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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. [ABSTRACT FROM AUTHOR]

Quantifying seasonal deformation is essential for accurately determining the thickness of the active layer and the distribution of water content within it, providing insights into the freeze‐thaw dynamics of permafrost environments and their sensitivity to climate change. Due to the limited hydraulic conductivity of the underlying permafrost, the freeze‐thaw processes are largely confined to the active layer, allowing for predictable seasonal deformations. This study employed Independent Component Analysis to isolate large‐scale seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) measurements taken from 2016 to 2020 in the Yangtze River Source Region (YRSR) of the Qinghai‐Tibet Plateau (QTP), covering 18,500 km2. We developed dedicated machine learning (ML) models that integrate these InSAR‐derived measurements with various environmental proxies. By applying these models to the YRSR, we generated a comprehensive, full‐coverage deformation map for permafrost terrains, achieving an R2 value of 0.91 and an Root Mean Squared Error of approximately 0.5 cm, thus confirming the model's strong predictability of seasonal deformation in permafrost regions. Deformation magnitude varied from less than 1 cm to over 10 cm. Our analysis suggests that terrain attributes, influenced by climate and soil conditions, are the primary factors driving these deformations. This research provides valuable insights into quantifying permafrost‐related seasonal deformation across expansive and rural landscapes. It also aids in assessing subsurface hydrological processes and the resilience and vulnerability of permafrost. The developed ML algorithm, with access to precise environmental data, is capable of forecasting seasonal deformations across the entire QTP and potentially throughout the Arctic. Plain Language Summary: Seasonal ground deformation, including both subsidence and uplift, is common in areas with a layer of ground that freezes and thaws seasonally, underlain by permafrost‐a type of ground that remains at or below 0°C for at least 2 years. These deformations are crucial indicators of changes in water content and thickness of this layer, offering insights into the freeze‐thaw dynamics of cold environments and their sensitivity to climate change. However, accurately mapping ground deformation over large areas has been challenging. In this study, we developed machine learning (ML) models that use radar remote sensing data, statistical methods, and a set of environmental variables to predict these seasonal ground movements. Our models can accurately forecast seasonal deformation using readily available environmental data. We find that slope of the terrain is the main factor influencing seasonal deformation, with climate and soil conditions also playing significant roles. This research offers new ways to measure and understand ground deformation in remote permafrost regions and demonstrates how ML can be used to predict such deformations on a continental or even global scale large. Our findings provide valuable insights for environmental scientists and could help inform strategies for managing these regions under changing climatic conditions. Key Points: Our results underscore the predictability of seasonal deformation with high accuracy in permafrost terrainsMachine learning models predict full‐coverage seasonal deformation with high accuracy (R2 = 0.91, Root Mean Squared Error [RMSE] = 0.5 cm)Seasonal deformation is primarily determined by terrain slope and regulated by climate and soil conditions [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Plateau is an area known to be sensitive to global climate change, and the problems caused by permafrost degradation in the context of climate warming potentially have far-reaching effects on regional hydrogeological processes, ecosystem functions, and engineering safety. Soil thermal conductivity (STC) is a key input parameter for temperature and surface energy simulations of the permafrost active layer. Therefore, understanding the spatial distribution patterns and variation characteristics of STC is important for accurate simulation and future predictions of permafrost on the Qinghai–Tibet Plateau. However, no systematic research has been conducted on this topic. In this study, based on a dataset of 2972 STC measurements, we simulated the spatial distribution patterns and spatiotemporal variation of STC in the shallow layer (5 cm) of the Qinghai–Tibet Plateau and the permafrost area using a machine learning model. The monthly analysis results showed that the STC was high from May to August and low from January to April and from September to December. In addition, the mean STC in the permafrost region of the Qinghai–Tibet Plateau was higher during the thawing period than during the freezing period, while the STC in the eastern and southeastern regions is generally higher than that in the western and northwestern regions. From 2005 to 2018, the difference between the STC in the permafrost region during the thawing and freezing periods gradually decreased, with a slight difference in the western hinterland region and a large difference in the eastern region. In areas with specific landforms such as basins and mountainous areas, the changes in the STC during the thawing and freezing periods were different or even opposite. The STC of alpine meadow was found to be most sensitive to the changes during the thawing and freezing periods within the permafrost zone, while the STC for bare land, alpine desert, and alpine swamp meadow decreased overall between 2005 and 2018. The results of this study provide important baseline data for the subsequent analysis and simulation of the permafrost on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

The hydrothermal dynamics of the active layer is a key issue in the study of surface processes in permafrost regions. Even though the soil energy budget is controlled by thermal conduction and latent heat transfer, few studies have focused on their effects upon the active layer thickness (ALT). In the present study, the community land model (CLM) version 5.0 is used to simulate the soil temperature and moisture of the active layers at the Tanggula (TGL) and Beiluhe (BLH) stations in permafrost regions of the Qinghai–Tibet Plateau based on the theory of soil enthalpy in order to estimate the soil energy state and analyze the energy changes in the active layer during freezing and thawing. The results indicate that the soil enthalpy has significant seasonal variation characteristics, which accurately reflected the freezing and thawing processes of the active layer. The change in soil enthalpy is significantly related to the thawing depth of the active layer in TGL and BLH, and its changing process can be expressed as an exponential relationship. Near the surface, the variation of the energy due to temperature gradient and actual evaporation can also be expressed as an exponential relationship. The promoting effect of heat conduction on the ALT is greater than the inhibiting effect of latent heat transfer, with the energy contribution from the phase change accounting for about 20–40% of the energy due to the temperature gradient. The thawing depth increases by 14.16–18.62 cm as the energy due to the temperature gradient increases by 1 MJ/m2 and decreases by 2.75–7.16 cm as the energy due to the phase change increases by 1 MJ/m2. Thus, the present study quantifies the effects of soil energy upon the ALT and facilitates an understanding of the hydrothermal processes in soils in permafrost regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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°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 × 106 km2 (0.80–1.28 × 106 km2), and the average MAGT and ALT are −1.35 ± 0.42°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. Key Points: The combined statistical method with machine learning is efficient to obtain the thermal regime of permafrost on the Qinghai‐Tibet Plateau (QTP)The present permafrost area on the QTP is ∼1.04 × 106 km2, and the average mean annual ground temperature and active layer thickness are −1.35 ± 0.42°C and 2.3 ± 0.60 m, respectivelyThe future changes of permafrost are projected to be pronounced due to climate change, but region‐specific [ABSTRACT FROM AUTHOR]

Soil hydrothermal regime of the active layer in the permafrost regions of the Qinghai‐Tibet Plateau (QTP) is important to the underlying permafrost and the climate change dynamics in Asia. However, a large bias still exists in current land surface models in the representation of soil temperature and moisture. This study assessed and augmented the Noah land surface model with multiparameterization options (Noah‐MP) for simulating soil hydrothermal dynamics at the Tanggula (alpine meadow) and Beiluhe (alpine swamp) stations located in the permafrost regions of the QTP. The results showed that the default Noah‐MP tended to underestimate soil temperature and moisture. Specifically, the default model overestimated the snow depth and duration due to the low snow sublimation rate. This resulted in a cold deviation in the soil temperature at two stations. Such underestimation was reduced by introducing a scheme that considered the sublimation loss from wind. Moreover, the remaining cold bias in the soil profiles of two stations was greatly resolved by a combined scheme of roughness length for heat (Z0h) and undercanopy aerodynamic resistance (ra,g). A soil thermal conductivity scheme, which can produce more realistic soil thermal conductivity in frozen soil, further improved the deep soil temperature simulation. The consideration of soil organic matter could mitigate the underestimation of the shallow soil moisture to some extent, but this improvement was more obvious at the Tanggula station, which had coarser mineral soil than the Beiluhe station. Key Points: Noah‐MP tends to underestimate soil temperature and moistureAdding snow sublimation from wind, combination of Z0h and ra,g, and realistic frozen soil thermal conductivity improves soil temperatureOrganic matter plays an important role in functioning coarse soil moisture [ABSTRACT FROM AUTHOR]

Warming permafrost on a global scale is projected to have significant impacts on engineering, hydrology and environmental quality. Greater warming trends are predicted on the Qinghai–Tibetan Plateau (QTP), but most models for mountain permafrost have not considered the effects of water phase change and the state of deep permafrost due to a lack of detailed information. To better understand historical and future permafrost change based on in situ monitoring and field investigations, a numerical heat conduction permafrost model was introduced which differentiated the frozen and thawed state of soil, and considered unfrozen water content in frozen soil, distribution of ground ice and geothermal heat flow. Simulations were conducted at two sites with validation by long‐term monitoring of ground temperature data. After forcing with reconstructed historical ground surface temperature series starting from 1966, the model predicted permafrost changes until 2100 under different RCP scenarios. The results indicate a slow thermal response of permafrost to climate warming at the two investigated sites. Even under the most radical warming scenario (RCP8.5), deepening of the permafrost table is not obvious before 2040. At both sites, the model indicates that shallow permafrost may disappear but deep permafrost may persist by 2100. Moreover, the simulation shows that the degradation modes may differ between zones of discontinuous and continuous permafrost. The main degradation mode of the site in the discontinuous zone appears to be upward thawing from the permafrost base, while that of the site in the continuous zone is downward thawing at the permafrost table with little change at the permafrost base. [ABSTRACT FROM AUTHOR]

The long-term and large-scale soil moisture (SM) record is important for understanding land atmosphere interactions and their impacts on the weather, climate, and regional ecosystem. SM products are one of the parameters used in some Earth system models, but these records require evaluation before use. The water resources on the Qinghai-Tibet Plateau (QTP) are important to the water security of billions of people in Asia. Therefore, it is necessary to know the SM conditions on the QTP. In this study, the evaluation metrics of multilayer (0-10, 10-40, and 40-100 cm) SM in different reanalysis datasets of the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim [ERA]), National Centers for Environmental Prediction Climate Forecast System and the Climate Forecast System version 2 (CFSv2), and China Meteorological Administration Land Data Assimilation System (CLDAS) are compared with in situ observations at 5 observation sites, which represent alpine meadow, alpine swamp meadow, alpine grassy meadow, alpine desert steppe, and alpine steppe environments during the thawing season from January 1, 2011, to December 31, 2013, on the QTP. The ERA SM remains constant at approximately 0.2 m3⋅m−3 at all observation sites during the entire thawing season. The CLDAS and CFSv2 SM products show similar patterns with those of the in situ SM observations during the thawing season. The CLDAS SM product performs better than the CFSv2 and ERA for all vegetation types except the alpine swamp meadow. The results indicate that the soil texture and land cover types play a more important role than the precipitation to increase the biases of the CLDAS SM product on the QTP. [ABSTRACT FROM AUTHOR]

Ground ice is a distinctive feature of permafrost, and its thawing under climate change can alter the regional hydrological and biogeochemical cycles. Spatial variations and determinants of ground ice isotopes are critical to understand subsurface water cycling during freeze-thaw process in the context of climate change, while they are not well known in permafrost region due to lack of field investigation. We examined spatial distributions and controlling factors of ground ice isotopes using data of 8 soil profiles surveyed in permafrost areas of the Qinghai-Tibet Plateau (QTP). The stable isotope values (δ2H and δ18O) of subsurface water on the QTP were higher than those in Arctic tundra ecosystem and East Siberian permafrost region. Isotopic values of water components differed each other, and varied significantly among the sampling sites. The spatial distribution of isotopes was complex. Isotopes generally decreased with depth within the soil profile, implying a general isotope depth gradient across different permafrost-affected areas. Water source, evaporative and freeze-out fractionation, and cryoturbation affect soil water isotopes. Correlation analyses showed that δ2H and δ18O in soil water positively related to air temperature and soil temperature, while negatively related to soil moisture, depth, active layer thickness, vegetation coverage, elevation, and precipitation. Elevation and soil depth mainly controlled spatial distributions of ground ice isotopes. The results could provide a new insight into soil moisture movement and cycling during freeze-thaw process in the permafrost region of the QTP, which is helpful to understand subsurface water cycle mechanism in the context of permafrost degradation. Unlabelled Image • The stable isotope values of ground ice on the Tibetan Plateau were measured. • Generally stable isotopes decreased as soil depth increased. • The spatial distributions of ground ice isotopes were mainly dominated by elevation and soil depth. [ABSTRACT FROM AUTHOR]

Abstract Stable isotopic tracing has proven to be a useful tool for assessing surface water source dynamics and hydrological connectivity in permafrost regions. This study has investigated the contribution of precipitation to water within the active layer at three long-term observation sites, including Fenghuoshan (FHS), Hoh Xil (KKXL) and Wudaoliang (WDL), by using isotopic tracer technique and two-component mixing model. The results showed that precipitation was the predominant source for water within the active layer, permafrost and ground ice near permafrost table at the three sites. Precipitation in August was the predominant source for water within the active layer at FHS, and precipitation in September was the main source at KKXL and WDL. The variation of isotopic values at different levels indicated that the water sources within the active layer could vary as the depth increases. The evaporation fractionation of water within the active layer at WDL was noticeable at depths of 0–50 cm, and the evaporation intensity decrease gradually from late June to late September. The relationship of isotopic tracing values between precipitation and water within the active layer at depths of 0–50 cm becomes more significant as the amount of the recently-occurring precipitation increases. Moreover, the relatively higher d-excess in precipitation indicates that local recycled moisture has greater contribution to precipitation. The differences of d-excess in most water within the active layer, permafrost and ground ice near permafrost table revealed that there were isotopic fractionation when precipitation supplying to above-mentioned three water bodies. The precipitation event amounted to 8.1 mm at KKXL can exert 49% ± 7.1% and 30.8% ± 3.6% contribution to water within the active layer at depths of 0–10 cm and 10–20 cm, respectively. While the long-period contribution cannot be identified because of the impact of evaporation. The results would provide new insights into the contribution of precipitation to water within the active layer on the QTP, which is also helpful to improve process-based hydrological models in the permafrost regions. Graphical abstract Unlabelled Image Highlights • Precipitation is the predominant source for water within the active layer. • Obvious evaporation fractionation sign for water within the active layer exists at depths of 0–50 cm. • The contribution ratio of single precipitation event to water within the active layer can be identified. [ABSTRACT FROM AUTHOR]

Highlights • Lower λ in frozen state was accounted for small initial freeze moisture content. • Soil thermal conductivity λ decreased with increase in freeze-thaw cycle times. • The critical soil moisture content was about 0.195 m3 m−3. • The critical degree of saturation was 0.37. • The contribution of soil moisture content to λ was different at different state. Abstract Soil thermal conductivity (λ) is one of the essential parameters relating to heat exchange, and it also plays a key role in verifying soil thermal hydrodynamics in permafrost regions. In this paper, the characteristic of in situ λ was analyzed based on data measured from June 2004 to December 2008 at Tanggula district on the Qinghai–Tibet Plateau. The result showed that diurnal λ strongly influenced by variation of soil moisture content. The daily λ exhibited distinct seasonal variation; on average, the largest value of λ occurred in summer, followed by the autumn and spring season, while the smallest value occurred in winter. As a whole, λ values in the unfrozen state were larger than those in the frozen state. Unsaturated soil and the huge difference in soil moisture content between the unfrozen state and initial freeze resulted in the lower λ in the frozen state. For the study area, the critical value of local soil saturation degree was about 0.37, the corresponding critical soil moisture content was about 0.195 m3 m−3. And soil moisture content was the main factor controlling in situ λ. Finally, an empirically-derived model was proposed for predicting daily λ , and which showed good performance in the study area. [ABSTRACT FROM AUTHOR]

Labile soil organic matter (SOM) plays a crucial role in nutrient and carbon cycling, particularly in permafrost ecosystems. Understanding its variation is therefore very important. In the present study, we evaluated the seasonal patterns of labile SOM from April 2013 to March 2014 under alpine swamp meadow (ASM), meadow (AM), steppe (AS) and desert (AD) vegetation in permafrost regions of the China's Qinghai–Tibet Plateau. The fractions (0 to 10 cm depth) included dissolved organic carbon (DOC), light-fraction carbon (LFC) and nitrogen (LFN), and microbial biomass carbon (MBC) and nitrogen (MBN). These fractions showed dramatic seasonal patterns in ASM and AM soils, but were relatively stable in AD soil. Soil DOC concentrations in the ASM, AM, and AD soils increased from April to May 2013, then increased again from July to August 2013 and from February to March 2014. The LFC and LFN concentrations in all four vegetation types were higher from June to August 2013. The highest MBC and MBN concentrations in the ASM, AM, and AS soils all occurred in the summer and the ASM soil showed a second peak in October or November 2013. Seasonal changes in climatic factors, vegetation types, and permafrost features were great causes of labile SOM variations in this study. Throughout the entire sampling period, the ASM soil generally had the highest labile SOM, followed by the AM, AS, and AD soils; thus, the ASM soil is the best system conserving soil nutrient (especially labile fractions) and microbial activity. Correlation analysis indicated that these fractions were not related to soil moisture and temperature in AS or AD soils, but soil temperature and moisture were significantly related to MBC and MBN in AM soil and DOC in ASM soil. Thus, the response of the labile SOM fractions in this high-altitude permafrost soils to climate change depended strongly on vegetation types. [ABSTRACT FROM AUTHOR]

• The Qinghai-Tibet Plateau has assimilated 43.16 Tg C/a from 1981 to 2016. • The carbon sink on the Qinghai-Tibet Plateau has weakened during 1981-2016. • The carbon sink on the Qinghai-Tibet Plateau is projected to weaken by 2080–2100. Cold regions contain a large amount of soil organic carbon, and the warming-accelerated loss of this carbon pool could cause important feedback to climatic change. The changes of carbon budgets in cold regions are poorly quantified especially for the Qinghai–Tibet Plateau (QTP) due to limited field observation data. By considering the soil freeze–thaw process and establishing new plant functional types with localized parameters, we used the Integrated Biosphere Simulator (IBIS) model to simulate the changes of carbon budget on the QTP during 1980–2016. The model was calibrated and validated using carbon flux data from eddy covariance observations at 16 sites. The results showed that the QTP has assimilated 43.16 Tg C/yr during 1980–2016, with permafrost and non-permafrost regions accounting for approximately 15% and 85% of the carbon sink, respectively. During the past four decades, the gross primary production and ecosystem respiration have increased by 1.74 and 2.04 Tg C/yr2, resulting in that the carbon sink on the QTP has weakened during 1980–2016. Moreover, the weakening of carbon sink is more pronounced in the non-permafrost regions. We project that the ecosystems will release 12.30 and 24.40 Tg C by 2080–2100 under the moderate and high shared socio-economic pathways (SSP 370 and SSP 585), respectively. This could largely offset the carbon sink and even shift the carbon sink to carbon source on the QTP. [ABSTRACT FROM AUTHOR]

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP-L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i.e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m3/m3, respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m3/m3, respectively) performed better than ERA5-Land (R = 0.46; ubRMSE = 0.03 m3/m3). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs. • SSM and RZSM products are evaluated over the permafrost region of QTP • Uncertainties of SSM and RZSM products are analyzed • SSM products generally overestimate the in-situ measurements • Large errors were found in RZSM for both satellite-based and reanalysis products [ABSTRACT FROM AUTHOR]