Search Results (1,023)

In recent years, large-scale linear infrastructure developments have been developed across hundreds of kilometers of permafrost regions on the Qinghai–Tibet Plateau. The implementation of major engineering projects, including the Qinghai–Tibet Highway, oil pipelines, communication cables, and the Qinghai–Tibet Railway, has spurred intensified research into permafrost dynamics. Climate warming has accelerated permafrost degradation, leading to a range of geological hazards, most notably widespread thermokarst landslides. This study investigates the spatiotemporal distribution patterns and influencing factors of thermokarst landslides in Qinghai Province through an integrated approach combining field surveys, remote sensing interpretation, and statistical analysis. The study utilized multi-source datasets, including Landsat-8 imagery, Google Earth, GF-1, and ZY-3 satellite data, supplemented by meteorological records and geospatial information. The remote sensing interpretation identified 1208 cryogenic hazards in Qinghai’s permafrost regions, comprising 273 coarse-grained soil landslides, 346 fine-grained soil landslides, 146 thermokarst slope failures, 440 gelifluction flows, and 3 frost mounds. Spatial analysis revealed clusters of hazards in Zhiduo, Qilian, and Qumalai counties, with the Yangtze River Basin and Qilian Mountains showing the highest hazard density. Most hazards occur in seasonally frozen ground areas (3500–3900 m and 4300–4900 m elevation ranges), predominantly on north and northwest-facing slopes with gradients of 10–20°. Notably, hazard frequency decreases with increasing permafrost stability. These findings provide critical insights for the sustainable development of cold-region infrastructure, environmental protection, and hazard mitigation strategies in alpine engineering projects. Full article

The Complementary Relationship (CR) principle of evapotranspiration provides an efficient approach for estimating actual evapotranspiration (ET_a), owing to its simplified computation and effectiveness in utilizing meteorological factors. Accurate estimation of actual evapotranspiration (ET_a) is crucial for understanding surface energy and water cycles, especially in permafrost regions. This study aims to evaluate the applicability of two Complementary Relationship (CR)-based methods—Bouchet’s in 1963 and Brutsaert’s in 2015—for estimating ETa on the Qinghai–Tibetan Plateau (QTP), using observations from Eddy Covariance (EC) systems. The potential evapotranspiration (ET_p) was calculated using the Penman equation with two wind functions: the Rome wind function and the Monin–Obukhov Similarity Theory (MOST). The comparison revealed that Bouchet’s method underestimated ET_a during frozen soil periods and overestimated it during thawed periods. In contrast, Brutsaert’s method combined with the MOST yielded the lowest RMSE values (0.67–0.70 mm/day) and the highest correlation coefficients (r > 0.85), indicating superior performance. Sensitivity analysis showed that net radiation (Rn) had the strongest influence on ET_a, with a daily sensitivity coefficient of up to 1.35. This study highlights the improved accuracy and reliability of Brutsaert’s CR method in cold alpine environments, underscoring the importance of considering freeze–thaw dynamics in ET modeling. Future research should incorporate seasonal calibration of key parameters (e.g., ε) to further reduce uncertainty. Full article

The Qiangtang Basin (Tibetan Plateau) poses significant geophysical challenges for seismic exploration due to near-surface widespread permafrost and steeply dipping Mesozoic strata induced by the Cenozoic Indo-Eurasian collision. These seismic geological conditions considerably contribute to lower signal-to-noise ratios (SNRs) with complex wavefields, to some extent reducing the reliability of conventional seismic imaging and structural interpretation. To address this, the common-reflection-surface (CRS) stack method, derived from optical paraxial ray theory, is implemented to transcend horizontal layer model constraints, offering substantial improvements in high-SNR prestack gather generation and prestack depth migration (PSDM) imaging, notably for permafrost zones. Using 2D seismic data from the basin, we detailedly compare the CRS stack with conventional SNR enhancement techniques—common midpoint (CMP) FlexBinning, prestack random noise attenuation (PreRNA), and dip moveout (DMO)—evaluating both theoretical foundations and practical performance. The result reveals that CRS-processed prestack gathers yield superior SNR optimization and signal preservation, enabling more robust PSDM velocity model building, while comparative imaging demonstrates enhanced diffraction energy—particularly at medium (20–40%) and long (40–60%) offsets—critical for resolving faults and stratigraphic discontinuities in PSDM. This integrated validation establishes CRS stacking as an effective preprocessing foundation for the depth-domain imaging of complex permafrost geology, providing critical improvements in seismic structural resolution and reduced interpretation uncertainty for hydrocarbon exploration in permafrost-bearing basins. Full article

Morphological problems for distinguishing between glacier ice, glacier ice with a debris cover (debris-covered glaciers), and rock glaciers are outlined with respect to recognising and mapping these features. Decimal latitude–longitude [dLL] values are used for geolocation. One model for rock glacier formation and flow discusses the idea that they consist of ‘mountain permafrost’. However, signs of permafrost-derived ice, such as flow features, have not been identified in these landsystems; talus slopes in the neighbourhoods of glaciers and rock glaciers. An alternative view, whereby rock glaciers are derived from glacier ice rather than permafrost, is demonstrated with examples from various locations in the mountain domain, 𝔻𝕞. A Google Earth and field examination of many rock glaciers shows glacier ice exposed below a rock debris mantle. Ice exposure sites provide ground truth for observations and interpretations stating that rock glaciers are indeed formed from glacier ice. Exposure sites include bare ice at the headwalls of cirques and above debris-covered glaciers; additionally, ice cliffs on the sides of meltwater pools are visible at various locations along the lengths of rock glaciers. Inspection using Google Earth shows that these pools can be traced downslope and their sizes can be monitored between images. Meltwater pools occur in rock glaciers that have been previously identified in inventories as being indictive of permafrost in the mountain domain. Glaciers with a thick rock debris cover exhibit ‘hidden ice’ and are shown to be geomorphological units mapped as rock glaciers. Full article

The complex climate in permafrost regions poses severe challenges to infrastructure, and freeze-thaw cycles accelerate the deformation and damage of road embankments. Conventional thermosyphon technology, though effective in lowering permafrost temperatures, has a limited range of effect, making it hard to meet the demand for large-scale temperature regulation. This paper proposes a V-shaped transverse thermosyphon design with bidirectional heat conduction. It connects at the embankment centerline and transversely penetrates the entire cross-section to expand the temperature regulation range. Using a hydro-thermal coupling model, the temperature regulation effects of vertical, inclined, and V-shaped thermosyphons were calculated. Results show that the V-shaped design outperforms the other two in temperature control across different embankment areas. Transverse temperature analysis indicates uniform cooling around the embankment center, while depth temperature analysis reveals more stable temperature control with lower and less fluctuating temperatures at greater depths. Long-term temperature analysis demonstrates superior annual temperature regulation, providing consistent cooling. This research offers a scientific basis for embankment temperature regulation design in permafrost regions and is crucial for ensuring long-term embankment stability and safety. Full article

Ice wedges, which are ubiquitous in permafrost areas, play a significant role in the evolution of permafrost landscapes, influencing the topography and hydrology of these regions. In this paper, we combine a detailed multi-generational, interdisciplinary, and international literature review along with our own field experiences to explore the development of low-centered ice-wedge polygons and their orthogonal networks. Low-centered polygons, a type of ice-wedge polygonal ground characterized by elevated rims and lowered wet central basins, are critical indicators of permafrost conditions. The formation of these features has been subject to numerous inconsistencies and debates since their initial description in the 1800s. The development of elevated rims is attributed to different processes, such as soil bulging due to ice-wedge growth, differential frost heave, and the accumulation of vegetation and peat. The transition of low-centered polygons to flat-centered, driven by processes like peat accumulation, aggradational ice formation, and frost heave in polygon centers, has been generally overlooked. Low-centered polygons occur in deltas, on floodplains, and in drained-lake basins. There, they are often arranged in orthogonal networks that comprise a complex system. The prevailing explanation of their formation does not match with several field studies that practically remain unnoticed or ignored. By analyzing controversial subjects, such as the degradational or aggradational nature of low-centered polygons and the formation of orthogonal ice-wedge networks, this paper aims to clarify misconceptions and present a cohesive overview of lowland terrain ice-wedge dynamics. The findings emphasize the critical role of ice wedges in shaping Arctic permafrost landscapes and their vulnerability to ongoing climatic and landscape changes. Full article

The screw piles used in permafrost regions represent a new type of pile, and their vertical bearing characteristics play a crucial role in ensuring the normal operation of engineering buildings. This study establishes a numerical calculation model to simulate the interaction between screw piles and soil in permafrost regions and verifies the numerical simulation results through model tests. The bearing mechanism of screw piles in permafrost areas is studied and compared with common, bored, cast-in-place piles widely used. Finally, a method for estimating the bearing capacity of screw piles in permafrost regions is proposed. The research indicates that approximately 90% of the bearing capacity of screw piles in permafrost regions is derived from the mechanical interaction between the concrete pile’s side and the permafrost soil. The shear strength of the permafrost is the primary determinant of the pile foundation’s bearing capacity, while the seasonally active layer has a minimal impact on its bearing capacity, resulting in a stable year-round performance. In permafrost regions, the equivalent friction resistance of screw piles is significantly greater than that of the conventional cast-in-place piles. When the pile reaches its ultimate bearing capacity, the plastic zone on the pile’s side becomes connected, and shear failure occurs in the surrounding soil. The design value of the bearing capacity of a single pile can be effectively estimated in engineering practice by improving the formula of the code for calculating the vertical bearing capacity. Full article

Tropical peatlands store approximately 105 gigatons of carbon (GtC), serving as vital long-term carbon sinks, yet remain critically underrepresented in climate policy. Indonesia peatlands contain 57GtC—the largest tropical peatland carbon stock in the Asia–Pacific. However, decades of drainage, fires, and lax enforcement practices have degraded vast peatland areas, turning them from carbon sinks into emission sources—as evidenced by the 1997 and 2015 peatland fires which emitted 2.57 Gt CO₂eq and 1.75 Gt CO₂eq, respectively. Using system theory validated against historical data (1997–2023), we develop a causal loop model revealing three interconnected feedback loops driving irreversible collapse: (1) drainage–desiccation–oxidation, where water table below −40 cm triggers peat oxidation (2–5 cm subsistence) and fires; (2) fire–climate–permafrost, wherein emissions intensify radiative forcing, destabilizing monsoons and accelerating Arctic permafrost thaw (+15% since 2000); and (2) economy–governance failure, perpetuated by palm oil’s economic dominance and slack regulatory oversight. To break these vicious cycles, we propose a precautionary framework featuring IoT-enforced water table (≤40 cm), reducing emissions by 34%, legally protected “Global Climate Stabilization Zones” for peat domes (>3 m depth), safeguarding 57 GtC, and ASEAN transboundary enforcement funded by a 1–3% palm oil levy. Without intervention, annual emissions may reach 2.869 GtCO₂e by 2030 (Nationally Determined Contribution’s business-as-usual scenario). Conversely, rewetting 590 km²/year aligns with Indonesia’s FOLU Net Sink 2030 target (−140 Mt CO₂e) and mitigates 1.4–1.6 MtCO₂ annually. We conclude that integrating peatlands as irreplaceable climate infrastructure into global policy is essential for achieving Paris Agreement goals and SDGs 13–15. Full article

Interferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties with deformation automatic detection. In this study, a full-coverage deformation rate map of the 10 km buffer of the Qinghai–Tibet Engineering Corridor (QTEC) was generated by combining nine driving factors and the deformation rate of the 5 km buffer along the QTEC based on three machine learning methods. The importance of the factors contributing to ground deformation was explored. The experimental results show that support vector regression (SVR) yielded the best performance (R2 = 0.98, RMSE = 0.76 mm/year, MAE = 0.74 mm/year). The 10 km buffer of deformation data obtained not only preserved the original deformation data well, but it also filled the blank areas in the deformation map. Subsequently, we trained the Faster R-CNN model on the deformation rate map simulated by SVR and used it for the automatic detection of permafrost thaw settlement areas. The results showed that the Faster R-CNN could identify the permafrost thawing slump quickly and accurately. More than 300 deformation areas along the QTEC were detected through our proposed method, with some of these areas located near thaw slump and thermokarst lake regions. This study confirms the significant potential of combining InSAR and deep learning techniques for permafrost degradation monitoring applications. Full article

Based on ascending and descending orbit SAR data from 2017–2025, this study analyzes the long time-series deformation monitoring and slip pattern of an active-layer detachment thaw slump, a typical active-layer detachment thaw slump in the permafrost zone of the Qinghai-Tibetan Plateau, by using the small baseline subset InSAR (SBAS-InSAR) technique. In addition, a three-dimensional displacement deformation field was constructed with the help of ascending and descending orbit data fusion technology to reveal the transportation characteristics of the thaw slump. The results show that the thaw slump shows an overall trend of “south to north” movement, and that the cumulative surface deformation is mainly characterized by subsidence, with deformation ranging from −199.5 mm to 55.9 mm. The deformation shows significant spatial heterogeneity, with its magnitudes generally decreasing from the headwall area (southern part) towards the depositional toe (northern part). In addition, the multifactorial driving mechanism of the thaw slump was further explored by combining geological investigation and geotechnical tests. The analysis reveals that the thaw slump’s evolution is primarily driven by temperature, with precipitation acting as a conditional co-factor, its influence being modulated by the slump’s developmental stage and local soil properties. The active layer thickness constitutes the basic geological condition of instability, and its spatial heterogeneity contributes to differential settlement patterns. Freeze–thaw cycles affect the shear strength of soils in the permafrost zone through multiple pathways, and thus trigger the occurrence of thaw slumps. Unlike single sudden landslides in non-permafrost zones, thaw slump is a continuous development process that occurs until the ice content is obviously reduced or disappears in the lower part. This study systematically elucidates the spatiotemporal deformation patterns and driving mechanisms of an active-layer detachment thaw slump by integrating multi-temporal InSAR remote sensing with geological and geotechnical data, offering valuable insights for understanding and monitoring thaw-induced hazards in permafrost regions. Full article

Engineering disturbances are increasing in permafrost regions of northeastern China, where soil microorganisms play essential roles in biogeochemical cycling and are highly sensitive to linear infrastructure disturbances. However, limited research has addressed how microbial communities respond to different post-engineering-disturbance recovery stages. This study investigated the impacts of the China–Russia Crude Oil Pipelines (CRCOPs) on soil microbial communities in a typical boreal forest permafrost zone of the Da Xing’anling Mountains. Soil samples were collected from undisturbed forest (the control, CK); short-term disturbed sites associated with Pipeline II, which was constructed in 2018 (SD); and long-term disturbed sites associated with Pipeline I, which was constructed in 2011 (LD). Pipeline engineering disturbances significantly increased soil clay content and pH while reducing soil water content (SWC), soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) (p < 0.05). No significant differences in these soil properties were observed between SD and LD. Bacterial diversity increased significantly, whereas fungal diversity significantly decreased following pipeline disturbances (p < 0.05). The beta diversity of both bacterial and fungal communities differed significantly among the three disturbance types. At the phylum level, pipeline disturbance increased the relative abundances of Proteobacteria, Acidobacteriota, Actinobacteriota, Ascomycota, and Mortierellomycota while reducing those of Bacteroidota and Basidiomycota. These shifts were associated with disturbance-induced changes in soil properties. Microbial co-occurrence networks in SD exhibited greater complexity and connectivity than those in CK and LD, suggesting intensified biotic interactions and active ecological reassembly during the early recovery phase. These findings suggest that pipeline disturbance could drive soil microbial systems into a new stable state that is difficult to restore over the long term, highlighting the profound impacts of linear infrastructure on microbial ecological functions in cold regions. This study provides a scientific basis for ecological restoration and biodiversity conservation in permafrost-affected areas. Full article

In high-elevation or high-latitude permafrost areas, persistent subzero temperatures significantly impact the freeze–thaw durability of concrete structures. Traditional methods for studying the frost resistance of concrete in permafrost regions do not provide a complete picture for predicting properties, and new approaches are needed using, for example, machine learning algorithms. This study utilizes four machine learning models—Support Vector Machine (SVM), extreme learning machine (ELM), long short-term memory (LSTM), and radial basis function neural network (RBFNN)—to predict freeze–thaw damage factors in concrete under low and subzero temperature conservation conditions. Building on the prediction results, the optimal model is refined to develop a new machine learning model: the Sparrow Search Algorithm-optimized Extreme Learning Machine (SSA-ELM). Furthermore, the SHapley Additive exPlanations (SHAP) value analysis method is employed to interpret this model, clarifying the relationship between factors affecting the freezing resistance of concrete and freeze–thaw damage factors. In conclusion, the empirical formula for concrete freeze–thaw damage is compared and validated against the prediction results from the SSA-ELM model. The study results indicate that the SSA-ELM model offers the most accurate predictions for concrete freeze–thaw resistance compared to the SVM, ELM, LSTM, and RBFNN models. SHAP value analysis quantitatively confirms that the number of freeze–thaw cycles is the most significant input parameter affecting the freeze–thaw damage coefficient of concrete. Comparative analysis shows that the accuracy of the SSA-ELMDE prediction set is improved by 15.46%, 9.19%, 21.79%, and 11.76%, respectively, compared with the prediction results of SVM, ELM, LSTM, and RBF. This parameter positively influences the prediction results for the freeze–thaw damage coefficient. Curing humidity has the least influence on the freeze–thaw damage factor of concrete. Comparing the prediction results with empirical formulas shows that the machine learning model provides more accurate predictions. This introduces a new approach for predicting the extent of freeze–thaw damage to concrete under low and subzero temperature conservation conditions. Full article

Analyses of thermokarst hazard risk are becoming increasingly crucial in the context of global warming. A significant aspect of thermokarst research is the mapping of landscapes based on their vulnerability to thermokarst processes. The exponential growth of remote sensing data and the advent of novel techniques have paved the way for the creation of sophisticated techniques for the study of natural disasters, including thermokarst phenomena. This study applies machine learning techniques to assess the vulnerability of tundra landscapes to thermokarst by integrating supervised classification using random forest with morphometric analysis based on the Topography Position Index. We recognized that the thermokarst landscape with the greatest potential for future permafrost thawing occupies 20% of the study region. The thermokarst-affected terrains and water bodies located in the undegraded uplands account for 13% of the total area, while those in depressions and valleys account for 44%. A small part (6%) of the study region represents areas with stable terrains within depressions and valleys that underwent topographic alterations and are likely to maintain stability in the future. This approach enables big geodata-driven predictive modeling of permafrost hazards, improving thermokarst risk assessment. It highlights machine learning and Google Earth Engine’s potential for forecasting landscape transformations in vulnerable Arctic regions. Full article

The Arctic region is rich in oil and gas resources and has great potential for development. It has become a new hot spot for international development. However, the harsh climatic and geological conditions and fragile ecosystems in the Arctic region put forward stringent technical requirements for oil and gas development. Polar permafrost has an impact on the growth of plant roots and the absorption of water. When drilling activities are carried out, the permafrost layer may be broken, resulting in the erosion of polar soil and disorder of the water balance, thus affecting local vegetation and ecosystems. Moreover, the legal system of polar environmental protection is lacking, and it is necessary to form a perfect risk assessment method to improve the relevant laws and regulations. Therefore, it is very important to study the environmental risk identification technology for polar drilling. For polar drilling, it is necessary to establish a risk source classification and identification method for environmental pollution events. However, at present, it mainly faces the following challenges: poor polar environment, lack of monitoring data, and lack of a legal system for polar environmental protection. By systematically discussing risk identification technology, the application and applicable models of different types of risk evaluation methods are categorized and summarized, the advantages and disadvantages of different types of risk evaluation methods and their application effects are analyzed based on the unique environment of the polar regions, and then the development direction of the future environmental risk identification technology for polar drilling is proposed. In order to accelerate the development of polar drilling environmental risk identification technology, research should be focused on the following three aspects: ① Promoting the multi-dimensional integration of polar drilling environmental pollution index data, to make up for the short board of less relevant data in the polar region. ② Combining the machine modeling algorithm with risk evaluation of polar drilling environmental pollution to improve the scientificity and accuracy of the evaluation results. ③ Establishing a scientific and accurate polar drilling environmental pollution risk identification system to reduce pollution risk. Full article

Accurate permafrost mapping in mountainous regions is hindered by sparse in situ observations and heterogeneous terrain. This study develops a GPR-augmented machine learning framework to map mountain permafrost in the northeastern Qinghai–Tibet Plateau. A total of 1037 presence–absence samples were compiled from boreholes, soil pits, 128 GPR transects collected in 2009, and 22 additional empirical points above 4700 m, covering diverse topographic and thermal conditions. Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. Aspect-controlled permafrost boundaries were captured, with modeled lower elevation limits varying by >200 m across slope directions. SHAP analysis showed that climate and soil variables contributed nearly 80% to model outputs, with LST, FDD, BD, and TDD being dominant. Several predictors exhibited threshold or nonlinear responses, reinforcing their physical relevance. Additional experiments confirmed that integration of GPR and high-elevation constraint samples significantly improved model generalization, especially in underrepresented terrain zones. This study demonstrates that a GPR-augmented machine learning framework can support cost-effective, physically informed mapping of frozen ground in complex alpine environments. Full article