In this study, we applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) to monitor the ground surface deformation from 2017 to 2020 in the permafrost region within an ~400 km × 230 km area covering the northern and southern slopes of Mt. Geladandong, Tanggula Mountains on the Tibetan Plateau. During SBAS-InSAR processing, we inverted the network of interferograms into a deformation time series using a weighted least square estimator without a preset deformation model. The deformation curves of various permafrost states in the Tanggula Mountain region were revealed in detail for the first time. The study region undergoes significant subsidence. Over the subsiding terrain, the average subsidence rate was 9.1 mm/a; 68.1% of its area had a subsidence rate between 5 and 20 mm/a, while just 0.7% of its area had a subsidence rate larger than 30 mm/a. The average peak-to-peak seasonal deformation was 19.7 mm. There is a weak positive relationship (~0.3) between seasonal amplitude (water storage in the active layer) and long-term deformation velocity (ground ice melting). By examining the deformation time series of subsiding terrain with different subsidence levels, we also found that thaw subsidence was not restricted to the summer and autumn thawing times but could last until the following winter, and in this circumstance, the winter uplift was greatly weakened. Two import indices for indicating permafrost deformation properties, i.e., long-term deformation trend and seasonal deformation magnitude, were extracted by direct calculation and model approximations of deformation time series and compared with each other. The comparisons showed that the long-term velocity by different calculations was highly consistent, but the intra-annual deformation magnitudes by the model approximations were larger than those of the intra-annual highest-lowest elevation difference. The findings improve the understanding of deformation properties in the degrading permafrost environment.

Soil temperature plays an essential role in the permafrost thermal state and degradation process. Especially the soil temperatures at 10 cm and 50 cm depths in the active layer, which are much easier to be observed in situ, have great effects on the surface water cycles and vegetation, and could be used as the upper boundary for permafrost models to simulate the thermal state of the permafrost and active layer thicknesses. However, due to the limitations of the observation data, there are still large uncertainties in the soil temperature data, including at these two depths, in the permafrost region of Qinghai–Tibet Plateau (QTP). In this study, we evaluated and calibrated the applicability of four daily shallow soil temperature datasets (i.e., MERRA-2, GLDAS-Noah, ERA5-Land, and CFSR) by using the in situ soil temperature data from eight observation sites from 2004 to 2018 in the permafrost region along the Qinghai–Tibet Engineering Corridor. The results revealed that there were different uncertainties for all four sets of reanalysis data, which were the largest (Bias = −2.44 °C) in CFSR and smallest (Bias= −0.43 °C) in GLDAS-Noah at depths of 10 cm and 50 cm. Overall, the reanalysis datasets reflect the trends of soil temperature, and the applicability of reanalysis data at 50 cm depth is better than at 10 cm depth. Furthermore, the GLDAS-Noah soil temperatures were recalibrated based on our observations using multiple linear regression and random forest models. The accuracy of the corrected daily soil temperature was significantly improved, and the RMSE was reduced by 1.49 °C and 1.28 °C at the depth of 10 cm and 50 cm, respectively. The random forest model performed better in the calibration of soil temperature data from GLDAS-Noah. Finally, the warming rates of soil temperature were analyzed, which were 0.0994 °C/a and 0.1005 °C/a at 10 cm and 50 cm depth from 2004 to 2018, respectively.

Permafrost has been warming and thawing globally, with subsequent effects on the climate, hydrology, and the ecosystem. However, the permafrost thermal state variation in the northern lower limit of the permafrost zone (Xidatan) on the Qinghai–Tibet Plateau (QTP) is unclear. This study attempts to explore the changes and variability in this permafrost using historical (1970–2019) and future projection datasets from remote-sensing-based land surface temperature product (LST) and climate projections from Earth system model (ESM) outputs of the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5, CMIP6). Our model considers phase-change processes of soil pore water, thermal-property differences between frozen and unfrozen soil, geothermal flux flow, and the ground ice effect. Our model can consistently reproduce the vertical ground temperature profiles and active layer thickness (ALT), recognizing permafrost boundaries, and capture the evolution of the permafrost thermal regime. The spatial distribution of permafrost and its thermal conditions over the study area were controlled by elevation with a strong influence of slope orientation. From 1970 to 2019, the mean annual ground temperature (MAGT) in the region warmed by 0.49 ^∘C in the continuous permafrost zone and 0.40 ^∘C in the discontinuous permafrost zone. The lowest elevation of the permafrost boundary (on the north-facing slopes) rose approximately 47 m, and the northern boundary of discontinuous permafrost retreated southwards by approximately 1–2 km, while the lowest elevation of the permafrost boundary remained unchanged for the continuous permafrost zone. The warming rate in MAGT is projected to be more pronounced under shared socioeconomic pathways (SSPs) than under representative concentration pathways (RCPs), but there are no distinct discrepancies in the areal extent of the continuous and discontinuous permafrost and seasonally frozen ground among SSP and RCP scenarios. This study highlights the slow delaying process of the response of permafrost in the QTP to a warming climate, especially in terms of the areal extent of permafrost distribution.