Extreme temperature events have intensified across Jordan over the past 40 a, increasing risks to agriculture, water availability, urban infrastructure, and public health. The purpose of this study is to assess the long-term spatial trends and regime shifts in extreme temperature indicators across Jordan's climate zones to explore climate adaptation strategies. This study presents a high-resolution and spatially explicit assessment of thermal extremes using daily data from 1982 to 2024 across 45 grid-based study points in Jordan. Thirteen temperature indices, including percentile-based thresholds, duration metrics, and absolute extremes, were computed using RClimDex and analyzed across four Köppen climate zones: hot desert (BWh), hot semi-arid (BSh), cold desert (BWk), and Mediterranean (Csa) climates. The analysis confirmed a statistically significant warming trend: annual mean maximum temperatures increased by 2.198°C, while annual mean minimum temperatures rose by 2.035°C. Cold extremes have sharply declined, with cold days (TX10p) decreasing by 70.0%-80.0%, and the cold spell duration indicator (CSDI) dropping from 12.6 to 4.0 d/a, particularly in the BWk zone. Heat indices intensified across all zones, with warm days (TX90p) increasing by over 300.0% in BWh, warm nights (TN90p) rising by 38.1%, and the warm spell duration indicator (WSDI) extending fourfold, indicating prolonged exposure to heatwaves. Mean value of maximum temperature (TXx) reached 45.600°C in most arid areas, while minimum temperature (TNx) exceeded 31.600°C, highlighting increased nocturnal heat stress. Change-point analysis indicated that 1998 was a pivotal year, marking a structural transition in both cold and warm temperature indices. Subsequent intensifications after 2010 in TN90p, TNx, and mean of daily maximum temperature (T_maxmean) reflected an ongoing trend toward sustained thermal extremes. In addition to time-series trends, the study employed network-based correlation analysis to explore the coherence among climate indices. Strong positive correlations were observed among TXx, TX90p, and mean of daily minimum temperature (T_minmean) (r≥0.94), as well as among TN90p, T_minmean, and TNx (r≥0.87), indicating a tightly clustered heat subsystem. Duration metrics like the WSDI showed a close alignment with percentile extremes (between WSDI and TX90p; r=0.88), suggesting integrated heatwave behavior. In contrast, cold indices (TX10p, TN90p, frost days, and CSDI) exhibited weak or negative correlations and displayed peripheral positioning in the climate network, indicating their limited role under a warming regime. Absolute extremes showed weak internal linkages, suggesting episodic rather than systemic response characteristics. This structural realignment indicated a shift from a previously balanced thermal profile to a heat-dominated climate system. Regional variations revealed that BWh and BSh were experiencing the steepest warming, while Csa was transitioning more slowly but was showing signs of reduced winter cooling and increased irrigation demands. The findings establish a robust climate baseline for Jordan and offer actionable insights for climate adaptation planning. Recommended measures include precision irrigation, the development of heat-resilient crops, improvements to urban cooling infrastructure, and early warning systems for thermal extremes. By integrating spatial climate zoning, regime shift analysis, and inter-index correlation structures, this study provides a replicable framework for monitoring climatic transformations and informing resilience strategies in arid and semi-arid areas.

Crude oil pollution is a significant global environmental challenge. The eastern Gansu Province on the Loess Plateau, an important agricultural region containing the Changqing Oilfield, is facing increasing crude oil contamination. Understanding how microbial communities respond to varying pollution levels is critical for developing effective bioremediation strategies. This study examined how different concentrations of crude oil affect soil properties and microbial communities in Qingyang City, eastern Gansu Province, China by comparing lightly polluted (1895.84-2696.54 mg/kg total petroleum hydrocarbons (TPH)), heavily polluted (4964.25-7153.61 mg/kg TPH), and uncontaminated (CK) soils. Results revealed that petroleum contamination significantly increased total organic carbon (TOC), pH, C:N:P ratio, and the activities of dehydrogenase (DHA) and polyphenol oxidase (PPO), while reducing total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), available potassium (AK), soil organic matter (SOM), soil water content (SWC), the activities of urease (URE) and alkaline phosphatase (APA), and microbial alpha diversity (P<0.050). Light pollution (LP) soils demonstrated an increase in culturable microorganisms, whereas heavy pollution (HP) soils exhibited increased hydrocarbon-degrading microbes and higher expression of key functional genes, such as alkane monooxygenase (AlkB), cytochrome P450 alkane hydroxylases (P450), catechol 2,3-dioxygenase (C23O), and naphthalene dioxygenase (Nah) (P<0.050). Non-metric multidimensional scaling (NMDS) and redundancy analysis (RDA) indicated evident variations in microbial community structure across different oil contamination levels. LP soils were dominated by bacterial genera Pseudoxanthomonas and Solimonadaceae, whereas Pseudomonas, Nocardioides, and hydrocarbon-degrading genera (Marinobacter, Idiomarina, and Halomonas) were predominant in HP soils. The fungal genus Pseudallescheria exhibited the most pronounced abundance shift between LP and HP soils (P<0.050). Environmental factor analysis identified AN, SWC, TN, SOM, and alpha diversity indices (Shannon index and Chao1 index) as the key differentiators of CK soils, whereas the pollutant levels and metal content were characterized in HP soils. Hydrocarbon-degrading microbial abundance was a defining trait of HP soils. Metabolic pathway analysis revealed enhanced aromatic hydrocarbon degradation in HP soils, indicating microbial adaptation to severe contamination. These findings demonstrated that crude oil pollution suppressed soil nutrients while reshaping the structure and function of microbial communities. Pollution intensity directly affected microbial composition and degradation potential. This study offers valuable insights into microbial responses across contamination gradients and supports the development of targeted bioremediation strategies for oil-contaminated loess soils.

With ongoing global climate change, drought has become the primary threat constraining food security in China. Traditional assessment frameworks based on administrative boundaries or macro-climatic zoning overlook variation in vulnerability affected by key agronomic practices, such as crop phenology and cropping systems, thereby limiting their accuracy. To address this research gap, this study developed and validated a novel drought risk assessment framework based on agricultural cropping zones (single-, double-, and triple-cropping zones). The framework coupled a Geographical and Temporal Neural Network Weighted Regression (GTNNWR) model for forecasting future crop vegetation dynamics with the Standardized Precipitation Evapotranspiration Index (SPEI) to assess drought risk under historical (2001-2020) and projected future (2021-2100) scenarios. The GTNNWR model achieved R² values ranging from 0.72 to 0.82 and RMSE values between 0.11 and 0.14 for NDVI prediction, significantly outperforming conventional models. Historical drought risk assessment revealed that drought events were most frequent during summer and concentrated in single-cropping and double-cropping zones. Future projections indicate a substantial intensification of drought risk. Under the Shared Socioeconomic Pathway (SSP)126 scenario, drought risk is projected to increase in the triple-cropping zones of the middle and lower reaches of the Yangtze River Plain. Under the SSP245 scenario, the frequency of spring and winter droughts is anticipated to rise markedly. Under the SSP585 scenario, drought intensity is projected to intensify in central-eastern single-cropping zones and southwestern double-cropping zones. This assessment framework based on agricultural cropping zones can precisely identify drought risks and facilitate adaptation in agricultural management, such as optimizing irrigation systems and adjusting crop structures.

Increasing human disturbance and climate change have threatened ecological connectivity and structural stability, especially in semi-arid mountain areas with sparse vegetation and weak hydrological regulation. Large-scale ecological restoration, such as adding ecological sources or corridors, is difficult in such environments and often faces poor operability and high implementation costs in practice. Taking the southern slope of the Qilian Mountains in China as the study area and 2020 as the baseline, this study integrated weighted complex network theory into the "ecological source-resistance surface-corridor" framework to construct a heterogeneous ecological network (EN). Circuit theory was integrated with weighted betweenness to identify critical barrier points for locally differentiated restoration, followed by assessment of the network optimization effects. The results revealed that 494 ecological sources and 1308 ecological corridors were identified in the study area. Fifty-one barrier points with restoration potential were identified along key ecological corridors and locally restored. After optimization, the network gained 11 additional ecological corridors, and the total ecological corridor length increased by approximately 1143 km. Under simulated attacks, the decline rates of maximum connected subgraph (MCS) and network efficiency (Ne) slowed compared with pre-restoration conditions, indicating improved robustness. These findings demonstrate that targeted local restoration can enhance network connectivity and stability while minimizing disturbance to the overall landscape pattern, providing a practical pathway for ecological restoration and sustainable management in semi-arid mountain areas.

Desertification is a global crucial ecological and environmental issue, and China is among the countries most seriously affected by desertification. In recent decades, numerous independent studies on desertification dynamics have been carried out using remote sensing technology, but there has been a lack of systematic research on desertification trends in China. This study employed the meta-analysis to integrate the findings of 140 published research cases and examined the dynamics of desertification in the eight major deserts, four major sandy lands, and their surrounding areas in China from 1970 to 2019, with a comparative analysis of differences between the eastern (including the Mu Us Sandy Land, the Otindag Sandy Land, the Hulunbuir Sandy Land, the Horqin Sandy Land, and the Hobq Desert) and western (including the Taklimakan Desert, the Gurbantunggut Desert, the Kumtagh Desert, the Ulan Buh Desert, the Qaidam Basin Desert, the Badain Jaran Desert, and the Tengger Desert) regions. The results revealed that from 1970 to 2019, desertification first expanded and then reversed in the whole region. Specifically, desertification expanded from 1980 to 1999 and reversed after 2000. The desertification trend exhibited distinct spatio-temporal variations between the eastern and western regions. From 1970 to 2019, the western region experienced relatively minor changes in desertified land area compared to the eastern region. In the context of global climate change, beneficial climatic conditions and ecological construction projects played a crucial role in reversing desertification. These findings provide valuable insights for understanding the development patterns of desertification in the most representative deserts and sandy lands in China and formulating effective desertification control strategies.

Territorial spatial ecological restoration is a crucial prerequisite for optimizing the territorial spatial patterns, enhancing the ecosystem functions, and achieving sustainable development at the regional scale. The Qaidam Basin, located in the alpine arid region of the Qinghai-Xizang Plateau, China, is experiencing desertification, biodiversity loss, soil erosion, and environmental pollution. Selecting the Qaidam Basin as the study area, we identified 9 ecological sources in the region using the Morphological Spatial Pattern Analysis (MSPA) method and the landscape connectivity assessment, and extracted 10 significant corridors and 26 general corridors using the Minimum Cumulative Resistance (MCR) and Gravity models. Then, we determined 114 ecological "pinch points" and 42 ecological barrier points by employing the Circuit Theory, thereby constructing the ecological security pattern of the area. Further, we evaluated the ecosystem health of the Qaidam Basin during 2003-2023 using the Vitality-Organization-Resilience-Service (VORS) model. Finally, we integrated ecosystem health assessment and ecological security pattern to comprehensively identify the key areas for ecological restoration in the Qaidam Basin. The results revealed that the ecosystem in the basin fluctuated toward a healthier state from 2003 to 2023. The average ecosystem health index (EHI) for the basin decreased from 0.34 in 2003 to 0.28 in 2013, followed by a substantial recovery to 0.36 in 2023. Higher EHI values were found in the northeastern, southeastern, and southwestern fringes and lower values were located in the basin interior and northwestern region. During 2003-2023, the areas that exhibited a decrease in EHI were primarily located in the interior and northwestern regions of the basin, while those that exhibited an increase in EHI were located in the northeastern, southeastern, and southwestern fringes, demonstrating expanded spatial differences. This may be attributed to the fact that once an eco-environment is damaged, the ecological recovery of the vulnerable areas within the eco-environment will be slow and difficult. This study identified four types of ecological restoration areas, including corridor connectivity, artificial restoration, ecological recovery, and ecological enhancement zones, covering a total area of 6034.7 km², and proposed targeted ecological restoration strategies according to these different categories. Our findings can serve as a valuable reference for optimizing the territorial spatial patterns, enhancing the ecosystem functions, and promoting sustainable development in the Qaidam Basin.

Drought is a natural disaster that significantly impacts the Earth's ecological environment, especially in arid and semi-arid areas. However, drought at a large watershed scale, which plays an important role in sustainable environmental development, has received limited attention. In this study, we analyzed the spatial and temporal variations in drought in the Yellow River Basin, China from 2002 to 2022 and its driving factors using a vegetation health index (VHI). Results showed that average VHI in the Yellow River Basin from 2002 to 2022 was 0.581, with the most severe drought occurring in summer and autumn. The basin showed a slow decreasing trend in drought during the study period. Regarding spatial distribution of monthly drought frequency and trend of VHI, the mean of the frequency was 13.00%, and 78.00% had a drought frequency of 10.00%-20.00%, with moderate drought generally prevailing. Regarding land use types, forest land, grassland, agricultural land, construction land, water body, and wasteland showed a descending order for the annual average VHI. VHI of each land use type was the lowest in summer and autumn, with pronounced seasonal characteristics. The uneven distribution of drought in the Yellow River Basin was primarily influenced by annual precipitation, solar-induced chlorophyll fluorescence, and relative humidity. VHI effectively quantified drought conditions at a regional scale and proved to be highly applicable in the Yellow River Basin. The results clarify the effectiveness of VHI for drought monitoring in the Yellow River Basin and can provide a reference for drought monitoring across the basin.

The Turpan-Hami (Tuha) Basin of China, a critical region on the Silk Road Economic Belt and a major national energy base, occupies a significant position in energy security and in the major industrial clusters in Xinjiang Uygur Autonomous Region, China. Understanding spatial and temporal evolution of human activities in this area is essential for harmonizing ecological protection with energy development, safeguarding the ecological security of the Silk Road Economic Belt, and promoting the sustainable development of the area. However, despite rapid socioeconomic advances, the trajectories of human activity intensity and the principal driving mechanisms over the past three decades remain inadequately understood. To address these gaps, this study constructed a land use dataset for the Tuha Basin from 1990 to 2020, utilizing Google Earth Engine (GEE) and random forest classification algorithm. We assessed the intensity of human activities and their spatial autocorrelation patterns and further identified key drivers influencing spatial and temporal variations using the Geodetector model. Our findings indicated that the intensity of human activities in the Tuha Basin has exhibited a "first decline and then recovery" trend over the past 30 a, accompanied by significant spatial clustering. In recent years, the aggregation of hot spots has diminished, while clustering of cold spots has intensified, suggesting a dispersion of human activity centers. Nevertheless, urban areas in the Hami and Turpan cities, along with their surrounding areas, continued to serve as core areas of human activities. Topographic features (slope gradient and aspect) and their interactions with economic variables emerged as dominant determinants shaping the spatial patterns and temporal dynamics of human activity intensity. This result provides critical insights into fostering sustainable regional development and ecological conservation in the Tuha Basin and offers valuable methodological and empirical references for studies on land use dynamics and human activity intensity in similar arid areas.

The Wuding River Basin, situated in the Loess Plateau of northern China, is an ecologically fragile region facing severe soil erosion and imbalanced ecosystem service (ES) functions. However, the mechanisms driving the spatiotemporal evolution of ES functions, as well as the trade-offs and synergies among these functions, remain poorly understood, constraining effective watershed-scale management. To address this challenge, this study quantified four ES functions, i.e., water yield (WY), carbon storage (CS), habitat quality (HQ), and soil conservation (SC) in the Wuding River Basin from 1990 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model, and proposed an innovative integration of InVEST with a Bayesian Belief Network (BBN) to nonlinearly identify trade-off and synergy relationships among ES functions through probabilistic inference. A trade-off and synergy index (TSI) was developed to assess the spatial interaction intensity among ES functions, while sensitivity and scenario analyses were employed to determine key driving factors, followed by spatial optimization to delineate functional zones. Results revealed distinct spatiotemporal variations: WY increased from 98.69 to 120.52 mm; SC rose to an average of 3.05×10⁴ t/hm²; CS remained relatively stable (about 15.50 t/km²); and HQ averaged 0.51 with localized declines. The BBN achieved a high accuracy of 81.9% and effectively identified strong synergies between WY and SC, as well as between CS and HQ, while clear trade-offs were observed between WY and SC versus CS and HQ. Sensitivity analysis indicated precipitation (variance reduction of 9.4%), land use (9.8%), and vegetation cover (9.1%) as key driving factors. Spatial optimization further showed that core supply and ecological regulation zones are concentrated in the central-southern and southeastern basin, while ecological strengthening and optimization core zones dominate the central-northern and southeastern margins, highlighting strong spatial heterogeneity. Overall, this study advances ES research by combining process-based quantification with probabilistic modeling, offering a robust framework for studying nonlinear interactions, driving mechanisms, and optimization strategies, and providing a transferable paradigm for watershed-scale ES management and ecological planning in arid and semi-arid areas.

Over the past six decades, the implementation of soil and water conservation measures has significantly reduced soil erosion and sediment yield on the Loess Plateau, China. However, while the overall reduction is well-documented, the dynamic interplay between soil erosion potential and sediment connectivity, specifically how they spatially covary under land use/cover changes, remains insufficiently understood. To address this gap, this study established a model framework by integrating the revised universal soil loss equation (RUSLE), index of connectivity (IC), and sediment delivery ratio (SDR) to evaluate the spatio-temporal variations in soil erosion and sediment yield in the Hantaichuan Watershed, northern Loess Plateau, China, from 1995 to 2020 and to estimate the effects of land use/cover changes and check dam construction on sediment yield. The results revealed that the soil erosion in the Hantaichuan Watershed decreased by 43.90% from 1995 to 2020 and the sediment yield decreased by 69.28% under the combination of land use/cover changes and check dam construction. The IC and soil erosion (IC-SE) map revealed both the coupling and decoupling covariation relationships between sediment connectivity and soil erosion. By 2020, areas with high connectivity and high erosion (I-E) covered only 18.67% of the watershed, while contributed more than 40.00% to the total erosion. The I-E zones were mainly located in the central part of the watershed where aeolian sands derived from the Hobq Desert are concentrated and were identified as critical areas for soil and water conservation. This study provides support for priority management of watershed conservation measures as well as a valuable reference for future studies.

Desert lakes are an important link in the water cycle and an important reservoir of water resources in arid and semi-arid areas, playing an important role in maintaining the stability of the regional natural environment. However, studies on the hydrochemical evolution and transformation relationships between desert lake groups and potential water sources are limited. Taking the Qixing Lake, the only lake group within the Hobq Desert in China, as the area of interest, this study collected samples of precipitation water, Yellow River water, lake water, and groundwater at different burial depths in the Qixing Lake region from July 2023 to October 2024. The hydrochemistry of different water bodies was analyzed using a combination of Piper diagrams, Gibbs diagrams, ratio of ions, and MixSIAR mixing models to reveal the transformational relationships of lake water with precipitation, groundwater, and Yellow River water. Results showed that both groundwater and surface water in the study area are weakly-to-strongly alkaline, with HCO₃^- as the dominant anion and Na⁺, Ca²⁺, and K⁺ as the main cations. The hydrochemical type of groundwater and some lakes was dominated by HCO₃^--Na⁺, whereas that of other lakes was dominated by Cl^--Na⁺ and HCO₃^--Mg²⁺. The hydrochemistry of groundwater and Yellow River water in the Qixing Lake region was controlled mainly by a combination of evaporite saline and silicate rock mineral dissolution. The local meteoric water line (LMWL) of the study area proved that regional water bodies are strongly affected by evaporative fractionation. The MixSIAR model revealed that shallow groundwater is the main recharge source of the lake group in the Qixing Lake region, accounting for 59.0%-64.2% of the total. The findings can provide references for the identification of water sources in desert lakes and the development and utilization of water resources in desert lake regions.

Continuous cropping can lead to soil environment deterioration, cause plant health problems, and reduce crop productivity. However, the response mechanisms of soil microbial co-occurrence patterns to the duration of continuous melon cropping remain poorly understood. Here, we employed the metagenomic techniques to comparatively investigate the bulk and rhizosphere soil microbial communities of major melon-producing regions (where the duration of continuous melon cropping ranges from 1 to 30 a) in the eastern and southern parts of Xinjiang Uygur Autonomous Region, China. The results showed that soil pH clearly decreased with increasing melon cropping duration, while soil electrical conductivity (EC) and the other soil nutrient indices increased with increasing melon cropping duration (with the exception of AN and TK in the southern melon-producing region). The most dominant bacterial phyla were Proteobacteria and Actinobacteria, and the most abundant fungal phyla were Ascomycota and Mucoromycota. Redundancy analysis (RDA) indicated that soil pH and EC had no significant effects on the bacterial communities. However, after many years of continuous melon cropping in the southern melon-producing region, fungal communities were significantly negatively correlated with soil pH and significantly positively correlated with soil EC (P<0.050). Co-occurrence network analysis showed that continuous melon cropping increased the complexity but decreased the connectivity of the cross-domain microbial networks. Moreover, the enrichment patterns of microorganisms in the main microbial network modules varied significantly with the duration of continuous melon cropping. Based on the analysis of keystone taxa, we found that continuous melon cropping increased some plant pathogens (e.g., Fusarium and Stagonospora) but decreased beneficial bacteria (e.g., Mesorhizobium and Pseudoxanthomonas). In conclusion, this study has greatly enhanced the understanding of the effects of continuous melon cropping on alterations in the microbial community structure and ecological networks in Xinjiang.

Soil erosion and shallow landslides in the upper reaches of the Yellow River, China, are increasing due to extreme climate events and human disturbances. The biomechanical properties of vegetation roots play an important role in soil stabilization and fixation, as they resist soil erosion and shallow landslides in this area. However, the biomechanical properties of the roots of dominant herbs and their influencing factors in this area remain poorly understood. Therefore, we selected two dominant herbs in this area, Stipa aliena Keng and Poa crymophila Keng, and carried out a series of uniaxial tensile tests on the roots of the two herbs under different treatments. Meanwhile, the effects of root diameter, plant species, gauge length, root water content, and loading rate on the biomechanical properties of the two herbs' roots were analyzed. The results showed that root diameter was the most significant factor affecting the root biomechanical properties (P<0.010), and root tensile force displayed a positive power law relationship with root diameter, whereas root tensile strength and Young's modulus followed negative power law correlations with root diameter, and fracture strain increased linearly with root diameter. Root tensile force, tensile strength, and fracture strain of S. aliena were significantly greater than those of P. crymophila (P<0.001), which was mainly due to the higher lignin content and lignin:cellulose ratio of S. aliena roots. During uniaxial tensile process, hydrated roots exhibited elastic-plastic-brittle behavior, whereas dried roots exhibited elastic-brittle behavior. Root fracture strain of the two herbs was significantly lower under 100 mm gauge length than under 50 mm gauge length (P<0.001), and the Young's modulus was significantly greater (P<0.050). Tensile strength and fracture strain of hydrated roots of the two herbs were significantly greater than those of dried roots (P<0.050), whereas the Young's modulus was significantly lower (P<0.001). Root tensile force, tensile strength, and fracture strain of S. aliena were significantly greater under 20 mm/min loading rate than under 200 mm/min loading rate (P<0.050), whereas loading rate had no significant effect on the root biomechanical properties of P. crymophila (P>0.050). Fibrous roots of the two herbs were well developed, with relatively high tensile strengths and Young's moduli of 78.498 and 837.901 MPa for S. aliena, and 67.541 and 901.184 MPa for P. crymophila, respectively. The two herbs can stabilize soil and prevent soil erosion and can be used as pioneer species for ecological restoration in the upper reaches of the Yellow River. These results provide a theoretical basis for soil erosion and shallow landslide control in the giant landslide area of the upper reaches of the Yellow River.

The rhizosphere bacteria play crucial roles in plant health and growth as they are involved in assimilating nutrients and resisting adverse conditions such as nutrient stress, drought, and wind erosion. Agriophyllum squarrosum (L.) Moq. is a pioneer plant used in sand fixation due to its strong resistance to drought and wind erosion. However, the bacterial community characteristics and ecological function in the rhizosphere of A. squarrosum are poorly understood. In this study, soil samples were collected from different developmental stages (seedling stage, vegetative stage, reproductive stage, and withering stage) of A. squarrosum. Illumina Miseq sequencing was used to detect differences in soil bacterial abundance. The Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) program was used to predict bacterial functions, and the relationships among bacteria, functional populations, and soil nutrients were examined using a heatmap analysis. The results showed that the Shannon and Sobs indices of rhizosphere bacteria were significantly higher during the reproductive stage than during the other stages. Pantoea sp. (7.03%) was the dominant genus during the seedling stage; Arthrobacter sp. was the dominant genus during the vegetative (13.94%), reproductive (7.57%), and withering (12.30%) stages. The relative abundances of Chloroflexi, Acidobacteria, and Gemmatimonadetes were significantly high during the reproductive stage. According to the PICRUSt analysis, membrane transport, signal transduction, and environmental adaptation of the bacterial functional population occurred during the seedling stage. Carbohydrate metabolism increased during the vegetative stage, while energy metabolism, lipid metabolism, and biosynthesis of other secondary metabolites of the bacterial functional population significantly increased during the reproductive stage. The abundances of bacterial communities, functional genes, and soil nutrients were synergistically altered during various developmental stages. Our findings suggest that the developmental stages of A. squarrosum play a significant role in defining the composition and structure of bacterial communities in the rhizosphere. The results will provide a basis for better prediction and understanding of soil bacterial metabolic potential and functions of A. squarrosum rhizosphere in sandy areas.

The diversity and discontinuity of plant communities in the oasis-desert ecotone are largely shaped by variations in groundwater depth, yet the relationships between spatial distribution patterns and ecological niches at a regional scale remain insufficiently understood. This study examined the oasis-desert ecotone in Qira County located in the Tarim Basin of China to investigate the spatial distribution of plant communities and groundwater depth as well as their relationships using an integrated approach that combined remote sensing techniques, field monitoring, and numerical modeling. The results showed that vegetation distribution exhibits marked spatial heterogeneity, with coverage ranked as follows: Tamarix ramosissima>Phragmites australis>Populus euphratica>Alhagi sparsifolia. Numerical simulations indicated that groundwater depths range from 2.00 to 65.00 m below the surface, with the system currently in equilibrium, sustaining an average annual recharge of 1.06×10⁸ m³ and an average annual discharge of 1.01×10⁸ m³. Groundwater depth strongly influences vegetation composition and structure: Phragmites australis dominates at average groundwater depth of 5.83 m, followed by Populus euphratica at average groundwater depth of 7.05 m. As groundwater depth increases, the community is initially predominated by Tamarix ramosissima (average groundwater depth of 8.35 m), then becomes a mixture of Tamarix ramosissima, Populus euphratica, and Karelinia caspia (average groundwater depth of 10.50 m), and finally transitions to Alhagi sparsifolia (average groundwater depth of 14.30 m). These findings highlight groundwater-dependent ecological thresholds that govern plant community composition and provide a scientific basis for biodiversity conservation, ecosystem stability, and vegetation restoration in the arid oasis-desert ecotone.

Spatiotemporal forecasting of surface soil moisture (SSM) is recognized as a critical scientific issue in precision agricultural irrigation, regional drought monitoring, and early warning systems for extreme precipitation. However, long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales. In this study, we used a daily SSM dataset at a 0.05°×0.05° spatial resolution over the Qilian Mountains, China and proposed a hybrid Convolutional Long Short-Term Memory (ConvLSTM)-Nudging model, which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting. We trained and evaluated the SSM predictive performance of four models (Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), ConvLSTM, and ConvLSTM with Squeeze-and-Excitation (SE) attention mechanism (ConvLSTM-SE)) in both short-term and long-term scenarios. The results showed that all the models perform well under short-term predictions, but the accuracy decrease substantially in long-term predictions. Therefore, we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases. Comprehensive evaluations demonstrate that Nudging significantly improves all the models, with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario. Relative to those of the best-performing ConvLSTM model for long-term forecasts, when observation noise δ=0.00 and observation fraction obs=50.0%, the coefficient of determination (R²) of ConvLSTM-Nudging increases by approximately 82.1%, while its mean absolute error (MAE) and root mean squared error (RMSE) decrease by approximately 84.8% and 77.3%, respectively; the average Pearson correlation coefficient (r) improves by approximately 23.6%, and Bias is reduced by 98.1%. These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions, they are prone to error accumulation and systematic drift in long-term autoregressive predictions. Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases, thereby achieving robust long-term SSM forecasting.

The Balkhash Lake Basin (BLB), a vital Central Asian watershed, faces hydrological uncertainty under climate warming. This study integrated multi-source remote sensing data (Sentinel-1 snow depth, Randolph Glacier Inventory (RGI) v.7.0 glacier inventory, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) mass balance) with a degree-day model to reconstruct decadal snow and ice dynamics across 13 sub-basins and analyzed their hydrological impacts from 1950 to 2014. The results showed that: (1) while flows from the downstream river of the BLB decreased from 1950 to 1982 due to land surface changes, runoff increased significantly after 1982 in the Ili River (18.0%) and moderately increased in most rivers in the east (1.3%-8.3%), driven by increased precipitation and glacier melt. Runoff in the Ayaguz catchment (no glaciers with the highest climate warming) declined (10.5%); (2) climate warming reduced precipitation falling as snow caused snow melt water to decline (0.03-0.22 mm/a) across the BLB, leading to downward shifts in runoff and runoff coefficient, especially in the rivers in the east. However, snow melt during April-June positively correlated with runoff coefficient, contributing to an upward shift in the Ili River Basin; and (3) meltwater from glacierized areas (<5.0% of basin area) contributed to 14.3% of total ablation water. Net glacier melt provided substantial excess flows (11.6 m³/s in the Ili River and <1.0 m³/s in the rivers in the east), generally counterbalancing the negative effect of rising potential evaporation at decadal scales and positively correlating with the runoff coefficient. Therefore, water stress in the BLB may be more severe in the future due to the accelerating glacier melt after the abrupt increase in air temperature in 2000, the continuing decline in snow melt, and the significant inter-annual variations in precipitation.

Sand and dust storms (SDSs) are natural disasters that frequently occur during spring in arid and semi-arid areas., causing serious impacts on human health, air quality, transportation, and agricultural production. Accurately simulating the occurrence and evolution of SDSs is of great significance for identifying dust sources and formulating effective disaster prevention measures. In this study, numerical simulations were conducted to reveal the dynamic spatiotemporal evolution and transport of dust load across East Asia. Using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) data, the most severe SDS events in the spring of 2023 in East Asia were numerically simulated. The simulated results were compared and validated using meteorological observations and multisource remote sensing data. The results showed that the simulated dust load in the peak regions showed close agreement with ground-based observations during the events. The primary dust sources in spring 2023 were identified as the western desert of Mongolia, the Gobi Desert, and the Taklimakan Desert in Xinjiang Uygur Autonomous Region of China. Peak dust load and maximum wind speed occurred almost simultaneously, indicating that high wind speed was the primary driver of sand and dust mobilization during individual SDS events. Increased surface vegetation covers partially mitigated wind-driven dust emissions. In April, strong winds over the Gobi Desert on the Mongolian Plateau predominantly drove cross-border SDSs along northwestern and northward transport pathways. Dust originating from Mongolia exerts a substantial influence on particulate dust load in the central and eastern parts of Inner Mongolia Autonomous Region of China. In contrast, their impact on the northwestern regions of China remains relatively limited. These findings contribute to understanding the source areas of SDS events in East Asia by simulating the dynamic evolution of SDSs and elucidating the relationships between SDS events and local geographical and environmental factors.

Land desertification severely compromises the core function of ecosystem and significantly disrupts biodiversity. Caragana korshinskii Kom. plays a pivotal role as a critical plant resource in the restoration and ecological reconstruction of desertified areas in Northwest China. Kytorhinus immixtus Motschulsky is the primary pest responsible for causing substantial damage to the seeds of C. korshinskii. In this study, field surveys were utilized in three distinct desertified types (lightly, moderately, and severely desertified areas) in north central Ningxia Hui Autonomous Region, Northwest China. This research was focused on investigating the population dynamics and damage rates of K. immixtus, with an emphasis on examining the relationships among K. immixtus distribution, levels of soil desertification, and associated environmental factors. The results revealed marked variations in the population distribution and abundance of K. immixtus across habitats with different degrees of desertification. Due to the sand-fixing ability of C. korshinskii, the severity of soil desertification decreased progressively from severe to moderate and light with C. korshinskii establishment. This reduction in desertification, along with habitat restoration and an increase in plant diversity, was correlated with a gradual increase in K. immixtus population size and damage rate. Generalized linear mixed model analysis revealed significantly positive correlations of soil total potassium, C. korshinskii height, maximum temperature during the survey, precipitation, and the plant species richness index with K. immixtus population. In contrast, the soil total phosphorus content, organic matter content, minimum temperature during the survey, C. korshinskii canopy width, and branch number were significantly and negatively correlated with K. immixtus population. Due to the sand-fixing capacity of C. korshinskii, the plant mitigated soil desertification, but as desertification severity decreased, habitat restoration and increased plant diversity drove a gradual increase in the population and damage rate of K. immixtus. Both biotic and abiotic factors in the habitat significantly influenced K. immixtus occurrence. To achieve the sustainable restoration of desert ecosystem, optimization of plant community structure with soil nutrient management in ecological rehabilitation is necessary to balance the benefits of sand fixation with pest risks.

Optimizing the spatial pattern of carbon sequestration service is essential for advancing regional low-carbon development, accelerating the achievement of the "dual carbon" goals, and promoting the high-quality development of ecological environment. The carbon sequestration capacity within the mountain-desert-oasis system (MDOS), a unique landscape pattern, exhibits significant gradient characteristics, and its carbon sink potential can be substantially improved through multi-scale spatial optimization. This study employed the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model to estimate carbon storage and sequestration (CSS) in the Gansu section of Heihe River Basin, China, a representative MDOS, based on land use/land cover (LULC) data from 1990 to 2020. The Patch-level Land Use Simulation (PLUS) model was coupled to simulate LULC and estimate carrying CSS under natural development (ND), ecological protection (EP), water constraint (WC), and economic development (ED) scenarios for 2035. Furthermore, the study constructed and optimized the CSS pattern on the basis of economic and ecological benefits, exploring the guiding significance of different scenarios for pattern optimization. The results showed that CSS spatial distribution is closely correlated with LULC pattern, and CSS is expected to improve in the future. CSS showed an overall increase across subsystems during 1990-2020, but varied across LULC types. CSS of construction land in all subsystems exhibited an increasing trend, while CSS of unused land showed a decreasing trend, with specific changes of 1.68×10³ and 3.43×10⁵ t, respectively. Regional CSS dynamics were mainly driven by conversions among unused land, cultivated land, and grassland. The CSS pattern of MDOS was divided into carbon sink functional region (CSFR), low carbon conservation region (LCCR), low carbon economic region (LCER), and economic development region (EDR). Water resources coordination served as the basis of pattern optimization, while the four dimensions—ecological carbon sink, low-carbon maintenance, agricultural carbon reduction and sink enhancement, and urban carbon emission reduction—framed the optimization framework. ND, EP, WC, and ED scenarios provided guidance as the basic reference, optimal benefit, "dual carbon" baseline, and upper development limit, respectively. Additionally, the detailed CSS sub-partitions of MDOS covered most potential scenarios of such ecosystems, demonstrating the applicability of these sub-partitions. These findings provide valuable references for enhancing CSS and hold important significance for low-carbon territorial spatial planning in the MDOS.

Tree growth is extremely vulnerable to climate change, especially in semi-arid areas. Although the response of stem radial growth (SRG) to climate change has been extensively studied, the intra-annual regulatory mechanisms of SRG in trees with different water use strategies and life types remain poorly understood. This study calculated the SRG of four native species in the semi-arid area of the Loess Plateau, China, including two isohydric species (Pinus tabuliformis Carrière and Populus × hopeiensis Hu & Chow) and two anisohydric species (Prunus sibirica L. and Platycladus orientalis (L.) Franco). The results revealed that the intra-annual SRG of all the four tree species exhibited a single peak, and greater SRG was found in anisohydric species. Principal component analysis and structural equation model revealed that atmospheric water, particularly relative humidity, was the main factor affecting the SRG of coniferous species (P. tabuliformis and P. orientalis), whereas the SRG was mainly affected by soil water content in broadleaf species (P. sibirica and P. × hopeiensis). These findings suggested that water use strategies and life types play important roles in SRG and environmental response of trees in semi-arid area. Considering the high climate sensitivity of wood formation in trees, our results highlight the importance of water use strategies and life types of trees in SRG prediction in the context of future climate change in arid and semi-arid areas.

Central Asia is characterized by an arid climate and widespread desert distribution, with its sustainable development severely constrained by dust events. An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area. Based on the meteorological observations from 2000 to 2020, we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries (Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, and Tajikistan) via Theil-Sen trend analysis and Geodetector modeling method, quantitatively revealing the influence of environmental factors, such as temperature, precipitation, and vegetation, on the frequency of dust weather. The results showed that: (1) dust weather in Central Asia was mainly distributed in a large ''dust belt'' extending from west to east from northern part of the Caspian lowland desert, and concentrated in basins, plains, and other low-altitude areas. Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia, with a maximum annual frequency of 21.9%; (2) strong dust weather in Central Asia has fluctuated and slightly decreased since 2001. The highest frequency (1.1%) occurred in spring (from March to June); (3) from 2000 to 2020, changes such as spot shifting and shrinking occurred in the four main source areas (north of the Aral Sea, Kyzylkum Desert, Karakum Desert, and Garabogazköl Bay region), where sandstorms occurred in Central Asia, and northern Caspian lowland desert became the most important low-emission dust source in Central Asia; and (4) the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia. This study provides a theoretical basis for sand prevention and sand control in Central Asia. In the future, Central Asia should focus on the rational utilization of land and water resources, and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.

Soil water content and salinity critically regulate soil microbial composition, plant community structure, and ecosystem multifunctionality (EMF) in semi-arid grasslands. However, the mechanisms through which drought (D), saline-alkaline (SA), and their combined (DSA) stress influence these ecological components remain poorly understood. This study investigated these mechanisms along natural gradients in a semi-arid grassland of China by analyzing soil physical-chemical properties, microbial communities, and vegetation characteristics. The results showed that as the environmental stress shifted from the D group to the DSA group and then to the SA group, soil electrical conductivity significantly increased, while urease and phosphatase activities significantly decreased. Soil organic carbon, total nitrogen, total phosphorus, and microbial biomass carbon and nitrogen were lower in the D and SA groups than in the DSA group. Meanwhile, plant biomass showed an increasing trend along the treatment gradient, primarily driven by dominant species, while plant diversity did not exhibit significant differences. Further analysis identified the soil water content and salinity as the key determinants of soil microbial diversity and community complexity. Soil enzyme activities exhibited contrasting relationships with microbial composition, correlating positively with the richness of bacterial amplicon sequence variants (ASVs) but negatively with the richness of fungal ASVs. Notably, microbial biomass, which varied significantly across different groups, emerged as a key predictor of changes in EMF, with its critical role confirmed through structural equation modeling. These findings collectively elucidate the responses of ecological communities to synergistic soil hydro-saline stress in semi-arid ecosystems, while highlighting the critical role of microbial biomass in maintaining EMF.

Rising temperatures and increased droughts caused by climate change significantly reduce crop yields. Halophytes with different photosynthetic metabolism types have specific mechanisms for resistance to climatic factors. This study analyzed the morphophysiological, biochemical, and molecular-genetic mechanisms of tolerance and adaptation in halophytes, promising candidates for the restoration of salt affected lands in arid and semi-arid areas. Experiments under drought (D) and elevated temperature (eT), as well as their combined action (eT+D), were performed on Atriplex verrucifera M. Bied. (C₃ plant) and Climacoptera crassa (M. Bieb.) Botsch. (C₄-NAD-ME plant) with different types of photosynthesis. The activity of photosystem I (PSI) and the efficiency of photosystem II (PSII) were measured, along with the expression of genes involved in the light (psaA, psaB, psbA, CAB, Fd1, PGR5, and ndhH) and dark (rbcL, Ppc2, and PPDK) reactions of photosynthesis. The content of key carboxylating enzymes ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) and phosphoenolpyruvate carboxylase (PEPC), as well as the photorespiration enzyme glycine decarboxylase (GDC), were assessed. Plant growth and water-salt balance parameters, and activity of enzymes in the malate dehydrogenase (MDH) system nicotinamide adenine dinucleotide (phosphate) (NAD(P))-MDH and NAD(P)-malic enzyme (ME) were also examined. A multivariate analysis of the experimental results revealed that A. verrucifera and C. crassa were both resistant to the effects of these climatic stressors. The tolerance mechanisms of both species were significantly influenced by a high level of photosynthetic plasticity. Nevertheless, differences were observed in the protective mechanisms underlying tolerance. In the C₃ species, dissipative processes associated with non-photochemical quenching (NPQ) of PSII and MDH system enzymes (malate valves) were activated, particularly under osmotic stress. The negative effects in the C₃ plants were caused by the combined action of eT+D, which was compensated by an increased expression of rbcL, psaA, CAB, and especially PGR5, i.e., genes encoding Rubisco large subunit and PSI components: apoproteins A, chlorophyll a/b-associated protein (CAB) of light-harvesting complex, and proton gradient regulation 5 (PGR5) protein of the main pathway of cyclic electron transport (CET) around PSI. In C₄ species, the protective MDH complex was expressed to a lesser extent, but activation of the C₄ carbon-concentrating mechanism (CCM) and upregulation of PGR5 expression were observed, particularly under the individual action of the factors. Under the combined stress of eT+D, C. crassa exhibited a synergistic effect, where the increase in NPQ level and NAD-ME activity, as well as decrease in NADP-ME activity was less pronounced compared with the effect of singular factors. Comparative physiological, biochemical, and molecular analyses of how C₃ and C₄ species response to individual and combined climatic factors provide new insights into sustainable plant adaptation strategies in the face of global climate change. Considering the high nutritional value of these two fodder species, a technological approach could be developed to improve the productivity of salt affected lands.

Climate change in High Mountain Asia (HMA) is characterized by elevation dependence, which results in vertical zoning of vegetation distribution. However, few studies have been conducted on the distribution patterns of vegetation, the response of vegetation to climate change, and the key climatic control factors of vegetation along the elevation gradient in this region. In this study, based on the Normalized Difference Vegetation index (NDVI), we investigated the evolution pattern of vegetation in HMA during 2001-2020 using linear trend and Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) methods. Pearson correlation analysis and partial correlation analysis were used to explore the response relationship between vegetation and climatic factors along the elevation gradient. Path analysis was employed to quantitatively reveal the dominant climatic factors affecting vegetation distribution along the elevation gradient. The results showed that NDVI in HMA increased at a rate of 0.011/10a from 2001 to 2020, and the rate of increase abruptly slowed down after 2017. NDVI showed a fluctuating increase at elevation zones 1-2 (<2500 m) and then decreased at elevation zones 3-9 (2500-6000 m) with the increase of elevation. NDVI was most sensitive to precipitation and temperature at a 1-month lag. With the increase of elevation, the positive response relationship of NDVI with precipitation gradually weakened, while that of NDVI with temperature was the opposite. The total effect coefficient of precipitation (0.95) on vegetation was larger than that of temperature (0.87), indicating that precipitation is the dominant control factor affecting vegetation growth. Spacially, vegetation growth is jointly influenced by precipitation and temperature, but the influence of precipitation on vegetation growth is dominant at each elevation zone. The results of this study contribute to understanding how the elevation gradient effect influences the response of vegetation to climate change in alpine ecosystems.

Arid mountain ecosystems are highly sensitive to hydrothermal stress and land use intensification, yet where net primary productivity (NPP) degradation is likely to persist and what drives it remain unclear in the Tianshan Mountains of Northwest China. We integrated multi-source remote sensing with the Carnegie-Ames-Stanford Approach (CASA) model to estimate NPP during 2000-2020, assessed trend persistence using the Hurst exponent, and identified key drivers and nonlinear thresholds with Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP). Total NPP averaged 55.74 Tg C/a and ranged from 48.07 to 65.91 Tg C/a from 2000 to 2020, while regional mean NPP rose from 138.97 to 160.69 g C/(m²•a). Land use transfer analysis showed that grassland expanded mainly at the expense of unutilized land and that cropland increased overall. Although NPP increased across 64.11% of the region during 2000-2020, persistence analysis suggested that 53.93% of the Tianshan Mountains was prone to continued NPP decline, including 36.41% with significant projected decline and 17.52% with weak projected decline; these areas formed degradation hotspots concentrated in the central and northern Tianshan Mountains. In contrast, potential improvement was limited (strong persistent improvement: 4.97%; strong anti-persistent improvement: 0.36%). Driver attribution indicated that land use dominated NPP variability (mean absolute SHAP value=29.54%), followed by precipitation (16.03%) and temperature (11.05%). SHAP dependence analyses showed that precipitation effects stabilized at 300.00-400.00 mm, and temperature exhibited an inverted U-shaped response with a peak near 0.00°C. These findings indicated that persistent degradation risk arose from hydrothermal constraints interacting with land use conversion, highlighting the need for threshold-informed, spatially targeted management to sustain carbon sequestration in arid mountain ecosystems.

In the northern Tarim River Basin, the Weigan River Basin is a critical endorheic system characterized by extreme aridity, where drought poses a major natural hazard to agricultural production and ecological stability. This study assessed the future evolution of drought under climate change by employing the standardized moisture anomaly index (SZI) on the basis of multi-model the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations under historical conditions (1970-2014) and future scenarios (shared socioeconomic pathway (SSP)1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 for 2015-2100). The results show that precipitation-evapotranspiration anomalies are projected to first decline but then increase over time, with increased fluctuations and uncertainty under high-emission scenarios (SSP5-8.5). These trends indicate intensifying drought risks and reveal a strong influence of emission pathways on regional water cycling. Temporal analysis of SZI indicates a transition from wetting to drying under low- and medium-emission pathways (SSP1-2.6 and SSP2-4.5), whereas high-emission scenarios are characterized by persistent drying and increased variability. The significant lower-tail dependence (0.271) observed under SSP2-4.5 and SSP5-8.5 suggests that extreme droughts may be subject to nonlinear co-amplification across scenarios. The frequency of moderate and more severe drought events is expected to increase substantially, especially under SSP5-8.5, where drought occurrence is predicted to extend into spring and autumn and become more evenly distributed throughout the year. Spatially, drought duration shows significant positive autocorrelation across all scenarios, with hot spots consistently concentrated in the southern and southeastern regions of the basin. Random forest analysis, interpreted as association-based pattern attribution, indicates that meteorological variables (precipitation and potential evapotranspiration (PET)) make the greatest contributions to the hot spot pattern, followed by topography and soil moisture. Among land use categories, farmland generally shows higher drought sensitivity than other land use types, as reflected by its relative contribution patterns across scenarios. The spatial pattern of drought is statistically structured by climatic forcing, surface conditions, and soil moisture status, reflecting their coupled associations with hot spot occurrence. In addition, a drought spatial uncertainty index was constructed from multi-scenario hot spot maps, revealing spatially heterogeneous structural variability throughout the basin. Correlation analysis further highlights strong internal couplings among environmental variables (e.g., elevation-linked hydroclimatic gradients and grassland-bare soil contrasts). These findings offer a scientific basis for developing region-specific drought monitoring and adaptation strategies under future climate change conditions.

Northwest China serves as a critical ecological barrier region for maintaining national water, energy, and food security, as well as transboundary ecological governance. However, under the dual pressures of climate change and human activities, ecosystem services (ESs) are facing severe challenges in this region. Based on multi-source remote sensing and statistical data during 2000-2020, this study investigated the spatiotemporal evolution characteristics of four key ESs (water yield, habitat quality, carbon storage, and food provisioning) in Northwest China using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Integrating morphological spatial pattern analysis (MSPA) and circuit theory, we identified ecological sources, corridors, pinch points, and barriers, and further designed three optimization scenarios (bottleneck optimization, high-resistance corridor buffering, and barrier removal optimization) to enhance landscape connectivity. The results revealed that ES supply and demand exhibited marked spatial heterogeneity, with high-supply areas concentrated in the southeastern sectors. Ecological sources primarily distributed in the southeastern and northern sectors, and ecological resistance surfaces continuously intensified. Water yield and habitat quality demands were increasing, food provisioning demand was decreasing, and carbon storage demand was surging. A total of 61 ecological sources (8% of the study area), 142 ecological corridors (24,957 km in total length), 237 ecological pinch points, and 89 barrier zones were identified. Among the three optimization scenarios, barrier removal achieved optimal connectivity improvement across all distance thresholds, with the probability of connectivity index improvement reaching up to 4%. This study provides scientific foundations and spatial decision support for ecological network optimization and sustainable governance in arid and semi-arid areas.

In recent years, intensive human activities have increased the intensity of desertification, driving continual desertification process of peripheral meadows. To investigate the effects of restoration on soil microbial communities, we analyzed vegetation-soil relationships in the Hulun Buir Sandy Land, northern China. Through the use of high-throughput sequencing, we examined the structure and diversity in the bacterial and fungal communities within the 0-20 cm soil layer after 9-15 a of restoration. Different slope positions were analyzed and spatial heterogeneity was assessed. The results showed progressive improvements in soil properties and vegetation with the increase of restoration duration, and the following order was as follows: bottom slope>middle slope>crest slope. During the restoration in the Hulun Buir Sandy Land, the bacterial communities were dominated by Proteobacteria, Actinobacteria, and Acidobacteria, whereas the fungal communities were dominated by Ascomycota and Basidiomycota. Eutrophic bacterial abundance increased with the restoration duration, whereas oligotrophic bacterial and fungal abundance levels decreased. The soil bacterial abundance significantly increased with the increasing restoration duration, whereas the fungal diversity decreased after 11 a of restoration, except that at the crest slope. Redundancy analysis showed that pH, soil moisture content, total nitrogen, and vegetation-related factors affected the bacterial community structure (45.43% of the total variance explained). Canonical correspondence analysis indicated that pH, total phosphorus, and vegetation-related factors shaped the bacterial community structure (31.82% of the total variance explained). Structural equation modeling highlighted greater bacterial responses (R²=0.49-0.79) to changes in environmental factors than those of fungi (R²=0.20-0.48). The soil bacterial community was driven mainly by pH, soil moisture content, electrical conductivity, plant coverage, and litter dry weight. The abundance and diversity of the soil fungal community were mainly driven by plant coverage, litter dry weight, and herbaceous aboveground biomass, while there was no significant correlation between the soil fungal community structure and environmental factors. These findings highlighted divergent microbial succession patterns and environmental sensitivities during sandy grassland restoration.

Land use in arid and semi-arid regions has a substantial effect on climate, environment, and biodiversity, thereby projecting the spatiotemporal changes in land use and the subsequent effects. This study employed the locally calibrated Future Land Use Simulation (FLUS) model, which coupled system dynamics with cellular automata and integrated an artificial neural network algorithm and a roulette wheel selection mechanism. We projected future land use (2020-2100) dynamics of Lanzhou, a typical river valley city in Northwest China, under three different Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The simulation results were validated and subsequently reclassified using the International Geosphere Biosphere Programme (IGBP) system to produce a dataset suitable for driving climatic and environmental models. Under the SSP1-2.6 scenario, urban and built-up land expanded consistently, whereas irrigated cropland and pasture as well as grassland contracted continuously. Conversely, the SSP5-8.5 scenario was characterized by a contraction of urban and built-up land, and relative stability of irrigated cropland and pasture as well as grassland. The SSP2-4.5 scenario presented a more complex trade-off, where urban and built-up land and grassland increased first and then decreased, whereas irrigated cropland and pasture followed an opposite trajectory. A significant inverse relationship between urban and built-up land and irrigated cropland and pasture was observed under all scenarios, underscoring the fundamental spatial competition that prevailed in this land-constrained valley city. Furthermore, the negative correlation of grassland with urban and built-up land, coupled with the positive correlation of grassland with irrigated cropland and pasture under both the SSP1-2.6 and SSP5-8.5 scenarios, indicated an evolution from broad confrontation to intricate internal trade-offs within the urban-agricultural-ecological system. This study underscored the critical influence of regional topographic and hydrological constraints on land-use evolution in arid regions, providing guidance for water resource management and ecosystem protection in Lanzhou, with applications for sustainable land-use planning in other arid and semi-arid river valley cities.

Ecological security patterns (ESPs) represent an effective way to maintain regional ecological security and promote regional sustainable development. This study investigated the spatiotemporal variations of ESPs in the West Liaohe River Basin (WLRB), China during 2000-2020 on the basis of five key ecosystem services (net primary production, soil conservation, habitat quality, water retention, and soil loss by wind). On the basis of the Geodetector model, we initially measured the explanatory rates of various natural and anthropogenic factors on the spatial differentiation of ecological sources and ecological corridors. The Geographically and Temporally Weighted Regression (GTWR) model was subsequently used to elucidate the driving mechanism of ESPs at the interannual scale. During 2000-2020, a "fan-shaped" ESP of "two zones, three belts, and many branches" formed in the WLRB. Natural factors dominated the spatial distribution of ESPs, and the average spatial explanation rate for ecological sources and ecological corridors was 23.86%, which was higher than that of anthropogenic activities (13.29%). However, anthropogenic activities amplified the spatiotemporal variations in ESPs. On this basis, this study proposed an ecological security protection and regulation strategy from three aspects, namely, regional priority, suitability analysis, and risk regulation, which might provide a working direction for regional practical management. This study extends the paradigm of ESP research and offers an important theoretical basis for regional ecological security, from "passive management" to "active management".

Climate change significantly affects vegetation dynamics. Thus, understanding interactions between vegetation and climatic factors is essential for ecological management. This study used kernel Normalized Difference Vegetation Index (kNDVI) and climatic data (temperature, precipitation, humidity, and vapor pressure deficit (VPD)) of China from 2000 to 2022, integrating Geographic Convergent Cross Mapping (GCCM) causal modeling, Extreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP) nonlinear threshold identification, and Geographical Simulation and Optimization Systems-Future Land Use Simulation (GeoSOS-FLUS) spatial prediction modeling to investigate vegetation spatiotemporal characteristics, driving mechanisms, nonlinear thresholds, and future spatial patterns. Results indicated that from 2000 to 2022, China's kNDVI showed an overall increasing trend (annual average ranging from 0.29 to 0.33) with distinct spatial differentiation: 52.77% of areas locating in agricultural and ecological restoration regions in the central-eastern plain) experienced vegetation improvement, whereas 2.68% of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation. The coefficient of variation (CV) of kNDVI at 0.30-0.40 (accounting for 10.61%) was significantly higher than that of NDVI (accounting for 1.80%). Climate-driven mechanisms exhibited notable library length (L) dependence. At short-term scales (L<50), vegetation-driven transpiration regulated local microclimate, with a causal strength from kNDVI to temperature of 0.04-0.15; at long-term scales (L>100), cumulative temperature effects dominated vegetation dynamics, with a causal strength from temperature to kNDVI of 0.33. Humidity and kNDVI formed bidirectional positive feedback at long-term scales (L=210, causal strength>0.70), whereas the long-term suppressive effect of VPD was particularly pronounced (causal strength=0.21) in arid areas. The optimal threshold intervals identified were temperature at -12.18°C-0.67°C, precipitation at 24.00-159.74 mm, humidity of lower than 22.00%, and VPD of <0.07, 0.17-0.24, and >0.30 kPa; notably, the lower precipitation threshold (24.00 mm) represented the minimum water requirements for vegetation recovery in arid areas. Future kNDVI spatial patterns are projected to continue the trend of "southeastern optimization and northwestern delay" from 2025 to 2040: the area proportion of high kNDVI value (>0.50) will rise from 40.43% to 41.85%, concentrated in the Sichuan Basin and the southern hills; meanwhile, the proportion of low-value areas of kNDVI (0.00-0.10) in the arid northwestern areas will decline by only 1.25%, constrained by sustained temperature and VPD stress. This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.

Nitrogen (N) and phosphorus (P) are essential nutrients regulating plant growth, yet their long-term impacts on grassland ecosystems in Tajikistan remain poorly understood. This study conducted a five-year (2018-2022) field experiment across four grassland sites (Tabakqi, Balkhi, Luchob, and Ziddi) along an elevation gradient in central and southwestern Tajikistan to explore the effects of varying N (0, 30, and 90 kg N/(hm²∙a)) and P (0 and 30 kg P/(hm²∙a)) additions on aboveground biomass (AGB) and plant species diversity. Nutrient addition significantly increased AGB across all sites. Compared with the control (without N or P addition), AGB increased by 20%-80% under moderate N treatment (adding 30 kg N/(hm²∙a)) and by up to 190%-200% under high N and P addition treatment (adding 90 kg N/(hm²∙a) and 30 kg P/(hm²∙a)). In 2022, AGB at the low-elevation site (Tabakqi) increased from 494 g/m² under the control to 650 g/m² under high N and P treatment, while at the high-elevation site (Ziddi), it rose from 552 to 1614 g/m². In contrast, biodiversity responses were elevation-dependent: species richness declined at mid-elevation grassland sites (Balkhi and Luchob) but showed little change at low-elevation (Tabakqi) and high-elevation (Ziddi) sites. Shannon-Wiener index, Simpson's dominance index, and Pielou's equitability index also varied, reflecting complex interactions among nutrient addition, precipitation, and temperature. The structural equation model (SEM) confirmed that nutrient addition directly enhance AGB but generally suppress plant species diversity, while precipitation promotes AGB, and temperature effects are inconsistent across sites. Overall, our findings demonstrate that nutrient enrichment can increase productivity but reduce biodiversity, with responses strongly mediated by elevation and climate. These results provide the first long-term experimental evidence from Tajikistan's grasslands and underscore the need to balance productivity gains with biodiversity conservation in sustainable grassland management.

Net ecosystem productivity (NEP) is a key indicator for estimating carbon sink dynamics in terrestrial ecosystems. Existing studies on carbon sink dynamics in Northwest China have uncertainties in quantifying spatiotemporal variations of NEP and their driving factors. This study estimated NEP across ecosystems in Northwest China during 2000-2020 using multi-model integration, and analyzed its spatiotemporal patterns and drivers. Results showed that the annual average NEP was 97.98 g C/(m²∙a), with higher values at eastern and western margins and lower values in central hinterland. Strong carbon sink areas included the Yili River Basin and northern slope of Tianshan Mountains, while low carbon sink areas concentrated in eastern Xinjiang Uygur Autonomous Region (Eastern Xinjiang) and Alxa-Ejin Plateau. NEP trended upward from 79.22 g C/(m²∙a) in 2000 to 109.03 g C/(m²∙a) in 2020 with low variability and strong persistence, suggesting continuous growth. NEP significantly and positively correlated with near-infrared reflectance of vegetation (NIRv), weakly with climate factors, and negatively with socio-economic density indicators. Topographically, NEP peaked at 2.0-2.4 km elevation, 15°-25° slopes, and north-facing aspects. Changes in ecosystem type significantly influenced NEP, with bare land conversion into grassland/cropland enhancing carbon sinks. Results of this study highlight the need for ecological restoration and rational land use to boost carbon sequestration in this ecologically sensitive region.

Aeolian sediments in the eastern Qinghai Lake region, China serve as sensitive paleoclimate archives, offering an ideal window into past environmental conditions. This study investigated the Dashuitang (QDST) profile in the eastern Qinghai Lake region by integrating sediment grain size, chroma, and magnetic susceptibility (MS) proxies to reconstruct the regional environmental evolution since the Last Glacial Interstadial and to investigate its relationship with the water level fluctuations of Qinghai Lake. Grain size end-member modeling analysis (EMMA) identified three end-members: end-member 1 (EM1) represented fine-grained material transported over longer distances through mixing processes, which could reveal the regional moisture conditions; end-member 2 (EM2) primarily consisted of coarse-grained material from nearby sources transported via saltation or creep, indicating the intensity of the winter monsoon; and end-member 3 (EM3) mainly reflected deposition from dust storm events controlled by regional low-altitude wind systems. In addition, the regional environmental sequence demonstrated coherence with other records, collectively elucidating the sub-orbital-scale dynamics of the Asian monsoon. The environmental sequence was divided into four principal phases on the basis of sedimentary characteristics and climatic responses: the late Last Glacial Interstadial, Last Glacial Maximum, Last Deglaciation, and Holocene phases. Additionally, the results of this study revealed that there is a close linkage between desertification and lake evolution in the eastern Qinghai Lake region. Since the Last Glacial Interstadial, desertification and lake evolution processes have generally exhibited a trade-off relationship, wherein lake level decline and desert expansion exhibited a direct positive feedback. However, during the early period of the Late Holocene (approximately 2.80-1.50 ka BP), a synergistic response pattern emerged, characterized by relatively high lake levels alongside moderate desert expansion, reflecting an asymmetric decoupling mechanism between the hydrological processes and aeolian dynamics during climatic transition periods. This study provides important insights for predicting the future evolution trends of lake-desert systems under climate change.

Climate warming and humidification trends have significantly influenced vegetation growth patterns in Chinese semi-arid areas. Exploring vegetation dynamics is crucial for understanding regional ecosystem structure and improving the efforts of ecosystem restoration. However, the applicability of various vegetation indices (VIs) in these arid areas remains uncertain. Evaluating the applicability of multiple VIs for vegetation monitoring can elucidate the variability of VIs performance at regional scale. Therefore, this study selected the Zuli River Basin (ZLRB), a typical loess hilly watershed in the semi-arid areas of China. Using Landsat data, we calculated the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel NDVI (kNDVI) for the ZLRB from 1990 to 2020. We analyzed the spatiotemporal variations of these VIs using trend analysis and the Mann-Kendall test, and quantified the contributions of climate change (considering time-lag effects) and human activities to VIs changes through wavelet and residual analyses. Results indicated that VIs generally exhibited an upward trend in the ZLRB, with significant improvements observed in 54.91% of the area for NDVI, 31.69% for EVI, and 33.71% for kNDVI. Among them, NDVI outperformed EVI and kNDVI in capturing vegetation changes in the semi-arid area. VIs responded to precipitation with 1-month time lag and no time lag to temperature during growing season. Moreover, precipitation had a stronger positive correlation with VIs than temperature. Climate change was identified as the dominant driver of vegetation dynamics in the ZLRB, accounting for 93.12% of NDVI variation, while human activities contributed only 6.88%. Comparative analysis of VIs suggests that NDVI was more suitable for describing vegetation changes in the typical arid area of the ZLRB. Our findings underscore the importance of selecting appropriate VIs for targeted ecological restoration and sustainable land management.

Identifying plant water sources is fundamental for elucidating ecohydrological processes and improving water resource management in arid zones under climate change. Stable hydrogen and oxygen isotopes are commonly used to trace plant water uptake; however, cryogenic vacuum extraction (CVE), the standard method for extracting plant xylem water, may induce deuterium depletion, thereby biasing source attribution. To systematically assess the effects of CVE-induced deuterium depletion across species, size classes, and habitats, we excavated five representative soil profiles along the mainstream of the Tarim River in northwestern China, in mid-July 2022. A total of 29 individuals, comprising both Populus euphratica and Tamarix ramosissima, were sampled. We divided P. euphratica individuals into four groups based on diameter at breast height (<50, 50-100, 100-250, and >250 cm), while categorized T. ramosissima individuals into four groups according to plant height (<1.0, 1.0-2.0, 2.0-4.0, and >4.0 m). Plant xylem water was extracted using CVE, and five deuterium depletion scenarios (-5.00‰, -7.00‰, -9.00‰, -11.00‰, and -13.00‰) were simulated. The Bayesian Mixing Model for Stable Isotope Analysis in R (MixSIAR) was applied under six input modes to quantify the proportional contributions of potential water sources and associated prediction errors. Model evaluation revealed that P. euphratica achieved the highest accuracy with a -9.00‰ correction of depletion, whereas a -11.00‰ correction was optimal for T. ramosissima, reducing relative prediction errors by 68.65% and 67.73%, respectively, compared with uncorrected scenario. Small-sized P. euphratica individuals exhibited less deuterium depletion, whereas no clear size-dependent pattern was observed for T. ramosissima. Spatially, plant individuals located farther from the river exhibited reduced deuterium depletion in xylem water. Despite differences in species traits and habitat conditions, both species predominantly relied on deep soil water and groundwater, which together contributed, on average, 61.45% and 59.95% for P. euphratica and T. ramosissima, respectively. These findings highlight the necessity of accounting for CVE-induced deuterium depletion when identifying plant water-use strategies and provide methodological guidance for isotope-based ecohydrological studies in arid environments.

The connection between climatic factors and grazing is essential for maintaining ecosystem function and vegetation productivity. This study examined the impact of grazing intensity on vegetation across a broad climatic gradient spanning the Espinal, Argentine Low Monte, and Patagonian Steppe ecoregions of Argentina. The research was carried out at eight sampling sites with radial grazing gradients generated around artificial water sources (piospheres), exhibiting two contrasting response patterns of vegetation to grazing pressure. One of the response patterns shows a typical vegetation response to grazing that the vegetation productivity increases with the distance to the water sources (decreasing grazing intensity). The second pattern is found in drier regions, where vegetation presents an inverse productivity response that vegetation productivity is higher near water sources (high grazing intensity) due to increased shrub cover. Vegetation productivity was measured using the Normalized Difference Vegetation Index (NDVI). Vegetation patch structure and cover were determined for each site with high, medium, and low grazing intensities. Results indicated that shrub cover is the primary driver of vegetation productivity, showing contrasting responses to grazing intensity between the two identified patterns. While NDVI proved to be a reliable proxy for shrub cover and total vegetation cover (R²>0.70), it failed to reflect grass cover dynamics. Furthermore, mean annual temperature was more strongly correlated with vegetation cover changes, while grazing intensity significantly altered vegetation patch structure and soil cover distribution. Specifically, in drier regions, high grazing intensity led to larger patches while, in wetter regions, it led to smaller patches (fragmentation). Shrubs, with their deeper roots and drought tolerance, were less preferred and more resistant to grazing in arid environments and thrived under grazing pressure in these arid conditions. Our results underscored the need for adaptive management strategies in grazing systems. Traditional approaches may require significant adjustments, as the efficacy of management hinges on the interplay of specific climatic conditions and the varied responses of vegetation. Furthermore, effective conservation efforts should prioritize the recognition and protection of shrubs given their critical contribution to ecosystem function and biodiversity. Ultimately, this research provides a valuable framework to understand the complex dynamics between grazing and vegetation in arid and semi-arid environments, highlighting that sustainable grazing practices should be tailored to account for both climatic variables and the unique characteristics of different plant communities.

Understanding the drivers of gross primary production (GPP) is essential for assessing vegetation productivity dynamics under climate change, particularly across regions with strong climatic heterogeneity. China spans diverse climate zones and ecosystems, yet the relative importance of climatic, environmental, and anthropogenic factors regulating GPP has remained poorly resolved. In this study, we investigated the spatiotemporal patterns of GPP across China from 2001 to 2020 and quantified the contributions of multiple driving factors across different climate zones. We combined ridge regression with an interpretable machine learning framework based on Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to disentangle the long-term linear controls on and short-term nonlinear responses driving GPP. Ridge regression was employed to address multicollinearity among predictors and to quantify their interannual contributions, while SHAP analysis was used to quantify feature contributions in nonlinear model predictions. Our results indicated that leaf area index (LAI) and human footprint dominated the long-term variability of GPP in most climate zones, whereas temperature and solar radiation exerted stronger influences on instantaneous GPP responses. The relative importance of drivers varied markedly among climate zones, reflecting region-specific climatic constraints and vegetation physiological characteristics. In addition, the contribution of atmospheric CO₂ to GPP variability was notably limited in the alpine climate zone and showed a declining fertilization effect nationally, suggesting increasing constraints imposed by water availability and nutrient limitations. By integrating linear attribution and nonlinear interpretability, this study provides a comprehensive assessment of the controls on GPP dynamics across China and highlights the importance of accounting for climatic heterogeneity and temporal scales when evaluating vegetation productivity responses to environmental change.

Sand control engineering plays a pivotal role in ensuring the safe operation of transportation corridors that traverse desertified areas. Evaluating the effectiveness of these interventions provides a crucial scientific basis for mitigating aeolian hazards and guiding the sustainable management of fragile and arid ecosystems. In this study, we investigated a representative section of Highway S315, which is prone to windblown sand hazards, in Ejin Banner, northern China. By integrating segmented measurements with unmanned aerial vehicle (UAV)-based oblique photogrammetry, we quantitatively characterized the spatial and temporal evolution of sand accumulation around multiple sand control structures and assessed their blocking efficiency. Complementary road sand-removal records and meteorological observations were analyzed to evaluate the long-term performance of engineering measures. Our results showed that sand accumulation behind high vertical sand barriers typically exhibited a triangular cross-sectional morphology, with a gently inclined stoss slope and a steep lee slope. The shape and volume of these deposits evolved dynamically in response to variations in the prevailing wind regime, reflecting strong feedback between barrier geometry and local airflow redistribution. In contrast, the low-profile checkerboard sand barriers displayed a three-stage morphological trajectory—initial accumulation, edge intensification, and functional decline—indicating a progressive loss of sand-trapping capacity as burial proceeded. Sand accumulation was markedly greater on the highway's western (upwind) side than on the eastern (downwind) side, with 70.0%-90.0% of the airborne sediment flux intercepted by the upwind structures. From 2015 to 2020, mean annual wind speeds remained stable (2.68±0.04 m/s), while precipitation varied from 22.6 to 103.7 mm. However, the annual sand removal volume from the road decreased consistently, confirming the enhanced mitigation effect of multi-level protective system. These findings highlight the coupled interactions between engineering design, wind-sand dynamics, and topographic context. Beyond their immediate protective role, well-designed sand control systems also contribute to the prevention of regional desertification by stabilizing mobile dunes and fostering conditions favorable for ecological restoration. The insights gained here provide both theoretical and practical support for optimizing sand control engineering and advancing sustainable hazard mitigation in arid and semi-arid areas.