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.

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.

Based on ascending and descending orbit SAR data from 2017-2025, this study analyzes the long time-series deformation monitoring and slip pattern of an active-layer detachment thaw slump, a typical active-layer detachment thaw slump in the permafrost zone of the Qinghai-Tibetan Plateau, by using the small baseline subset InSAR (SBAS-InSAR) technique. In addition, a three-dimensional displacement deformation field was constructed with the help of ascending and descending orbit data fusion technology to reveal the transportation characteristics of the thaw slump. The results show that the thaw slump shows an overall trend of south to north movement, and that the cumulative surface deformation is mainly characterized by subsidence, with deformation ranging from -199.5 mm to 55.9 mm. The deformation shows significant spatial heterogeneity, with its magnitudes generally decreasing from the headwall area (southern part) towards the depositional toe (northern part). In addition, the multifactorial driving mechanism of the thaw slump was further explored by combining geological investigation and geotechnical tests. The analysis reveals that the thaw slump's evolution is primarily driven by temperature, with precipitation acting as a conditional co-factor, its influence being modulated by the slump's developmental stage and local soil properties. The active layer thickness constitutes the basic geological condition of instability, and its spatial heterogeneity contributes to differential settlement patterns. Freeze-thaw cycles affect the shear strength of soils in the permafrost zone through multiple pathways, and thus trigger the occurrence of thaw slumps. Unlike single sudden landslides in non-permafrost zones, thaw slump is a continuous development process that occurs until the ice content is obviously reduced or disappears in the lower part. This study systematically elucidates the spatiotemporal deformation patterns and driving mechanisms of an active-layer detachment thaw slump by integrating multi-temporal InSAR remote sensing with geological and geotechnical data, offering valuable insights for understanding and monitoring thaw-induced hazards in permafrost regions.

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R2: 0.873, and MAPE: 0.104), and its inversion of QTP's ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost.

Alpine wet meadow (AWM), an important wetland type on the Qinghai-Tibet Plateau (QTP), is sensitive to climate change, which alters the soil hydrothermal regime and impacts ecological and hydrological functions in permafrost regions. The mechanisms underlying extreme AWM degradation in the QTP and hydrothermal factors controlling permafrost degradation remain unclear. In this study, soil hydrothermal processes, soil heat migration, and the permafrost state were measured in AWM and extremely degraded AWM (EDAWM). The results showed that the EDAWM exhibited delayed onset of both soil thawing and freezing, shortened thawing period, and extended freezing period at the lower boundary of the active layer. The lower ground temperatures resulted in a 0.2 m shallower active layer thickness in the EDAWM compared with the AWM. Moreover, the EDAWM altered soil thermal dynamics by redistributing energy, modifying soil moisture, preserving soil organic matter, and adjusting soil thermal properties. As for energy budget, a substantial amount of heat in the EDAWM was consumed by turbulent heat fluxes, particularly latent heat flux, which reduced the amount of heat transferred to the ground. Additionally, the higher soil organic matter content in EDAWM decreased the annual mean soil thermal conductivity from 1.42 W m- 1 K-1 in AWM to 1.26 W m- 1 K-1 in EDAWM, slowing down heat transfer within the active layer and consequently mitigating permafrost degradation. However, with continued climate warming, the soil organic matter content in EDAWM will inevitably decline due to microbial decomposition in the absence of new organic inputs. As the soil organic matter content diminishes, soil heat transfer processes will likely accelerate, and the permafrost warming rate may surpass that in undistributed AWM. These findings enhance our understanding of how alpine ecosystem succession influences regional hydrological cycles and greenhouse gas emissions.

Study region: The Qinghai Lake basin, including China's largest saltwater lake, is located on the Qinghai-Tibetan Plateau (QTP). Study focus: This study focuses on the hydrological changes between the past (1971-2010) and future period (2021-2060) employing the distributed hydrological model in the Qinghai Lake basin. Lake evaporation, lake precipitation, and water level changes were estimated using the simulations driven by corrected GCM data. The impacts of various factors on the lake water levels were meticulously quantified. New hydrological insights: Relative to the historical period, air temperatures are projected to rise by 1.72 degrees C under SSP2-4.5 and by 2.21 degrees C under SSP5-8.5 scenarios, and the future annual precipitation will rise by 34.7 mm in SSP2-4.5 and 44.1 mm in SSP5-8.5 in the next four decades. The ground temperature is projected to show an evident rise in the future period, which thickens the active layer and reduces the frozen depth. The runoff into the lake is a pivotal determinant of future water level changes, especially the runoff from the permafrost degradation region and permafrost region dominates the future water level changes. There will be a continuous rapid increase of water level under SSP5-8.5, while the water level rising will slow down after 2045 in the SSP2-4.5 scenario. This study provides an enhanced comprehension of the climate change impact on QTP lakes.