Search Results (347)

Accurate prediction of embankment settlement and evaluation of its serviceability in permafrost regions are significantly challenged by scarce monitoring data and dynamic, non-stationary settlement processes. To address this, an integrated framework combining change-point detection with a novel dynamic prediction model is proposed. Analysis of long-term monitoring data from the Qinghai–Tibet Railway using the Pettitt test revealed a key change point around 2015, indicating a transition towards stabilization. Subsequently, an SAA-GRU-LSTM hybrid model, employing a dynamic compensation prediction strategy, was developed. The model successfully utilized only early-stage data to forecast future settlement trends, demonstrating robust performance in adapting to the identified abrupt change. Furthermore, by applying established engineering serviceability criteria to both historical and predicted data, the framework enables a dynamic and prospective serviceability assessment. This methodology provides a practical tool for the maintenance and risk management of infrastructure in permafrost environments under conditions of data scarcity and process uncertainty. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

Emerging contaminants (ECs) comprise diverse pollutant classes that are increasingly detected in remote environments due to their persistence and long-range transport potential. In cold regions, atmospheric cold-trapping processes favour their accumulation in high-altitude and high-latitude snow and ice, which act as sensitive archives and secondary sources of contamination. While previous studies have addressed individual environmental compartments (e.g., snowpack, glacier ice, meltwater), focusing on specific contaminant classes, a systematic review integrating the occurrence, behaviour and impacts of major EC groups in polar and alpine snow and ice is still lacking. To fill this gap, this work synthesised current knowledge on the environmental fate of three key EC categories in the cryosphere: metals and metalloids (MMs), industrial chemicals and by-products (ICBs), and pharmaceuticals and personal care products (PPCPs). PRISMA guidelines were accurately followed for research, which was based on a Google Scholar search combining keywords on cryospheric matrices (snow, firn, ice cores), geographical regions (Arctic, Antarctic, Alps, high mountains), and contaminant classes. Of 350 records initially identified, 300 met the eligibility criteria (post-industrial snow, firn, or ice cores studies) after excluding studies focused on aerosol or meltwater-only, method-focused papers, pre-industrial datasets, urban-only investigations, and duplicates. Risk of bias was qualitatively assessed through manual screening, evaluating matrix eligibility, temporal consistency, analytical methods, detection limits, and duplicate data, with particular attention to inconsistencies in ECs classification. Strict operational definitions were therefore applied to ensure methodological coherence. Concentration data were harmonised into a standardised database, and findings were synthesised through a structured narrative supported by tabulated datasets organised by matrix and site. Overall, the evidence indicates widespread occurrence of ECs in the global cryosphere, with spatial variability linked to emission sources, long-range transport pathways, and snow physicochemical properties. Climate-change-driven alterations of snow dynamics, glacier retreat and permafrost thaw are expected to modify partitioning equilibria and enhance the secondary release of legacy and contemporary contaminants. However, significant limitations persist, including geographical gaps, variability in analytical sensitivity, lack of long-term monitoring for certain EC classes, and inconsistencies in contaminant classification frameworks. Despite these constraints, the synthesis highlights consistent emerging patterns and underscores the need to strengthen existing environmental protocols to mitigate potential risks to ecosystems and human health. Full article

Rock glaciers are key indicators of mountain permafrost and act as climatically resilient water reservoirs in arid mountains. This study presents the first inventory and kinematic classification of rock glaciers in Western Tien Shan (Kazakhstan and Kyrgyzstan), combining geomorphological mapping with InSAR time-series analysis. Using high-resolution optical imagery (Google Earth Pro (version 7.3.6.10441), Bing Maps, SAS Planet (version 200606.10075), digital elevation models, and Small Baseline Subset InSAR processing, 741 rock glaciers covering more than 70.5 km² were identified. Activity classification revealed 232 transitional and 509 active forms, with mean seasonal displacement rates of ~15 cm yr⁻¹ calculated based on August and September observations. Spatial analysis showed a strong rock glacier concentration on north-facing slopes (>66% of total area) with reduced potential incoming solar radiation. Rock glaciers mainly occur between 2800 and 3800 m a.s.l., with a mean elevation of 3340 m a.s.l. However, their kinematic activity varies across mid-altitudinal ranges, underscoring the influence of slope, aspect, shading, and local topography. Integration with the Global Permafrost Zonation Index (PZI) indicated a lower permafrost boundary at ~1922 m a.s.l., with the largest and most active glaciers occurring at intermediate PZI values (0.5–0.7). This first rock glacier inventory for the Western Tien Shan establishes a benchmark dataset that supports the validation and refinement of global models at a regional scale, guides priorities for permafrost monitoring, and provides a replicable framework for inventory development in other data-scarce mountain regions. Full article

The response of Arctic vegetation to climate warming exhibits pronounced spatial heterogeneity, driven partly by widespread permafrost degradation. However, the role of thermokarst lake development in mediating vegetation-climate interactions remains poorly understood, particularly across heterogeneous landscapes of northeastern Siberia. This study integrated multi-source remote sensing data (2001–2021) with trend analysis, partial correlation, and a Shapley Additive Explanation (SHAP)-interpreted random forest model to examine the drivers of normalized difference vegetation index (NDVI) variability across five levels of thermokarst lake coverage (none, low, moderate, high, very high) and two vegetation types (forest, tundra). The results show that although greening dominates the region, browning is disproportionately observed in areas with high thermokarst lake coverage (>30%), highlighting the localized reversal of regional greening trends under intensified thermokarst activity. Air temperature was identified as the dominant driver of NDVI change, whereas soil temperature and soil moisture exerted secondary but critical influences, especially in tundra ecosystems with extensive thermokarst lake development. The relative importance of these factors shifted across thermokarst lake coverage gradients, underscoring the modulatory effect of thermokarst processes on vegetation-climate feedbacks. These findings emphasize the necessity of incorporating thermokarst dynamics and landscape heterogeneity into predictive models of Arctic vegetation change, with important implications for understanding cryospheric hydrology and ecosystem responses to ongoing climate warming. Full article

Permafrost is an important carbon pool for terrestrial ecosystems and a significant source of atmospheric greenhouse gases, but the effects of ground vegetation and snow cover on permafrost greenhouse gas fluxes are still unclear. The soil–atmosphere exchange fluxes of greenhouse gases (mainly carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)) occupy key roles during the winter snow and the vegetation growing seasons. Here, a typical Larix gmelinii forest, located in the permafrost region of the Daxing’an Mountains, northeast China, was studied. Using the static chamber-gas chromatograph method, the relationship between soil greenhouse gas emissions, ground vegetation, and snow cover was investigated. We found that the CO₂, CH₄, and N₂O cumulative fluxes from vegetative soils had increased by 19.5%, 37.5%, and 10.7%, compared with fluxes from areas where the ground vegetation had been removed. Snow cover increased soil CO₂ cumulative flux by 53.1% and soil N₂O cumulative flux by 28.6%, and soil CH₄ cumulative flux decreased by 39.3%. Our results show that snow cover and ground vegetation removal reduce CO₂ and N₂O emissions from permafrost soils. Ground vegetation removal also increases the absorption of CH₄ in permafrost soils, while snow cover removal promotes CH₄ emissions. These findings confirm the effects of ground vegetation and snow cover on the transformation processes of greenhouse gases from forest ecosystems in permafrost regions. Therefore, this research provides scientific data support for the improvement of land surface climate models and the mitigation of climate change in cold regions. Full article

The Altai Mountains rank among the world’s most notable and valuable archaeological regions. Within the sprawling Altai Mountains area, burial mounds (kurgans) of past civilizations, which are sometimes well preserved in permafrost, are a particularly precious trove of archaeological insights. This study investigates the application of deep learning-based object detection techniques for automatic kurgan identification in high-resolution satellite imagery. We compare the performance of various object detection methods utilizing both convolutional neural network and Transformer backbones. Our results validate the effectiveness of different approaches, especially with larger models, in the challenging task of detecting small archaeological structures. Techniques addressing the class imbalance can further improve performance of off-the-shelf methods. These findings demonstrate the feasibility of employing deep learning techniques to automate kurgan identification, which can improve archaeological surveying processes. It suggests the potential of deep learning technology for constructing a comprehensive inventory of Altai Mountain kurgans, particularly relevant in the context of global warming and archaeological site preservation. Full article

Permafrost degradation under climate warming has profound implications for ecological processes, hydrology, and human activities. Northeast China, characterized by sporadic and isolated patch permafrost near the southern limit of latitudinal permafrost (SLLP), represents one of the most sensitive and complex permafrost regions. This study aims to improve the reliability of permafrost mapping by incorporating parameter uncertainty into simulations. We developed a Monte Carlo–Temperature at the Top of Permafrost (TTOP) (MC–TTOP) framework that integrates an equilibrium model with Monte Carlo sampling to quantify parameter sensitivity and model uncertainty. Using all-sky daily air temperature data and land use and land cover information, we generated probabilistic estimates of mean annual ground temperature (MAGT), permafrost occurrence probability (PZI), and associated uncertainties. Model validation against borehole observations demonstrated improved accuracy compared with global-scale simulations, with a reduced bias and RMSE. Results reveal that permafrost in Northeast China was relatively stable during 2003–2010 but experienced pronounced degradation after 2011, with the total area decreasing to ~2.79 × 10⁵ km² by 2022. Spatial uncertainty was greatest in transitional zones near the southern boundary, where PZI-based delineations tended to overestimate permafrost extent. Regional comparisons further showed that permafrost in Northeast China is more fragmented and uncertain than that on the Tibetan Plateau, owing to complex snow–vegetation–topography interactions and intensive human disturbances. Overall, the MC-TTOP simulations indicate an accelerated permafrost degradation after 2011, with the highest uncertainty concentrated in southern transitional zones near the SLLP, demonstrating that the MC-TTOP framework provides a robust tool for probabilistic permafrost mapping, offering improved reliability for regional-scale assessments and important insights for future risk evaluation under climate change. Full article

The source region of the Yellow River on the Tibetan Plateau constitutes a critical ecological security barrier and a key water-conservation region, where permafrost dynamics exercise primary control over ecosystem stability and hydrological processes. Although observations document intensifying freeze–thaw processes under climate warming, the historical and future evolution of maximum freezing depth, abbreviated as MFD, in the source region of the Yellow River remains poorly constrained. Using ground-temperature and meteorological records from 15 stations for 1981–2014, we estimated MFD with a Stefan-type formulation, assessed trend significance using the Mann–Kendall test and Sen’s slope, and characterized changes through 2100 using CMIP6 projections under four shared socioeconomic pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. We found a strong inverse association between MFD and annual mean ground temperature, such that a 1 °C increase corresponds to an average decrease of approximately 13.2 cm. Historically, MFD has progressively shallowed and exhibits a clear meridional gradient—deeper in the north and shallower in the south; low-value zones declined from 0.75 to 0.50 m, whereas high-value zones decreased from 2.92 to 2.83 m. Across future scenarios, MFD continues to shallow; the strongest signal occurs under SSP5-8.5, yielding an additional decline of approximately 42 percent relative to the historical baseline, with degradation most pronounced at lower elevations. These findings provide actionable guidance for understanding ecohydrological processes and for water resource management in the source region of the Yellow River under climate warming. Full article

Changes in vegetation coverage reflect the status and dynamic processes of ecosystems and serve as a crucial foundation for regional ecological protection. Using Landsat-5 and Sentinel-2 data, this study calculated the vegetation coverage in the Three-River Headwaters (TRH) region from 1990 to 2022 with the pixel dichotomy model, identified land cover changes over the past three decades via a deep neural network, and analyzed the primary influencing factors behind vegetation coverage dynamics. The results indicate that vegetation coverage in TRH has generally increased, as very high vegetation coverage expanded by 10.3%, while very low and low vegetation coverage decreased by 4.2%. Extensive bare land in the western region decreased and transformed into grassland, while the areas of shrubland and forest in the central and eastern TRH areas increased. The areas of grassland, shrubland, and forest increased by 3.7 × 10⁴ km², 2.1 × 10⁴ km², and 4.7 × 10³ km², respectively. Precipitation, elevation, and temperature are the main factors influencing the spatial variation in vegetation coverage. We found that the contributions of the permafrost active layer thickness and precipitation to changes in vegetation coverage are high. Finally, we provide a detailed and timely analysis of recent vegetation distribution and type changes on the Tibetan Plateau, offering a strengthened scientific foundation for monitoring, assessment, and ecological conservation efforts aimed at supporting ecosystem restoration in the region. Full article

The soil freeze–thaw process is a dominant disturbance in the seasonally frozen ground and the active layer of permafrost, which plays a crucial role in the surface energy balance, water cycle, and carbon exchange and has a pronounced influence on vegetation phenology. This study proposes a novel density-based Freeze–Thaw Disturbance Index (FTDI) based on the identification of the freeze–thaw disturbance region (FTDR) over the Qinghai–Tibet Plateau (QTP). FTDI is defined as an areal density metric based on geomorphic disturbances, i.e., the proportion of FTDRs within a given region, with higher values indicating greater areal densities of disturbance. As a measure of landform clustering, FTDI complements existing freeze–thaw process indicators and provides a means to assess the geomorphic impacts of climate-driven freeze–thaw changes during permafrost degradation. The main conclusions are as follows: the FTDR results that are identified by the random forest model are reliable and highly consistent with ground observations; the FTDRs cover 8.85% of the total area of the QTP, and mainly in the central and eastern regions, characterized by prolonged freezing durations and the average annual ground temperature (MAGT) is close to 0 °C, making the soil in these regions highly susceptible to warming-induced disturbances. Most of the plateau exhibits low or negligible FTDI values. As a geomorphic indicator, FTDI reflects the impact of potential freeze–thaw dynamic phase changes on the surface. Higher FTDI values indicate a greater likelihood of surface thawing processes triggered by rising temperatures, which impact surface processes. Regions with relatively high FTDI values often contain substantial amounts of organic carbon, and may experience delayed vegetation green-up despite general warming trends. This study introduces the FTDI derived from the FTDR as a novel index, offering fresh insights into the study of freeze–thaw processes in the context of climate change. Full article

Thermokarst lakes play a crucial role in regulating hydrological, ecological, and biogeochemical processes in permafrost regions. However, due to the limited spatial resolution of earlier satellite imagery, small thermokarst lakes—highly sensitive to climate change and permafrost degradation—have often been overlooked, hindering accurate spatiotemporal analyses. To address this limitation, five water indices—Modified Normalized Difference Water Index (MNDWI), Multi-Band Water Index (MBWI), Automated Water Extraction Index (AWEI_sh and AWEI_nsh), and Red Edge Water Index (RWI)—were employed based on Sentinel-2 imagery from 2021 to extract thermokarst lakes in the Qinghai–Tibet Highway (QTH) region, China. Visual validation indicated that the Red Edge Water Index (RWI) yielded the best performance, with an error of only 10.21%, significantly lower than other indices (e.g., MNDWI: 41.36%; MBWI: 38.80%). Seasonal comparisons revealed that the applicability of different water indices varies, with autumn months (September to October) being the optimal period for lake extraction due to stable and unfrozen surface conditions. Using the RWI, 56 thermokarst lake drainage events were identified in the study area from 2016 to 2025 (as of September 2025), most occurring after 2019—likely associated with climatic factors—and small lakes were found to be more prone to drainage, accompanied by notable surface subsidence in drained regions. These findings are applicable across the Qinghai–Tibet Plateau (QTP) and provide a scientific basis for monitoring thermokarst lakes, delineating accurate lake boundaries, and exploring drainage mechanisms. Full article

Land surface temperature (LST) is a key indicator reflecting the ecological environmental disturbance caused by open-pit coal mining activities and determining the ecological status in alpine permafrost regions. Thus, it is crucial to study the spatiotemporal variations and influencing mechanisms of LST throughout all stages of small-scale mining–large-scale land surface damage–ecological restoration. Landsat imagery over nine periods was extracted from the growing seasons between 1990 and 2024. This study retrieved LST while simultaneously calculating albedo, soil moisture, and normalized difference vegetation index (NDVI) for each time phase. By integrating land use/cover (LUCC) data, the spatiotemporal evolution patterns of LST in the mining area throughout all stages were revealed. Based on the Geodetector method, an identification approach for factors influencing LST spatial differentiation was established. This approach was applicable to the entire process characterized by significant land type transitions. The results indicate that the spatiotemporal variations in LST were significantly correlated with land surface damage and restoration caused by human activities in the mining area. With the implementation of ecological restoration, high and ultra-high temperatures decreased by about 25.98% compared to the period when the surface damage was the most severe. The main influencing factors of LST differentiation were identified for different land use types, i.e., natural and restored meadows (soil wetness, albedo, and NDVI), mine pits (albedo, aspect, and elevation), and mining waste dumps (aspect and albedo before restoration; aspect and NDVI after restoration). This study can provide a reference for monitoring the ecological environment changes and ecological restoration of global coalfields with the same climatic characteristics. Full article

Songhua River Basin, northeast China, has seen significant changes due to climate change and human activities from 1990 to 2000, when forests were largely reclaimed and agricultural land was taken up to change the terrestrial water cycle drastically. This paper investigates hydrological changes in three basins: the main stream basin of the Songhua River, the Second Songhua River Basin, and the Nenjiang River Basin. Machine learning and signal processing techniques have been applied to reconstruct historical river records with high accuracy, achieving determination coefficients exceeding 0.97. The physically based WEP model effectively simulates both natural hydrological patterns and human-induced hydrological processes in the northern Nenjiang region. Climate projections indicate clear temperature increases across all scenarios. The most significant warming is observed under the SSP5-8.5 scenario, where runoff increases by 8.52% to 12.02%t, with precipitation driving 62% to 78% of the changes. Summer runoff shows the most significant increase, while autumn runoff decreases, particularly in the Nenjiang Basin, where permafrost loss alters spring melt patterns. This change elevates flood risk in summer, with the rate of increase strongly dependent on the scenario. Water resources show strong scenario dependence, with the average growth rate of SSP5-8.5 being 4 times that of SSP1-2.6. A critical threshold is reached at a 2.5 °C increase in temperature, triggering system instability. These results emphasize the need for adaptation to spatial differences to address emerging water security challenges in rapidly changing northern regions, including nonlinear hydroclimatic responses, infrastructure resilience to flow changes, and cross-basin coordination. Full article