The thermal stability of permafrost, a foundation for engineering infrastructure in cold regions, is increasingly threatened by the dual stressors of climate change and anthropogenic disturbance. This study investigates the dynamics of the crushed rock revetted embankment at the Kunlun Mountain Section of the Qinghai-Tibet Railway, systematically investigating the coupled impacts of climate warming and engineering activities on permafrost thermal stability using borehole temperature monitoring data (2008-2024) and climatic parameter analysis. Results show that under climate-driven effects, the study area experienced an air temperature increase of 0.2 degrees C per decade over the 2015-2024. Concurrently, the mean annual air thawing degree-days (TDD) rose by 13.8 degrees C center dot d/a, leading to active-layer thickening at a rate of 3.8 cm center dot a- 1at natural ground sites. From 2008 to 2024, the active layer had thickened by 0.7-0.8 m. At the embankment toe (BH 5), the active-layer thickening rate (3.3 cm center dot a- 1) was 25 % lower than that at the natural ground borehole (3.8 cm center dot a- 1); correspondingly, the underlying permafrost temperature increase rate at the toe (0.3 degrees C per decade) was lower than that at the natural borehole (0.5-0.6 degrees C per decade). Permafrost warming rates decreased with depth. Shallow layers (above -2 m) were significantly influenced by climate, with warming rates of 0.3-0.6 degrees C per decade. In contrast, deep layers (below -10 m) showed warming rates converging with the background atmospheric temperature trend (0.2 degrees C per decade). Thermal regime disturbance was most pronounced at horizontal distances of 3.0-5.0 m from the embankment. Nevertheless, the crushed-rock revetment maintained a permafrost table 0.6 m shallower than that of natural ground, confirming its thermal diode effect (facilitating convective cooling in winter), which partially offset climate warming impacts. This study provides critical empirical data and validates the cooling mechanism of crushed-rock revetment, which is essential for predicting the long-term thermal stability and informing adaptive maintenance strategies for railway infrastructure in warming permafrost regions.

Near-surface temperature and moisture are key boundary conditions for simulating permafrost distribution, projecting its response to climate change, and evaluating the surface energy balance in alpine regions. However, in desertified permafrost zones of the Qinghai-Tibet Plateau (QTP), the observations remain sparse, and reported trends vary considerably among sites. This lack of consistent evidence limits the ability to represent microenvironmental processes in models and to predict their influence on permafrost stability. From September 2021 to August 2024, we conducted continuous observations at a desertified permafrost site on the central QTP, covering the vertical range from 150 cm above to 100 cm below the ground surface (boundary layer). Measurements included air and ground temperature, air humidity, soil moisture, wind speed, and net radiation. Results showed that the mean annual air temperature increased with decreasing height at a gradient of approximately 0.42 degrees C/m, while mean annual air humidity remained nearly constant at 56.8 +/- 1.1 % (150-0 cm). In the near-surface soil layer (0 similar to -10 cm), temperature rose by 3.6 +/- 0.1 degrees C and moisture decreased by 34.0 +/- 2.7 %. The mean annual ground temperature increased with depth at a rate of about 0.55 degrees C/m, whereas soil moisture decreased between -20 and -60 cm (52.86 %/m) and increased between -60 and -100 cm (56.30 %/m). Seasonal patterns showed marked difference: in the freezing season, the calculated total temperature increment within the boundary layer (1.91 degrees C) was 61 % lower than the observed value (4.88 degrees C), while in the thawing season, it was 58 % higher (4.38 degrees C > 2.77 degrees C). These results reveal strong vertical gradients and seasonal contrasts in thermal and moisture regimes, emphasizing the need to integrate coupled temperature-moisture processes into boundary layer parameterizations for cold-region environments. Improved representations can enhance permafrost modeling and inform infrastructure design in regions experiencing both warming and desertification.

Understanding changes in water balance and land-atmosphere interaction under climate change is crucial for managing water resources in alpine regions, especially in the Qinghai-Tibet Plateau (QTP). Evapotranspiration (ET), a key process in the land-atmosphere interaction, is influenced by permafrost degradation. As the active layer in permafrost regions deepens due to climate warming, the resulting shifts in surface hydrologic connectivity and water storage capacity affect vegetation's ability to access water, thereby influencing its growth and regulating ET dynamics, though the full complexity of this process remains unclear. This study employs the Budyko-Fu model to assess the spatiotemporal dynamics of ET and the ET ratio (the ratio of ET to precipitation) on the QTP from 1980 to 2100. While ET shows a continuous upward trend, the ET ratio exhibits a non-monotonic pattern, increasing initially and then decreasing. More than two-thirds of permafrost areas on the QTP surpassed the critical ET ratio threshold by 2023, under three emission scenarios. By 2100, nearly all areas are projected to reach the tipping point, with 97 % affected under the SSP5-8.5 scenario. Meadow and steppe regions are expected to encounter this threshold earlier, whereas forested areas will be less affected, with over 80 % unlikely to reach the tipping point by 2100. Basin-level differences are notable: nearly 90 % of the Qaidam basin exceeded the threshold before 2023, compared to less than 50 % in the Yangtze basin. By 2100, more than 80 % of regions in all basins are expected to cross the tipping point due to ongoing permafrost degradation. This study advances understanding of land-atmosphere interactions in alpine regions, providing critical insights for water resource management and improving extreme weather predictions.

The development of thermokarst lakes on the Qinghai-Tibetan Plateau (QTP) serves as a prominent indicator of permafrost degradation driven by climate warming and increased humidity. However, quantitative observations of surface change and relationships between lakes and permafrost during thermokarst development remain inadequate. This study utilized long-term terrestrial laser scanning (TLS) to capture high-resolution data on the surface contour changes of the lake in the Beiluhe Basin over 3 years. Between June 2021 and September 2023, the area of BLH-B Lake increased by 19.23% to 6634 m2, with a maximum shoreline retreat distance of 14.37 m. Lake expansion exhibited pronounced seasonal characteristics, closely correlated with temperature and precipitation variations, with the most significant changes occurring during thawing periods. Notably, the lake expanded by up to 505 m2 during extreme rainfall events in the 2022 thawing period, accounting for 47.20% of the total expansion observed over 3 years. Integrated geophysical methods, including electrical resistivity tomography (ERT) and ground-penetrating radar (GPR), revealed substantial permafrost degradation, particularly along the northwestern and western shores, where talik formation occurred within 40 m of the lakeshore. Heat from groundwater flow within the active layer and direct solar radiation contributes to accelerated permafrost degradation around the lake. The integration of TLS with geophysical methods revealed both surface contour changes and subsurface permafrost conditions, providing a comprehensive view of the dynamics of thermokarst lakes. This integrated monitoring approach proves effective for studying thermokarst lake evolution, offering critical quantitative insights into permafrost degradation processes on the QTP and providing essential baselines for climate change impact assessment.

Suprapermafrost groundwater (SPG) plays a critical role in hydrological and ecological functioning of permafrost regions, yet its spatiotemporal dynamics and controlling mechanisms remain poorly understood on the Qinghai-Tibet Plateau (QTP). Here, we integrated in situ observations, geophysical surveys, and machine learning (ML) models (including XGBoost, LightGBM, and RandomForest) to investigate the seasonal variation, drivers, and projections of SPG dynamics in alpine meadow (AM) and alpine wet meadow (AWM) ecosystems. Results showed that SPG tables ranged from -1.1 to -0.1 m in AM and from -1.3 to -0.2 m in AWM during the warm season. SPG fluctuations were primarily driven by thaw depth (TD) and rainfall infiltration and exhibited similar seasonal patterns across both ecosystems. A greater TD was associated with a deeper SPG table, as deeper thawing expanded the unsaturated zone and enhanced vertical drainage, indicating an exponential relationship between TD and SPG table position, and a linear relationship with aquifer thickness. In contrast, rainfall infiltration increased shallow soil moisture and elevated SPG tables, with responses influenced by rainfall intensity, duration, and infiltration pathways. Spatial heterogeneity in SPG distribution was further shaped by vegetation structure and microtopographic variation. Furthermore, ML models projected that mean summer SPG table depths in the 2090s would increase by 0.06 m under SSP126 and 0.64 m under SSP585 in AWM ecosystems, and by 0.37 m under SSP126 and 0.87 m under SSP585 in AM ecosystems. These findings provide new insights into how climate warming affects hydrological processes in permafrost regions of the QTP.

Highlights What are the main findings? Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Thermal hazard susceptibility is expected to increase greatly by the end of the century due to permafrost degradation. What is the implication of the main findings? Segmented assessment can effectively improve evaluation accuracy and model interpretability. Thermal hazards exhibit significant sensitivity to climate change, while gravity hazards do not.Highlights What are the main findings? Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Thermal hazard susceptibility is expected to increase greatly by the end of the century due to permafrost degradation. What is the implication of the main findings? Segmented assessment can effectively improve evaluation accuracy and model interpretability. Thermal hazards exhibit significant sensitivity to climate change, while gravity hazards do not.Abstract With climate change, the Qinghai-Tibet Highway (QTH) is facing increasingly severe risks of natural hazards, posing a significant threat to its normal operation. However, the types, distribution, and future risks of hazards along the QTH are still unclear. In this study, we established an inventory of multi-hazards along the QTH by remote sensing interpretation and field validation, including landslides, debris flows, thaw slumps, and thermokarst lakes. The QTH was segmented into three sections based on hazard distribution and environmental factors. Susceptibility modelling was performed for each hazard within each using machine learning models, followed by further evaluation of hazard susceptibility under future climate change scenarios. The results show that, at present, approximately 15.50% of the area along the QTH exhibits high susceptibility to multi-hazards, with this proportion projected to increase to 20.85% and 23.32% under the representative concentration pathways (RCP) 4.5 and RCP 8.5 distant future scenarios, respectively. Variations in hazard-prone environments dominate the spatial heterogeneity of multi-hazard distribution. Gravity hazards demonstrate limited sensitivity to climate change, whereas thermal hazards exhibit a more pronounced response. Our geomorphology-based segmented assessment framework effectively enhances evaluation accuracy and model interpretability. The results can provide critical insights for the operation, maintenance, and hazard risk management of the QTH.

Permafrost in the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change, but its evolution over past century remains unclear. Based on the QTP climate change analysis since the 20th century, our study employed machine learning technique with field observations to construct permafrost simulation models, clarify its evolution processes, and reveal its changes on vertical zonation and sunny-shady slope distribution under climate warming. The results indicated that the QTP air temperature trends included initial warming (1900-1940 s, 0.13 degrees C/10a), cooling (1950-1960 s, - 0.20 degrees C/10a), and warming again (1970s to 2010s, 0.21 degrees C/10a). Precipitation patterns showed a slight decrease (- 0.33 mm/10a), rapid decrease (- 6.75 mm/10a), and gradual increase (6.57 mm/10a). Correspondingly, significant permafrost changes were recorded during the periods of 1900s, 1940s, 1960s, and 2010s, with the permafrost areas of 1.28, 1.19, 1.30, and 1.10 x 106 km2, respectively, and average mean annual ground temperature (active layer thickness) were - 2.82 +/- 1.93 degrees C (1.89 +/- 0.72 m), - 2.58 +/- 1.91 degrees C (2.21 +/- 0.78 m), - 2.86 +/- 1.94 degrees C (2.10 +/- 0.79 m), and - 2.26 +/- 1.72 degrees C (2.23 +/- 0.75 m) (mean +/- standard deviation), respectively. The southern Qiangtang Plateau and Three Rivers Source region exhibited significant permafrost changes during both the warming and cooling stages. Climate warming over the past 50 years has raised the average permafrost distribution altitude by 43 m, and accelerated its degradation on sunny slopes. These findings exhibit new knowledges on the QTP permafrost evolution and provide scientific references for permafrost degradation research under climate warming.

The spatial distribution of saturated hydraulic conductivity (Ks) is controlled by soil processes at multiple scales, and this spatial variability is crucial to simulating soil moisture movement. Nevertheless, few studies focus on the spatial variability of Ks and how changes through alpine meadow degradation or the specific scales at which the controlling factors function. This study therefore examines the scale-dependent relationships between Ks and several primary driving factors. Soil samples were collected at an interval of 3 m along three transects on a slope in the Qinghai-Tibet Plateau (QTP) and Ks, bulk density (BD), above-ground biomass (AGB), soil organic carbon content (SOC), sand content (SAND), silt content (SILT) and clay content (CLAY) were analysed. Ks showed strong spatial dependency and irregular distribution due to alpine meadow degradation. Pearson correlation analysis revealed a significant correlation between BD, AGB and Ks (p < 0.001). Furthermore, cross-semivariograms showed that Ks exhibited strong spatial correlation with AGB and SAND. Using the state space method, we determined that BD, SOC, AGB and CLAY are the main factors that control the spatial distribution of Ks on the slope. A two-factor state-space equation based on CLAY and BD provides a good representation of Ks, enabling the prediction and estimation of Ks distribution characteristics. These findings enhance our understanding of the crucial parameters that govern hydrological processes at the slope-scale of alpine grassland on the QTP, thereby helping to elucidate permafrost-related hydrological processes related to climate change.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R2) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R2 and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R2 of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

Background and aimsAlpine swamp meadows play a vital role in water conservation and maintaining ecological balance. However, the response mechanisms of its area and hydrological functions under global climate change remain unclear, particularly the impact of permafrost degradation on water storage capacity, which urgently requires quantification.MethodsWe integrated multi-temporal Landsat data (2000-2023) and phenological features to construct a classification framework for alpine swamp meadows. A multi-source remote sensing-based water balance assessment method was developed. Random forest importance evaluation and piecewiseSEM were employed to quantify the impacts and pathways of multidimensional driving factors on changes in alpine swamp meadow area and water storage.ResultsThe phenology-based classification method effectively extracted alpine swamp meadows with a mean producer's accuracy of 92.84%, user's accuracy of 92.14%, and a Kappa coefficient of 0.95. The study found that the spatial expansion of alpine swamp meadows in the watershed showed an initial decrease followed by an increase trend, while the water storage capacity continued to decline, indicating a significant decoupling between the two.ConclusionUnder climate change, increased precipitation and reduced snow cover albedo have led to the expansion of alpine swamp meadows, while enhanced evapotranspiration and the degradation of permafrost aquicludes have caused a systematic decline in their water storage capacity. These findings provide a scientific basis for assessing the health of alpine ecosystems and managing water resources under climate change.