Search Results (62)

The development of arid-oasis agriculture and territorial spatial restructuring strongly influence regional eco-environmental quality. Clarifying production–living–ecological space (PLES) transitions and the nonlinear mechanisms driving eco-environmental quality is important for sustainable agricultural land use and ecological governance. Northern Xinjiang combines economic agglomeration with ecological sensitivity, making it a representative region for examining changes in eco-environmental quality and their underlying drivers. Land-use data for 1990, 2000, 2010, and 2020 were analyzed using land-use transition matrices, the eco-environmental quality index (EEQI), spatial autocorrelation, and hotspot analysis to characterize spatiotemporal changes under PLES transitions. XGBoost–SHAP was applied to identify the dominant factors, nonlinear responses, and interactions. The results showed that: (1) ecological space remained dominant from 1990 to 2020, but its share declined from 91.72% to 87.36%. Production and living spaces increased from 7.78% and 0.50% to 11.72% and 0.92%, respectively. Agricultural production space expanded most markedly, and spatial restructuring was most intense during 2000–2010. (2) EEQI decreased from 0.3100 to 0.2922 and showed an overall fluctuating decline. Significant spatial clustering was observed, with Moran’s I values consistently above 0.81 (p < 0.001). High-value areas were mainly distributed in the Ili River Valley and along the northern slope of the Tianshan Mountains, whereas low-value areas were concentrated in the Junggar Basin interior and the arid areas along its eastern margin. (3) During 2000–2020, LAI and NPP in agricultural production space increased by 45.7% and 48.2%, respectively. These increases indicated overall improvements in canopy condition and productivity. After 2010, NPP growth slowed markedly while LAI remained relatively stable, indicating a stage-specific divergence. (4) XGBoost–SHAP showed good robustness and spatial generalizability, with R² values of 0.868–0.918 and RMSE values of 0.059–0.075. NDVI remained the primary driver, while Elev contributed consistently. The dominant interaction shifted from NDVI ∩ PopDen in 1990 and 2000 to NDVI ∩ Elev in 2010 and NDVI ∩ GDP in 2020. This shift indicated fundamental natural constraints and stronger joint effects of population and economic activities during the later stages. These findings provide a scientific basis for optimizing PLES, regulating arid-oasis agriculture, and safeguarding regional ecological security. Full article

The propagation of meteorological drought into agricultural drought is a critical stage in drought hazard development and risk accumulation. Characterizing its spatiotemporal evolution, propagation processes, and triggering conditions is therefore essential for agricultural drought mitigation and water-resource regulation in arid regions. Taking the northern slope of the Tianshan Mountains in Xinjiang, China, as the study area, this study used monthly ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts for 1980–2022 to derive the Standardized Precipitation Evapotranspiration Index and Standardized Soil Moisture Index. Three-dimensional drought-event identification, the Mann–Kendall trend test, cross-wavelet transform, wavelet coherence analysis, and Copula analysis were integrated to investigate drought dynamics, multiscale propagation, and triggering thresholds. This event–process–threshold framework links individual drought-event evolution with lagged propagation characteristics and probabilistic triggering conditions, extending previous studies that focused primarily on correlation or propagation time. The results showed that a total of 92 meteorological drought events and 65 agricultural drought events were identified, with agricultural droughts generally exhibiting greater persistence and cumulative severity. Meteorological droughts were concentrated mainly in the central-eastern region, whereas agricultural droughts occurred predominantly across the western-to-central agro-pastoral ecotone. The two drought types exhibited predominantly in-phase coupling, with dominant propagation periods of 2–8 months, particularly 3–6 months, and agricultural drought generally lagging meteorological drought by 1–3 months. Propagation was fastest in summer, most persistent in autumn, and comparatively weak in winter. Propagation probability increased markedly with meteorological-drought severity, while the triggering threshold ranged from −0.81 to −0.71 and exhibited pronounced seasonal and spatial heterogeneity. These findings provide quantitative criteria for tiered drought early warning. The identified propagation periods and thresholds can support timely soil-moisture monitoring, irrigation preparation, and seasonal water allocation before meteorological drought develops into severe agricultural drought, particularly during sensitive crop-growth stages and in vulnerable oasis-agricultural areas. Full article

Asymmetric diurnal warming is a salient feature of global climate change, and its reshaping of hydrological and carbon processes in terrestrial ecosystems has attracted increasing attention. Water use efficiency (WUE), defined as the ratio of gross primary productivity to evapotranspiration, serves as a key indicator coupling vegetation water consumption with carbon sequestration; however, how daytime and nighttime warming directly and indirectly drive divergent WUE responses through soil moisture and evapotranspiration pathways—and whether these responses are governed by soil-moisture thresholds—remains insufficiently explored in arid grasslands. In this study, the arid and semi-arid grasslands of northern Xinjiang were selected as the study area, and the spatiotemporal heterogeneity of diurnal land surface temperature, evapotranspiration components, soil moisture, and WUE during the growing season was systematically analyzed to identify the key hydrological factors and driving pathways through which diurnal warming regulates WUE. The results showed that: (1) nighttime warming in the grasslands of northern Xinjiang was significantly stronger than daytime warming during the study period, with significant nighttime warming (p < 0.05) detected over 86.31% of the area, far exceeding the 16.62% observed for daytime warming, and the diurnal temperature range narrowing continuously at a rate of 0.02 °C per year; (2) over the same period, WUE exhibited an overall non-significant declining trend with pronounced spatial heterogeneity, with areas of significant increase concentrated in the Junggar Basin, whereas areas of significant decrease were clustered on the northern slope of the Tianshan Mountains, reflecting divergent evapotranspiration and productivity responses under contrasting moisture regimes; (3) LAI emerged as the most critical independent factor regulating WUE variation, with daytime warming exerting a significant negative effect on WUE primarily by depleting soil moisture, suppressing LAI, and intensifying vapor pressure deficit-driven evapotranspiration stress, whereas nighttime warming across the broader region exerted a positive compensatory effect by promoting LAI growth and sustaining soil moisture availability; (4) these pathway reversals are governed by a soil-moisture threshold: in the arid basin where WUE increased significantly, the effects of nighttime warming on soil moisture and LAI shifted from promotive to inhibitory, and the direct effect of daytime warming likewise reversed from positive to negative, revealing that the apparent WUE increase reflects a passive hydrological response in which transpiration is preferentially suppressed under severe water deficit rather than an improvement in ecosystem function. These findings demonstrate that diurnal warming effects on grassland water use exhibit pronounced soil-moisture threshold dependence and spatial heterogeneity. Our study highlights that substituting daily mean temperature for separate daytime and nighttime temperatures masks the opposing effects of asymmetric warming on coupled water consumption and carbon processes, and underestimates the sensitivity of arid grassland hydrological cycles to climate warming. This study provides a new mechanistic basis for optimizing parameters in carbon–water cycle models and for differentiated water resource management in arid grassland regions. Full article

Soil quality is a critical factor impacting agricultural productivity and ecosystem functions. Accurate assessment of soil quality is crucial for sustainable agricultural development. Xinjiang is the primary cotton-producing region in China. Its unique geographical condition, characterized by two basins surrounded by three mountains, results in distinct climatic conditions, soil-forming factors, and soil physical and chemical properties across different cotton-growing areas. The spatial differentiation patterns of soil quality and their primary factors in cotton-growing regions of different river basins are not yet fully understood. This study focused on four typical cotton-growing areas of Xinjiang, China. A total of 1588 plow-layer soil samples were collected, and 21 indicators covering soil physical, chemical, and environmental properties were measured. By constructing a minimum data set (MDS) and comparing the performance of linear (LS) and non-linear (NLS) scoring functions, the effects of geographical environmental factors on the spatial distribution patterns of soil quality in cotton fields of different basins were analyzed. The results showed the MDS, composed of data on soil bulk density and the contents of organic matter, available iron, available zinc, nickel, sand, and silt, could replace the total data set. The NLS-MDS was identified as the optimal assessment model. Its Nash–Sutcliffe efficiency coefficient (E_f = 0.84) and coefficient of determination (R² = 0.70) were both higher than those of the linear model (E_f = 0.79, R² = 0.66). The study also revealed significant spatial heterogeneity in soil quality across different cotton-growing areas. The average soil quality index (SQI) in the Aksu River Basin (SQI_NLS-MDS = 0.53) and Xiaohaizi Basin (SQI_NLS-MDS = 0.50) was significantly higher than that in the Kuitun River Basin (SQI_NLS-MDS = 0.44) and Manas River Basin (SQI_NLS-MDS = 0.41). Random forest analysis demonstrated that the relative importance of topographic (digital elevation model) and climatic factors (annual mean temperature, annual mean precipitation) on SQI was higher than that of the vegetation factor (normalized difference vegetation index). The strong interaction between topographic and climatic factors was the primary driver of the spatial distribution of soil quality. This study confirms significant spatial heterogeneity of soil quality in cotton fields across different river basins in Southern and Northern Xinjiang, and identifies the synergistic interaction between topographic and climatic factors as the dominant driver of this heterogeneity in arid regions. These findings provide a scientific basis for implementing site-specific agricultural management in arid regions. Full article

Against the backdrop of global climate change and carbon neutrality strategies, land use carbon emissions have become a prominent topic amid regional efforts toward low-carbon transformation. However, existing studies on land-use carbon emissions have predominantly focused on humid and economically developed regions, while the unique carbon metabolism pathways of arid oasis–desert ecosystems, which are characterized by extremely low environmental carrying capacity and high sensitivity to land-use disturbance, remain largely unexplored. This study takes the oasis urban cluster in the Tarim Basin in southern Xinjiang Uygur Autonomous Region as the research object. This region belongs to a typical oasis–desert composite ecosystem, with a simple structure and low environmental carrying capacity (reflected by sparse vegetation cover < 20%, annual precipitation < 100 mm, extremely limited water resources, and high sensitivity to land disturbance). Its carbon metabolism pathway (i.e., the dynamic balance between carbon sources and sinks induced by land-use change) is fundamentally different from that in humid areas, and thus merits dedicated investigation. This study selects the period from 2000 to 2020 as the research period, which completely covers the acceleration period of urbanization and agricultural expansion in the Tarim Basin oasis urban cluster since the advancement of China’s Western Development Initiative. The data have a temporal resolution of 5 years (samples in 2000, 2005, 2010, 2015, 2020) and a spatial resolution of 30 m for land use and prefecture level for socio-economic indicators. Based on this, to fill the above-mentioned research gap, a research framework integrating the carbon emission coefficient accounting method, landscape pattern index, spatial autocorrelation analysis and geographic detector is adopted. Specifically, this study aims to systematically quantify the spatio-temporal evolution of land use carbon emissions and identify the most robust driving factors in the Tarim Basin oasis urban cluster by integrating multiple models, an approach that has not been previously applied to arid oasis regions. The research results show: (1) Based on the carbon emission coefficient method, total carbon emissions increased from 1.4455 million tons to 22.364 million tons, following a ‘slow-then-fast’ trajectory. In terms of temporal evolution, the study period can be further divided into three sub-stages: 2000–2005 (slow diffusion, with emission center skewed toward the northern energy-intensive zone), 2005–2015 (rapid restructuring, characterized by a ‘unipolar surge’ in Aksu and spread to the central oasis belt), and 2015–2020 (high-intensity stabilization, forming a cross-regional emission belt). Meanwhile, the land use structure has undergone a significant transformation. Construction land and cultivated land have continued to expand, while ecological land has significantly shrunk, resulting in a complex transformation pattern of oasis–desert ecotone. (2) The overall landscape became increasingly fragmented and diversified, the integrity of ecological space was damaged, and the regional carbon sink function was weakened. (3) The spatial autocorrelation analysis indicates that the spatial distribution of carbon emissions shows a heterogeneous pattern, forming a high-emission concentration area centered around Aksu-Bayingol. However, the global Moran’s I index is negative (such as −0.171 in 2020, p > 0.05), suggesting that carbon emissions have not formed a significant spatial clustering. (4) Carbon emissions are dominated by human and economic factors, and the interaction of factors is significant. The geographic detector identifies population density (average q value 0.904) and the proportion of construction land (average q value 0.858) as the key determinants of spatial variation in carbon emissions, reflecting the sensitive response of the human-nature system of arid zones to the urbanization process. These findings not only clarify the spatio-temporal features and driving forces of land use carbon emissions in the Tarim Basin oasis urban cluster, but also provide a replicable analytical framework for carbon-emission research in other arid and semi-arid regions worldwide. Based on these findings, we discuss the unique driving mechanisms of carbon emissions in arid regions, conclude that construction land expansion and population density are the dominant factors, and recommend a three-tier zoning governance system (carbon source control zone, carbon sink enhancement zone, coordinated development zone) for low-carbon spatial planning in arid areas. Full article

In modern livestock production, a reliable supply of high-quality forage is essential for sustaining animal productivity and product quality. Although nitrogen (N) fertilization can promote forage growth, excessive N inputs often result in low agronomic nitrogen use efficiency (NAUE) and increased environmental risks. Biochar, owing to its porous structure, high specific surface area, and physicochemical stability, can improve soil physical properties, enhance water and nutrient retention, and regulate soil N availability. However, the mechanisms by which biochar combined with reduced N rate fertilization affects NAUE in artificial grasslands remain insufficiently quantified. A two-year field experiment was conducted at the Grassland Science Experimental Station of Xinjiang Agricultural University on the northern slope of the Tianshan Mountains, Xinjiang, China. Eight treatments were established using a factorial design with two biochar rates (0 and 20 t·ha⁻¹; B0 and B20) and four N application rates (0, 75, 150, and 225 kg·ha⁻¹; N0, N75, N150, and N225). Results showed that biochar application significantly decreased soil bulk density and increased soil water content and electrical conductivity. It also elevated soil total carbon, total nitrogen, total phosphorus, NH₄⁺–N, and NO₃⁻–N concentrations, with B20N150 exhibiting the highest overall nutrient status. Plant community diversity indices did not differ significantly among treatments (p > 0.05), though B20 slightly enhanced Shannon–Wiener and Simpson indices under N0 and N75. Moderate N application significantly increased hay yield, whereas the highest N rate (225 kg·ha⁻¹) did not further improve yield and reduced NAUE. Biochar combined with N75 or N150 improved NAUE, and B20N150 achieved the best balance of high hay yield and high NAUE. Structural equation modeling revealed that soil water content (path coefficient = 0.45), NH₄⁺–N (0.27), and plant community diversity (0.20) were key positive drivers of NAUE, with biochar exerting indirect effects primarily via improving soil water and available N. Collectively, applying 20 t·ha⁻¹ biochar with 150 kg·ha⁻¹ N (B20N150) is recommended as an optimal strategy for N rate reduction and NAUE enhancement in artificial grasslands of arid and semiarid regions. Full article

The Temperature-Vegetation-Precipitation Drought Index (TVPDI) performs poorly in complex terrain due to Normalized Difference Vegetation Index (NDVI) saturation and land surface temperature (LST) retrieval inaccuracies. To address this, we adopted an improved TVPDI (ITVPDI) by incorporating Leaf Area Index (LAI) and the land surface–air temperature difference (LST−T). By using multi-source data from 2000 to 2022 in Xinjiang, China, we validated ITVPDI and analyzed drought dynamics. Results show: (1) ITVPDI correlates better with solar-induced chlorophyll fluorescence (SIF) (r = 0.17) and the moisture index (MI) (r = 0.22) than the traditional TVPDI, demonstrating superior performance in densely vegetated and topographically complex areas. (2) Drought frequency ranked as follows: severe (31.55%) > moderate (29.04%) > extreme (23.44%) > mild (15.94%). Mild and moderate droughts occurred in Northern Xinjiang and the Tianshan Mountains, while severe and extreme droughts clustered around the Tarim Basin and Eastern Xinjiang desert margins. As drought intensity increases, its center of gravity shifts “from north to south” and “from mountains to basins.” (3) ITVPDI showed a slight upward trend over the 23-year period, with autumn experiencing the most severe drought (mean ITVPDI = 0.293). (4) A mean Hurst index of 0.468 indicates weak anti-persistence, suggesting the current wetting trend may reverse, and increasing future drought risk. The ITVPDI proves to be a robust tool for drought monitoring in arid and semi-arid regions with complex terrain. This study provides crucial scientific support for regional water resource allocation, precision irrigation, and collaborative drought resistance and disaster mitigation in Northwest China. Full article

Dryland sustainability depends on how vegetation productivity and water-use processes respond to climatic variability and human intervention. Focusing on Xinjiang, China, this study assessed eco-hydrological change from 2000 to 2023 using multi-source remote sensing and climatic datasets. We integrated vegetation productivity and water-use efficiency into a composite EcoIndex, combined anomaly-based diagnostics with eco-hydrological synchrony analysis, and used pixel-level random forest attribution to identify dominant climatic and anthropogenic controls. The results show clear regional differentiation. Northern Xinjiang remained primarily climate-driven and maintained relatively stronger vegetation–water coupling, whereas Southern Xinjiang exhibited more pronounced human-induced restructuring, especially in oasis and cultivated areas. Eastern Xinjiang functioned as a transitional zone with weak coupling and high sensitivity to multiple pressures. Across Xinjiang, 63.27% of the area was classified as climate-dominated, 22.41% as human-dominated, and 14.32% as mixed influence. The results indicate that improvements in vegetation condition do not necessarily imply improved eco-hydrological coordination, and that mixed-influence zones may represent early-warning areas of sustainability risk. This study provides a spatial diagnostic framework for supporting sustainable land and water management, regional adaptation planning, and resilience-oriented governance in arid and semi-arid regions. Full article

Locust outbreaks are major biological disturbances in grassland ecosystems of arid and semi-arid regions. Accurate identification of locust suitable habitats is important for regional monitoring and management. However, direct comparisons between multi-criteria analysis (MCA) and species distribution models (SDMs) under a unified framework remain limited. In this study, we compared these two approaches for dominant locust species in Xinjiang, China, including Calliptamus italicus, Gomphocerus sibiricus, and Locusta migratoria manilensis. We used the same environmental variables and occurrence records for all models. The MCA methods included the analytic hierarchy process (AHP), technique for order preference by similarity to ideal solution (TOPSIS), and ordered weighted averaging (OWA). The SDMs included the generalized linear model (GLM), maximum entropy model (MaxEnt), extreme gradient boosting (XGBoost), and an ensemble model. The results showed that SDMs had higher area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) values than MCA under the internal point-based evaluation framework, although both approaches effectively identified locust-suitable habitats. The two approaches also showed high spatial agreement in moderately and highly suitable habitats, with Jaccard indices of 0.88–0.92, and consistently identified the northern slopes of the Tianshan Mountains, the Ili River Valley, and the margins of the Junggar Basin as core suitable areas. These results indicate that the two approaches are complementary for locust monitoring and management. Full article

Drought is a highly destructive natural disaster that inflicts severe economic losses. Its formation mechanisms are complex, yet existing studies have often focused on single driving factors, leaving the synergistic effects of multiple factors insufficiently explored. Based on multi-source data from Xinjiang spanning 1981–2020, this study systematically examined the combined impacts of atmospheric circulation, underlying surface conditions, and human activities on drought, using the multi-temporal-scale Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSI), along with partial correlation analysis, spatial autocorrelation, and principal component analysis. The results show that Xinjiang experienced a pronounced drying trend over the past 40 years, with the seasonal SPEI and SSI both exhibiting significant declines. Drought intensity was higher in northern Xinjiang than in the south. Correlations between drought indices and circulation indices, such as Atlantic Multidecadal Oscillation (AMO), were relatively weak, indicating a limited regulatory influence of large-scale circulation on regional drought under the dual constraints of topography and an inland setting. Among underlying surface factors, slope significantly influenced drought spatial patterns. Mountainous areas and basin interiors showed positive spatial correlations, characterized respectively by high–high clustering (high slope and high drought index) and low–low clustering (low slope and low drought index). In contrast, basin margins exhibited low–high clustering (low slope surrounded by high drought index), reflecting negative spatial correlation. Aspect showed no significant effect. Vegetation cover displayed clear seasonal coupling with drought, with strong negative correlations in spring due to intensified water stress. Human activities also played a prominent role. Since the mid-1990s, the expansion of built-up land and increased agricultural water use have shifted drought–land use relationships toward low–high clustering (low drought index surrounded by high land-use intensity) in southern Xinjiang oases, and toward low–low clustering (low drought index and low land-use intensity) in eastern Xinjiang. Meanwhile, ecological restoration projects promoted a transition from low–high to high–high clustering (high drought index and high land-use intensity) in some areas, alleviating local drying trends. Principal component analysis further revealed a shift in the dominant driver: land-use change was the primary factor before 2005, whereas vegetation cover became the key driver thereafter. By clarifying the mechanisms underlying multi-factor interactions in drought in Xinjiang, this study provides scientific support for integrated water resource management, ecological conservation, and climate adaptation strategies in arid regions. Full article

Against the global push for “carbon peak and carbon neutrality” and Xinjiang’s role as a major arid-region agricultural base in China, balancing agricultural development with low-carbon transitions remains challenging due to its fragile ecology and resource-intensive farming. However, county-scale dynamics of cultivated land carbon emission intensity (CEI) and its drivers in Xinjiang are understudied, limiting targeted mitigation. This study analyzed Xinjiang’s cultivated land CEI (2000–2020) using the Geographically and Temporally Weighted Regression and Stochastic Impacts by Regression on Population, Affluence and Technology (GTWR-STIRPAT) model, geodetector, and spatiotemporal analysis, with counties as units. Data included 30 m-resolution land use data and socioeconomic statistics. Results showed CEI rose from 0.270 to 0.377 t/hm², with marked spatial differences: northern Xinjiang saw fluctuating growth and a 58.65 km northeastward shift of emission gravity, while southern Xinjiang had lower western CEI (ecological constraints) and higher eastern CEI (agricultural expansion). Key drivers were total sown area (TSAC), agricultural film usage (UAPF), and rural agricultural population (RAP). Factor interactions (machinery power × sown area, q = 0.844) non-linearly amplified CEI. The GTWR-STIRPAT model (R² = 0.97) outperformed OLS and captured heterogeneity—mechanization/area expansion dominated northern CEI, while film use/population mattered more in the south. Region-specific strategies are needed: northern Xinjiang should optimize machinery energy and control area expansion; southern Xinjiang, strengthen ecology and promote low-carbon inputs; eastern Xinjiang, leverage efficient oasis agriculture. This study supports precise carbon management in Xinjiang and similar arid regions globally. Full article

This study investigated the distribution patterns of zooplankton species composition and functional groups, their correlations with aquatic environmental factors, and the mechanisms underlying community stability under the influence of regional barriers in arid areas of Xinjiang, China. The aim was to elucidate the ecological processes driving zooplankton communities in artificial aquatic ecosystems in Central Asia. A systematic survey was conducted on water environmental parameters and zooplankton community structures across 10 artificial water bodies, including the southern foot of the Altai Mountains and both northern and southern slopes of the Tianshan Mountains. The survey encompassed physical and nutrient indicators, and the results revealed significant spatial variation among water bodies across regions. Artificial water bodies in the southern Altai Mountains and northern Tianshan Mountains exhibited substantial fluctuations in temperature, dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). In contrast, water bodies in the southern Tianshan Mountains showed less variation in nutrient indicators. Zooplankton identification results indicated marked differences in zooplankton communities across regions, which were further confirmed by cluster analysis and non-metric multidimensional scaling (NMDS). A total of 19 dominant zooplankton species were identified across the three basins, classified into 6 functional groups. The composition of zooplankton functional groups also varied considerably, which may be closely associated with significant fluctuations in nutrient indicators of aquatic environmental factors across regional barriers. Additionally, there were specific differences in zooplankton diversity among the three basins: the SA region ranged from α-mesosaprobic to polysaprobic and β-mesosaprobic; the NT region was classified as β-mesosaprobic; and the ST region ranged between β-mesosaprobic and lightly polluted. These results may be attributed to differences in regional barriers and glacial meltwater conditions. Canonical Correspondence Analysis (CCA) showed that environmental factors collectively explained 71.1% of the variation in species distribution. Exploring the zooplankton species composition and their relationships with aquatic environmental factors under different regional barriers provides a scientific basis for regional water resource management and environmental protection. Full article

Net Primary Productivity (NPP) stands as a crucial metric for evaluating the condition and performance of terrestrial ecosystems. This study focuses on northern Xinjiang, China, as the research site. By employing the Carnegie Ames Stanford Approach (CASA) model alongside meteorological data, we examined the spatiotemporal variations in vegetation NPP from 2001 to 2022. The model utilized monthly NDVI, climate drivers, and vegetation type raster data as inputs, while the Mann–Kendall test, We utilized Theil–Sen trend analysis and residual analysis to investigate how climatic factors and human activities drove NPP changes. Results show that from 2001 to 2022, vegetation NPP in northern Xinjiang generally rose with fluctuations, averaging 127.96 gC·m⁻²·a⁻¹ annually and growing linearly at 0.58 gC·m⁻²·a⁻¹. Spatially, NPP displayed a pattern of “high in the west and low in the east, high in mountainous areas and low in deserts.” High NPP areas are mainly clustered in the Ili River Valley and adjacent mountainous regions, encompassing eastern and southwestern Ili Prefecture, northern Tianshan slopes, Balq Mountains, and southern Borokunu foothills, where hydrothermal conditions are relatively advantageous. In the last 22 years, the mean temperature in northern Xinjiang showed a fluctuating upward trend, precipitation exhibited a fluctuating downward trend, and solar radiation demonstrated a significant declining trend. Partial correlation analysis revealed that, compared with temperature and solar radiation, precipitation had a stronger positive correlation with NPP. Residual analysis showed that in areas where vegetation NPP exhibited recovery, human activities were the dominant driving factor, accounting for 23.58% of the total area, whereas the influence of climate change was relatively minor. Conversely, in regions where vegetation NPP degraded, climate change exerted a greater impact than human activities. This research clarifies the combined impacts of climate and human actions on ecosystem productivity in arid areas, offering a scientific foundation and reference for ecological protection and regional carbon control in such regions. This provides a scientific basis for formulating rational response strategies to restore vegetation and enhance the quality of ecosystems in arid regions. Full article

Rapid and uncoordinated urban expansion in arid oasis city clusters intensifies land use conflicts and ecological pressure, threatening regional sustainability. This study investigates the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains (UANSTM) in Xinjiang, northwestern China—an arid region urban cluster. A multi-source spatial data framework was established to delineate urban built-up areas and to construct land use efficiency (LUE) indicators, thereby facilitating an integrated analysis of the spatial coupling between urban expansion intensity (UEI) and LUE from 2000 to 2020. The results indicate that: (1) The urban built-up area expanded from 322 km² to 1096 km², shifting northward and northwestward, producing fragmented and decentralized patterns; (2) LUE improved but exhibited clear spatial disparities. Core cities like Urumqi showed strong synergy between rapid expansion and rising efficiency, whereas peripheral cities such as Wusu expanded quickly without corresponding efficiency gains, reflecting evident trade-offs; (3) The relationship between UEI and LUE exhibited a nonlinear evolution—trade-offs dominated during 2000–2005, synergy strengthened from 2005 to 2015, and trade-offs resurged again after 2015.These findings reveal the cyclical vulnerability of arid region urbanization and highlight the effectiveness of the proposed framework for diagnosing spatial mismatches and guiding compact, efficiency-oriented urban development toward long-term sustainability. Full article

Investigating the diurnal variation characteristics of precipitation (DVCP) in Xinjiang, an arid region of Northwest China, is essential for improving water resource management and disaster mitigation strategies. This study examines the DVCP associated with diverse underlying surfaces in Eastern Xinjiang (EX)—one of the most arid regions in China—during summer (June–August) from 2015 to 2019, using hourly simulation data from the real-time forecasting system of Nanjing University (WRF_NJU). Evaluation against automatic weather station (AWS) observations indicates that WRF_NJU outperforms reanalysis (ERA5), satellite (CMORPH), and MESWEP datasets, demonstrating its reliability for regional precipitation analysis. Further investigation reveals that in the Turpan-Hami Basin (THB), below 1000 m above sea level (ASL), peaks in precipitation amount (PA), intensity (PI), and frequency (PF) occur at 06 local solar time (LST), whereas in mountainous areas above 3000 m ASL, these peaks are delayed until 13 LST. Analysis of the coefficient of variation (CV) shows that the most pronounced differences in DVCP between mountainous and basin regions are associated with PF and PI. Specifically, regions with high CV for PF are concentrated in the central to northern parts of the THB, while high CV for PI is found in the eastern Mid-Tianshan Mountains (MTM) and East Tianshan Mountains (ETM). Moreover, significant differences in DVCP are observed across land surface types: PA peaks over grasslands, forests, and water bodies occur around noon, whereas over impervious surfaces, croplands, and barren areas, they occur during the early morning hours. Full article