5 precipitation datasets alongside 24 atmospheric circulation indices, this study delves into the variances in Tianshan’s precipitation patterns and their correlation with large-scale atmospheric circulation within the timeframe of 1981 to 2020. We observe a seasonally driven dichotomy, with the mountains exhibiting increasing moisture during the spring, summer, and autumn months, contrasted by drier conditions in winter. There is a pronounced spatial variability; the western and northern reaches exhibit more pronounced increases in precipitation compared to their eastern and southern counterparts. Influences on Tianshan’s precipitation patterns are multifaceted, with significant factors including the North Pacific Pattern (NP), Trans-Niño Index (TNI), Tropical Northern Atlantic Index (TNA*), Extreme Eastern Tropical Pacific SST (Niño 1+2*), North Tropical Atlantic SST Index (NTA), Central Tropical Pacific SST (Niño 4*), Tripole Index for the Interdecadal Pacific Oscillation [TPI(IPO)], and the Western Hemisphere Warm Pool (WHWP*). Notably, NP and TNI emerge as the predominant factors driving the upsurge in precipitation. The study further reveals a lagged response of precipitation to atmospheric circulatory patterns, underpinning complex correlations and resonance cycles of varying magnitudes. Our findings offer valuable insights for forecasting precipitation trends in mountainous terrains amidst the ongoing shifts in global climate conditions. Full article

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana (Picea schrenkiana var. tianschanica) forest area in the Kashi River Basin of the Ili River Valley in the western Tianshan Mountains was selected as the research area. Based on forest resources inventory data, Gaofen-1 (GF-1), Gaofen-6 (GF-6), Gaofen-3 (GF-3) Polarimetric Synthetic Aperture Radar (PolSAR), and DEM data, we classified the Picea schrenkiana forests in the study area into three cases: the Whole Forest without vertical zonation and stand age, Vertical Zonality Classification without considering stand age, and Stand-Age Classification without considering vertical zonality. Then, for each case, we used eXtreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Residual Networks (ResNet), respectively, to estimate the AGB of forests in the study area. The results show that: (1) The integration of multi-source remote-sensing data and the ResNet can effectively improve the remote-sensing estimation accuracy of the AGB of Picea schrenkiana. (2) Furthermore, classification by vertical zonality and stand ages can reduce the problems of low-value overestimation and high-value underestimation to a certain extent. Full article

Urumqi is located at the northern foot of the Tianshan Mountains. Its topographical features have a significant impact on the transport and dispersion of air pollutants. Moreover, its winter is extremely long, lasting up to six months. A combination of an irrational energy consumption structure, unique meteorological conditions, and complex geographical terrains has led to a substantial increase in NO₂ emissions, severely damaging the local ecological environment. In this study, we integrate Geographic Information System (GIS) and Computational Fluid Dynamics (CFD). By leveraging GIS’s powerful spatial analysis capabilities and CFD’s high-precision fluid simulation technology, we significantly enhance the simulation accuracy of complex phenomena like airflow and pollutant diffusion. Additionally, the inverse distance weighted interpolation method is comprehensively employed to analyze the Air Quality Indices (AQIs) of typical pollutants in different districts of Urumqi during winter. The results reveal that high altitude causes instability of the dominant near-surface winds within the atmospheric boundary layer. The increasing frequency of surface calm winds reduces the advective transport of atmospheric pollutants. Topography and winter meteorological conditions are identified as the primary factors contributing to pollutant accumulation. This research not only unveils the fundamental mechanisms of pollutant dispersion in mountainous terrains but also validates the practicality of coupling GIS and CFD, providing a theoretical basis for pollution dispersion studies in this region. This study reveals the general laws of pollutant dispersion in mountainous terrain, resolves the issue of establishing complex geographical models, and demonstrates the feasibility of coupling the GIS and CFD. Meanwhile, it provides a theoretical basis for pollution dispersion in this region. Full article

Overwintering frost damage is a major challenge for the wine grape industry in northern China. This study investigates overwintering treatments to improve survival rates and mitigate frost damage in the wine grape production area of the northern foothills of the Tianshan Mountains. Seven overwintering treatments were tested: soil-covered striped cloth, striped cloth, sandwiched striped cloth, thickened striped cloth, double-layered striped cloth, heat-insulating striped cloth, and heat-insulating sandwich striped cloth. Temperature and humidity were continuously monitored during the overwintering period, both aboveground and at depths of 20 and 40 cm underground. By analyzing temperature trends, the duration of low temperatures, and temperature fluctuations, comprehensive overwintering indices were derived through principal component analysis to assess heat retention, moisture preservation, and the impact on grapevine survival. The results showed that the sandwiched striped cloth treatment provided the best insulation, with a 4.4 °C higher minimum daily temperature and a 356% increase in overwintering indices compared to striped cloth alone. The double-layer striped cloth treatment also improved safety, with a 130% increase in overwintering indices. Other treatments, including the soil-covered and the heat-insulating striped cloth, showed reduced performance. The sandwiched striped cloth and double-layer striped cloth treatments are recommended for northern China’s wine grape regions, with further research needed to evaluate their economic viability. Full article

High-altitude mountainous regions are highly vulnerable to climate and environmental shifts, with the current global climate change exerting a profound influence on the ecological landscape of the Tianshan Mountains in China. This study assesses the ecological security quality in the Tianshan Mountains of China from 2001 to 2020 by employing various remote sensing techniques such as the Remote Sensing Ecological Index (RSEI) for evaluation, Normalized Difference Vegetation Index (NDVI) for fractional vegetation cover (FVC) analysis, the CASA model for estimating vegetation primary productivity (NPP), and a carbon source/sink model for calculating the net ecosystem productivity (NEP) of vegetation. The research also delves into the evolutionary trends and impact mechanisms on the ecological environment using land use and meteorological data. The findings reveal that the RSEI’s principal component (PC1) exhibits significant explanatory power, showing a notable increase of 5.90% from 2001 to 2020. Despite relatively stable changes in the RSEI over the past two decades covering 61.37% of the study area, there is a prevalent anti-persistence pattern at 72.39%. Notably, NDVI, FVC, and NPP display upward trends in vegetation characteristics. While most areas in the Tianshan Mountains continue to emit carbon, there is a marked increase in NEP, signifying an enhanced carbon absorption capacity. The partial correlation coefficients between the RSEI and temperature, as well as precipitation, demonstrate statistically significant relationships (p < 0.05), encompassing 6.36% and 1.55% of the study area, respectively. Temperature displays a predominantly negative correlation in 98.71% of the significantly correlated zones, while precipitation exhibits a prevalent positive correlation. An in-depth analysis of how climate change affects the quality of the ecological environment provides crucial insights for strategic interventions to enhance regional environmental protection and promote ecological sustainability. Full article

(1) Background: Endophytic fungi play an important role in plant growth and stress resistance. The presence of a special fungal taxon such as the dark septate endophytic (DSE) fungi in alpine environments is particularly important for plant resistance to environmental stresses. However, the composition of root endophytic fungi in different environments and between different host plants has not been well studied. (2) Results: A total of 408 culturable endophytic fungi were isolated from the roots of Saussurea involucrata and Rhodiola crenulata which were collected in 5 plots from the Tianshan and Karakoram Mountains of the Xinjiang region, belonging to 91 species, 54 genera, 31 families, and 3 phyla based on the morphological characteristics and molecular sequence. Among them, DSE fungi were the dominant group, accounting for 52.94%, and Leptodontidium orchidicola was the dominant species. In addition, we also compared the composition and diversity of root endophytic fungi from different plants and different sites, with emphasis on special fungal taxa such as DSE. (3) Conclusions: The composition and diversity of cultural endophytic fungi are significantly different in the two alpine medicinal plant species and across various locations. Some fungi showed the preferences of the host or environment. The endophytic fungal resources, especially DSE, were very rich in the two alpine medicinal plants, indicating that these fungi may play a crucial role in the ecological adaptation of host plants in harsh environments. Full article

Western Tianshan National Nature Reserve in Xinjiang, China stands out for its uniqueness and high biodiversity, including lichens. This study aims to characterize lichen diversity and compare distribution patterns of different life forms, substratum affinities and photobiont types. Surveys were conducted from June to August 2024 using stratified sampling methods at elevation ranging from 1100 m to 3400 m in the study area. Morphological, anatomical and chemical studies revealed 173 lichen species from 24 families and 58 genera, of which 100 species were identified as crustose, 46 as foliose and 27 as fruticose. Among the different habitat groups, strictly saxicolous lichens were dominant with 89 species, followed by corticolous lichens with 44 species and terricolous lichens with 40 species. The total species richness of lichens has a bimodal pattern: one peak appears at a low altitude (1701–2000 m) and the other at a high altitude (2901–3200 m). Among the three substratum categories studied, the species richness of terricolous lichens showed a unimodal relationship with elevation, and the saxicolous lichen had a bimodal pattern. The species richness of corticolous lichens was highest at lower and medium elevations and decreased at higher elevations. With respect to photobiont type, the species richness of cyanolichens showed a unimodal relationship with elevation. Maximum richness occurred at 2700 m, contrary to the chlorolichens, which had a bimodal pattern. Species richness of all three growth forms of lichens showed a bimodal pattern related to elevation. Among the three morphological types, crustose and foliose species richness had their highest values of 38 and 19, respectively, at 1701–2000 m, and fruticose lichens peaked with a maximum of 13 species at 2301–2600 m. The species richness of crustose lichens is lowest between altitudes 2300 and 2600 m, while the lowest species richness of fruticose and foliose lichens occurs at 2001–2300 m and elevations above 2900 m. Full article

In past decades, urbanization has entered a phase of rapid development, resulting in an intensified utilization of land resources. The finite nature of these resources has led to increased pressure on land availability, giving rise to a phenomenon known as land use conflict. This conflict is particularly evident in the frequent conversion of land categories, with urban impervious surfaces increasingly encroaching upon forests, grasslands, and agricultural land. Such encroachments trigger a series of land use conflict issues, which subsequently impact the function and structure of regional ecosystems. This paper analyzes the spatial and temporal changes in land use and land cover (LULC) within the urban agglomeration on the northern slope of Tianshan Mountain. It measures and evaluates the spatial and temporal evolution of land use conflicts in the study area from 1990 to 2020, utilizing conflict-related theories and the landscape risk evaluation model. Additionally, the paper explores the spatial and temporal dimensions of land use conflicts under three scenarios—natural development (ND), cultivation priority (CP), and ecological priority (EP)—for the years 2030 and 2050, informed by the Future Land Use Simulation (FLUS) model. The results indicate that unused land constitutes the predominant land use type, accounting for over 50% of the total area. The areas of cultivated land, water bodies, and urban land are experiencing an increasing trend, while the areas of forestland, grassland, and unused land are witnessing a decreasing trend. The level of land use spatial conflicts during the study period showed a decreasing and then increasing trend, with an overall upward trend and an increase in the average value of 0.03. In terms of the proportion of spatial units, mild and general conflicts exhibited a decreasing trend, with reductions of 4.21% and 2.95%, respectively. Conversely, the proportion of medium conflicts increased significantly, rising by 7.33%, while severe conflicts experienced a slight increase of 0.23%. Under the ND, CP, and EP scenarios, the spatial and temporal dynamics of future land use conflicts varied. However, the study area was predominantly characterized by general conflicts in both 2030 and 2050. In 2030, the proportions of spatial units experiencing general conflicts in the three scenarios are projected to be 61.20%, 60.39%, and 57.51%, respectively. In comparison, these proportions are projected to be 59.24%, 62.70%, and 56.29% in 2050, respectively. The anticipated future changes in land use spatial conflicts vary across different scenarios. Notably, the ND scenario indicates a rising conflict level in the study area over the next 30 years, with an overall increase of 0.03 in the mean value. In contrast, the changes in the index under the CP and EP scenarios are relatively stable. Full article