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.

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.

The freeze-thaw (F-T) cycle of the active layer (AL) causes the frost heave and thaw settlement deformation of the terrain surface. Accurately identifying its amplitude and time characteristics is important for climate, hydrology, and ecology research in permafrost regions. We used Sentinel-1 SAR data and small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain the characteristics of F-T cycles in the Zonag Lake-Yanhu Lake permafrost-affected endorheic basin on the Qinghai-Tibet Plateau from 2017 to 2019. The results show that the seasonal deformation amplitude (SDA) in the study area mainly ranges from 0 to 60 mm, with an average value of 19 mm. The date of maximum frost heave (MFH) occurred between November 27th and March 21st of the following year, averaged in date of the year (DOY) 37. The maximum thaw settlement (MTS) occurred between July 25th and September 21st, averaged in DOY 225. The thawing duration is the thawing process lasting about 193 days. The spatial distribution differences in SDA, the date of MFH, and the date of MTS are relatively significant, but there is no apparent spatial difference in thawing duration. Although the SDA in the study area is mainly affected by the thermal state of permafrost, it still has the most apparent relationship with vegetation cover, the soil water content in AL, and active layer thickness. SDA has an apparent negative and positive correlation with the date of MFH and the date of MTS. In addition, due to the influence of soil texture and seasonal rivers, the seasonal deformation characteristics of the alluvial-diluvial area are different from those of the surrounding areas. This study provides a method for analyzing the F-T cycle of the AL using multi-temporal InSAR technology.

The Qilian Mountains, located on the northeastern edge of the Qinghai-Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai-Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze-thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze-thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east-west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai-Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

Global climate warming has led to the deepening of the active layer of permafrost on the Tibetan Plateau, further triggering thermal subsidence phenomena, which have profound effects on the carbon cycle of regional ecosystems. This study conducted warming (W) and thermal subsidence (RR) control experiments using an Open-Top Chamber (OTC) device in the river source wetlands of the Qinghai Lake basin. The aim was to assess the impacts of warming and thermal subsidence on soil temperature, volumetric water content, biomass, microbial diversity, and soil respiration (both autotrophic and heterotrophic respiration). The results indicate that warming significantly increased soil temperature, especially during the colder seasons, and thermal subsidence treatment further exacerbated this effect. Soil volumetric water content significantly decreased under thermal subsidence, with the RRW treatment having the most pronounced impact on moisture. Additionally, a microbial diversity analysis revealed that warming promoted bacterial richness in the surface soil, while thermal subsidence suppressed fungal community diversity. Soil respiration rates exhibited a unimodal curve during the growing season. Warming treatment significantly reduced autotrophic respiration rates, while thermal subsidence inhibited heterotrophic respiration. Further analysis indicated that under thermal subsidence treatment, soil respiration was most sensitive to temperature changes, with a Q10 value reaching 7.39, reflecting a strong response to climate warming. In summary, this study provides new scientific evidence for understanding the response mechanisms of soil carbon cycling in Tibetan Plateau wetlands to climate warming.

Soil parameters form the foundation of hydrogeological research and are crucial for studying engineering construction and maintenance, climate change, and ecological environment effects in cold regions. However, the soil properties in the permafrost region of the Qinghai-Tibet Plateau (QTP) remain unclear. Hence, in this study, soil temperature (Ts), volumetric specific heat capacity (C), thermal conductivity (K), thermal diffusivity (D), soil water content (SWC), electric conductivity (EC), vertical (Kv) and horizontal (Kh) saturated hydraulic conductivity, bulk density (rho b), and soil texture near the Qinghai-Tibet Railway were measured, and their effects on the freeze-thaw process were evaluated. The results revealed a predominantly sandy loam soil texture, with Kh and Kv showing strong spatial variability, while the other parameters presented moderate spatial variability. Thermokarst lake had a limited influence on D, C, K, and rho b but significantly reduced Kh and Kv. Groundwater affected SWC, Ts, and EC. The model results showed that all parameters indicated small sensitivities to the maximum thawing depth (MTD), with MTD positively responding to all parameters except for Kv and porosity (rho p). Except for Kh and Kv, all parameters showed high sensitivities to the time from starting to complete freezing (TSCF). TSCF responded positively to C, rho p, and density (rho d) and negatively to K and Kh. This study expanded the quantification of soil properties in the QTP, which can help improve the accuracy of cryohydrogeologic models, thus guiding the construction and maintenance of infrastructure engineering.

Exploring the complex relationship between the freeze-thaw cycle and the surface energy budget (SEB) is crucial for deepening our comprehension of climate change. Drawing upon extensive field monitoring data of the Qinghai-Tibet Plateau, this study examines how surface energy accumulation influences the thawing depth. Combined with Community Land Model 5.0 (CLM5.0), a sensitivity test was designed to explore the interplay between the freeze-thaw cycle and the SEB. It is found that the freeze-thaw cycle process significantly alters the distribution of surface energy fluxes, intensifying energy exchange between the surface and atmosphere during phase transitions. In particular, an increase of 65.6% is observed in the ground heat flux during the freezing phase, which subsequently influences the sensible and latent heat fluxes. However, it should be noted that CLM5.0 has limitations in capturing the minor changes in soil moisture content and thermal conductivity during localized freezing events, resulting in an imprecise representation of the complex freeze-thaw dynamics in cold regions. Nevertheless, these results offer valuable insights and suggestions for improving the parameterization schemes of land surface models, enhancing the accuracy and applicability of remote sensing applications and climate research.

The comprehensive understanding of the occurred changes of permafrost, including the changes of mean annual ground temperature (MAGT) and active layer thickness (ALT), on the Qinghai-Tibet Plateau (QTP) is critical to project permafrost changes due to climate change. Here, we use statistical and machine learning (ML) modeling approaches to simulate the present and future changes of MAGT and ALT in the permafrost regions of the QTP. The results show that the combination of statistical and ML method is reliable to simulate the MAGT and ALT, with the root-mean-square error of 0.53 degrees C and 0.69 m for the MAGT and ALT, respectively. The results show that the present (2000-2015) permafrost area on the QTP is 1.04 x 10(6) km(2) (0.80-1.28 x 10(6) km(2)), and the average MAGT and ALT are -1.35 +/- 0.42 degrees C and 2.3 +/- 0.60 m, respectively. According to the classification system of permafrost stability, 37.3% of the QTP permafrost is suffering from the risk of disappearance. In the future (2061-2080), the near-surface permafrost area will shrink significantly under different Representative Concentration Pathway scenarios (RCPs). It is predicted that the permafrost area will be reduced to 42% of the present area under RCP8.5. Overall, the future changes of MAGT and ALT are pronounced and region-specific. As a result, the combined statistical method with ML requires less parameters and input variables for simulation permafrost thermal regimes and could present an efficient way to figure out the response of permafrost to climatic changes on the QTP.