From July 29 to August 1, 2023, the North China region experienced a rare extreme rainstorm event (referred to as the “23.7” rainstorm), which triggered widespread flood disasters. This study systematically analyzed the water vapor transport characteristics and key driving mechanisms of this rainstorm based on ground meteorological station observation data, ERA5 reanalysis data, S-band Doppler radar data, and a qualitative Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The research results show that persistent southeasterly moisture transport from the Western Pacific Ocean and the South China Sea was strongly associated with sustaining the rainstorm in the Hebei region. This transport established a significant moisture convergence center, providing an abundant moisture source for the extreme precipitation. At upper levels, the southwest airflow formed around the western Pacific subtropical high strengthened vertical divergence over the rainstorm area. This pronounced upper-level divergence acted as a dynamic driver for deep ascent, maintaining the vertical circulation necessary to transport high- θ_e air from the lower troposphere into the storm core, thereby prolonging the duration of heavy precipitation.During this rainstorm, the 55 dBZ strong radar echo centers of the convective system exhibited significant vertical development, extending into the upper troposphere. Although radar-derived heights serve primarily as qualitative indicators due to influences such as hydrometeor population, beam geometry, and attenuation effects, these elevated echo tops are consistent with the presence of exceptionally vigorous updrafts that facilitated the rapid lofting of hydrometeors and intensified surface rainfall. Additionally, trajectory simulations qualitatively identified the primary moisture transport routes for this heavy rainfall event. The primary driving mechanism behind the “23.7” rainstorm was the synergistic interaction among sustained and abundant horizontal moisture transport, intense vertical lifting within the storm region, and a robust upper-level divergence field. The dynamic coupling of these factors was strongly associated with prolonged convective activity. Moreover, a continuous moisture influx maintained a near-saturated thermodynamic profile, which significantly enhanced precipitation efficiency. Combined with the quasi-stationary nature of the system, this thermodynamic environment was critical in sustaining the massive rainfall totals. Further investigation into the triggering mechanisms of vertical water vapor transport is crucial for enhancing the forecasting capabilities of localized rainfall, particularly in mountainous regions, and for improving the accuracy of disaster risk assessment. This study advances the understanding of extreme precipitation formation mechanisms in North China, providing scientific support for optimizing numerical weather prediction models and strengthening early warning capabilities for extreme weather events.

With global warming, rising atmospheric vapor pressure deficit (VPD) has emerged as a critical driver of vegetation productivity and the terrestrial carbon sink. Yet, the thresholds beyond which VPD constrains tree growth remain poorly quantified. Using 2,953 tree-ring sites and combining generalized additive models with threshold regression models, we demonstrate that growth-related VPD thresholds are widespread. Approximately 63% of the studied species groups exhibited identifiable thresholds, which were generally lower in cool, humid regions and higher in warm, dry environments. For the same tree species occurring under contrasting aridity conditions, thresholds also differed substantially. As VPD has risen, the proportion of sites exceeding these thresholds has increased rapidly since about 1970 and is projected to rise further under different emissions scenarios. Under SSP5-8.5 in particular, approximately 79.8% of the sites are projected to be exposed to VPD above their thresholds by the end of the century. Although potential upward threshold shifts associated with acclimation or adaptation could partly reduce future exceedance, this buffering effect becomes increasingly limited under stronger climate forcing. Without effective climate mitigation, rising VPD will increasingly exceed the tolerance thresholds of trees, placing more trees under sustained growth constraints and undermining terrestrial carbon sequestration.

Ecological Security (ES) is an essential safeguard for regional sustainable development. Scientifically elucidating the multiscale evolution of ES patterns and their driving mechanisms is critical for ecological governance and conservation in Mountainous Urban Agglomerations (MUAs). Taking the central Yunnan Urban Agglomeration (CYUA) as a representative MUA, this study constructs a three-dimensional ES assessment framework integrating ecological health, ecological sensitivity, and ecological risk. By integrating ES slope-spectrum analysis with spatial autocorrelation, Geodetector, Multiscale Geographically Weighted Regression (MGWR), and machine learning, we analyze the spatiotemporal evolution of regional ES patterns and their driving mechanisms from a multiscale perspective. Results show that from 2000 to 2020, ES in the CYUA exhibited an overall improving trend with clear scale dependency. At the micro-scale, urban expansion intensified ecological fragmentation, whereas at the macro-scale, regional integration under policy guidance was evident. ES shows significant differentiation along slope gradients, forming a typical pattern of “low-slope–high-risk and high-slope–high-security,” with the 10–25° interval identified as a “conflict front” between ecological conservation and urban development, facing elevated degradation risks. Human Activity Intensity (HAI) is the dominant driver of ES spatial differentiation, with a critical pressure threshold of 0.29, and exhibits significant nonlinear interactive effects with slope and NDVI, with q-values exceeding 0.6. Overall, this study reveals complex human–environment interactions in MUAs and provides scientific evidence for balancing topographic constraints with urbanization, optimizing territorial spatial patterns, and promoting coordinated development of ecological conservation and high-quality urbanization.

Permafrost degradation has substantially accelerated the development of thaw slumps in response to climate warming on the Qinghai-Tibet Plateau (QTP). Thaw slumps represent thermokarst-related hydro-geomorphic features that significantly affect landscape stability and hydrological processes. Thaw slump susceptibility assessment is an effective tool for understanding permafrost landscape responses to climate warming and supporting risk-informed management. However, existing assessments often overlook permafrost surface deformation and the spatially coupled interactions of environmental factors, limiting understanding of thaw slump development mechanisms. In this study, an interpretable two-dimensional convolutional neural network (2DCNN) framework was employed to assess thaw slump susceptibility, incorporating surface deformation derived from small baseline subset (SBAS) InSAR to capture spatially coupled environmental controls. In addition, the influences of environmental factors were quantitatively analyzed using Shapley additive explanations (SHAP) and structural equation modeling (SEM). Results indicate that integrating SBAS-InSAR-derived surface deformation substantially improved thaw slump susceptibility prediction, with the model achieving an AUC of 0.981 and outperforming models without deformation or pixel-based approaches. Based on the susceptibility assessment, incorporating surface deformation enhanced the spatial targeting of high-risk areas, with 177.28 km2 covering 85.23% of mapped thaw slumps, compared with 223.61 km2 and 84.86% without deformation data, indicating a substantial reduction in false-positive predictions. The NDVI, slope, and surface deformation rate were identified as the primary factors associated with thaw slump development, and further analysis revealed that non-linear interactions among these factors are closely related to surface instability. This study presents a robust and interpretable framework for evaluating thaw slump susceptibility, providing new insights into environmental controls while supporting landscape management in permafrost regions.

Salt crusts formed on inland arid playas are an important global source of saline dust, posing severe risks to ecosystem stability and human health. Understanding salt crust dynamics and driving factors is essential for assessing saline dust emissions and ecological risks. The Ebinur Lake Basin (ELB) is a significant source of saline dust in northwest China and features extensive salt crusts. However, the spatiotemporal evolution of salt crusts has not been well quantified due to the lack of consistent long-term observations. In this study, an improved nonlinear spectral unmixing model based on ensemble tree models is developed to estimate salt crust coverage in the ELB spanning 2005-2025. The model performs robustly (R2 = 0.769) for medium-to-high coverage salt crusts (MHSCs; fractional cover >= 0.3). From 2005 to 2025, the MHSC area in the ELB increased with interannual fluctuations, peaking at 377.8 km2 in 2021-approximately 5.03 times the 2005-2009 mean. Spatially, the MHSCs were categorized into four types: lake-basin, desert, fluvial-depressional and anthropogenic. Among these, the lake-basin type covered the largest area and exhibited the strongest variability, accounting for most of the interannual fluctuations in MHSC area. Analysis of the driving factors indicates that lake area, drought conditions, and human activities were the primary drivers of the MHSC changes, with drought having a distinct lagged effect. This study estimates the long-term spatiotemporal dynamics of the MHSCs in the ELB and provides a scientific basis for the ecological risk assessment and the management of saline dust hazards in arid and semiarid regions.

The impact of elevated atmospheric vapor pressure deficit (VPD) on vegetation productivity is well-documented at monthly and annual scales. However, the influence of daytime VPD (VPDday) and nighttime VPD (VPDnight) is often overlooked. Using multiple long-term remote sensing proxies of vegetation productivity, we reveal distinct effects of VPDday and VPDnight on growing season vegetation productivity over the extratropical Northern Hemisphere (> 25° N). VPDday was negatively associated with vegetation productivity in 73.2% of vegetated pixels, and robustness analyses across alternative datasets and methods yielded a range of 67.4%-75.6%. By contrast, positive effects of VPDnight were detected in 51.8% of vegetated pixels, with corresponding estimates ranging from 36.5% to 55.7% across robustness analyses. This contrast was strongly related to aridity conditions. Vegetation productivity in drylands is more vulnerable to the double negative effects of high VPDday and VPDnight, while in humid regions, it benefits from increased VPDnight. Sap-flow observations helped explain this contrast from the perspective of plant hydraulic transport. In humid regions, relatively ample soil moisture allowed nocturnal water transport to be maintained under elevated VPDnight, helping restore plant water status overnight and providing favorable hydraulic conditions for daytime carbon uptake and vegetation productivity. In drylands, sap flow declined more strongly under high atmospheric demand and limited moisture during both daytime and nighttime, suggesting stronger hydraulic limitation and reduced overnight recovery, and thereby creating less favorable hydraulic conditions for vegetation productivity. These findings underscore the different roles of VPDday and VPDnight in regulating vegetation productivity and highlight the importance of incorporating both into models to improve predictions of climate change impacts on terrestrial ecosystems.

Accurate, large-scale, and temporally explicit lake mapping is critical for water resource management, hazard risk assessment, and understanding lake responses to climate change. The Tibetan Plateau (TP) hosts a high density of lakes, many of which are small and difficult to detect due to highly heterogeneous environments, including mountain shadows, glacier and snow cover, clouds, and turbid waters. Existing studies often produce multi-year composite datasets, which enhance the detectability of lakes by emphasizing their long-term occurrence. However, these datasets ultimately remain static, reflecting the long-term aggregated distribution of lakes without capturing their spatial extent in any specific year. In this study, we processed 17,942 Sentinel-2 images acquired from July to October 2020 using an automated, deep learning-based water extraction framework to extract lakes across the TP. The method demonstrated high accuracy under challenging conditions, with Intersection over Union (IoU) values exceeding 92% in cloudy, glacial, and mountainous test areas. Applying this framework, we generated a comprehensive 2020 lake inventory, identifying 57,841 lakes larger than 0.01 km(2). Approximately 65% of the total lake coverage was concentrated in the Inner Plateau Basin, with the 4,500-5,000 m elevation exhibiting the highest density (29,200 lakes covering 32,892.51 km(2), representing 50.5% of all lakes and 59.3% of total lake area). Compared with previous studies, this dataset improves the detection of small lakes and provides a temporally explicit, year-specific map of lake distributions, distinguishing multiple lake types (large natural lakes, glacial lakes, thermokarst lakes, and reservoirs). This high-resolution, comprehensive dataset constitutes a valuable resource for hydrological, climatic, and ecohydrological research across TP. The lake dataset generated in this study has been archived and made publicly available through Zenodo (https://doi.org/10.5281/zenodo.15639602).

Floods are one of the most frequent natural hazards worldwide. Accurate flood risk mapping is critical for effective flood management in flood-prone areas. In this study, we employed the multi-criteria decision analysis (MCDA) method to develop a flood risk map that combines flood susceptibility and vulnerability factors. Three machine learning models—random forest (RF), XGBoost, and LightGBM—were selected as the basic classifiers for creating flood susceptibility maps. Historical flood data and 13 flood-influencing factors were extracted for machine learning training. Model performance was assessed using precision, recall, accuracy, F1-score, and AUC through 5-fold cross-validation. All three models performed well, but RF slightly outperformed the other two according to the evaluation results. We used the analytic hierarchy process (AHP) method to combine the flood susceptibility map generated by the RF model with flood vulnerability indicators to produce a flood risk map. Our findings demonstrate that integrating advanced machine learning techniques with MCDA method offers an effective approach for flood risk assessment, providing a robust foundation for decision making in flood risk management.

Investigating aeolian saltation over typical desert surfaces is crucial for understanding aeolian patterns and geomorphic development in arid and semi-arid regions. However, ground-based synchronous measurements on various land surfaces are scarce, and usually in limited measurement duration. The Alxa Plateau is characterized by strong wind, extensive wind erosion, and is one of the main dust source areas in China. In this study, we conducted year-long synchronous sand saltation measurements on five typical desert surfaces (gravel Gobi, fine-gravel flat, sand sheet, mud-flat nebkha, and salt-flat nebkha), in the transitional zone between the downstream plain of Heihe River and Badain Jaran Sand Desert. The measurements revealed that, under the local arid climate, the average annual 2 m-high wind speed on gravel Gobi was similar to that on sand sheet, but 1.26 to 1.43 times higher than that on the other three surface types. Sand saltating number on sand sheet was 1 order of magnitude greater than gravel Gobi and 2-3 orders greater than the other surfaces. The majority of sand saltation activities occurred within 10-30℃ air temperature and 10-30% relative humidity. Surface roughness and sand availability might be important factors affecting wind speed and aeolian saltation activity. The majority of sand saltation was chiefly caused by high wind speed and long-duration sand-transporting events. We detected the average particle kinetic energy decreased with increasing wind speed and concentration of saltating sand, implying that enhanced collisions among numerous sand particles at higher wind speeds might substantially reduce the abrasive efficiency of individual sand particles.

On 7 January 2025, at 09:05 local time, a M6.8 earthquake occurred in Tingri County, Tibet Autonomous Region, China, causing significant casualties and widespread building damage. In response to the urgent need for post-earthquake loss assessment, we proposed a comprehensive rapid assessment framework. This framework synergistically integrates remote sensing data, seismic intensity maps, building distribution data, building damage matrices, national census data, and regional building surveys to expedite the estimation of affected areas and the quantification of damaged bulidings. The methodology involved the development of structural damage matrices and seismic fragility curves specific to various structural types, facilitating the assessment of direct economic losses. Our findings reveal that over 70% of the buildings within the high seismic intensity zones (IX and VIII) near the epicenter were earth-timber structures, characterized by limited seismic resistance. This structural vulnerability status led to disproportionately higher rates of building collapse and severe damage compared to the areas with better seismic-resistant buildings. The assessment identified approximately 254,000 affected buildings within the epicentral and surrounding regions, with a total affected building area of 12.30 million m2. The analysis of buildings at different damage levels showed that 26% of the buildings were slightly damaged, 15% were moderately damaged, 6% were severely damaged, and 3% collapsed. The direct economic losses from building damage was estimated at approximately CNY 3.62 billion. This study established a practical technical framework for rapid building loss assessment for earthquakes in the Qinghai-Tibet Plateau and adjacent regions, enabling timely and reliable evaluation of seismic impacts. The proposed methodology enhances decision-making efficiency during emergency response by providing critical information for response strategies. Furthermore, this study could offer a fundamental reference for post-earthquake recovery planning, supporting the development of targeted reconstruction efforts. More broadly, this study holds significant potential for improving disaster management in seismically vulnerable regions, particularly those with similar structural weaknesses and high seismic risk.

This study explored the change trend and driving factors of aerosol pH under severe control measures in four cities in northern China during the 2022 Winter Olympics (WOG). The results of positive matrix factorization (PMF) source apportionment show that the decline in vehicle emissions and secondary inorganic aerosol formation played a significant role in reducing PM2.5 concentrations during the control periods. Based on calculations using the thermodynamic model ISORROPIA II, the aerosol pH values in the four cities during the Winter Olympics ranged from 2.75 to 4.66, indicating acidity. The aerosol pH in Zhangjiakou was the lowest among the four cities, while Baoding had the highest aerosol pH. One-at-a-time sensitivity analysis revealed that during the strict control period, the key factors driving aerosol pH were SO42-, NHx (NH3 + NH4+) and TNO3 (HNO3 + NO3-). These factors contributed to 66 %, 54 %, 61 %, and 65 % of the changes in aerosol pH in the four cities, respectively. With decreasing control intensity, meteorological factors began to drive changes in aerosol pH. The factors affecting the gas-aerosol partition of semivolatile components were explored. During the study period, the aerosol pH, temperature, and aerosol liquid water content (ALWC) played important roles in the partition of the semivolatile components in the four cities. This research helps clarify the relationships between aerosol composition, aerosol pH, and meteorological factors.

Since 2000, China has invested over RMB 300 billion in large-scale ecological restoration projects in northern China to combat desertification. Over the past two decades, significant progress has been achieved in vegetation restoration, particularly in key areas such as Erdos, Nunkiang, and Horqin. However, is the trend of vegetation restoration in the Desertification Control Zones (DCZ) increasing continuously? What are the driving factors for the change in the vegetation restoration trend? Existing research has not yet reached a definitive conclusion on this matter. To figure out the changes and drivers of vegetation recovery trends, we assessed shifts in vegetation trends within the DCZ over the period 2000-2023 using Pettitt's test and residual trend analysis based on fractional vegetation cover (FVC) and climate data. The results indicate that: The recovery trend of FVC in the DCZ began to slow down since 2012; The FVC recovery trend was 0.208 %yr- 1 from 2000 to 2011, then slowed to 0.065 %yr- 1 after 2011. Climatic factors caused the slowdown in FVC recovery; the average contribution of climatic factors to FVC recovery in the DCZ was 0.071 %yr- 1 before 2011 and declined to - 0.086 % yr- 1 after 2011. Precipitation (-0.049 %yr- 1) and temperature (-0.026 %yr- 1) exhibited the most substantial negative effects after 2011. Human activities have had a stable positive impact on FVC recovery, contributing 0.137 %yr- 1 before 2011 and 0.151 %yr- 1 after 2011. The negative impact of climate change on vegetation recovery should be considered in desertification control practice. Future desertification control practices should be based on natural conditions to ensure the continued effectiveness of investments.

From 10 to 15 April 2025, China experienced a rare persistent extreme wind-dust compound disaster that swept from north to south. Based on observational data, historical disaster records, and situations of various exposed elements, this study analyzed the formation mechanisms and evolution of this extreme event and conducted a rapid assessment of the associated loss and damage. The results indicate that the direct cause of this extreme wind-dust compound disaster was a strong cold vortex system generated in Mongolia, which moved eastward and southward, combined with the amplification effects of topography and urban structures, and the downward transmission of momentum from higher troposphere. The analysis revealed that approximately 697.47 million people were exposed to strong winds, while about 1,374.54 million people were exposed to high concentrations of PM10. The strong winds also caused varying degrees of damage to buildings, transportation networks, agricultural greenhouses, and forests. Based on vulnerability curves for wind-related loss and damage, it was estimated that the number of victims affected by this extreme wind-dust compound disaster ranged from 0.209 to 1.044 million, with casualties between 5 and 13 individuals. The number of damaged buildings was estimated to be between 2115 and 4607, and the area of affected crops was between 229 and 783 km2. The direct economic losses could reach as high as RMB 0.076–3.501 billion yuan. This study revealed the causes of this extreme wind-dust compound disaster and quantified the disaster loss and impact, providing new insights for the prevention of associated disasters.

The intensifying global warming and the increasing frequency of extreme weather events have created an urgent need for targeted resilience building in mountainous villages. This study focuses on three typical villages in the Hengduan Mountains region. From the perspective of individual villagers, a disaster resilience evaluation index system was constructed, encompassing four dimensions: disaster prevention capacity, disaster resistance capacity, disaster relief capacity, and recovery capacity. Using the entropy method and a village disaster resilience assessment model, the disaster resilience levels of each village were quantitatively evaluated. The results indicate the following: (1) Disaster resistance capacity is the key factor constraining the disaster resilience level of mountain villages. (2) The overall disaster resilience of mountain villages is at a medium level, with minor differences among villages. (3) Significant disparities exist in capacity dimensions across villages: Qina Village demonstrates the strongest disaster resistance capacity, while Xiamachang Village excels in disaster prevention capacity but shows relative weakness in recovery capacity. (4) Household material endowment has a significant positive impact on disaster prevention, resistance, relief, and recovery capacities, while individual self-rescue capability and individual–government collaboration capacity also significantly enhance disaster prevention, resistance, and relief capacities. We propose the following: Leveraging the rural revitalization strategy as a pivotal point, this approach promotes the diversified development of the village economy. It facilitates the increase in villagers’ income through the implementation of employment skill training programs, thereby strengthening household material foundations to enhance individual disaster resilience. By relying on the mass monitoring and mass prevention mechanism and a disaster information sharing platform, real-time exchange of disaster situation information is achieved, which enhances communication and collaboration between villagers and the government, consequently improving the synergistic efficiency between individuals and governmental bodies. Simultaneously, a villager-centered disaster prevention system is constructed. Through measures such as disaster prevention publicity and practical disaster response drills, villagers’ awareness of disasters and their capabilities for self and mutual rescue are elevated, ultimately strengthening the overall disaster resilience of rural areas in the Hengduan Mountains region.

The physicochemical characteristics of dustfall particles are essential for the in-depth understanding on the aerodynamic processes of aeolian dust and its environmental effects. In this study, we conducted continuous high-frequency sampling of atmospheric dustfall in the Taklimakan hinterland during spring 2022, analyzing particle micromorphology, size distribution, mineral composition, deposition fluxes, and vertical dust characteristics. The results showed that the dustfall particles sampled in the Taklimakan hinterland were mostly microaggregates, angular, and subrounded based on the statistical analysis of the Focused Ion Beam Scanning Electron Microscope (FIB-SEM). As determined by the Laser Diffraction Particle Size Analyzer (LDPSA), the dustfall particles were predominately coarse particles, with particles between 20 mu m and 80 mu m accounting for 83.73 % of the total particle number. Volume proportion of dustfall particles with particle size of 60-150 mu m was 72.41 %. Mineralogical analysis of dustfall particles using the Intelligent Scanning Electron Microscope Environmental Particle Analysis System (IntelliSEM EPAS) revealed that calcite was the dominant component (31.15 %), followed by quartz (18.52 %), chlorite (11.84 %), kaolinite (8.11 %), smectite (6.28 %), and illite (5.25 %). Halite was identified as the primary salt component, making up 9.52 % of detected particles. Vertical dust profiles derived by the ground-based Mie-scattering lidar indicated that large amounts of irregular dust floated in the tropospheric atmosphere over the Taklimakan Desert, causing a high depolarization ratio of more than 0.6 within 5 km of the surface. These dust aerosols suspended in the upper air with long periods were attributed to the frequent windblown dust weather over the Tarim Basin in spring, resulting in high ambient particulate concentration and dust deposition.

This study explores the application of numerical simulation in debris flow disaster early warning, using the Shiyang Gully in China as a case study. Using both the HEC-HMS and FLO-2D, the 18 June 2017 debris flow event was reconstructed to analyze the impacts of cumulative rainfall, rainfall intensity, and rainfall range on debris flow hazards. Simulation results showed that cumulative rainfall exceeding 90 mm or rainfall intensity surpassing 200 mm/8 h significantly increases debris flow depth, impact force, and affected areas, leading to severe structural damage. Expanding the rainfall range to the entire basin further amplifies disaster risks, increasing both inundation depth and exposed elements. Based on these findings, a four-tier debris flow early warning system was developed: (1) blue (IV) warning for cumulative rainfall of up to and including 20 mm or intensity of 200 mm/24 h, indicating preparation and monitoring; (2) yellow (III) warning for rainfall exceeding 20 mm but below 60 mm, requiring enhanced inspections and safety measures; (3) orange (II) warning for rainfall between 60 and 90 mm or intensity of 200 mm/12 h, necessitating immediate evacuation preparations; and (4) red (I) warning for rainfall over 90 mm or intensity of 200 mm/8 h, demanding full evacuation and emergency responses. This study demonstrates the value of numerical simulation in refining early warning systems by integrating multi-scenario analyses of rainfall parameters. The proposed system offers scientific and practical insights for enhancing debris flow disaster management, particularly in small, high-risk watersheds, providing a framework for cross-regional disaster mitigation strategies.

The authors have requested that this preprint be removed from Research Square due to a pending patent application.

Tarim Basin in western China is home to the world's second-largest mobile dune desert, Taklimakan Desert, and it's one of Asia's primary sources of sand and dust storm. Observations of windblown dust are insufficient over this hyper-dry inland region. Here we present a comprehensive study based on consecutive in-situ field observations, meteorological records, environmental monitoring data and satellite measurements over the Tarim Basin for a full year in 2015. The results show that during the severe sand and dust storm events, the observed ambient PM10 (particulate matter with an aerodynamic diameter ≤ 10 μm) concentration rises rapidly, with a maximum value exceeding 10,000 µg/m3 per hour, while wind speeds reach 10-30 m/s and visibility is reduced to less than 10 m. Soil particulates can be blown vertically into the atmosphere at a height of 3-12 km. High volumes of dust deposition were measured at environmental monitoring stations, ranging from 1764 to 3800 g/m2 yr. Those significant flux levels of ambient particulate matter (PM) concentrations and dust depositions are strongly associated with frequent dust occurrence in the arid environment of the Tarim Basin. Satellite measurements of aerosol optical depths (AOD) show a broad spatial pattern of dust aerosols distribution over the basin, with dense dust remaining suspended for long periods of time (3-5 months in spring and summer seasons). The wind regimes, basin-like topography, thermodynamic condition, and loose sandy surfaces greatly affect the regional aeolian dust environment in the Tarim Basin, which lead to a significantly high dust emission, ambient PM concentration and dust deposition.

Near-surface wind speed (NSWS) is one of the most important factors shaping local terrain and geomorphological features, and its variations have significant environmental impacts, strongly influencing global dune dynamics and dust emissions. In recent years, the reduction in wind speed may have mitigated drought stress induced by rising temperatures, further weakening dust emissions. In this study, we present changes in global dust aerosol optical thickness (DOT) alongside variations in dust events, in response to alterations in desert regions associated with climate change. We find that global near-surface dust events (excluding Europe) declined annually from 2000 to 2023. Concurrently, the DOT in desert regions, specifically in the eastern Thar Desert, the Sahara Desert, and the Badain Jaran Desert, also decreased annually. This decline is primarily attributed to the reduced intensity of sand-moving wind regime in these areas, with drift potential (DP) decreasing by 37 %, 10 %, and 8 %, respectively. Additionally, dust activity has also diminished to varying degrees in parts of North Africa, Northeast Asia, South America, and Southern Africa. Finally, we project future changes in aeolian dust conditions in desert areas over the next three decades under the SSP 2-4.5 and SSP 5-8.5 scenarios. Under future simulation scenarios, the declining trend in DP is projected to intensify over the next three decades, with significant regional disparities. Notably, under the high emission scenario, the median DP of global deserts is projected to decrease by 1.1 m3 s-3 compared to the median under the medium emission scenario, with the reductions primarily occurring in the southern Sahara Desert and northeastern Australian deserts. Conversely, significant increases in DP are projected under high-emission scenarios in the Arabian Desert, Taklamakan Desert, and Gobi Desert. These changes are anticipated to heighten the risk of global dust events, which may be associated with large-scale ocean-atmosphere oscillations. Such projected changes will impact dune erosion and dust emissions, influencing urban and rural planning in desert regions and posing potential risks to human health.

ABSTRACTThe Qinghai‐Tibet Plateau (QTP) has an extensive frozen soil distribution and intense geological tectonic activity. Our surveys reveal that Qinghai‐Tibet Plateau earthquakes can not only damage infrastructure but also significantly impact carbon dioxide emissions. Fissures created by earthquakes expose deep, frozen soils to the air and, in turn, accelerate soil carbon emissions. We measured average soil carbon emission rates of 968.53 g CO2 m−2·a−1 on the fissure sidewall and 514.79 g CO2 m−2·a−1 at the fissure bottom. We estimated that the total soil carbon emission flux from fissures caused by M ≥ 6.9 earthquakes on the Qinghai‐Tibet Plateau from 326 B.C. to 2022 is 1.83 × 1012 g CO2 a−1; this value is equivalent to 0.51% ~ 1.48% and 2.34% ~ 5.14% of the increased annual average carbon sink resulting from the national ecological restoration projects targeting forest protection and grassland conservation in China, respectively. These earthquake fissures thus increased the soil carbon emission rate by 0.71 g CO2 m−2·a−1 and significantly increased the total carbon emissions. This finding shows that repairing earthquake fissures could play a very important role in coping with global climate change.