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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 permafrost degradation can cause long‐term ground surface subsidence, and the surface undergoes frost heave and thaw settlement due to the ice‐water phase change in the active layer. The multi‐year surveys by leveling observations and Interferometric Synthetic Aperture Radar monitoring (InSAR) are helpful to understand the characteristics of seasonal deformation and to model the permafrost terrain surface deformation. In this paper, we studied the characteristics of seasonal deformation over permafrost terrain in Xidatan, obtained by leveling measurements from 2010 to 2018, Sentinel‐1 data from 2014 to 2020, and hydrothermal‐data‐based simulation. The results consistently showed a four‐stage pattern of seasonal deformation characteristics: Summer thaw subsiding, warm‐season stable‐standing, winter freeze heaving and stable‐standing. The leveling measurements proved that spatial heterogeneity also exists on a small spatial scale (400 m2). The deformation amplitude obtained by leveling data is the largest, and the InSAR data is the smallest. Plain Language Summary: The active layer plays a key role in land surface processes in permafrost areas. Due to the volume change associated with the ice‐water phase transition, this causes periodic deformation of the ground surface. Surface deformation in permafrost areas has a great negative impact on cold‐region infrastructures, and it is also an indicator of permafrost changes in a warming climate. Since 2010, we have been conducting a large number of observations along the Qinghai‐Tibet Highway. The meteorological observation field provides data for simulation and the analysis of deformation. And Interferometric Synthetic Aperture Radar (InSAR) has significant advantages in providing high time‐resolved deformation results. Here, the characteristics of permafrost surface intra‐annual deformation are firstly investigated in detail by using a multi‐data source. The applicability of InSAR and simulation results in the permafrost region was evaluated using leveling measured data, and the reasons for the difference were analyzed. Key Points: Seasonal deformation was studied in detail by leveling measurement, InSAR monitoring and hydrothermal‐data‐based simulationThe leveling observations of surface deformation in the permafrost site of Xidatan from 2010 to 2018 were first presentedA four‐stage intra‐annual deformation pattern: summer thaw subsiding, warm‐season stable‐standing, winter freeze heaving and stable‐standing [ABSTRACT FROM AUTHOR]

Permafrost that exists near the boundary of the permafrost zone is generally more sensitive to climate change. By analyzing ground temperatures observed from two 30‐m‐deep boreholes, a case study was conducted to present some characteristics of recent permafrost warming in the Xidatan area, near the northern limit of the permafrost zone on the Qinghai–Tibetan Plateau. The rate of permafrost degradation from top to bottom in the area was far less than that from bottom to top. Local conditions produced spatial differences in permafrost characteristics, and thus the site covered by alpine meadow had a thinner active layer and lower rate of change than the site with desert steppe. With permafrost warming, the depths of zero annual amplitude at the two sites showed significant decreasing trends, suggesting that the warming could change the proportion of unfrozen water and ice in permafrost, and then lead to a decrease in the mean thermal diffusivity of formation. Mean annual permafrost temperatures at depth of zero annual amplitude of the two boreholes were respectively −0.4°C and −0.7°C, indicating that high‐temperature permafrost is widely distributed in the study area. The lower temperature permafrost had a higher warming rate and a higher upward shift rate of the permafrost base. The pattern of permafrost degradation near the limit of permafrost was characterized by nonuniform speed and staged development. [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]

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]