Search

The landscape and landforms in permafrost regions are transforming due to climate change and permafrost thaw. This study uses optical and radar remote sensing, alongside spatial analysis, to examine thermokarst features and their driving factors in the source region of the Yangtze River (SRYR) on the central Tibetan Plateau. We analyse the distribution, interaction, and key environmental factors influencing thermokarst ponds and ground surface deformation, which are the two widespread and noticeable thermokarst features. Since the 1960s, the number of small water bodies has doubled from approximately ~2 × 104 to ~4 × 104 by the 2020s, with the median size of these water bodies decreasing from 2.3 × 104 m2 to 1.4 × 104 m2. The permafrost terrain has an average subsidence rate of 6.8 mm/a. About 50.9% of the SRYR exhibits evident thermokarst features. Surficial geological factors, especially geomorphology and slope, are primary factors in shaping the spatial distributions of thermokarst features. Both seasonal deformation and long‐term subsidence rates are more pronounced in areas with thermokarst ponds. However, once pond coverage exceeds around 5%, the amplifying effect on long‐term subsidence rates and seasonal deformation diminishes. The investigation further reveals that the relationship between seasonal deformation and long‐term subsidence is not strictly linear and that the combined increase in seasonal deformation and long‐term subsidence applies only to areas with seasonal deformation below approximately 20 mm. Beyond this threshold, the long‐term subsidence rate is no longer exacerbated by increased seasonal deformation. [ABSTRACT FROM AUTHOR]

Permafrost ground ice melting could alter hydrological processes in cold regions by releasing water. Currently, there is a lack of gridded data of ground ice from the Qinghai‐Tibet Plateau (QTP). Using 664 borehole sample records, we applied a random forest (RF) method to predict the ground ice content of permafrost between 2 and 10 m depth in three layers (2–3, 3–5, and 5–10 m) at a spatial resolution of 1 km. The RF predictions demonstrated an R2 value exceeding 0.80 for all three layers with a negligible positive overestimation (0.98%–1.85%). The ground ice content of the first layer (2–3 m) can be predicted primarily using climate variables, but the contribution of terrain and soil variables increases as the depth increases. The total water storage of ground ice across the QTP permafrost (2–10 m depth) is approximately 3330.0 km3, with 403.5 km3 in the 2–3 m layer, 857.2 km3 in the 3–5 m layer, and 2069.3 km3 in the 5–10 m layer. This study generated for the first time a gridded dataset of the shallow permafrost ground ice content across the entire QTP which can be used to improve simulations of hydrological processes in the permafrost regions. [ABSTRACT FROM AUTHOR]

The Qinghai‐Tibet Plateau (QTP) is the largest permafrost region in the world at low and middle latitudes and high elevation. Permafrost is being degraded on the QTP due to global warming, which has a significant effect on regional climate, hydrological, and ecological processes. This paper provides a summary of recent progress in methods used in permafrost research, the permafrost distribution, and basic data relevant to permafrost research on the QTP. The area of permafrost was 1.32 × 106 km2 over the QTP, which accounts for approximately 46% of the QTP. Moreover, simulation studies of the hydrothermal process and permafrost change were reviewed and evaluated the effect of permafrost degradation on hydrological and ecological processes. The results revealed that the effects of permafrost on runoff were closely related to soil temperature, and the effect of permafrost degradation on the carbon cycle requires further study. Finally, current challenges in simulation of permafrost change processes on the QTP were discussed, emphasizing that permafrost degradation under climate change is a slow and non‐linear process. This review will aid future studies examining the mechanism underlying the interaction between permafrost and climate change, and environmental protection in permafrost regions on the QTP. [ABSTRACT FROM AUTHOR]

The ground temperature at a fixed depth is a crucial boundary condition for understanding the properties of deep permafrost. However, the commonly used mean annual ground temperature at the depth of the zero annual amplitude (MAGTdzaa) has application limitations due to large spatial heterogeneity in observed depths. In this study, we utilized 231 borehole records of mean annual ground temperature at a depth of 15 meters (MAGT15m) from 2010 to 2019 and employed support vector regression (SVR) to predict gridded MAGT15m data at a spatial resolution of nearly 1 km across the Qinghai-Tibet Plateau (QTP). SVR predictions demonstrated a R2 value of 0.48 with a negligible negative overestimation (-0.01 °C). The average MAGT15m of the QTP permafrost was -1.85 °C (±1.58 °C), with 90% of values ranging from -5.1 °C to -0.1 °C and 51.2% exceeding -1.5 °C. The freezing degree days (FDD) was the most significant predictor (p<0.001) of MAGT15m, followed by thawing degree days (TDD), mean annual precipitation (MAP), and soil bulk density (BD) (p<0.01). Overall, the MAGT15m increased from northwest to southeast and decreased with elevation. Lower MAGT15m values are prevail in high mountainous areas with steep slopes. The MAGT15m was the lowest in the basins of the Amu Darya, Indus, and Tarim rivers (-2.7 to -2.9 °C) and the highest in the Yangtze and Yellow River basins (-0.8 to -0.9 °C). The baseline dataset of MAGT15m during 2010–2019 for the QTP permafrost will facilitates simulations of deep permafrost characteristics and provides fundamental data for permafrost model validation and improvement. [ABSTRACT FROM AUTHOR]

We develop a moving-mesh permafrost model that contains a ground subsidence computation module to estimate the effects of ground subsidence on permafrost simulation under different warming scenarios. Including the ground subsidence process in the permafrost simulation produces only a relatively small improvement in the simulation performance of the ground temperature field, as validated by observations from two sites on the Qinghai–Tibetan Plateau (QTP). It is shown that ignoring ground subsidence tends to achieve a larger active layer thickness (ALT) but a smaller original thickness of permafrost that has thawed when simulating permafrost changes in a warming climate. The heat consumed by permafrost changes will be underestimated in simulations that do not consider ground subsidence. The effects that ground subsidence exerts within the permafrost simulation are clearly demonstrated under a strong warming scenario, which will influence the global energy budget. Projections indicate that the permafrost in the continuous permafrost area of the QTP may be close to the phase transition temperature to become zero thermal gradients in 2030–2040 under the SSP5-8.5 scenario, and there will be a great risk of ground subsidence by that stage. For permafrost regions with rich ground ice, the downward propagating temperature signals caused by ground subsidence are more attenuated. However, the heat calculation error will be larger in a simulation that does not consider ground subsidence there. This study quantifies the effects of ground subsidence, which can provide a better understanding of the permafrost thaw and energy budget of the QTP. [ABSTRACT FROM AUTHOR]

The source region of the Yangtze River (SRYR, 1.4 × 105 km2 above Zhimenda station) on the Tibetan Plateau (TP) has 78% permafrost coverage. The streamflow depth increased at a rate of 2.5 mm/a since 2000. Quantification of the water contribution brought by permafrost thawing is a difficult task. In this study, we used Sentinel‐1 data and the SBAS‐InSAR technique to monitor terrain deformation from September 2016 to December 2021, and then utilized the long‐term deformation rate to assess ground ice meltwater release and the seasonal deformation to evaluate water storage in the active layer. Results reveal that 55.3% of the terrain in the SRYR has subsidence >2.5 mm/a, indicating widespread ground ice melting. The release rate of ground ice meltwater is 4.3 mm/a in the entire SRYR, above 6 mm/a at the Dangqu and Tuotuo River subbasins. The water release rate is relatively small (∼3%) in comparison to the streamflow depth of 151 mm per year during the investigation period of 2017–2021. We did not detect a strong increasing or decreasing trend among the 5‐year seasonal deformation, which reflects that the total soil water content in the active layer did not change significantly during the short investigation period. The results provide a data basis for ground ice richness and loss information in the SRYR and help to understand the impact of permafrost thawing on the regional water cycle in the permafrost environment. Plain Language Summary: The Yangtze River is the longest river in China; its source region (1.4 × 105 km2, above Zhimenda station) has 78% permafrost coverage and a 935 km3 ground ice reserve. The hydrological processes may have been significantly impacted by the permafrost degradation over the past few decades. This study presented a quantitative analysis of the effects of permafrost thawing on the water balance using terrain deformation derived from InSAR monitoring. Subsidence rates and seasonal deformation were first used combined to estimate permafrost thawing release water. The entire source region of the Yangtze River experienced extensive thaw subsidence and ground ice melting, and the rate of ground ice meltwater release was 4.3 mm per year during 2016–2021. The 5‐year seasonal deformation for 2017 to 2021 did not show strong increasing or decreasing trends. It suggests that the water storage in the active layer doesn't have significant changes. It may also suggest that most of the water from melting ground ice is released as surface or subsurface runoff and the supply to the active layer is small during the investigation period. Key Points: Quantitative analysis of the effects of permafrost thawing on the water balance in the permafrost‐affected watershedSubsidence rates and seasonal deformation were used combined to evaluate the amount of ground ice melting release waterRunoff depth of 4.3 mm/a by ground ice melting water and small variation of active layer water storage in the study area during 2016–2021 [ABSTRACT FROM AUTHOR]

The source region of the Yellow River (SRYR) is situated on the permafrost boundary in the northeast of the Qinghai-Tibet Plateau (QTP), which is an area highly sensitive to climate change. As a result of increasing global temperatures, the permafrost in this region has undergone significant degradation. In this study, we utilized Sentinel-1 to obtain ground surface deformation data in the SRYR from June 2017 to January 2022. We then analyzed the differences in terrain deformation under various environmental conditions. Our findings indicated an overall subsidence trend in the SRYR, with a long-term deformation velocity of −4.2 mm/a and seasonal deformation of 8.85 mm. Furthermore, the results showed that terrain deformation varied considerably from region to region, and that the Huanghe' yan sub-basin with the highest permafrost coverage among all sub-basins significantly higher subsidence rates than other regions. Topography strongly influenced ground surface deformation, with flat slopes exhibiting much higher subsidence rates and seasonal deformation. Moreover, the ground temperature and ground ice richness played a certain role in the deformation pattern. This study also analyzed regional deformation details from eight boreholes and one profile line covering different surface conditions, revealing the potential for refining the permafrost boundary. Overall, the results of this study provide valuable insights into the evolution of permafrost in the SRYR region. [ABSTRACT FROM AUTHOR]

Climate changes significantly impact the hydrological cycle. Precipitation is one of the most important atmospheric inputs to the terrestrial hydrologic system, and its variability considerably influences environmental and socioeconomic development. Atmospheric warming intensifies the hydrological cycle, increasing both atmospheric water vapor concentration and global precipitation. The relationship between heavy precipitation and temperature has been extensively investigated in literature. However, the relationship in different percentile ranges has not been thoroughly analyzed. Moreover, a percentile-based regression provides a simple but effective framework for investigation into other factors (precipitation type) affecting this relationship. Herein, a comprehensive investigation is presented on the temperature dependence of daily precipitation in various percentile ranges over the Qinghai–Tibet Plateau. The results show that 1) most stations exhibit a peaklike scaling structure, while the northeast part and south margin of the plateau exhibit monotonic positive and negative scaling structures, respectively. The scaling structure is associated with the precipitation type, and 2) the positive and negative scaling rates exhibit similar spatial patterns, with stronger (weaker) sensitivity in the south (north) part of the plateau. The overall increase rate of daily precipitation with temperature is scaled by Clausius–Clapeyron relationship. 3) The higher percentile of daily precipitation shows a larger positive scaling rate than the lower percentile. 4) The peak-point temperature is closely related to the local temperature, and the regional peak-point temperature is roughly around 10°C. Significance Statement: This study aims to better understand the relationship between precipitation and surface air temperature in various percentile ranges over the Qinghai–Tibet Plateau. This is important because percentile-based regression not only accurately describes the response of precipitation to warming temperature but also provides a simple but effective framework for investigating other factors (precipitation type) that may be affecting this relationship. Furthermore, the sensitivity and peak-point temperature are evaluated and compared among different regions and percentile ranges; this study also attempts to outline their influencing factors. To our knowledge, this study is the first integration of percentile-based analysis of the dependence of daily precipitation on surface air temperature. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) products presently available in permafrost regions, especially on the Qinghai–Tibet Plateau (QTP), hardly meet the demands of evaluating and modeling climatic, hydrological, and ecological processes, due to their significant bias and low spatial resolution. This study developed an algorithm to generate high-spatial-resolution SM during the thawing season using Sentinel-1 (S1) and Sentinel-2 (S2) temporal data in the permafrost environment. This algorithm utilizes the seasonal backscatter differences to reduce the effect of surface roughness and uses the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) to characterize vegetation contribution. Then, the SM map with a grid spacing of 50 m × 50 m in the hinterland of the QTP with an area of 505 km × 246 km was generated. The results were independently validated based on in situ data from active layer monitoring sites. It shows that this algorithm can retrieve SM well in the study area. The coefficient of determination (R2) and root-mean-square error (RMSE) are 0.82 and 0.06 m3/m3, respectively. This study analyzed the SM distribution of different vegetation types: the alpine swamp meadow had the largest SM of 0.26 m3/m3, followed by the alpine meadow (0.23), alpine steppe (0.2), and alpine desert (0.16), taking the Tuotuo River basin as an example. We also found a significantly negative correlation between the coefficient of variation (CV) and SM in the permafrost area, and the variability of SM is higher in drier environments and lower in wetter environments. The comparison with ERA5-Land, GLDAS, and ESA CCI showed that the proposed method can provide more spatial details and achieve better performance in permafrost areas on QTP. The results also indicated that the developed algorithm has the potential to be applied in the entire permafrost regions on the QTP. [ABSTRACT FROM AUTHOR]

Isolating seasonal deformation from Interferometric Synthetic Aperture Radar (InSAR) time‐series is critical to quantitative understanding the freeze‐thaw processes in permafrost regions. Physics‐ or statistics‐based approaches have been developed to extract seasonal deformation, yet both constraining their evolution in time domain, and thus impeded the quantification of their amplitude variability especially over large scales. By applying Independent Component Analysis (ICA) on Sentinel‐1 InSAR measurements during 2015–2019 on the central Qinghai‐Tibet Plateau, we reveal that the averaged seasonal deformation is increasing with a linear trend of around 0.17 cm/year. The growing seasonal amplitude is attributed to an 8 cm increase of the Equivalent Water Thickness in the active layer. The results demonstrate the capability of ICA‐based decomposition on isolating freeze‐thaw‐related deformation from other components. The large‐scale spatial distribution of varied seasonal deformation can provide new insight into quantifying the water mass balance in vast permafrost regions. Plain Language Summary: Permafrost is ground that remains at or below 0°C for at least 2 years. The variations of water content in the seasonally thawed layer underlain by permafrost have strong impacts on the water storage balance, the ecosystems and the infrastructure stability in cold regions. Their inter‐annual variations can be reflected by ground deformation, but are hard to quantify over large scales. Here, we apply a satellite remote sensing technique to measure ground deformation during 2015–2019 on the central Qinghai‐Tibet Plateau. Using a multivariate statistical method, called independent component analysis, we successfully extract the ground deformation related to the inter‐annual soil water variations. We infer the total water content increase in our study area and corroborate it with independent remote sensing products and in‐situ measurements. This study demonstrates the potential of remote sensing measurements in characterizing the subsurface soil water variations in cold regions with permafrost. Key Points: Interferometric Synthetic Aperture Radar and independent component Analysis capture varied seasonal deformation in a large permafrost regionIncreasing amplitude of seasonal deformation is observed on the central Qinghai‐Tibet Plateau between 2015 and 2019Increase of equivalent water thickness in active layer may correspond to intensified hydrologic cycle [ABSTRACT FROM AUTHOR]

Selin Co, located within permafrost regions surrounded by glaciers, has exhibited the greatest increase in water storage among all the lakes on the Tibetan Plateau over the last 50 years. Most of the increased lake water volume has been attributed to increased precipitation and the accelerated melting of glacier ice, but these processes are still not sufficient to close the water budget with the expansion of Selin Co. Ground ice meltwater released by thawing permafrost due to continuous climate warming over the past several decades is regarded as another source of lake expansion. This study presents the first attempt to quantify the water contribution of ground ice melting to the expansion of Selin Co by evaluating the ground surface deformation. We monitored the spatial distribution of surface deformation in the Selin Co basin using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) technique and compared the results with the findings of field surveys. Then, the ground ice meltwater volume in the watershed was calculated based on the cumulated settlement. Finally, this volume was compared with the lake volume change during the same period, and the contribution ratio was derived. SBAS-InSAR monitoring during 2017–2020 illustrated widespread and large subsidence in the upstream section of the Zhajiazangbu subbasin, where widespread continuous permafrost is present. The terrain subsidence rate was normally between 5 and 20 mm a -1 , indicating rapid ground ice loss in the region. The ground ice meltwater was released at a rate of ∼57×106 m 3 a -1 , and the rate of increase in lake water storage was ∼485×106 m 3 a -1 during the same period, with ground ice meltwater contributing ∼12 % of the lake volume increase. This study contributes to explaining the rapid expansion of Selin Co and equilibrating the water balance at the watershed scale. More importantly, the proposed method can be extended to other watersheds underlain by permafrost and help in understanding the hydrological changes in these watersheds. [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]

Thaw settlement caused by permafrost degradation has great impacts on engineering infrastructure, hydrological cycles and ecosystems in recent decades under climate warming. We combined the moving‐grid method (Lagrangian method) with the heat conduction equation to develop a new moving‐grid permafrost model. Our model takes into account not only the cryostratigraphy of permafrost, but also the thaw settlement. We used the model to simulate the thaw settlement and the permafrost changes at the three sites along the Qinghai‐Tibet Engineering Corridor from 1966 to 2018. The monitoring of the ground temperature and the geodetic leveling in situ were used for model validation. The results show that our model can accurately reproduce the observed interannual thaw settlement and ground temperature fields. The risk of thawing settlement in ice‐rich permafrost regions in the zero‐temperature‐gradient stage is high. Plain Language Summary: Under climate warming, the thawing of permafrost in Arctic and alpine mountain has induced ground surface settlement (thaw settlement). Thaw settlement may lead to geologic hazards damaging engineering foundation and changing local hydrological cycles and ecosystems. It is essential to accurately assess permafrost changes and thaw settlement by models. Few current permafrost simulations, however, take thaw settlement into account. We developed a new permafrost model based on the moving‐grid method. Our model can quantitatively assess not only permafrost changes but also associated thaw settlement. We used the new model to simulate the permafrost changes and the thaw settlement at three sites along the Qinghai‐Tibet Engineering Corridor (Qinghai‐Tibet Engineering Corridor) over a span of 50 recent years. The monitoring of the ground surface settlement and the ground temperatures in nearly a decade from the three sites were used to validate the simulated results. The simulated results were well consistent with the observations, which indicated that our model could capture thaw settlement processes and permafrost changes in a warming climate. The simulation results show that those permafrost regions with slowly rising ground temperature but quickly ground ice thawing may have a great risk of thawing settlement. Key Points: We develop a new moving‐grid permafrost model taking thaw settlement into account based on the Lagrangian moving‐grid methodThaw settlement and permafrost changes at the three sites along the Qinghai‐Tibetan Engineering Corridor from 1966 to 2018 are modeledHigh risk of thawing settlement in ice‐rich permafrost regions occurs in the zero‐temperature‐gradient stage [ABSTRACT FROM AUTHOR]

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 °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. [ABSTRACT FROM AUTHOR]

Permafrost has great influences on the climatic, hydrological, and ecological systems on the Qinghai–Tibet Plateau (QTP). The changing permafrost and its impact have been attracting great attention worldwide like never before. More observational and modeling approaches are needed to promote an understanding of permafrost thermal state and climatic conditions on the QTP. However, limited data on the permafrost thermal state and climate background have been sporadically reported in different pieces of literature due to the difficulties of accessing and working in this region where the weather is severe, environmental conditions are harsh, and the topographic and morphological features are complex. From the 1990s, we began to establish a permafrost monitoring network on the QTP. Meteorological variables were measured by automatic meteorological systems. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature (GT) was observed from boreholes. In this study, a comprehensive dataset consisting of long-term meteorological, GT, soil moisture, and soil temperature data was compiled after quality control from an integrated, distributed, and multiscale observation network in the permafrost regions of QTP. The dataset is helpful for scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will significantly promote the verification, development, and improvement of hydrological models, land surface process models, and climate models on the QTP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/ , last access: 24 August 2021, 10.11888/Geocry.tpdc.271107, Lin et al., 2021). [ABSTRACT FROM AUTHOR]

Permafrost on the Qinghai–Tibet Plateau (QTP) has undergone degradation as a result of recent climate change. This may alter the thermo‐hydrological processes and unlock soil organic carbon, and thereby affect local hydrological, ecological, and climatic systems. The relationships between permafrost and climate change have received extensive attention, and in this paper we review climate change for permafrost regions of the QTP over the past 30 years. We summarize the current state and changes in permafrost distribution and thickness, ground temperature, and ground ice conditions. We focus on changes in permafrost thermal state and in active‐layer thickness (ALT). Possible future changes in ground temperature and ALT are also discussed. Finally, we discuss the changes in hydrological processes and to ecosystems caused by permafrost degradation. Air temperature and ground temperature in the permafrost regions of the QTP have increased from 1980 to 2018, and the active layer has been thickening at a rate of 19.5 cm per decade. The response of permafrost to climate change is not as fast as in some reports, and permafrost degradation is slower than projected by models that do not account for conditions deep in permafrost. [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]

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. [ABSTRACT FROM AUTHOR]

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies. [ABSTRACT FROM AUTHOR]

Spatial information of particle size fractions (PSFs) is primary for understanding the thermal state of permafrost in the Qinghai-Tibet Plateau (QTP) in response to climate change. However, the limitation of field observations and the tremendous spatial heterogeneity hamper the digital mapping of PSF. This study integrated log-ratio transformation approaches, variable searching methods, and machine learning techniques to map the surficial soil PSF distribution of two typical permafrost regions. Results showed that the Boruta technique identified different covariates but retained those covariates of vegetation and land surface temperature in both regions. Variable selection techniques effectively decreased the data redundancy and improved model performance. In addition, the spatial distribution of soil PSFs generated by four log-ratio models presented similar patterns. Isometric log-ratio random forest (ILR-RF) outperformed the other models in both regions (i.e., R2 ranged between 0.36 to 0.56, RMSE ranged between 0.02 and 0.10). Compared with three legacy datasets, our prediction better captured the spatial pattern of PSFs with higher accuracy. Although this study largely improved the accuracy of spatial distribution of soil PSFs, further endeavors should also be made to improve model accuracy and interpretability for a better understanding of the interaction and processes between environmental predictors and soil PSFs at permafrost regions. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (θ) and the soil dielectric constant (ε). The θ–ε relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai–Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai–Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as υ-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within ±0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m3/m3. While when the GPR velocity interpretation error is larger than ±0.004 m/ns, a piecewise formula calculation method, combined with the υ-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai–Tibet plateau permafrost regions. [ABSTRACT FROM AUTHOR]

Abstract The variations in land surface heat fluxes affect the ecological environment, hydrological processes and the stability of surface engineering structures in permafrost regions of the Qinghai-Tibetan Plateau (QTP). Based on observation data from a meteorological station in the Tanggula site in 2005, which is located in a permafrost region on the QTP, the performances of seventeen selected the phase 5 of the Coupled Model Intercomparison Project (CMIP5) were evaluated. The results showed that these simulations did not perform well using sensible heat flux, downward shortwave radiation or upward shortwave radiation, and differences exist among the models. The average multimodel ensemble results were similar to the observed land surface heat fluxes. The results revealed that the monthly average latent heat flux and the net radiation were small in December and January and large in May, June and July. The fluctuation in the soil heat flux was well correlated with the net radiation, and the sensible heat flux was negative in January and December in northwest of the Plateau. The latent heat flux was the strongest over the southeastern QTP from May to August, and it decreased over the northwestern QTP. In contrast, the sensible heat flux was the weakest over the southeastern QTP, and it gradually increased and became dominant over the northwestern QTP. The results also indicated that there was a good correlation between the surface heating field intensity and the net radiation, with a correlation coefficient of 0.99; this indicates stronger heating over the eastern QTP than over the western QTP and stronger heating over the southern QTP than over the northern QTP. Furthermore, the Bowen ratio was higher during the freezing and thawing stages than that during the completely thawed stage. This ratio was larger over the central and northeastern QTP and smaller along the northwest edge of the QTP, which was lower (range from −0.81 to 4.86) due to the overestimation of precipitation, a smaller difference between the simulated monthly average surface temperature and the observed air temperature, and a decrease in wind speed when using the CMIP5 models in the permafrost region of the QTP. This research provides a foundation for understanding land surface heat flux characteristics in the permafrost regions on the QTP under climate change. Highlights • Evaluate the performance of the CMIP5 model in the permafrost regions of the QTP. • Analyze variations in the spatial distribution of land surface heat flux values on the QTP. • Demonstrate the effect of the freezing and thawing process on land surface heat fluxes. [ABSTRACT FROM AUTHOR]

The spatial and temporal variations of the seasonal freeze–thaw cycles are important in understanding the ecological and hydrological processes and biogeochemical cycle associated with permafrost degradation caused by climate change, although observational data on the soil hydrothermal dynamics within the active layer of the permafrost region at the central and northern Qinghai–Tibet Plateau (QTP) are extremely scarce. In this study, soil temperature and moisture date from 11 observational sites along the Qinghai–Tibet Highway from 2010 to 2014 were used to analyze the freeze-thaw cycles of the active layer. The results revealed that mean annual ground surface temperature (MAGST) and mean annual temperature at the top of permafrost (TTOP) were the most closely related to the onset dates of soil freezing and thawing. The onset dates of soil freezing from bottom to top did not occur earlier than those from top to bottom. The differences between the onset dates of the two freezing directions and the proportion of bottom-up freezing depth increased with decreasing TTOP. The unfrozen water content of the cooling process was always higher than that of the warming process during the freezing stage. The hysteresis effect of the unfrozen water content could also be observed in the field experiment, and the maximum hysteresis levels occurred at their corresponding soil freezing points. Soil organic matter and soil moisture associated with vegetation cover are essential for water-heat exchanges between atmosphere and permafrost beneath active layer. We suggest that a better protected plant ecosystem, helps preserving the underlying permafrost on the Qinghai–Tibet Plateau. Unlabelled Image • A huge spatial variation of seasonal freeze-thaw cycles was observed. • The hysteresis effect of the unfrozen water can also be observed in the field. • TTOP and MAGST are the most important indicators of the freeze-thaw cycles. • Local factors are also vital for the freeze-thaw cycles in regional scale. [ABSTRACT FROM AUTHOR]