Uzbekistan is a core country of the Silk Road Economic Belt; thus, its long-term climate variations play key roles in achieving the objectives set by Sustainable Development Goal 13 (SDG-13) to address climate change and its impacts. This study utilized temperature and precipitation data from 32 meteorological stations across Uzbekistan (1950-2023), Climatic Research Unit Time-Series (CRU-TS) version 4.08 (1901-2023), European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5^th Generation (ERA5) (1950-2022), and Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) (1980-2024) to thoroughly investigate the spatiotemporal characteristics of temperature and precipitation in Uzbekistan from 1901 to 2024. The Distance between Indices of Simulation and Observation (DISO) metric was employed to evaluate the accuracy of the gridded datasets, and the Mann-Kendall method was implemented to analyze the linear trends of temperature and precipitation. Results indicated that ERA5 temperature data are the most consistent with the observed temperature data, whereas CRU-TS data perform best in determining long-term precipitation variability. The century-scale trend represented by the CRU-TS data showed significant warming at a rate of nearly 0.17°C/10a, exceeding the Northern Hemisphere average. Uzbekistan showed no clear upward trend in precipitation during 1901-2023. Rapid warming and heightened interannual precipitation variability were observed in ERA5 (1950-2022) and MERRA2 (1980-2024) over their respective coverage periods. CRU-TS, ERA5, and MERRA2 data all revealed a shift of the warming center from the Aral Sea Basin to the central and southeastern regions of Uzbekistan. Nevertheless, the Aral Sea Basin remained in the core warming zone, with CRU-TS data indicating a centennial warming rate of 0.16°C/10a-0.20°C/10a. The precipitation patterns revealed a complex "wetter southeast and drier central-west" and "wetter summer and drier autumn-winter" dichotomy. These findings could enhance the understanding of long-term climate change in Uzbekistan and provide a scientific foundation for implementing SDG-13 to mitigate the impacts of climate change in the region.

The establishment of a regional ecological security pattern (ESP) is crucial for the sustainable development of Xinjiang Uygur Autonomous Region (hereinafter referred as to Xinjiang), China, which is characterized by a fragile ecological foundation and a highly vulnerable ecosystem. However, traditional ESP frameworks often insufficiently account for long-term environmental changes because landscape connectivity is not fully integrated into ecological source identification. They also struggle to capture the cumulative and spatially heterogeneous impacts of human disturbances on ecological resistance surfaces. To bridge these gaps, we developed an advanced ESP framework for Xinjiang—a vast and ecologically sensitive arid region—covering a 20 a span from 2000 to 2020. We identified ecological sources through a comprehensive assessment of ecosystem services (ESs) and landscape connectivity while simultaneously establishing an ecological resistance surface using an extreme gradient boosting (XGBoost) model integrated with the human activity intensity (HAI) index. Finally, ecological corridors were determined via the minimum cumulative resistance (MCR) model and prioritized using the gravity model to thoroughly determine the network evolution. Validation revealed that the XGBoost model achieved a robust accuracy of 0.7388-0.7995 indicating its superior applicability in resistance surface construction. The HAI consistently increased, peaking along the Northern Slope Economic Belt of the Tianshan Mountains and in urban areas of the Tarim Basin. All ESs displayed significant spatial heterogeneity, with high-value clusters in the north and much lower intensities in the south. Driven by intensifying human pressure, the spatial share of ecological sources decreased from 8.81% to 4.57%, with increased landscape fragmentation. Ecological source fragmentation was more severe in 2020, whereas ecological source stability was stronger in 2015. In 2015, the core corridors reached a maximum share of 44.44%, despite their minimum absolute quantity. Conversely, in 2020, the total number of corridors peaked, while the proportion of core corridors decreased to 17.64%. This divergent trend reveals that between 2015 and 2020, larger ecological sources were heavily compromised by external factors, leading to severe source fragmentation and a subsequent loss of corridor connectivity. These findings suggest that ecological management in Xinjiang must shift toward differentiated zonation, prioritizing intensive corridor restoration on the northern slope of the Tianshan Mountains and stricter boundaries for agricultural and urban expansion in the Tarim Basin.

The Mu Us Sandy Land, a critical agro-pastoral ecotone in northern China, served as a key region for understanding landscape connectivity dynamics under coupled climate-human pressures. This study used landscape pattern indices and the Geodetector model to systematically quantify the spatiotemporal dynamics of structural landscape connectivity in the Mu Us Sandy Land from 1990 to 2020 by integrating multi-temporal land use and meteorological data. The driving mechanisms of the landscape connectivity dynamics and present status of regional ecological restoration were analyzed. The results showed an overall decline in structural landscape connectivity over the past three decades, marked by a distinct spatial pattern of "decreasing in the east and increasing in the west". Fragmentation in the east was driven by intensive human expansion, while aggregation in the west resulted from long-term ecological restoration. Factor analysis revealed that structural landscape connectivity was jointly influenced by climatic and anthropogenic factors, with wide-ranging synergistic interactions. The dominant drivers changed temporally, i.e., in the early phases (1990-2000), natural factors (e.g., wind speed and temperature) and agricultural activities were the main drivers; in the transitional phase (2000-2010), precipitation played an important role; and in the later phases (2010-2020), the human-land synergy regime characterized by human activity factors, particularly residential density, gained predominant explanatory power. This study revealed the multifactor synergistic driving mechanisms and their temporal evolution, providing a scientific basis for adaptive ecological restoration and spatial planning in semi-arid regions.

Dust emission is initiated when the wind friction velocity exceeds a surface-dependent threshold; however, whether low-, moderate-, or high-threshold surfaces contribute most to dust emission, particularly across the Gobi Desert, remains unclear. This study addressed this question by detecting dust emission point sources across southern Mongolia and northern China using nine years (January 2017-May 2025) of high- and moderate-resolution optical satellite imagery from Sentinel-2, Landsat-8/9, and Sentinel-3 through visual interpretation. We estimated effective threshold friction velocities (u_*t) from Sentinel-1 C-band synthetic aperture radar (SAR) data and analyzed these values under dry spring conditions and during confirmed dust emission events. Our analysis of 4984 dust emission point sources revealed five major and nine minor dust source regions, primarily associated with lake basins, topographic depressions, mountain foothills, and gully systems. The major dust source regions included the Gaxun Nur Basin, Urad Rear Banner, and Alagxa Left Banner in China and the Ulaan Lake Basin, Orog Lake Basin, and Taatsiin Tsagaan Lake Basin in Mongolia. These regions exhibited moderate effective u_*t ranging from 0.50 to 0.95 m/s. In contrast, little or no dust emission was detected from mountain ranges characterized by high effective u_*t (>1.20 m/s) or from major sandy deserts (e.g., the Badain Jaran, Tengger, and Ulan Buh deserts), despite their extremely low effective u_*t (<0.20 m/s). Limited dust emission was observed in settings with moderate-to-high effective u_*t (0.95-1.10 m/s), such as mountain foothills and gully systems, and on surfaces with low-to-moderate effective u_*t (0.20-0.50 m/s). These findings indicated that frequent dust emission is confined to geomorphological settings with moderate effective u_*t and active fluvial sediment supply, whereas mountain foothills and gully systems act as secondary, episodic sources because of their relatively high effective u_*t. Identifying this moderate effective u_*t range for frequent dust emission refines the representation of surface erodibility in dust emission models and highlights priority regions for future field-based measurements of sediment properties.

Booming sands occur in more than a dozen countries worldwide. However, many sites have lost their acoustic properties as a result of human disturbance. As one of the earliest recorded booming sand sites, the Mingsha Mountain in Dunhuang, China remains important for both acoustic-landscape conservation and cultural heritage preservation. This study proposes a novel approach for identifying the spatial distribution of booming sands by integrating high-resolution topographic parameters derived from Gaofen-7 (GF-7) satellite imagery with laboratory-measured properties (physical, mineralogical, and acoustic characteristics) of field-collected sand samples and certain threshold parameters obtained from in-situ field observations. Key identification thresholds include the slope≥30.00°, dune height≥30.00 m, and slope length≥60.00 m. The collected sand samples exhibit excellent sorting and high flowability, consistent with the physical characteristics typically associated with booming sands. Light minerals dominate the sand samples (93.56%), with quartz being the most abundant mineral. Applying the remote sensing-based framework to the Mingsha Mountain identifies approximately 22.46 km² of potential booming sand areas, primarily concentrated in the central-northern region, where sand dunes are characterized by large heights, stable morphology, steep slopes, and relatively uniform aspect distributions. Acoustic analysis reveals that dominant frequencies are concentrated within a narrow range of 842-1020 Hz, supporting the robustness of the proposed identification framework. Based on these findings and the natural recovery capacity of booming sands in the Mingsha Mountain, this study proposes a management strategy involving rotational sand-sliding and sand-sliding rest to conserve booming-sand resources and support sustainable tourism in Dunhuang.

Climate extremes strongly influence vegetation dynamics in arid and semi-arid regions, but the mechanisms of vegetation responses remain unclear. Using the Yellow River Basin, China as a case study, we integrated 20 extreme climate indices (11 precipitation-related and 9 temperature-related) and a drought index to assess vegetation responses to climate extremes. An integrated framework combining random forest (RF) and structural equation modeling (SEM) was applied to quantify lagged and cumulative effects and separate the relative impacts of climate change and human activities. The results indicated that: (1) vegetation greenness in the basin increased by 0.004/a, with nearly three-quarters of the area showing a statistically significant upward trend; (2) the frequency of extreme precipitation events increased, while extreme high-temperature events became more frequent and intense, and extreme low-temperature events showed little change; (3) drought exhibited the strongest lagged effect on vegetation, lasting up to 6.7 months, whereas individual extreme event mainly induced cumulative effects on normalized difference vegetation index (NDVI); (4) drought and precipitation primarily affected the duration of vegetation growth, while temperature mainly controlled its disturbance intensity, with diurnal temperature range (DTR) and extreme temperature indices being the most important; and (5) climate change and human activities jointly dominated NDVI increases over 89.5% of the vegetated area, highlighting the high sensitivity and spatial heterogeneity of vegetation responses. These findings improve our understanding of vegetation-climate thresholds in dryland ecosystems and provide a transferable framework for environmental impact assessment and adaptive ecosystem management in arid regions.

Sandy vegetation plays a critical role in maintaining ecological balance and combating desertification in arid regions. Accurately elucidating the vegetation dynamics and analyzing how it responds to the impacts of natural and human activity hold vital scientific significance for promoting the sustainable preservation and development of sandy lands. This research focuses on four major sandy lands (Hulun Buir, Mu Us, Horqin, and Otindag) in the typical fragile ecological zones of China, aiming to analyze the vegetation change and identify the contribution of driving factors to vegetation dynamics. Using normalized difference vegetation index (NDVI) data from the Google Earth Engine, we analyzed the spatiotemporal patterns of fractional vegetation cover (FVC) during the period 2000-2020. Furthermore, by integrating the optimal parameter-based geographical detector (OPGD) and structural equation modeling (SEM), we systematically assessed the relationships of natural factors and human activities with FVC changes. Compared with the period 2000-2010, FVC degradation in the Horqin and Mu Us sandy lands decreased by 5.74% and 3.49%, respectively, while FVC improvement increased by 6.12% and 1.24% during the period 2011-2020. Meanwhile, the Hulun Buir Sandy Land showed reductions in both degradation (14.92%) and improvement (0.08%), whereas the Otindag Sandy Land exhibited increased degradation (1.43%) and reduced improvement (0.41%). Climate was the dominant driver of FVC changes in the Hulun Buir and Otindag sandy lands, whereas human activity played a key role in the Horqin and Mu Us sandy lands. Specifically, temperature and precipitation were the key drivers in the Hulunbuir and Otindag sandy lands, while FVC dynamics in the Horqin and Mu Us sandy lands were mainly driven by land use intensity. These findings provide insights into vegetation dynamics and driving mechanisms in sandy land ecosystems, and offer practical implications for ecological management in vulnerable regions.

Shrub mortality is accelerating globally, yet the mechanisms and thresholds driving vegetation mortality in dryland shrubs remain insufficiently understood. Caragana korshinskii Kom., a key species for dryland restoration in China, has experienced widespread die-off in recent decades. This study aimed to identify the important drivers of C. korshinskii mortality on the Loess Plateau, China. We surveyed 35 C. korshinskii plantation plots (three 30 m×30 m subplots per sampling plot) across the western Loess Plateau. In each subplot, we measured the plant height, canopy diameter, and basal diameter of 30 randomly selected individuals and recorded the total number of live and dead shrubs to calculate the annual mortality probability (AMP). Environmental factors encompassing climate, soil, topography, and disturbance were compiled for each sampling plot. Spearman's correlation and ridge regression analyses were applied to determine the most driving factors. Piecewise linear models were then used to determine their breakpoints, and partial least squares path model (PLS-PM) was employed to quantify their effects on AMP. The results revealed that AMP exhibited a clear spatial gradient, decreasing from the west to the east across the study area. AMP decreased with increasing mean annual precipitation (MAP) but increased sharply when the MAP fell below 311 mm, suggesting a discontinuous response around this statistical breakpoint. Similarly, AMP increased markedly when the mean annual temperature (MAT) exceeded its statistical breakpoint of 9.13°C. Higher total nitrogen (TN) was associated with a gradual decline in AMP, whereas wind speed and soil thickness were not clearly related. PLS-PM confirmed that MAP is the principal direct driving factor, with TN and MAT making secondary contributions. Our findings reveal that MAP is the key environmental driver of C. korshinskii mortality, with the statistical breakpoint (311 mm) providing critical insights into vegetation stability and implications for the sustainable management and restoration of dryland ecosystems under climate change.

Understanding scale-dependent plant-plant relationships is essential for revealing population organization and species coexistence in extremely arid ecosystems. In this study, Calligonum mongolicum Turcz., a dominant sand-fixing shrub in mobile dunes of the Gurbantunggut Desert, Northwest China, was selected as the focal species to investigate its intra- and interspecific spatial relationships across multiple scales. Univariate, bivariate, and marked point pattern analyses combined with different null models were applied to characterize spatial patterns between different life stages, survival statuses, individual sizes, and co-occurring shrubs, and to evaluate the potential contributions of habitat heterogeneity and dispersal processes. The results show that the overall population and different life stages of C. mongolicum exhibited significant aggregation under the complete spatial randomness model, whereas the aggregation patterns were largely weakened after accounting for habitat heterogeneity and clustering processes. Adult-juvenile spatial associations showed clear scale dependence, whereas survival-related spatial patterns exhibited limited spatial dependence. After accounting for habitat heterogeneity, no persistent spatial pattern supporting strong adult-mediated facilitation or density-dependent effects was detected across most scales. Interspecific associations between C. mongolicum and co-occurring shrubs (Haloxylon persicum Bunge ex Boiss. & Buhse, Artemisia ordosica Krasch., and Ephedra distachya L.) also exhibited pronounced scale dependence, with positive associations mainly occurring at smaller scales and negative associations emerging at some larger scales. These findings indicate that the spatial organization of dominant desert shrubs is shaped by multiple ecological processes, including habitat heterogeneity, dispersal limitation, and potential biotic interactions, providing new insights into plant coexistence and population maintenance in extremely arid ecosystems.