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.

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.

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.

Numerous endorheic lakes in the Qinghai-Tibet Plateau (QTP) have shown a dramatic increase in total area since 1996. These expanding lakes are mainly located in the interior regions of the QTP, where permafrost is widely distributed. Despite significant permafrost degradation due to global warming, the impact of permafrost thawing on lake evolution in QTP has been underexplored. This study investigated the permafrost degradation and its correlation with lake area increase by selecting four lake basins (Selin Co, Nam Co, Zhari Namco, and Dangqiong Co) in QTP for analysis. Fluid-heat-ice coupled numerical models were conducted on the aquifer cross-sections in these four lake basins, to simulate permafrost thawing driven by rising surface temperatures, and calculate the subsequent changes in groundwater discharge into the lakes. The contribution of these changes to lake storage, which is proportional to lake area, was investigated. Numerical simulation indicates that from 1982 to 2011, permafrost degradation remained consistent across the four basins. During this period, the active layer thickness first increased, then decreased, and partially transformed into talik, with depths reaching up to 25 m. By 2011, groundwater discharge had significantly risen, exceeding 2.9 times the initial discharge in 1988 across all basins. This increased discharge now constitutes up to 17.67 % of the total lake water inflow (Selin Co). The dynamic lake water budget further suggests that groundwater contributed significantly to lake area expansion, particularly since 2000. These findings highlight the importance of considering permafrost thawing as a crucial factor in understanding the dynamics of lake systems in the QTP in the context of climate change.

Accurate understanding and modeling of soil hydrothermal dynamics in permafrost regions is essential for reliably assessing future permafrost changes and their impacts. However, the inadequate representation of soil water-heat transport processes in current land surface models (LSMs) introduces large uncertainty in simulating permafrost dynamics, particularly on the Qinghai-Tibet Plateau (QTP). In this study, we modified the parameterizations of soil thermal conductivity, unfrozen water and soil evaporation resistance in version 5.0 of the Community Land Model (CLM5.0) and assessed their effects on soil hydrothermal dynamics in permafrost regions on the QTP using in-situ measurements the depths of 10-40 cm. The results showed that soil temperature was more sensitive to the modified soil thermal conductivity and unfrozen water schemes, with average RMSE reduced by approximately 0.60 degrees C compared to the default CLM5.0. Soil moisture was mainly affected the unfrozen water scheme during freezing and by the optimized soil evaporation resistance scheme during thawing, with maximum accuracy improvements of 8% and 25%, respectively. All three schemes significantly improved soil thermal conductivity simulations, reducing RMSE over 80%. Overall, our modifications remarkably reduced simulation errors compared to the default schemes, improving the average accuracy soil temperature, soil moisture and soil thermal conductivity by approximately 16%, 21% and 81% respectively. Additionally, this study emphasized the importance of accurately representing permafrost-related processes in LSMs, as they significantly affected simulation results. Specifically, soil thermodynamics is strongly sensitive to subtle changes in soil moisture transport processes, such as the hysteresis effect unfrozen water content, and parameterizations of snowpack and vegetation. Therefore, future work should focus on enhancing the accurate representations of these processes and optimized parameters in LSMs to improve the simulation accuracy in permafrost regions on the QTP. This study enhanced the understanding of soil hydrothermal processes in LSMs and provided valuable insights for the future model development for permafrost regions under the context of climate change.

Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.