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.

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.

Challenges in land use and land cover (LULC) include rapid urbanization encroaching on agricultural land, leading to fragmentation and loss of natural habitats. However, the effects of urbanization on LULC of different crop types are less concerned. The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region, Mexico, from 1994 to 2024, and predicted the LULC in 2034 using remote sensing data, with the goals of sustainable land management and climate resilience strategies. Despite increasing urbanization and drought, the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region. Using Landsat imagery, we assessed crop attributes through indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference moisture index (NDMI), and vegetation condition index (VCI), alongside watershed delineation and spectral features. The random forest model was applied to classify LULC, providing insights into both historical and future trends. Results indicated a significant decline in vegetation cover (109.13 km²) from 1994 to 2024, accompanied by an increase in built-up land (75.11 km²) and bare land (67.13 km²). Projections suggested a further decline in vegetation cover (41.51 km²) and continued urban land expansion by 2034. The study found that paddy crops exhibited the highest values, while common bean and maize performed poorly. Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024, highlighting the increasing vulnerability of agriculture to climate change. The study concludes that sustainable land management, improved water resource practices, and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area. These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.

Increasing concerns regarding aquatic ecological health and eutrophication driven by urbanization and human activities have highlighted the need to understand primary productivity (PP) dynamics in aquatic ecosystems. This study investigated the spatial distribution of PP across the Weihe River Basin, China using inverse distance weighting and analyzed the influence of different land uses and water physical-chemical parameters on PP using Mantel test and Spearman analysis. Significantly spatial heterogeneity in PP concentrations, ranging from 0.458 to 3262.807 mg C/(m²•d), was observed with high-PP sites clustered in the middle-lower reaches dominated by farmland-construction land mosaics. Core drivers included light availability (Secchi depth and sunlight duration) and phytoplankton biomass (chlorophyll-a (Chl-a)), while water temperature exhibited threshold-dependent effects. Total organic carbon played dual roles, promoting PP concentrations in low-Chl-a regions, but suppressing it under high-Chl-a regions. Dual-scale buffer analysis (500 and 1000 m buffer zones) revealed PP heterogeneity stemed from interactive land use configurations, rather than isolated types. Balanced construction land-to-farmland ratio (0.467-2.890) elevated PP concentrations in human-dominated basins (the main stem of the Weihe River and Jinghe River), whereas excessive agricultural homogenization reduced PP likely due to fertilizer saturation and algal self-shading. Ecologically sensitive basins (the Beiluohe River Basin) demonstrated distinct patterns, in which PP concentration was regulated through natural-agricultural synergies. These results deepened the understanding of land use effects on aquatic PP, providing a theoretical basis for optimizing land use strategies to reconcile eutrophication control with ecological productivity in human-stressed basins.

Comprehensively revealing the intensity of drought propagation from meteorological to hydrological drought is crucial for effective drought monitoring and management. However, existing assessments often fail to integrate multiple drought characteristics, resulting in incomplete evaluations. To address this limitation, this study introduced the drought comprehensive propagation intensity (DCPI) index, a systematic tool that quantifies propagation intensity and incorporates five drought characteristic indicators (drought frequency, total duration, maximum duration, coverage, and degree) to assess the comprehensive drought intensity in the upper Shiyang River Basin, China from 1961 to 2023. The results indicated that pre-1980s drought propagation was relatively weak (DCPI<0.964), reflecting stable hydrological homeostasis. After the 1980s, the intensity significantly increased, peaking at 5.530 (rather strong drought) in the 2000s due to human-induced alterations in surface runoff and ecological changes. Spatially, the western tributaries (e.g., the Xida River Watershed) presented stronger hydrological drought intensity, whereas the eastern tributaries (e.g., the Huangyang, Gulang, and Dajing river watersheds) presented higher meteorological drought intensity. The DCPI values decreased from west to east, with near peer-to-peer propagation observed in the Dongda, Huangyang, and Jinta river watersheds, suggesting minimal human interference. A nonlinear relationship between meteorological and hydrological droughts was identified, with severe drought frequency and duration emerging as critical drivers of propagation intensity. Notably, trends of meteorological humidification coexist with hydrological aridification, highlighting systemic challenges for water resource management. The DCPI framework enhances the understanding of drought mechanisms by enabling a structured evaluation of drought impacts, which is essential for developing effective water resource strategies and ecological restoration efforts in arid areas. This study underscores the importance of integrating multi-dimensional drought characteristics to improve prediction accuracy and inform policy decisions.

The western alpine region is an important freshwater supply and water conservation area for China and its surrounding areas. As ecological civilization construction progresses, the ecohydrology of the western alpine region in China, which is a crucial ecological barrier, has undergone significant changes. In this study, we collected 1077 sampling points and presented a comprehensive overview of research results pertaining to the hydrochemistry of river water, meltwater, groundwater, and precipitation in the western alpine region of China using piper diagram, end-member diagram, and hydrological process indication. Water resources in the western alpine region of China were found to be weakly alkaline and have low total dissolved solids (TDS). The mean pH values for river water, meltwater, groundwater, and precipitation are 7.92, 7.58, 7.72, and 7.32, respectively. The mean TDS values for river water, meltwater, groundwater, and precipitation are 280.99, 72.48, 544.41, and 67.68 mg/L. The hydrochemical characteristics of the water resources in this region exhibit significant spatial and temporal variability. These characteristics include higher ion concentrations during the freezing period and higher ion concentrations in inland river basins, such as the Shule River Basin and Tarim River Basin. The principal hydrochemical type of river water and meltwater is HCO₃^-•SO₄^2--Ca²⁺, whereas the principal cations in groundwater are Mg²⁺ and Ca²⁺, and the principal anions are HCO₃^- and SO₄^2-. In terms of precipitation, the principal hydrochemical type is SO₄^2--Ca²⁺. The chemical ions in river water and groundwater are primarily influenced by rock weathering and evaporation-crystallization, whereas the chemical ions in meltwater are mainly affected by rock weathering and atmospheric precipitation, and the chemical ions in precipitation are derived primarily from terrestrial sources. The main forms of water input in the western alpine region of China are precipitation and meltwater, and mutual recharge occurs between river water and groundwater. Hydrochemical characteristics can reflect the impact of human activities on water resources. By synthesizing the regional hydrochemical studies, our findings provide insights for water resources management and ecological security construction in the western alpine region in China.

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.

Precipitation is scarce in semi-arid areas, which results in serious drought. Occurrence of flash drought is quite often in these areas, and flash drought may also cause significant disasters. However, monitoring flash drought is still weak and remains a challenge. This study aims to identify, evaluate, and monitor flash drought events that occurred from 1961 to 2020 in reservoirs of the Ceará State, Brazil. The Christian's method, standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and evaporative demand drought index (EDDI) were used to assess the severity and persistence of flash drought. Moreover, analyses conducted in 2001, 2008, 2011, 2012, 2016, and 2020 revealed the complexity and interaction of flash drought with environmental and meteorological factors. The results indicated that in dry years such as 2001, 2012, and 2016, drought indices pointed to the intensification of drought conditions, with impacts on major reservoirs in the area, such as Banabuiú, Castanhão, and Orós. Low precipitation, associated with high evaporative demand, intensified water stress, reducing water availability for the population and local ecosystems. In wet years such as 2008, 2011, and 2020, SPEI and EDDI indicated higher moisture levels and drought relief, favoring the recovery of reservoirs. It was also observed that most flash drought episodes evolved into conventional droughts, highlighting their persistence and potential long-term impact. Moreover, the months of May and November presented a higher frequency of flash drought during the wet and dry periods, respectively, negatively impacting most of the studied reservoirs. These findings underscore the need for effective drought monitoring and mitigation strategies to reduce its impacts on agriculture and water resources in the semi-arid area. Early detection and analysis of flash drought are important for improving water resource management and for continuous adaptation to changing drought conditions.

The evolution of land use patterns and the emergence of urban heat islands (UHI) over time are critical issues in city development strategies. This study aims to establish a model that maps the correlation between changes in land use and land surface temperature (LST) in the Mashhad City, northeastern Iran. Employing the Google Earth Engine (GEE) platform, we calculated the LST and extracted land use maps from 1985 to 2020. The convolutional neural network (CNN) approach was utilized to deeply explore the relationship between the LST and land use. The obtained results were compared with the standard machine learning (ML) methods such as support vector machine (SVM), random forest (RF), and linear regression. The results revealed a 1.00°C-2.00°C increase in the LST across various land use categories. This variation in temperature increases across different land use types suggested that, in addition to global warming and climatic changes, temperature rise was strongly influenced by land use changes. The LST surge in built-up lands in the Mashhad City was estimated to be 1.75°C, while forest lands experienced the smallest increase of 1.19°C. The developed CNN demonstrated an overall prediction accuracy of 91.60%, significantly outperforming linear regression and standard ML methods, due to the ability to extract higher level features. Furthermore, the deep neural network (DNN) modeling indicated that the urban lands, comprising 69.57% and 71.34% of the studied area, were projected to experience extreme temperatures above 41.00°C and 42.00°C in the years 2025 and 2030, respectively. In conclusion, the LST predictioin framework, combining the GEE platform and CNN method, provided an effective approach to inform urban planning and to mitigate the impacts of UHI.

Scientifically constructing an ecological security pattern (ESP) is an important spatial analysis approach to improve ecological functions in arid areas and achieve sustainable development. However, previous research methods ignored the complex trade-offs between ecosystem services in the process of constructing ESP. Taking the mainstream of the Tarim River Basin (MTRB), China as the study area, this study set seven risk scenarios by applying Ordered Weighted Averaging (OWA) model to trade-off the importance of the four ecosystem services adopted by this study (water conservation, carbon storage, habitat quality, and biodiversity conservation), thereby identifying priority protection areas for ecosystem services. And then, this study identified ecological sources by integrating ecosystem service importance with eco-environmental sensitivity. Using circuit theory, the ecological corridors and nodes were extracted to construct the ESP. The results revealed significant spatial heterogeneity in the four ecosystem services across the study area, primarily driven by hydrological gradients and human activity intensity. The ESP of the MTRB included 34 ecological sources with a total area of 1471.38 km², 66 ecological corridors with a length of about 1597.45 km, 11 ecological pinch points, and 13 ecological barrier points distributed on the ecological corridors. The spatial differentiation of the ESP was obvious, with the upper and middle reaches of the MTRB having a large number of ecological sources and exhibiting higher clustering of ecological corridors compared with the lower reaches. The upper and middle reaches require ecological protection to sustain the existing ecosystem, while the lower reaches need to carry out ecological restoration measures including desertification control. Overall, this study makes up for the shortcomings of constructing ESP simply by spatial superposition of ecosystem service functions and can effectively improve the robustness and stability of ESP construction.

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.

Soil moisture (SM) is a critical variable in terrestrial ecosystems, especially in arid and semi-arid areas where water sources are limited. Despite its importance, understanding the spatiotemporal variations and influencing factors of SM in these areas remains insufficient. This study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation (ETC), Mann-Kendall test, Theil-Sen estimator, ridge regression analysis, and other relevant methods. The following findings were obtained: (1) at the pixel scale, the long-term monthly SM data from the European Space Agency Climate Change Initiative (ESA CCI) exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error (RMSE) of 0.014 m³/m³; (2) from 2000 to 2022, the study area experienced significant increase in annual average SM, with a rate of 0.408×10^-3 m³/(m³•a). Moreover, higher altitudes showed a notable upward trend, with SM increasing rates at 0.210×10^-3 m³/(m³•a) between 1000 and 2000 m, 0.530×10^-3 m³/(m³•a) between 2000 and 4000 m, and 0.760×10^-3 m³/(m³•a) at altitudes above 4000 m; (3) land surface temperature (LST), root zone soil moisture (RSM) (10-40 cm depth), and normalized difference vegetation index (NDVI) were identified as the primary factors influencing annual average SM, which accounted for 34.37%, 24.16%, and 22.64% relative contributions, respectively; and (4) absolute contribution of LST was more significant in subareas at higher altitudes, with average absolute contributions of 0.800×10^-3 m³/(m³•a) between 2000 and 4000 m and 0.500×10^-2 m³/(m³•a) above 4000 m. This study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas, highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas, providing valuable insights for ecological research and water resource management in these areas.

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.

The loess plains cover approximately 2000.00 km² of the northern Negev Desert, accounting for about 9% of Israel's total land area. As elsewhere, the loess in the Negev Desert is composed of wind-transported dust and sand particles that have been deposited in sink sites. The loess deposits are characteristically covered by biocrusts, which constitute a substantial share of the region's primary productivity. The biocrusts regulate the vascular vegetation communities, including herbaceous and woody plants, many of which are endemic and/or endangered plant species. Throughout history, the region's main land-uses have been based on extensive livestock grazing and runoff-harvesting agriculture, which both still exist to some extent. These land-uses did not challenge the sustainability of the geo-ecosystems over centuries and millennia. At present, predominant land-uses include intensive rangelands (1016.81 km², encompassing 51% of the loess plains' area), croplands (encompassing both rainfed and irrigated cropping systems: 930.92 km², 47% of the loess plains' area), and afforestation lands (158.75 km²). These current land-uses impose substantial challenges to the functioning of the loess plains. Further, urban and rural settlements have expanded considerably in the last decades (158.45 km²), accompanied by mass construction of infrastructures. Altogether, these new land-uses have caused widespread soil erosion, soil structure deformation, depletion of soil organic carbon, environmental contamination, native vegetation removal, invasion of plant species, and habitat fragmentation. Recent climate change has intensified these stressors, exacerbating adverse impacts and forming feedback loops that intensify land degradation and desertification. The declining ecosystem functioning over recent decades emphasizes the urgent need for passive and active restoration schemes. While some of these efforts have proven to be successful, other have failed. Therefore, proactive policy making and environmental legislation are needed to plan and develop schemes aimed at halting land degradation, while simultaneously maximizing nature conservation and restoration of degraded lands across the loess plains. Such actions are expected to increase the regions' capacity for climate change mitigation and adaptation.

Recent years have witnessed increasingly frequent extreme precipitation events, especially in desert steppes in the semi-arid and arid transition zone. Focusing on a desert steppe in western-central Inner Mongolia Autonomous Region, China, this study aimed to determine the principle time-varying pattern of extreme precipitation and its dominant climate forcings during the period 1988-2017. Based on the generalized additive models for location, scale, and shape (GAMLSS) modeling framework, we developed the best time-dependent models for the extreme precipitation series at nine stations, as well as the optimized non-stationary models with large-scale climate indices (including the North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), Southern Oscillation (SO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and North Pacific Oscillation (NPO)) as covariates. The results indicated that extreme precipitation remained stationary at more than half of the stations (Hailisu, Wuyuan, Dengkou, Hanggin Rear Banner, Urad Front Banner, and Yikewusu), while linear and non-linear time-varying patterns were quantitatively identified at the other stations (Urad Middle Banner, Linhe, and Wuhai). These non-stationary behaviors of extreme precipitation were mainly reflected in the mean value of extreme precipitation. The optimized non-stationary models performed best, indicating the significant influences of large-scale climate indices on extreme precipitation. In particular, the NAO, NPO, SO, and AMO remained as covariates and significantly influenced the variations in the extreme precipitation regime. Our findings have important reference significance for gaining an in-depth understanding of the driving mechanism of the non-stationary behavior of extreme precipitation and enable advanced predictions of rainstorm risks.

Precipitation isotopes (δ¹⁸O and δ²H) are closely related to meteorological conditions for precipitation generation and the initial state of water vapor source areas, and are essential to the study of the regional hydrological cycle. The deuterium excess (d-excess) indicates deviation in isotope fractionation during evaporation and can trace water vapor sources. This study analyzed 443 precipitation samples collected from the Gannan Plateau, China in 2022 to assess precipitation isotope variations and their driving factors. Water vapor sources were evaluated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), Concentration Weighted Trajectory (CWT), and Potential Source Contribution Factor (PSCF) models. Results showed that precipitation isotope values showed significant spatial and temporal variations on the Gannan Plateau. Temporally, precipitation isotope values peaked in June (when evaporation dominated) and minimized in March (depletion effect of air masses in the westerly wind belt). Spatially, the isotope values showed a distribution pattern of "high in the east and low in the west", which was mainly regulated by the differences in altitude and local meteorological conditions. Compared with the global meteoric water line (GMWL) with equation of δ²H=8.00δ¹⁸O+10.00, the slope and intercept of local meteoric water line (LMWL) for precipitation on the Gannan Plateau were smaller (7.49 and 7.63, respectively), reflecting the existence of a stronger secondary evaporation effect under the clouds in the region. The sources of water vapor on the Gannan Plateau showed significant seasonality and spatial heterogeneity. Specifically, the westerly belt and monsoon were the main water vapor transport paths at each sampling point, with Central Asian continental water vapor dominating in spring (53.49%), Indian Ocean water vapor dominating in summer (52.53%), Atlantic Ocean water vapor dominating in autumn (46.74%), and Atlantic Ocean and Mediterranean Sea water vapor dominating in winter (42.30% and 33.68%, respectively). Changes in the intensity of convective activity and Outgoing Longwave Radiation (OLR) affected the enrichment of isotopic values, which exhibited the same change trends as δ¹⁸O. During the precipitation process, the δ¹⁸O value first decreased and then increased. During the initial and final stages of precipitation process, precipitation was mainly influenced by continental air masses, while during the middle stage, it was controlled by marine air masses. The systematic research on precipitation isotopes and water vapor sources is important for climate change research and extreme precipitation prediction on the Gannan Plateau and other similar areas.

Understanding plant community assembly is crucial for effective ecosystem conservation and restoration. The ecological filter framework describes community assembly as a process shaped by dispersal, environmental, and biotic filters. Additionally, functional traits and phylogenetic relationships are increasingly recognized as important factors influencing species coexistence and community structure. However, both the ecological filter framework and the roles of functional traits and phylogeny in community assembly remain underexplored in the Algerian steppes—particularly in the El Bayadh region, where ongoing vegetation degradation threatens ecosystem stability. This study applied Hierarchical Modeling of Species Communities (HMSC) as an integrative approach to assess how ecological filters influence plant community assembly in the El Bayadh steppe and to evaluate the roles of functional traits and phylogenetic relationships in this process. Environmental data—including soil properties, topography, precipitation, and land use types (grazing and exclosure)—were collected across 50 plots in April and October, 2023, along with functional traits from 24 species. These traits include root length, leaf area, specific leaf area, clonality, life history, and seed mass. HMSC results revealed that soil properties and precipitation were the primary drivers of community structure, while sand height and elevation had a moderate influence. In contrast, competition and grazing played relatively minor roles. Species responses to environmental covariates were heterogeneous: soil fertility and texture had mixed effects, benefiting some species while limiting others; sand encroachment and precipitation variability generally had negative impacts, whereas grazing exclusion favored many species. A weak phylogenetic signal was recorded, indicating that community assembly was driven more by environmental filtering than by shared evolutionary history. Functional trait responses to environmental variation reflected plant strategies that balanced resource acquisition and conservation. Specifically, seed mass, leaf area, and root length increased under higher soil moisture and nutrient availability but declined in response to salinity, precipitation variability, and sand height. Clonality and perennial life history traits enhanced the survival of plant species under harsh conditions. Overall, this study provides a holistic understanding of community assembly processes in the El Bayadh steppe and offers valuable insights for ecosystem management and restoration in arid and degraded ecosystem environments.

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.

Flash drought is characterized by a period of rapid drought intensification with impacts on agriculture, water resources, ecosystems, and human environment. In the Qilian Mountains, northwestern China, flash droughts are becoming more frequently due to the global climate warming. However, the spatiotemporal variations and their driving factors of flash droughts are not clear in this region. In this study, the European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v5-Land (ERA5-Land) dataset was utilized to identify two types of flash drought events (heatwave-induced and water scarcity-induced flash drought events) that occurred in the growing season (April‒September) during 1981-2020 in this area. The results showed that the frequency of heatwave-induced flash droughts has decreased since 2010, while the frequency of water scarcity-induced flash droughts has declined markedly. Spatially, heatwave-induced flash droughts were predominantly concentrated in the western Qilian Mountains, whereas water scarcity-induced flash droughts were primarily concentrated in the central and eastern Qilian Mountains. A significantly increasing temporal trend in both types of flash droughts in the eastern Qilian Mountains was found. Meanwhile, there was a decreasing temporal trend of heatwave-induced flash droughts in the southwestern part of the region. Additionally, the influence of two major atmospheric modes, i.e., the El Niño‒Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), on these two types of flash droughts was explored by the Superposed Epoch Analysis. The ENSO mainly influences flash droughts in the central and eastern parts of the Qilian Mountains by altering the strength of the East Asian monsoon, while the NAO mainly affects flash droughts in the entire parts of the Qilian Mountains by inducing anomalous westerlies activity. Our findings have important implications for predicting the evolution of flash drought events in the Qilian Mountains region under continued climate warming.

Amid global climate change, rising levels of nitrogen (N) deposition have attracted considerable attention for their potential effects on the carbon cycle of terrestrial ecosystems. The desert steppes are a crucial yet vulnerable ecosystem in arid areas, but their response to the combination of N addition and precipitation (a crucial factor in arid areas) remains underexplored. This study systematically explored the impact of N addition and precipitation on net ecosystem exchange (NEE) in a desert steppe in northern China. Specifically, we conducted a 2-a experiment from 2022 to 2023 with eight N addition treatments in the Urat desert steppe of Inner Mongolia Autonomous Region, China, to examine changes in NEE and explore its driving factors. The structural equation model (SEM) and multiple regression model were applied to determine the relationship of NEE with plant community characteristics and soil physical-chemical properties. Statistical results showed that N addition has no significant effect on NEE. However, it has a significant impact on the functional traits of desert steppe plant communities. SEM results further revealed that N addition has no significant effect on NEE in the desert steppe, whereas annual precipitation can influence NEE variations. The multiple regression model analysis indicated that plant functional traits play an important role in explaining the changes in NEE, accounting for 62.15% of the variation in NEE. In addition, plant height, as an important plant functional trait indicator, shows stronger reliability in predicting the changes in NEE and becomes a more promising predictor. These findings provide valuable insights into the complex ecological mechanisms governing plant community responses to precipitation and nutrient availability in the arid desert steppes, contributing to the improved monitoring and prediction of desert steppe ecosystem responses to global climate change.

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.

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 Loess Plateau (LP), one of the most ecologically fragile regions in China, is affected by severe soil erosion and environmental degradation. Despite large-scale ecological restoration efforts made by Chinese government in recent years, the region continues to face significant ecological challenges due to the combined impact of climate change and human activities. In this context, we developed a kernal Remote Sensing Ecological Index (kRSEI) using Moderate Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine (GEE) platform to analyze the spatiotemporal patterns and trends in ecological environmental quality (EEQ) across the LP from 2000 to 2022 and project future trajectories. Then, we applied partial correlation analysis and multivariate regression residual analysis to further quantify the relative contributions of climate change and human activities to EEQ. During the study period, the kRSEI values exhibited significant spatial heterogeneity, with a stepwise degradation pattern in the southeast to northwest across the LP. The maximum (0.51) and minimum (0.46) values of the kRSEI were observed in 2007 and 2021, respectively. Trend analyses revealed a decline in EEQ across the LP. Hurst exponent analysis predicted a trend of weak anti-persistent development in most of the plateau areas in the future. A positive correlation was identified between kRSEI and precipitation, particularly in the central and western regions; although, improvements were limited by a precipitation threshold of 837.66 mm/a. A moderate increase in temperature was shown to potentially benefit the ecological environment within a certain range; however, temperature of -1.00°C-7.95°C often had a negative impact on the ecosystem. Climate change and human activities jointly influenced 65.78% of LP area on EEQ, primarily having a negative impact. In terms of contribution, human activities played a dominant role in driving changes in EEQ across the plateau. These findings provide crucial insights for accurately assessing the ecological state of the LP and suggest the design of future restoration strategies.

The Qinghai-Xizang Plateau of China faces challenges like thaw slumping, threatening slope stability and infrastructure. Understanding the mechanical properties of the roots of the dominant herbaceous plant species in the alpine meadow layer of the permafrost regions on the Qinghai-Xizang Plateau is essential for evaluating their role in enhancing soil shear strength and mitigating slope deformation in these fragile environments. In this study, the roots of four dominant herbaceous plant species—Kobresia pygmaea, Kobresia humilis, Carex moorcroftii, and Leontopodium pusillum—that are widely distributed in the permafrost regions of the Qinghai-Xizang Plateau were explored to determine their mechanical properties and effects in enhancing soil shear strength. Through indoor single root tensile and root group tensile tests, we determined the root diameter, tensile force, tensile strength, tensile ratio, and strength frequency distributions. We also evaluated their contributions to inhibiting slope deformation and failure during the formation and development of thermal thaw slumps in the alpine meadow. The results showed that the distribution of the root diameter of the dominant plant species is mostly normal, while the tensile strength tends to be logarithmically normally distributed. The relationship between the root diameter and root tensile strength conforms to a power function. The theoretical tensile strength of the root group was calculated using the Wu-Waldron Model (WWM) and the Fiber Bundle Model (FBM) under the assumption that the cumulative single tensile strength of the root bundle is identical to the tensile strength of the root group in the WWM. The FBM considers three fracture modes: FBM-D (the tensile force on each single root is proportional to its diameter relative to the total sum of all the root diameters), FBM-S (the cross-sectional stress in the root bundle is uniform), and FBM-N (each tensile strength test of individual roots experiences an equal load). It was found that the model-calculated tensile strength of the root group was 162.60% higher than the test value. The model-derived tensile force of the root group from the FBM-D, FBM-S, and FBM-N was 73.10%, 28.91%, and 13.47% higher than the test values, respectively. The additional cohesion of the soil provided by the roots was calculated to be 25.90-45.06 kPa using the modified WWM, 67.05-38.15 kPa using the FBM-S, and 57.24-32.74 kPa using the FBM-N. These results not only provide a theoretical basis for further quantitative evaluation of the mechanical effects of the root systems of herbaceous plant species in reinforcing the surface soil but also have practical significance for the effective prevention and control of thermal thaw slumping disasters in the permafrost regions containing native alpine meadows on the Qinghai-Xizang Plateau using flexible plant protection measures.

Understanding the impact of meteorological and topographical factors on snow cover fraction (SCF) is crucial for water resource management in the Qilian Mountains (QLM), China. However, there is still a lack of adequate quantitative analysis of the impact of these factors. This study investigated the spatiotemporal characteristics and trends of SCF in the QLM based on the cloud-removed Moderate Resolution Imaging Spectroradiometer (MODIS) SCF dataset during 2000-2021 and conducted a quantitative analysis of the drivers using a histogram-based gradient boosting regression tree (HGBRT) model. The results indicated that the monthly distribution of SCF exhibited a bimodal pattern. The SCF showed a pattern of higher values in the western regions and lower values in the eastern regions. Overall, the SCF showed a decreasing trend during 2000-2021. The decrease in SCF occurred at higher elevations, while an increase was observed at lower elevations. At the annual scale, the SCF showed a downward trend in the western regions affected by westerly (52.84% of the QLM). However, the opposite trend was observed in the eastern regions affected by monsoon (45.73% of the QLM). The SCF displayed broadly similar spatial patterns in autumn and winter, with a significant decrease in the western regions and a slight increase in the central and eastern regions. The effect of spring SCF on spring surface runoff was more pronounced than that of winter SCF. Furthermore, compared with meteorological factors, a variation of 46.53% in spring surface runoff can be attributed to changes in spring SCF. At the annual scale, temperature and relative humidity were the most important drivers of SCF change. An increase in temperature exceeding 0.04°C/a was observed to result in a decline in SCF, with a maximum decrease of 0.22%/a. An increase in relative humidity of more than 0.02%/a stabilized the rise in SCF (about 0.06%/a). The impacts of slope and aspect were found to be minimal. At the seasonal scale, the primary factors impacting SCF change varied. In spring, precipitation and wind speed emerged as the primary drivers. In autumn, precipitation and temperature were identified as the primary drivers. In winter, relative humidity and precipitation were the most important drivers. In contrast to the other seasons, slope exerted the strongest influence on SCF change in summer. This study facilitates a detailed quantitative description of SCF change in the QLM, enhancing the effectiveness of watershed water resource management and ecological conservation efforts in this region.

Diversity of soil microorganisms in different habitats of arid and semi-arid areas plays an important role in the soil texture and nutrient, promoting the growth of vegetation in those areas. To clarify the response of soil bacterial community diversity to the changes of environmental factors in different habitats, this study collected soil samples under the canopies of Tamarix ramosissima Ledeb. in oasis, transition zone, and desert habitats in the upper reaches of the Tarim River, Northwest China. High-throughput sequencing technology and PICRUSt2 software were used to explore the composition and function of soil bacterial communities in different habitats of T. ramosissima. The results showed that: (1) soil environmental factors under the canopy of T. ramosissima in the three habitats differed significantly, with soil moisture and nutrient conditions being better in the oasis; (2) Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria, and Gemmatimonadetes were the major bacterial communities in the three habitats; (3) soil bacterial community composition under the canopy of T. ramosissima varied greatly, and the richness was significantly different among the three habitats; (4) redundancy analysis indicated that soil water content and available phosphorous were the most important environmental factors influencing the composition of soil bacterial community; and (5) 6 primary functions and 21 secondary functions were obtained by PICRUSt2 function prediction, with metabolism being the most dominant function. This study revealed the response of soil bacterial community composition to habitat changes and their driving factors in the upper reaches of the Tarim River, which could improve the understanding of ecological sensitivity of soil microorganisms in arid and semi-arid areas, and provide a theoretical foundation for improving soil quality and ecological protection.

Investigating the spatiotemporal evolution of vegetation and its response mechanisms to natural and anthropogenic elements is crucial for regional vegetation restoration and ecological preservation. The Mu Us Sandy Land (MUSL), which is situated in the semi-arid zone of northwestern China adjacent to the Loess Plateau, has been at the forefront of desertification and oasis formation over the past two millennia. This study is based on the synthesis of the Normalized Difference Vegetation Index (NDVI) data from MOD13A3 data in the MODIS (Moderate-Resolution Imaging Spectroradiometer) dataset (2002-2021) and climate data (temperature and precipitation) at annual and monthly scales from the National Earth System Science Data Center. A range of analytical methods, including univariate linear regression, Theil-Sen trend analysis and Mann-Kendall significance test, correlation analysis, residual analysis, and Hurst index, were used to explore the response mechanisms of the NDVI to climate change and human activities and to predict the future trends of the NDVI in the MUSL. The results showed that through the method of correlation analysis, in terms of both spatially averaged correlation coefficients and area proportion, the NDVI was positively correlated with temperature and precipitation in 97.59% and 96.51% of the study area, respectively, indicating that temperature has a greater impact on the NDVI than precipitation. Residual analysis quantified the contributions of climate change and human activities to the NDVI changes, revealing that climate change and human activities contribute up to 30.00% and 70.00%, respectively, suggesting that human activities predominantly affect the NDVI changes in the MUSL. The Hurst index was used to categorize the future trend of the NDVI into four main directions of development: continuous degradation (0.05% of the study area), degradation in the past but improvement in the future (54.45%), improvement in the past but degradation in the future (0.13%), and continuous improvement (45.36%). In more than 50.00% of the regions that have been degraded in the past but were expected to improve in the future, the NDVI was expected to exhibit a stable trend of anti-persistent improvement. These findings provide theoretical support for future ecological protection, planning, and the implementation of ecological engineering in the MUSL, and also offer a theoretical basis for the planning and execution of construction projects, environmental protection measures, and the sustainable development of vegetation.

Research on grassland carrying capacity (GCC) and forage-livestock balance is of great significance for promoting the harmonious development of human and grassland. However, the lack of understanding of GCC and forage-livestock balance in the agro-pastoral transition zone of northern China has limited the grassland sustainable development. Here, the spatial and temporal characteristics of GCC and forage-livestock balance in the grassland of agro-pastoral transition zone of northern China from 2000 to 2022 were analyzed using meteorological data and remote sensing data. Geographical detectors and geographically weighted regression were also used to identify the driving factors and their interactions with GCC changes. Moreover, future GCC trends were predicted using the Coupled Model Intercomparison Project Phase 6 dataset. Results revealed that: (1) GCC showed an overall upward trend from 2000 to 2022 but with significant inter-annual fluctuations. Its spatial distribution decreased gradually from north to south and from east to west. Precipitation, temperature, and cumulative solar radiation were the main drivers of the inter-annual variation of GCC, and the interaction between precipitation and temperature was the main influencing factor of the spatial distribution of GCC; (2) the forage-livestock balance was in an overloaded state in most years, but its index remained basically stable. Spatially, grazing overloading was mainly distributed in northeastern area and the severe overloading was mainly distributed in northwestern area; and (3) future projections indicated a downward trend in potential GCC. Under shared socioeconomic pathway (SSP)2-4.5 scenario, the potential GCC had a ranged of 1.38×10⁷-1.86×10⁷ standard sheep unit (SHU) and a mean of 1.60×10⁷ SHU. Meanwhile, the potential GCC under SSP5-8.5 scenario had a range of 1.18×10⁷-1.69×10⁷ SHU and a mean of 1.49×10⁷ SHU. These results indicated that although GCC of the agro-pastoral transition zone of northern China showed an overall increasing trend from 2000 to 2022, the forage-livestock balance index remained basically stable. The GCC was predicted to show a decreasing trend in the future. The findings provide a scientific basis for the sustainable development of grassland and the optimization of grazing management policies in this area.

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.

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.

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.

Given that Xinjiang Uygur Autonomous Region of China possesses exceptionally abundant solar radiation resources that can be harnessed to develop clean energy, accurately characterizing their spatiotemporal distribution is crucial. This study investigated the applicability of the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product downward surface shortwave radiation dataset (DSSR_CER) under clear-sky conditions in Xinjiang. By integrating multi-source data and utilizing techniques like multivariate fitting and model simulation, we established a two-layer aerosol model and developed a clear-sky downward surface shortwave radiation (DSSR) retrieval model specific to Xinjiang using the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model. We further explored the spatiotemporal distribution characteristics of DSSR under clear-sky conditions in Xinjiang from 2017 to 2019 based on the localized DSSR retrieval model. Our findings revealed a significant discrepancy in DSSR_CER under clear-sky conditions at the Xiaotang station in Xinjiang. By comparing, screening, and correcting core input parameters while incorporating the two-layer aerosol model, we achieved a more accurate SBDART simulated DSSR (DSSR_SBD) compared to DSSR_CER. The annual mean DSSR exhibited a distinct distribution pattern with high values in mountainous regions such as the Altay Mountains, Kunlun Mountains, and Tianshan Mountains and significantly lower values in adjacent lowland areas, including the Tarim River Basin and Junggar Basin. In the four typical administrative regions in northern Xinjiang, the annual mean DSSR (ranging from 551.60 to 586.09 W/m²) was lower than that in the five typical administrative regions in southern Xinjiang (ranging from 522.10 to 623.62 W/m²). These spatial variations stem from a complex interplay of factors, including latitude, altitude, solar altitude angle, and sunshine duration. The variations in seasonal average DSSR aligned closely with variations in the solar altitude angle, with summer (774.76 W/m²) exhibiting the highest values, followed by spring (684.86 W/m²), autumn (544.76 W/m²), and winter (422.74 W/m²). The monthly average DSSR showed a unimodal distribution, peaking in June (792.94 W/m²) and reaching its lowest level in December (363.06 W/m²). Overall, our study findings enhance the current understanding of the spatiotemporal distribution characteristics of DSSR in Xinjiang and provide certain references for the management of clean energy development in this region.

Intense evaporation in areas with loess-like sulfate saline soils has resulted in significant ecological challenges that include water shortages and soil salinization. Investigating evaporation rate in loess-like sulfate saline soils under varying salt contents carries crucial implications for understanding regional water loss processes, predicting soil salinization advancement, and formulating effective ecological management strategies. Therefore, this study sampled the loess-like sulfate saline soil that is widely distributed in western China as experimental materials and investigated the impact of different initial salt contents (0.00%, 0.50%, 1.50%, 3.00%, and 5.00%) on the evaporation rate, water content, and temperature of soil. The results showed that the evaporation rate decreased with increasing initial salt content. After a salt accumulation layer formed on the soil surface, the water content of the surface soil fluctuated. An increase in the initial salt content resulted in a corresponding increase in the surface temperature. Considering the evaporation characteristics of loess-like sulfate saline soil and the impact of an anomalous increase in surface soil water content on soil surface resistance, this study proposed a modified evaporation model on the basis of Fujimaki's evaporation model of saline soil by introducing a correction coefficient β to modify the soil surface resistance. A comparison of the calculated evaporation rates before and after the modification with the measured evaporation rates revealed a significant improvement in the calculation accuracy of the modified model, indicating that the modified model is capable of more accurately simulating the evaporation rate of sulfate saline soil with different initial salt contents. This paper proposes an effective method for calculating the evaporation rate of loess-like sulfate saline soils, providing a theoretical basis for evaporation research in saline soil.

Cotton, as one of important economic crops, is widely planted in the saline-alkaline soil of southern Xinjiang, China. Moreover, in order to control the saline-alkaline content for seed germination and seedlings survive of cotton, farmers always adopt salt leaching during winter and spring seasons. However, excessive amount of salt leaching might result in the waste of water resources and unsuitable irrigation seasons might further increase soil salinization. In this study, a field experiment was conducted in the saline-alkaline soil in 2020 and 2021 to determine the effects of leaching amount and period on water-salinity dynamics and cotton yield. Five leaching amounts (0.0 (W0), 75.0 (W1), 150.0 (W2), 225.0 (W3), and 300.0 (W4) mm) and three leaching periods (seedling stage (P1), seedling and squaring stages (P2), and seedling, squaring, flowering, and boll setting stages (P3)) were used. In addition, a control treatment (CK) with a leaching amount of 300.0 mm in spring was performed. The soil water-salt dynamics, cotton growth, seed cotton yield, water productivity (WP), and irrigation water productivity (WPI) were analyzed. Results showed that leaching significantly decreased soil electrical conductivity (EC), and W3P2 treatment reduced EC by 11.79% in the 0-100 cm soil depth compared with CK. Plant height, stem diameter, leaf area index, and yield under W3 and W4 treatments were greater than those under W1 and W2 treatments. Compared with W3P1 and W3P3 treatments, seed cotton yield under W3P2 treatment significantly enhanced and reached 6621 kg/hm² in 2020 and 5340 kg/hm² in 2021. Meanwhile, WP and WPI under W3P2 treatment were significantly higher than those under other leaching treatments. In conclusion, the treatment of 225.0 mm leaching amount and seedling and squaring stages-based leaching period was beneficial for the salt control, efficient water utilization, and yield improvement of cotton in southern Xinjiang, China.

Snow cover plays a critical role in global climate regulation and hydrological processes. Accurate monitoring is essential for understanding snow distribution patterns, managing water resources, and assessing the impacts of climate change. Remote sensing has become a vital tool for snow monitoring, with the widely used Moderate-resolution Imaging Spectroradiometer (MODIS) snow products from the Terra and Aqua satellites. However, cloud cover often interferes with snow detection, making cloud removal techniques crucial for reliable snow product generation. This study evaluated the accuracy of four MODIS snow cover datasets generated through different cloud removal algorithms. Using real-time field camera observations from four stations in the Tianshan Mountains, China, this study assessed the performance of these datasets during three distinct snow periods: the snow accumulation period (September-November), snowmelt period (March-June), and stable snow period (December-February in the following year). The findings showed that cloud-free snow products generated using the Hidden Markov Random Field (HMRF) algorithm consistently outperformed the others, particularly under cloud cover, while cloud-free snow products using near-day synthesis and the spatiotemporal adaptive fusion method with error correction (STAR) demonstrated varying performance depending on terrain complexity and cloud conditions. This study highlighted the importance of considering terrain features, land cover types, and snow dynamics when selecting cloud removal methods, particularly in areas with rapid snow accumulation and melting. The results suggested that future research should focus on improving cloud removal algorithms through the integration of machine learning, multi-source data fusion, and advanced remote sensing technologies. By expanding validation efforts and refining cloud removal strategies, more accurate and reliable snow products can be developed, contributing to enhanced snow monitoring and better management of water resources in alpine and arid areas.

In dryland ecosystems, nitrogen (N) is the primary limiting factor after water availability, constraining both plant productivity and organic matter decomposition while also regulating ecosystem function and service provision. However, the distributions of different soil N fraction stocks in drylands and the factors that influence them remain poorly understood. In this study, we collected 2076 soil samples from 173 sites across the drylands of northern China during the summers of 2021 and 2022. Using the best-performing eXtreme Gradient Boosting (XGBoost) model, we mapped the spatial distributions of the soil N fraction stocks and identified the key drivers of their variability. Our findings revealed that the stocks of total nitrogen (TN), inorganic nitrogen (IN), and microbial biomass nitrogen (MBN) in the top 30 cm soil layer were 1020.4, 92.2, and 40.8 Tg, respectively, with corresponding mean densities of 164.6, 14.9, and 6.6 g/m². Climate variables—particularly mean annual temperature and aridity—along with human impacts emerged as the dominant drivers of soil N stock distribution. Notably, increased aridity and intensified human impacts exerted mutually counteracting effects on soil N fractions: aridity-driven moisture limitation generally suppressed N accumulation, whereas anthropogenic activities (e.g., fertilization and grazing) promoted N enrichment. By identifying the key environmental and anthropogenic factors shaping the soil N distribution, this study improves the accuracy of regional and global N stock estimates. These insights provide a scientific foundation for developing more effective soil N management strategies in dryland ecosystems, contributing to sustainable land use and long-term ecosystem resilience in drylands.

Water quality is a pressing issue affecting the sustainable development of lakes. To elucidate the spatial and temporal characteristics of water quality in Bosten Lake, China, this study constructed a comprehensive water quality index (CWQI) based on key water quality indicators, utilizing water quality data collected from 17 sampling sites spaning from 2011 to 2019. Key water quality indicators were determined using factor analysis, and the spatial and temporal characteristics of key water quality indicators and the CWQI were examined using multivariate statistical analysis. The key water quality indicators included pH, chemical oxygen demand (COD), water transparency (SD), NO₃^-, total dissolved solids (TDS), Cl^-, SO₄^2-, and electrical conductivity (EC). Furthermore, the contribution rates of all water quality indicators to the water quality were quantitatively elucidated using the SHapley Additive exPlanations (SHAP) values, thereby validating the factor analysis outcomes. Among the eight key water quality indicators, the COD had the most significant influence on the water quality of Bosten Lake. The water quality condition of Bosten Lake has remained at Class III from 2011 to 2019 (CWQI ranging from 3.19 to 3.90). The water quality of Bosten Lake was characterized by distinct regional differences that arose from hydrodynamic processes within the lake and upstream water quality. The southwestern region exhibited the best water quality (mean CWQI of 3.47), whereas the northwestern region exhibited the worst (mean CWQI of 3.58). It is crucial to acknowledge that alongside the increase in industrial and agricultural effluent discharge monitoring, a series of ecological restoration projects for the lake basin have been initiated. Over time, the water quality of Bosten Lake showed gradual improvement (improvement rate of CWQI at 0.05/a). This study provides a critical scientific basis for enhancing the understanding and effective management of water quality in the Bosten Lake Basin through a comprehensive analysis of its spatial and temporal evolution and driving mechanisms.

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.

Agriophyllum squarrosum (L.) Moq., commonly known as sandrice, is an annual medicinal plant prevalent in the dunes across China's deserts. A garden trial revealed that flavonoid content varies among sandrice ecotypes due to long-term local adaptation to water variability. To investigate how sandrice responds to drought stress through the molecular metabolic regulation of flavonoids, we employed transcriptomic and metabolomic analyses during a 9-d ambient drought stress, examining three ecotypes along a precipitation gradient. The three ecotypes located in Dengkou (DK) County, Dulan (DL) County, and Aerxiang (AEX) village of northern China, which had 137, 263, and 485 mm precipitation, respectively. Soil moisture content was 4.04% after drought stress, causing seedlings of the three sandrice ecotypes to display collapsed structures, yellowing leaves, wilting, and curling. Among these, DL exhibited superior drought tolerance, in which plant height increase (PHI) and leaf area (LA) were significantly higher than those of DK and AEX. Flavonoid-targeted metabolomics identified that rutin, isoquercitrin, and astragalin constituted over 95.00% of the 15 flavonoid metabolites detected. A total of 12 differentially accumulated flavonoids (DAFs) were found, with rutin being the most abundant (1231.57-2859.34 ng/100 mg fresh weight (FW)), showing a gradual increase along the precipitation gradient. Transcriptomic analysis revealed 14 common differentially expressed genes (DEGs) associated with flavonoid synthesis among the three ecotypes. Integrative analysis of DEGs and DAFs indicated that sandrice adapts to drought stress by activating different flavonoid synthesis pathways. In DK, the dihydrokaempferol-dihydroquercetin pathway, regulated by flavonoid 3'-monooxygenase (CYP75B1), likely enhances drought adaptation. In AEX, transcriptional repression by O-methylatransferase (OMT) shifts the metabolic flux from the quercetin-isorhamnetin pathway to the quercetin-isoquercetin-rutin pathway in response to drought. DL, the most drought- tolerant ecotype, appears to activate the naringenin-apigenin-luteolin route and employs a unique flavonoid accumulation pattern in response to drought stress. Our data reveal that flavonoid synthesis in sandrice is fine-tuned among ecotypes to cope with drought, offering valuable germplasm resources and evaluation methods for sandrice acclimation and providing insights into drought response in non-model plants.

Vegetation change is the most intuitive and sensitive bioindicator reflecting seasonal and interannual variations in the external environment, and it can directly reflect the rapid response of terrestrial ecosystems to climate change. Using remote sensing and meteorological data, this study revealed the spatiotemporal characteristics of leaf area index (LAI) in the north of China during 1982-2022, clarified the response of LAI change to different meteorological factors, quantified the impacts of climate change and human activities on LAI change, and predicted the future trends in LAI change. From 1982 to 2022, the vegetation in the north of China generally showed a greening trend with a change rate of 0.0071 m²/(m²•a). Temperature was strongly positively correlated with LAI and was the main climate factor driving LAI change. Residual analysis revealed that vegetation improvement occurred in across 74.53% of the study area, and vegetation improvement in about 96.83% of the improved zone was attributed to a combination of climate change and human activities. The regions where anthropogenic contribution exceeded 60.00% covered 36.83% of human-affected areas, while the regions where climatic contribution exceeded 60.00% covered 19.77% of climate-affected areas, demonstrating that human activities influenced the intensity of LAI change more deeply despite the broad spatial impact of climate change. Human activities such as afforestation and the Three-North Protective Forest Program played the dominant role in vegetation greening compared to climate change. Hurst index analysis indicated that 80.30% of vegetation in the north of China is expected to experience a non-sustained improvement in the future. These findings will provide a scientific basis for optimizing the protection strategies of the national ecological barrier areas and evaluating the effectiveness of major ecological projects.