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.

In the context of global warming, landscapes with ice-rich permafrost, such as the Qinghai-Tibet Plateau (QTP), are highly vulnerable. The expansion of thermokarst lakes erodes the surrounding land, leading to collapses of various scales and posing a threat to nearby infrastructure and the environment. Assessing the susceptibility of thermokarst lakes in remote, data-scarce areas remains a challenging task. In this study, Landsat imagery and human-computer interaction were employed to improve the accuracy of thermokarst lake classification. The study also identified the key factors influencing the occurrence of thermokarst lakes, including the lake density, soil moisture (SM), slope, vegetation, snow cover, ground temperature, precipitation, and permafrost stability (PS). The results indicate that the most susceptible areas cover 19.02% of the QTP's permafrost region, primarily located in southwestern Qinghai, northeastern Tibet, and the Hoh Xil region. This study provides a framework for mapping the spatial distribution of thermokarst lakes and contributes to understanding the impact of climate change on the QTP.

The 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 x 10(12) g CO2 a(-1); this value is equivalent to 0.51% similar to 1.48% and 2.34% similar to 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.

Long-term, high-resolution soil moisture (SM) is a vital variable for understanding the water-energy cycle and the impacts of climate change on the Qinghai-Tibet Plateau (QTP). However, most existing satellite SM data are only available at coarse scale (+/- 25 km) and suffer a lot from data gaps due to satellite orbit coverage and snow cover, especially on the QTP. Although substantial efforts have been devoted to downscale SM utilizing multiple soil moisture indices (SMIs) or diverse machine learning (ML) methods, the potentials of different SMIs and ML approaches in SM downscaling on the complex plateau remain unclear, and there is still a necessity to obtain an accurate, long-term, high-resolution and seamless SM data over the QTP. To address this issue, this study generated the long-term, high-accuracy and seamless soil moisture dataset (LHS-SM) over the QTP during 2001-2020 using a two-step downscaling method (first downscaling then merging). Firstly, the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1 km utilizing five ML approaches. Then, a dynamic data merging method that considers spatiotemporal nonstationary error was applied to derive the final LHS-SM data. The performance of fifteen SMIs was also assessed and the optimal indexes for downscaling were identified. Results indicated that the shortwave infrared band-based indices had better performance than the near infrared band-based and energy-based indices. The generated LHS-SM data exhibited satisfying accuracy (mean R = 0.52, ubRMSE = 0.047 m(3)/m(3)) and certain improvement to the ESA CCI SM data both at station and network scales. Compared with existing 1 km SM datasets, the LHS-SM data also showed the best performance (mean R = 0.62, ubRMSE = 0.047 m(3)/m(3)), while existing datasets either failed to fully characterize the spatial details or had some data gaps and unreasonable distributions. Strong spatial heterogeneity was observed in the SM dynamics during 2001-2020 with the southwest and northeast showing a dry gets wetter scheme and the southeast presenting a wet gets drier trend. Overall, the LHS-SM dataset gained its added values by compensating the drawbacks of existing 1 km SM products over the QTP and was much valuable for many regional applications.

Monitoring and modelling surface deformation are crucial components of understanding the freeze-thaw process and preventing disasters in permafrost regions. However, previous methods had limitations that inhibited the interpretation of freeze-thaw deformation, such as a lack of physical meaning, an inability to reflect the physical freeze-thaw process and consideration of only a single external factor's impact on permafrost deformation. This study proposes an improved degree-day model (IDM) for quantitatively isolating surface deformation using interferometric synthetic aperture radar (InSAR) technology over permafrost. We considered the effect of soil moisture variation on permafrost deformation and incorporated interannual variation in the freeze-thaw process due to climate change. By applying small baseline subset (SBAS) technology to Sentinel-1 InSAR measurements over the Wudaoliang permafrost region on the Qinghai-Tibet Plateau from 2018 to 2019, we estimated long-term and seasonal permafrost deformation. The reliability of InSAR results was validated using in situ measurements, with root mean square errors (RMSEs) less than 10 mm. The results showed that the average linear deformation rates in 2018 and 2019 were -3.8 mm a-1 and -11.0 mm a-1, respectively, and the maximum seasonal deformations were 15.7 mm and 13.2 mm, respectively. Compared with the original degree-day model (ODM), the method used in this study produced smaller residual deformations of 6.9 mm and 6.4 mm, highlighting its ability to improve a quantitative description of permafrost deformation.

With the global climate change, glaciers on the Qinghai-Tibet Plateau (QTP) and its adjacent mountainous regions are retreating rapidly, leading to an increase in active rock glaciers (ARGs) in front of glaciers. As crucial components of water resources in alpine regions and indicators of permafrost boundaries, ARGs reflect climatic and environmental changes on the QTP and its adjacent mountainous regions. However, the extensive scale of rock glacier development poses a challenge to field investigations and sampling, and manual visual interpretation requires substantial effort. Consequently, research on rock glacier cataloging and distribution characteristics across the entire area is scarce. This study statistically analyzed the geometric characteristics of ARGs using high- resolution GF-2 satellite images. It examined their spatial distribution and relationship with local factors. The findings reveal that 34,717 ARGs, covering an area of approximately 6873.54 km2, with an average area of 0.19 +/- 0.24 km2, a maximum of 0.0012 km2, and a minimum of 4.6086 km2, were identified primarily in north-facing areas at elevations of 4300-5300 m and slopes of 9 degrees-25 degrees, predominantly in the Karakoram Mountains and the Himalayas. Notably, the largest concentration of ARGs was found on north-facing shady slopes, constituting about 42 % of the total amount, due to less solar radiation and lower near-surface temperatures favorable for interstitial ice preservation. This research enriches the foundational data on ARG distribution across the QTP and its adjacent mountainous regions, offering significant insights into the response mechanisms of rock glacier evolution to environmental changes and their environmental and engineering impacts.