Permafrost degradation on the Tibetan Plateau (TP) will significantly affect local water cycle processes, downstream water ecology, and water security. In this study, we evaluate the long-term interannual dynamics of permafrost distribution and active layer thickness (ALT) on the TP based on historical data from Climatic Research Unit gridded Time Series (CRU TS) downscaling and projected data under four shared socio-economic pathways (SSPs) in Scenario Model Intercomparison Project (ScenarioMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP 6). To achieve this, we employ a data-driven scheme at 1 km resolution for both historical and future periods (1901-2100) that compares the performance of four machine learning algorithms to select the optimal algorithm for permafrost distribution and ALT simulations. Our results indicate that the permafrost on the TP has been undergoing degradation in both historical and future periods, with a decrease in permafrost area and an increase in ALT. The changing rates of permafrost area and regionally averaged ALT during the historical period (1901-2020) are -1.05 x 104 km2 decade-1 and 0.012 m decade-1, while an accelerated degradation is observed after the 1970 s (with changing rates of permafrost area and regionally average ALT of -3.62 x 104 km2 decade-1 and 0.055 m decade-1). Our results also suggested that permafrost degradation on the TP will continue in the future under the four SSP scenarios. The individual global climate models (GCMs) exhibit a consistent degradation trend but great uncertainty in degradation speed. The ensemble mean of simulations across 15 selected GCMs showed that the degradation percentage of permafrost area on the TP under scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 was 26.0 +/- 6.8 %, 50.4 +/- 5.6 %, 79.2 +/- 4.5 %, and 89.0 +/- 4.0 % by 2100, and the regionally average ALT increased by 0.301 +/- 0.112 m, 0.628 +/- 0.113 m, 1.204 +/- 0.119 m, and 1.486 +/- 0.125 m, respectively. We also analyze permafrost stability and elevationdependent changes of ALT on the TP. The permafrost stability increases with elevation and latitude, and ALT changes more intensely with increasing elevation. This study will provide valuable data for hydrological and ecological studies related to permafrost on the TP.

The Tibetan Plateau (TP) is experiencing extensive permafrost degradation due to climate change, which seriously threatens sustainable water and ecosystem management in the TP and its downstream areas. Understanding the evolution of permafrost is critical for studying changes in the water cycle, carbon flux, and ecology of the TP. In this study, we mapped the spatial distribution of permafrost and active layer thickness (ALT) at 1 km resolution for each decade using empirical models and machine learning methods validated with borehole data. A comprehensive comparison of model results and validation accuracy shows that the machine learning method is more advantageous in simulating the permafrost distribution, while the ALT simulated by the empirical model (i.e., Stefan model) better reflects the actual ALT distribution. We further evaluated the dynamics of permafrost distribution and ALT from 1980 to 2020 based on the results of the better-performing models, and analyzed the patterns and influencing factors of the changes in permafrost distribution and ALT. The results show that the permafrost area on the TP has decreased by 15.5 %, and the regionally average ALT has increased by 18.94 cm in the 2010s compared to the 1980s. The average decreasing rate of permafrost area is 6.33 x 104 km2 decade-1, and the average increasing rate of ALT is 6.31 cm decade-1. Permafrost degradation includes the decreasing permafrost area and the thickening active layer mainly related to the warming of the TP. Spatially, permafrost area decrease is more susceptible to occur at lower latitudes and lower altitudes, while ALT increases more dramatically at lower latitudes and higher altitudes. In addition, permafrost is more likely to degrade to seasonally frozen ground in areas with deeper ALT.

The Qinghai-Tibet Railway (QTR) is the railway with the highest elevation and longest distance in the world, spanning more than 1142 km from Golmud to Lhasa across the continuous permafrost region. Due to climate change and anthropogenic activities, geological disasters such as subsidence and thermal melt collapse have occurred in the QTR embankment. To conduct the large-scale permafrost monitoring and geohazard investigation along the QTR, we collected 585 Sentinel-1A images based on the composite index model using the multitrack time-series interferometry synthetic aperture radar (MTS-InSAR) method to retrieve the surface deformation over a 3.15 x 10(5) km(2) area along the QTR. Meanwhile, a new method for permafrost distribution mapping based on InSAR time series deformation was proposed. Finally, the seasonal deformation map and a new map of permafrost distribution along the QTR from Golmud to Lhasa were obtained. The results showed that the estimated seasonal deformation range of the 10 km buffer zone along the QTR was -50-10 mm, and the LOS deformation rate ranged from -30 to 15 mm/yr. In addition, the deformation results were validated by leveling measurements, and the range of absolute error was between 0.1 and 4.62 mm. Most of the QTR was relatively stable. Some geohazard-prone sections were detected and analyzed along the QTR. The permafrost distribution results were mostly consistent with the simulated results of Zou's method, based on the temperature at the top of permafrost (TTOP) model. This study reveals recent deformation characteristics of the QTR, and has significant scientific implications and applicational value for ensuring the safe operation of the QTR. Moreover, our method, based on InSAR results, provides new insights for permafrost classification on the Qinghai-Tibet Plateau (QTP).

Permafrost distribution is of great significance for the study of climate, ecology, hydrology, and infrastructure construction in high-cold mountain regions with complex topography. Therefore, updated high-resolution permafrost distribution mapping is necessary and highly demanded in related fields. This case study conducted in a small catchment in the northeast of the Qinghai Tibet Plateau proposes a new method of using ground-penetrating radar (GPR) to detect the stratigraphic structure, interpret the characteristics of frozen ground, and extract the boundaries of permafrost patches in mountain areas. Thus, an empirical-statistical model of mountain frozen ground zonation, along with aspect (ASP) adjustment, is established based on the results of the GPR data interpretation. The spatial mapping of the frozen ground based on this model is compared with a field survey dataset and two existing permafrost distribution maps, and their consistencies are all higher than 80. In addition, the new map provides more details on the distribution of frozen ground. In this case, the influence of ASP on the distribution of permafrost in mountain areas is revealed: the adjustment of ASP on the lower limit of continuous and discontinuous permafrost is 180-200 m, the difference in the annual mean ground temperature between sunny and shady slopes is up to 1.4-1.6 degrees C, and the altitude-related temperature variation and uneven distribution of solar radiation in different ASPs comprehensively affect the zonation of mountain frozen ground. This work supplements the traditional theory of mountain permafrost zonation, the results of which are of value to relevant scientific studies and instructive to engineering construction in this region.

Permafrost is a key element of the cryosphere and sensitive to climate change. High-resolution permafrost map is important to environmental assessment, climate modeling, and engineering application. In this study, to estimate high-resolution Xing'an permafrost map (up to 1 km(2)), we employed the surface frost number (SFN) model and ground temperature at the top of permafrost (TTOP) model for the 2001-2018 period, driven by remote sensing data sets (land surface temperature and land cover). Based on the comparison of the modeling results, it was found that there was no significant difference between the two models. The performances of the SFN model and TTOP model were evaluated by using a published permafrost map. Based on statistical analysis, both the SFN model and TTOP model efficiently estimated the permafrost distribution in Northeast China. The extent of Xing'an permafrost distribution simulated by the SFN model and TTOP model were 6.88 x 10(5) km(2) and 6.81 x 10(5) km(2), respectively. Ground-surface characteristics were introduced into the permafrost models to improve the performance of models. The results provided a basic reference for permafrost distribution research at the regional scale.

Dynamics of the frozen ground are key to understand the changes of eco-environment in cold regions, especially for areas with limited field observations. In this study, we analyzed the spatial and temporal variations of the ground surface freezing and thawing indices from 1900 to 2017 for the upper Brahmaputra River (also called the Yarlung Zangbo River in China) Basin (UBRB), southwestern Tibetan Plateau, with the air freezing and thawing indices using the University of Delaware (UDEL) monthly gridded air temperature dataset. The abrupt change years for air freezing index (AFI) and ground surface freezing index (GFI) were detected in 1999 and 2002, respectively, and for both air thawing index (ATI) and ground surface thawing index (GTI) were 2009. With the air temperature rising at a rate of 0.006 degrees C per year over 1900-2017, the AFI and GFI decreased at a rate of -0.1 degrees C d per year, while the ATI and GTI increased at rates of 0.3 and 0.5 degrees C d per year before the abrupt change year, respectively; all changing trends of freezing/thawing indices increased after the abrupt year, which was -2.9, -0.8, 7.3, and 21.7 degrees C d per year for the AFI, GFI, ATI, and GTI, respectively. We utilized the surface frost number model to obtain the dynamics of the frozen ground over the UBRB. When the empirical coefficient of E was assigned to 1.2, the simulated frozen ground occupied about 53.2% of the whole UBRB in the 1990s, which agreed well with the existing permafrost map published in 1996. The area of frozen ground accounts for 51.5%-54.5% of the UBRB during 1900-2017. This result may facilitate further studies of the multi-interactions among the frozen ground and relevant eco-environment, such as the air-ground surface energy exchange, hydrological cycles, and changes of the active layer thickness over the UBRB.

The spatial distribution of permafrost and associated mean annual ground temperature (MAGT) and active layer thickness (ALT) are crucial data for hydrological studies. In this paper, we present the current state of knowledge on the spatial distribution of the permafrost properties of 29 river basins in Mongolia. The MAGT and ALT values are estimated by applying TTOP and Kudryavtsev methods. The main input of both methods is the spatially distributed surface temperature. We used the 8-day land surface temperature (LST) data from the day- and night-time Aqua and Terra images of the moderate resolution imaging spectroradiometer (MODIS). The gaps of the MODIS LST data were filled by spatial interpolation. Next, an LST model was developed based on 34 observational borehole data using a panel regression analysis (Baltagi, Econometric analysis of panel data, 3 edn, Wiley, New York, 2005). The model was applied for the whole country and covered the period from August 2012 to August 2013. The results show that the permafrost covers 26.3% of the country. The average MAGT and ALT for the permafrost region is - 1.6 degrees C and 3.1 m, respectively. The MAGT above -2 degrees C (warm permafrost) covers approximately 67% of the total permafrost area. The permafrost area and distribution in cold and warm permafrost varies highly over the country, in particular in regions where the river network is highly developed. High surface temperatures associated with climate change would result in changes of permafrost conditions, and, thus, would impact the surface water availability in these regions. The data on permafrost conditions presented in this paper can be used for further research on changes in the hydrological conditions of Mongolia.

Permafrost is degrading on the Qinghai-Tibet Plateau (QTP) due to climate change. Permafrost degradation can result in ecosystem changes and damage to infrastructure. However, we lack baseline data related to permafrost thermal dynamics at a local scale. Here, we model climate change impacts on permafrost from 1986 to 2075 at a high resolution using a numerical model for the Beiluhe basin, which includes representative permafrost environments of the QTP. Ground surface temperatures are derived from air temperature using an n-factor vs Normalized Differential Vegetation Index (NDVI) relationship. Soil properties are defined by field measurements and ecosystem types. The climate projections are based on long-term observations. The modelled ground temperature (MAGT) and active-layer thickness (ALT) are close to in situ observations. The results show a discontinuous permafrost distribution (61.4%) in the Beiluhe basin at present. For the past 30 years, the permafrost area has decreased rapidly, by a total of 26%. The mean ALT has increased by 0.46 m. For the next 60 years, 8.5-35% of the permafrost area is likely to degrade under different trends of climate warming. The ALT will probably increase by 0.38-0.86 m. The results of this study are useful for developing a deeper understanding of ecosystem change, permafrost development, and infrastructure development on the QTP.

The Qinghai-Tibet Plateau (QTP), where is underlain by the highest and most extensive mid-altitude permafrost, is undergoing more dramatic climatic warming than its surrounding regions. Mapping the distribution of permafrost is of great importance to assess the impacts of permafrost changes on the regional climate system. In this study, we applied logistic regression model (LRM) andmulti-criteria analysis (MCA) methods to map the decadal permafrost distribution on the QTP and to assess permafrost dynamics from the 1980s to 2000s. The occurrence of permafrost and its impacting factors (i.e., climatic and topographic elements) were constructed from in-situ field investigation-derived permafrost distribution patterns in 4 selected study regions. The validation results indicate that both LRM and MCA could efficiently map the permafrost distribution on the QTP. The areas of permafrost simulated by LRM and MCA are 1.23 x 10(6) km(2) and 1.20 x 10(6) km(2), respectively, between 2008 and 2012. The LRM and MCA modeling results revealed that permafrost area has significantly decreased at a rate of 0.066 x 10(6) km(2) decade(-1) over the past 30 years, and the decrease of permafrost area is accelerating. The sensitivity test results indicated that LRM did well in identifying the spatial distribution of permafrost and seasonally frozen ground, and MCA did well in reflecting permafrost dynamics. More parameters such as vegetation, soil property, and soil moisture are suggested to be integrated into the models to enhance the performance of both models. (C) 2018 Published by Elsevier B.V.

Accurate information on the distribution of permafrost and its thermal and hydrological properties is critical for environmental management and engineering development. This study modeled the current state of permafrost on the Qinghai-Tibet Plateau (QTP), including the spatial distribution of permafrost, active-layer thickness (ALT), mean annual ground temperature (MAGT), depth of zero annual amplitude (DZAA) and ground-ice content using an improved Noah land surface model (LSM). The improved model was examined at a typical permafrost site and then applied to the entire QTP using existing gridded meteorological data and newly developed soil data. The simulated permafrost distribution and properties were validated against existing permafrost maps in three representative survey areas and with measurements from 54 boreholes. The results indicate that the Noah LSM with augmented physics and proper soil data support can model permafrost over the QTP. Permafrost was simulated to underlie an area of 1.113 x 10(6) km(2) in 2010, accounting for 43.8% of the entire area of the QTP. The modeled regional average ALT and MAGT were 3.23m and -1.56 degrees C, respectively. Spatially, MAGT increases and DZAA becomes shallower from north to south. Thermally unstable permafrost (MAGT above -0.5 degrees C) is predominant, accounting for 38.75% of the whole permafrost area on the QTP. Ice-rich permafrost was mainly simulated around lakes across the north-central QTP.