Permafrost Regions Research Articles

Assessment of a hydrologic-land surface model to simulate thermo-hydrologic evolution of permafrost regions

Hydrologic-land surface models (H-LSMs) offer a physically-based framework for representing and predicting the present and future states of the extensive high-latitude permafrost areas worldwide. Their primary challenge, however, is that soil temperature data are severely limited, and traditional model validation, based only on streamflow, can show the right fit to these data for the wrong reasons. Here, we address this challenge by (1) collecting existing data in various forms including in-situ borehole data and different large-scale permafrost maps in addition to streamflow data, (2) comprehensively evaluating the performance of an H-LSM with a wide range of possible process parametrizations and initializations, and (3) assessing possible trade-offs in model performance in concurrently representing hydrologic and permafrost dynamics, thereby pointing to the possible model deficiencies that require improvement. As a case study, we focus on the sub-arctic Liard River Basin in Canada, which typifies vast northern sporadic and discontinuous permafrost regions. Our findings reveal that different process parameterizations tend to align with different data sources or variables, which largely exhibit inconsistencies among themselves. We further observe that a model may fail to represent permafrost occurrence yet seemingly fit streamflows adequately. Nonetheless, we demonstrate that accurately representing essential permafrost dynamics, including the active soil layer and insulation effects from snow cover and soil organic matter, is crucial for developing high-fidelity models in these regions. Given the complexity of processes and the incompatibility among different data sources/variables, we conclude that employing an ensemble of carefully designed model parameterizations is essential to provide a reliable picture of the current conditions and future spatio-temporal co-evolution of hydrology and permafrost.

Assessing the compression properties of Gravel-bearing forest soil in northeast China’s seasonally frozen regions under Freeze-thaw cycles and varying gravel content

Due to climate change, human activities and natural disturbances in high-latitude permafrost and seasonally frozen areas are gradually increasing, attracting more attention from scholars. However, research primarily focuses on soil biology and chemistry in these regions, with limited exploration of their mechanical properties, especially compression properties. This study aims to evaluate the effects of gravel content and freeze–thaw (F-T) cycles on the compression properties of coarse-grained layered forest soil from northeast China’s seasonally frozen regions, with the goal of predicting the soil’s compressive changes under heavy mechanical loads. Specifically, using uniaxial confined compression tests (UCCT) on 252 disturbed soil samples (including two soil layers: AB and Bhs; six gravel contents; and seven F-T cycles), three characteristic compression coefficients—precompression stress (σpc), compression index (Cc), and swelling index (Cs)—were measured. Additionally, scanning electron microscopy (SEM) was used to analyze the mesostructure evolution of coarse-grained gravel-bearing soil. Volume changes of samples were measured after 15F-T cycles with varying gravel contents. Results indicate non-linear effects of gravel content and F-T cycles on σpc. Gravel content below 50% positively influences σpc, while content above 50% increases soil pore content, decreasing σpc. Cc and Cs exhibit an approximately negative correlation with gravel content and initially increase followed by a decrease with more F-T cycles. Moreover, the σpc and Cc of the AB layer are higher than those in the Bhs layer, likely due to differences in clay and organic carbon contents. Notably, the observed trends differ from previous studies on other soil types such as farmland and paddy fields. This study fills a gap in understanding the compression characteristics of layered gravel-bearing forest soil in seasonally frozen regions, providing valuable insights for evaluating soil compression in both seasonally frozen and permafrost regions, and understanding mechanical vehicle-soil interactions. It also lays the theoretical groundwork and provides data support for constructing compression models of layered gravel-bearing forest soil.

Dynamic Thermomechanical Modeling of Rock‐Ice Avalanches: Understanding Flow Transitions, Water Dynamics, and Uncertainties

AbstractThe rapid melting of glaciers and thawing of permafrost in mountainous regions have heightened the danger of rock‐ice avalanches. These avalanches pose a severe threat due to their potential to transform into water‐saturated debris flows. The catastrophic event in Chamoli, India, on 7 February 2021, illustrates the devastating consequences of such processes. Developing a model capable of predicting the dynamics and extent of these events is imperative for natural hazard science and disaster mitigation. In response, we propose a depth‐averaged rock‐ice avalanche model encompassing four distinct materials: rock, ice, snow, and water. The model integrates crucial physical processes, including frictional heating, phase changes, ground material entrainment, and air‐blast hazards. Through a system of mass and momentum balance equations extended with grain flow and internal energy equations, the model captures heat exchanges and resulting phase changes as the fragmented material flows. Focusing on identifying the primary water source in the flow and testing the model on the 2021 Chamoli event, we quantify water's influence on flow dynamics and regime transitions. However, uncertainties persist in heat transfer physics and quantifying the hydro‐meteorological state of the flow path. Our thermo‐mechanical model enables the simulation of complex avalanches and identifies key flow transitions: powder cloud formation and potential debris flow transformation. The study underscores the pivotal role of water in avalanche dynamics and the challenge of accurately quantifying water content within the flow, necessitating comprehensive ground assessments for effective disaster management.

Performance and reinforcement of air-cooled embankments traversing degrading permafrost of the Qinghai-Tibet Plateau

The reliable operation of railway embankments traversing degrading permafrost regions is challenged by climate warming. This study examines performances of four main types of railway embankments on the Qinghai-Tibet Plateau in thermally stabilizing permafrost foundation over warm permafrost using numerical modelling and 10-year monitoring data. Then, a reinforcement measure that combines a thermal conductivity variable system (TCVS) was designed to improve the cooling capacity of the crushed-rock sloped embankment (CRSE) by countering the heat absorption of slopes during summers. A coupled thermal-fluid-solid model was built to simulate and assess the cooling performance and reinforcing capacity of the new design. Results show that the crushed-rock embankments can produce convection cooling on the permafrost subgrade but the performances vary with different structures. The CRSE has insufficient cooling capacity to withstand the underlying permafrost degradation in warm permafrost regions. The optimized CRSE that combines the TCVS can effectively cool the underlying warm permafrost and decrease the shady-sunny slope effect under a warming climate, and can be used as an effective reinforcement measure. This study confirms the application of air-cooled embankments in protecting permafrost subgrade and provides guidance for structural design of embankment traversing degrading permafrost.

Investigating soil properties and their effects on freeze-thaw processes in a thermokarst lake region of Qinghai-Tibet Plateau, China

Soil parameters form the foundation of hydrogeological research and are crucial for studying engineering construction and maintenance, climate change, and ecological environment effects in cold regions. However, the soil properties in the permafrost region of the Qinghai–Tibet Plateau (QTP) remain unclear. Hence, in this study, soil temperature (Ts), volumetric specific heat capacity (C), thermal conductivity (K), thermal diffusivity (D), soil water content (SWC), electric conductivity (EC), vertical (Kv) and horizontal (Kh) saturated hydraulic conductivity, bulk density (ρb), and soil texture near the Qinghai-Tibet Railway were measured, and their effects on the freeze-thaw process were evaluated. The results revealed a predominantly sandy loam soil texture, with Kh and Kv showing strong spatial variability, while the other parameters presented moderate spatial variability. Thermokarst lake had a limited influence on D, C, K, and ρb but significantly reduced Kh and Kv. Groundwater affected SWC, Ts, and EC. The model results showed that all parameters indicated small sensitivities to the maximum thawing depth (MTD), with MTD positively responding to all parameters except for Kv and porosity (ρp). Except for Kh and Kv, all parameters showed high sensitivities to the time from starting to complete freezing (TSCF). TSCF responded positively to C, ρp, and density (ρd) and negatively to K and Kh. This study expanded the quantification of soil properties in the QTP, which can help improve the accuracy of cryohydrogeologic models, thus guiding the construction and maintenance of infrastructure engineering.

Hydrothermal coupling model between wellbore and permafrost for drilling in arctic cold regions

The abundant oil and natural gas resources in the Arctic cold regions have driven people to conduct drilling operations in these harsh environments. During drilling operations in Arctic permafrost regions, the linear heat source effect generated by the flow of drilling fluid in the wellbore continuously melts the surrounding permafrost, which can lead to wellhead settlement and instability of the wellbore wall, among other accidents. In order to provide guidance for drilling engineering in cold regions, there is an urgent need to study the coupled heat transfer between the wellbore and the permafrost in Arctic drilling. Due to the significant influence of hydrothermal coupling process in permafrost on temperature transfer, conventional wellbore heat transfer models are not suitable. Therefore, this paper proposes a hydrothermal coupling model between the wellbore and permafrost for drilling in Arctic cold regions and analyzes the heat transfer in the wellbore and the hydrothermal processes of the formation using numerical methods. By applying this model, the insulation effects of existing main wellbore temperature control and insulation technologies were studied. The main research findings indicate that drilling process can cause the permafrost around the wellbore to thaw, and the thawed permafrost around the wellbore presents a “horn” shape that expands from the wellhead to the bottom of permafrost layer; Without insulation measures, the volume of thawed permafrost around the wellbore can reach 315 m³ after 7 days of circulation; During the drilling process, moisture migration occurs. Under the effect of gravitational seepage, unfrozen water content in the thawed permafrsot around the wellbore increases from the wellhead to the bottom of the permafrsot layer; Vacuum insulated tubing has almost negligible impact on the temperature of the drilling fluid in the wellbore, while it can reduce the volume of thawed permafrost around the wellbore by 62.5 % to 88 % or even higher; Drilling fluid cooling systems can significantly lower the temperature of the drilling fluid in the wellbore but cannot effectively prevent the thawing of permafrost around the wellbore. Therefore, in practical drilling operations, priority should be given to using high-quality vacuum insulated tubing or combining both methods simultaneously.

Spatial Distribution of Thaw Depth in Palsas Estimated From Optical Unoccupied Aerial Systems Data

ABSTRACTMaximum seasonal thaw depth, referred to as active layer thickness (ALT), is one of the key parameters used to monitor permafrost conditions. ALT maps based on interpolation of point measurements or derived from coarse or moderate spatial resolution satellite data often hide small‐scale spatial variations in thaw depth resulting from differences in surface characteristics and microtopography. To model and predict changes in hydrological and biogeochemical processes in permafrost areas accurately, high‐resolution remote sensing‐based estimations of ALT are needed. Therefore, we applied random forest (RF) regression on a set of topographical and spectral vegetation indices derived from optical unoccupied aerial systems data, Landsat 8 land surface temperature (LST) data, and field measurements to estimate thaw depths in palsas at three mires in north‐west Finland. We also analyzed differences in thaw depths between mires located at different elevations, between dome and plateau‐shaped palsas, and between different vegetation and surface cover classes. The RF models resulted in root mean square errors from 2.4 to 5.7 cm between predicted and observed thaw depths and the R2 values of 0.57–0.96. Height from the surrounding fen surface and LST were the most important variables in thaw depth models, although high‐accuracy results were also achieved without LST. The mean thaw depths did not differ between the sites with lowest and highest elevation, whereas the thaw depths were significantly deeper in dome‐shaped palsas compared to plateaus. The thaw depths were significantly different between vegetation cover classes only on plateau‐shaped palsas. The results indicate the high impact of the topography on the palsa thaw depth, thus highlighting the importance of accurate elevation models in spatial modeling of palsa ALT. The methodology presented in this study can be applied to other permafrost regions where field measurements of ALT are accompanied with high‐resolution topographical and multispectral data.

A novel framework for multiple thermokarst hazards risk assessment and controlling environmental factors analysis on the Qinghai-Tibet Plateau

Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. Currently, high-precision and harmonized assessment tools for multiple thermokarst hazards risk assessment are lacking, and the mechanisms governing the environmental interactions of thermokarst hazards have not been fully clarified. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). In addition, model performance was improved by model optimization. Finally, structural equation modeling (SEM) was used to assess the controlling environmental factors for the thermokarst hazards in the YRSR. The results show that slope and precipitation contribute the most to the modeling accuracy of thermokarst lakes and thaw slumps, respectively. Model optimization improved the base model modeling accuracy by approximately 2 % ∼ 7 %, with XGBoost having the highest sensitivity to model optimization and the highest modeling accuracy. In terms of the ensemble strategy, the stacking model and ensemble model significantly improved the risk mapping accuracy, and the stacking model was better than the ensemble model, with accuracies of 92.39 % and 93.36 % for thermokarst lakes and thaw slumps, respectively. Compared with previous results, the results of this study are more representative of the YRSR. Finally, via SEM, terrain factors and soil factors were identified as controlling environmental factors for the risk of thaw slumps and thermokarst lakes, respectively. This study proposes a high-precision risk assessment method for thermokarst hazards in permafrost regions, and contributes to a deeper understanding of the interaction mechanisms between thermokarst hazards and environmental factors.

Summer heat wave in 2022 led to rapid warming of permafrost in the central Qinghai-Tibet Plateau

Extreme events with increasing frequency and intensity are significantly affecting the permafrost environment. Analysis using the ERA5-Land reanalysis data revealed that the permafrost region of the central Qinghai-Tibet Plateau (QTP) experienced the summer heat wave in 2022. Four active layer sites experienced maximum active layer thicknesses (ALT) in 2022 (mean: 207.7 cm), which was 20% higher than the mean ALT during 2000–2021 (mean: 175.9 cm). The mean annual ground temperature (MAGT) observed in 2022 was also the highest, exceeding the average of the previous years by 10%. The contribution fraction of heat wave to the seasonal thaw depth of active layer was quantified using Stefan model with ranging from 6.6% to 13.6%, and the maximum contribution fraction occurs in 2022. These findings are helpful to better understand the impact processes of extreme events on the active layer and permafrost.

Experimental study on the thermal performance of non-white near-infrared solar reflective coatings in a permafrost region

Solar reflective coating (SRC) offers a method to reduce surface temperature without the need for additional energy consumption, making it highly applicable in permafrost regions. However, many SRCs with high solar reflectance are white, which can cause glare and not meet the aesthetic pavement requirements of high-grade highways. Herein, we developed two non-white SRCs (black and gray) designed to achieve low reflectance in the visible spectrum while maintaining high reflectance in the near-infrared region. We then evaluated the thermal performance of a white SRC, two non-white SRCs and asphalt (as a control) by analyzing surface temperature, ground temperature, and the heat flux between the soil and pavement in a permafrost region of the Qinghai-Tibet Plateau. The results showed that three SRCs reduced the annual mean surface temperature and surface temperature fluctuation compared to the asphalt. The white SRC exhibited annual heat release, whereas the non-white SRCs (black and gray) reduced heat absorption by 34.53% and 69.44%, respectively, compared to the asphalt. By directly reducing the heat absorption of asphalt, the non-white SRCs can potentially slow permafrost degradation beneath asphalt pavements. This study provides a theoretical foundation and technical support for applying non-white near-infrared SRC on high-grade highways in permafrost regions.

Pavement damage characteristics in the permafrost regions based on UAV images and airborne LiDAR data

The rapid degradation of “Xing'an-Baikal permafrost” in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan section (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

Permafrost thaw and thermokarst in the source region of the Yangtze river in the central Tibetan plateau revealed by radar and optical remote sensing

AbstractThe 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.

An indicator of relative distribution probability of field-scale permafrost in Northeast China: Using a particle swarm optimization (PSO)-based indicator composition algorithm

Under the influence of climate changing, permafrost in Northeast China (NEC) has been consistently degrading in recent years. Numerous scholars have investigated the spatial and temporal distribution patterns of permafrost in the NEC region. However, due to constraints in data availability and methodological approaches, only a limited number of studies have extended their analyses to the field scale. In this study, we established a particle swarm optimization (PSO)-based indicator composition algorithm (PSO-ICA) to obtain an indicator factor, η, that indicates the relative distribution probability of permafrost at the field scale. PSO-ICA screened and combined 12 high-resolution environmental variables to compose η. The spatial distribution data of permafrost with a length of 765.378 km provided by the engineering geological investigation report (EGIR) of six highways were used to train and validate the effectiveness of η in indicating permafrost. At the field scale, η was found to be similar to the surface freezing number (SFN) in its ability to indicate permafrost, with AUC values of 0.7046 and 0.7063 for the two by the ROC test. In addition, η has a good performance in predicting highway distresses in the permafrost region in the absence of survey data. This study also confirmed that the resolution and accuracy of permafrost mapping results can be improved by utilizing η. After downscaling the 1 km resolution SFN to 30 m resolution using η, the R2 of the linear relationship between SFN and permafrost temperatures from 43 monitoring boreholes was improved from 0.7010 to 0.8043. If η can help understand the distribution of permafrost at field scale, many engineering and environmental practices could potentially benefit.

Assessing machine learning models to generate permafrost distribution map in Solukhumbu, Nepal

Permafrost is one of the key components of the cryosphere. Previous studies show that the extent of permafrost has shifted to higher elevations in Nepal. These researches, however, has been hampered by inconsistency in their study period. Proxies like rock glaciers and climatic variables, such as multi-decadal annual air temperature, are used to link towards the likely occurrence of permafrost. Here, the rock glacier inventory of Solukhumbu was prepared, and classified based on their activity (Intact/Relict) from Google Earth. Talus-based rock glaciers were observed more than glacier-derived ones. These rock glaciers were highly correlated with Mean Annual Air Temperature, followed by potential incoming solar radiation and slope. Three machine learning models (Logistic Regression, Random Forest and Support Vector Machines) were trained to generate permafrost probability distribution maps based on their prediction of the probability of rock glaciers being intact as opposed to relict. Logistic Regression and Support Vector Machines were able to produce a similar spatial distribution of permafrost. However, the Random Forest has low precision of spatial variation. The permafrost distribution map suggests the likely occurrence of permafrost to be above 5000 m, indicating a potential for rock and landslides should it thaw in the future. While higher-resolution input data can improve the results, this approach remains promising for application in permafrost regions where information about the ice content of rock glaciers is very limited.

Long-Term Field Subgrade Monitoring and Quantitative Hazard Assessment along the Qinghai–Tibet Highway in the Permafrost Region

Long-Term Field Subgrade Monitoring and Quantitative Hazard Assessment along the Qinghai–Tibet Highway in the Permafrost Region

Predicting Seasonal Deformation Using InSAR and Machine Learning in the Permafrost Regions of the Yangtze River Source Region

AbstractQuantifying 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.

Gas hydrate technological applications: From energy recovery to carbon capture and storage

Hydrates are crystalline structures that trap small gas molecules inside hydrogen-bonded water cages. These structures form at high pressure and low temperature. In recent years, there has been a growing interest in gas hydrates for technological applications, specifically in energy recovery, as well as carbon dioxide capture and storage. In the CO2/CH4 exchange using gas hydrates, researchers have reported a large amount of natural gas trapped in the form of gas hydrates under the ocean seafloor and permafrost regions. This large amount of natural gas trapped inside hydrate cages in oceanic and permafrost deposits has driven interest in the energy sector to investigate the possibility of safely harvesting gas hydrates as one of energy resources. However, there are still unanswered fundamental questions including the mechanism of natural gas recovery from gas hydrates. Another gas hydrate-based technology that has a growing interest is the use of gas hydrates for separation including carbon dioxide capture and storage. Carbon dioxide molecules have been shown to be preferentially trapped in the hydrate phase, demonstrating the possibility of the usage for carbon capture technology. Similarly, there are still underlying concerns of these applications, such as thermodynamics and stability of gas hydrates in porous materials, and the crystallization kinetics and mechanism of hydrate formation. This paper provides an overview of laboratory investigations conducted to understand the mechanism and evaluate the feasibility of energy recovery as well as discussion on recent advances in laboratory investigations on gas hydrate-based technology.

Water budgets in an arid and alpine permafrost basin: Observations from the High Mountain Asia

Ground freeze‒thaw processes have significant impacts on infiltration, runoff and evapotranspiration. However, there are still critical knowledge gaps in understanding of hydrological processes in permafrost regions, especially of the interactions among permafrost, ecology, and hydrology. In this study, an alpine permafrost basin on the northeastern Qinghai‒Tibet Plateau was selected to conduct hydrological and meteorological observations. We analyzed the annual variations in runoff, precipitation, evapotranspiration, and changes in water storage, as well as the mechanisms for runoff generation in the basin from May 2014 to December 2015. The annual flow curve in the basin exhibited peaks both in spring and autumn floods. The high ratio of evapotranspiration to annual precipitation (>1.0) in the investigated wetland is mainly due to the considerably underestimated ‘observed’ precipitation caused by the wind-induced instrumental error and the neglect of snow sublimation. The stream flow from early May to late October probably came from the lateral discharge of subsurface flow in alpine wetlands. This study can provide data support and validation for hydrological model simulation and prediction, as well as water resource assessment, in the upper Yellow River Basin, especially for the headwater area. The results also provide case support for permafrost hydrology modeling in ungauged or poorly gauged watersheds in the High Mountain Asia.

Spatiotemporal variations of permafrost extent in Mongolia during 1950–2022

Permafrost in Mongolia is located in the transition zone between high-latitude and high-altitude permafrost regions of the Northern Hemisphere, with large temperature differences and complex subsurface characteristics. In this study, the reliability of the skin temperature data from the ERA5-Land product covering Mongolia is assessed via site observations. The ERA5-Land skin temperature dataset shows a cold bias, which is more pronounced in the cold season. Following calibration based on elevation differences, significant improvements are observed at both the annual scale (92 % improvement in RMSE (root mean square error) and 98 % improvement in MBE (mean bias error)) and the seasonal scale (78 % improvement in RMSE and 82 % improvement in MBE). Additionally, the spatial variations in the surface freezing index (SFI) and surface thawing index (STI) are most pronounced in the central and northeastern Mongolia. The SFI exhibits a significant decreasing trend of 7.16 °C·d/year, while the STI shows a significant increasing trend of 4.49 °C·d/year. Furthermore, the permafrost extent in Mongolia is simulated from 1950 to 2022 using the frost number (Fn) model and the temperature on top of permafrost (TTOP) model. The validated results indicate that the accuracy of the Fn model is relatively high, with an overall accuracy of 0.9 and a Kappa coefficient of 0.47. The permafrost extent in Mongolia has declined from 734.7 × 103 km2 in the 1950 s to 480.1 × 103 km2 in the 2010 s, with a prominent decrease of 3.2 × 103 km2/decade after 1994. According to the variations in permafrost extent during past 72 years, the Hovsgol and Khentii Mountain ranges have experienced significant permafrost degradation.

Numerical Simulations of the Prethawing Performance of Lime-Energy-Column Embankments in Warm Permafrost Regions

Numerical Simulations of the Prethawing Performance of Lime-Energy-Column Embankments in Warm Permafrost Regions