Search Results (1,098)

The Source Region of the Yangtze River is a high-altitude area with extensive permafrost on the Tibetan Plateau. While temperature, precipitation, and radiation significantly affect vegetation phenology, the influence of permafrost changes remains unclear. Using the daily Long-term Seamless NOAA AVHRR NDVI Dataset of China (2003–2022), we extracted the start (SOS) and end (EOS) of the growing season in the Source Region of the Yangtze River (SRYR). Soil thawing date (SOT) was obtained from freeze–thaw state products, while active layer thickness (ALT) was estimated using the Stefan model based on MODIS land surface temperature (LST). Partial least squares regression and mediation analysis quantified the direct and indirect effects of permafrost degradation. Results show: (1) The end of the growing season (EOS) became significantly earlier in 64.33% of the region, while the start of the growing season (SOS) showed little change. (2) The effect of SOT on SOS depends on moisture conditions. Earlier SOT leads to earlier SOS in wetter areas by supplying meltwater, but delays SOS in cold–dry areas by increasing soil water loss. (3) Thicker ALT strongly promotes earlier EOS, accounting for up to 42.61% of EOS variation in cold–dry zones, because a deeper active layer potentially promotes downward movement of water, which may further lead to the potential leaching of nutrients from the shallow root zone, limiting resources for shallow-rooted plants. (4) Alpine meadows respond more strongly to permafrost changes than alpine grasslands. Overall, water loss caused by permafrost degradation may reduce the potential lengthening of the growing season under climate warming, highlighting the key role of soil water in linking permafrost and vegetation dynamics. Full article

Antarctica’s extreme cryospheric conditions impose severe thermodynamic constraints on the natural attenuation of hydrocarbon pollutants. Despite the Antarctic Treaty System’s protections, the footprint of human logistics has left persistent reservoirs of petroleum hydrocarbons that threaten endemic biodiversity. This review critically synthesizes the state-of-the-art in Antarctic bioremediation, moving beyond traditional culture-dependent studies to integrate recent multi-omics breakthroughs (2020–2025). We analyze the molecular mechanisms limiting bioavailability in frozen soils and highlight the adaptive strategies of psychrophilic consortia, including the modification of membrane fluidity and the expression of cold-active enzymes (e.g., RHDs, AlkB). Notably, we discuss emerging findings on novel long-chain alkane degradation genes (almA, ladA) identified in 2025, which challenge previous assumptions about recalcitrance. Furthermore, the review evaluates the engineering bottlenecks of in situ versus ex situ strategies, emphasizing the synergistic potential of bacterial–fungal co-cultures and the ecological necessity of “climate-smart” remediation to mitigate methane emissions from thawing permafrost. By bridging the gap between fundamental microbial genetics and applied field engineering, we propose a roadmap for the next generation of biotechnological solutions in the warming polar environment. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Seasonal permafrost areas undergo long-term freeze–thaw cycles, severely compromising the strength of foundation soils. Consequently, deformation and settlement under long-term cyclic traffic loads are greater than in normal temperature areas, leading to potential safety hazards. This study focuses on soft clay soils in seasonal permafrost areas. Remoulded soft clay is subjected to freeze–thaw cycles, followed by a series of long-term cyclic traffic load tests using the GDS dynamic triaxial testing system and pore size analyses using the nuclear magnetic resonance (NMR) technology. The study aims to investigate the effects of varying freeze–thaw cycles, compaction coefficients, and types of curing agents on the cumulative plastic strain of soft clay. The findings indicate that under identical freeze–thaw conditions, both the presence of curing agents and the increase of the soil’s compaction coefficient significantly restrain the deformation of freeze–thawed soils. In the micro perspective, freeze–thaw cycles cause irreversible fracturing of the soil’s internal framework, while the addition of curing agents effectively mitigates the pore enlargement effect. The resulting pore size distribution differs by about 4% from the original distribution, which is consistent with the patterns observed in dynamic triaxial tests. Full article

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

Variations in terrestrial carbon flux influence atmospheric CO₂ exchange and related climate feedback, with Net ecosystem productivity (NEP) serving as a key metric for assessing ecosystem carbon source–sink dynamics. Given the vital ecological barrier function of the Tibetan Plateau (TP), understanding the spatiotemporal variability of NEP and its climatic controls is essential for elucidating carbon sink and climate interactions under ongoing climate change. The spatiotemporal dynamics of NEP across the TP from 1979 to 2018 are investigated using the process-based Community Land Model version 5.0 (CLM5.0). And climate sensitivity experiments are conducted to quantify the relative contributions of different climate factors to NEP variability. Furthermore, future changes in NEP for the period 2025–2100 under multiple Shared Socioeconomic Pathway (SSP) scenarios are projected. The results indicate that the TP functioned predominantly as a net carbon sink during the historical period, with a multi-year mean NEP of 23.96 g C m² yr⁻¹. Spatially, NEP showed a significantly increasing gradient from the northwest to the southeast. During 1979–2018, NEP exhibited an overall decreasing trend across most regions of the TP. Air temperature was identified as the dominant controlling factor, accounting for approximately 68% of the interannual NEP variability, followed by solar radiation (21%) and precipitation (11%). The dominant climatic drivers of NEP variation differ among regions: air temperature predominates in the southwestern and southeastern regions, radiation dominates in the northwestern and central areas, and precipitation exerts a controlling effect in the northern and western regions. Future projections suggest that NEP remains positive under all SSP scenarios, indicating that the TP is likely to persist as a carbon sink throughout the 21st century. This study provides important reference for the development of ecological protection, restoration planning, and regional carbon neutrality strategies. 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

A substantial number of potential underground carbon storage reservoirs exist in regions that contain permafrost (continuously frozen layers of the subsurface), such as in the Alaskan North Slope. The extent and depth of these permafrost layers are changing globally at a rapid pace on the geologic timescale, which warrants continued research and observation. In order to prepare for successful carbon sequestration projects in these regions, in this work, we investigate the outcome from the potential scenario of carbon dioxide encountering the permafrost at depth. This article reviews currently available literature pertaining to the characteristics of permafrost for carbon storage in the case of the injection of carbon dioxide into deep onshore underground reservoirs. This study compares research showing evidence of both the flow of carbon dioxide gas through permafrost and the storage of carbon dioxide gas by permafrost. The findings suggest more research is needed, and several future research areas are outlined in this work. Full article

Permafrost regions serve as sensitive indicators of global warming due to their ecological sensitivity and role as climate archives. To study how soil microbial communities in seasonal permafrost respond to freeze–thaw alternations, we analyzed composition and diversity during freezing, freeze–thaw, and thawing stages, identifying key taxa and environmental drivers. Our results identified 11 known fungal phyla and 13 dominant genera in permafrost regions. Most dominant fungi showed stable abundance during soil warming. However, the genera Inocybe and Sebacina were significantly suppressed when transitioning from frozen to freeze–thaw conditions. Fungal species diversity gradually increased with rising temperature and freeze–thaw frequency, with thawed soil showing higher richness and evenness. Frozen, freeze–thaw, and thawed soil were respectively associated with 90.48%, 71.43%, and 66.67% of node species. Adjacent stages shared 57.14% of coexisting species. Keystone node species declined progressively from frozen to thawed stages, indicating substantial yet continuous community reorganization. Furthermore, total carbon, organic carbon, available nitrogen, and phospholipid fatty acids peaked in freeze–thaw alternating soil. Active fungal biomass and species richness were most strongly correlated with soil carbon, temperature, and moisture. Overall, the influence of nutrients on soil fungi was limited across different freeze–thaw stages, while temperature emerged as the primary driver reshaping fungal community structure during freeze–thaw dynamics. Full article

The study of microorganisms inhabiting extreme environments offers a valuable opportunity to explore their potential ecological roles. This study aimed to reveal and compare the microbial taxonomic diversity of largely unexplored permafrost regions located in different climatic zones (dry and wet) in the Chilean Andes, separated by thousands of kilometers. Permafrost active layer samples were collected from the Ojos del Salado (Atacama Desert) and the Torres del Paine (Patagonia) from different sampling depths. Illumina 16S rRNA gene-based amplicon sequencing revealed that the Andean permafrost active layer provides diverse habitats for distinct microbial communities, with higher taxonomic diversity of Bacteria than Archaea. The wet Patagonian Andes samples showed higher diversity, with a greater abundance of Chloroflexota and Bacteroidota, while the dry Ojos del Salado samples were dominated by Actinomycetota, indicating desiccation stress. Archaea were classified as ammonia-oxidizing members of the Thermoproteota phylum. Beta-diversity analyses suggested that differences in environmental conditions (mainly available moisture) contributed more to community structure differentiation than geographical distances. Nevertheless, the effect of sampling depth on microbial diversity was insignificant. Full article

Thaw settlement (TS) in warm and ice-rich permafrost presents a challenge to highway subgrade stability in the Qinghai–Tibet Engineering Corridor (QTEC). To conduct a regional risk assessment, this study develops a framework coupling multi-source data fusion with Random Forest (RF) machine learning. By connecting site-specific thermo-mechanical simulations with corridor-scale remote sensing predictors, a 30 m resolution thaw settlement zoning map for 13 m wide separated subgrades was generated. The results indicate the following: (1) Thaw settlement exhibits significant spatial variability, with Level III settlement (20–30 cm) being the dominant category, accounting for 40.85% of the total area; Level IV and V settlements are mainly distributed in warm and ice-rich permafrost regions such as the Chumar River, Wuli, and Tuotuo River areas. (2) Mean annual ground temperature (MAGT) and ice content type (ICT) are key factors influencing the spatial settlement pattern, with differentiated dominant mechanisms: 50% of the zones are dominated by ICT, corresponding to higher settlement (26.76–43.31 cm); 35.71% are influenced by both MAGT and ICT; and 14.29% are dominated by MAGT, with lower settlement (16.23–24.19 cm). This suggests a distinct spatial pattern where “high-temperature zones are largely controlled by ice content, while low-temperature zones are controlled by temperature.” (3) Among multi-source remote sensing factors, land surface temperature (LST) and the thawing index (TI) show significant correlations with thaw settlement, confirming their applicability for hazard identification in high-altitude regions. This study provides a scientific reference and decision support for engineering maintenance and route selection on the Qinghai–Tibet Plateau. 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

Long-term fire histories are well-documented across most North American temperate forest systems, yet the fire regimes of high-alpine treeline environments remain poorly understood. Here, we present a millennial-scale fire history from the Sawtooth Fen Palsa (SFP), a rare permafrost fen palsa located in the high-alpine treeline ecotone of the Beartooth Plateau, Wyoming, a permafrost system now unraveling due to recent decades of rapid warming. Analysis of paleoenvironmental proxies from peat sediments overlying the permafrost reveals a multi-century peak in fire activity at the beginning of the record, ca. 10,000 cal yr BP, coinciding with the afforestation of newly deglaciated, ice-free sites. This initial surge in high-severity fire activity was followed by a decline when solar-orbitally driven increases in growing-season temperatures likely promoted forest opening and more surface fire activity within the SFP watershed. High-severity fire activity increased again during the mid-Holocene (ca. 5800–5000 cal yr BP), when effective moisture increased, favoring subalpine forest expansion and increased connectivity of woody biomass (sagebrush and forest), enhancing the potential for canopy fire spread. Only two small fire episodes were recorded in recent millennia when a rapid change in the sedimentation rate may indicate a partial loss of the sediment record. Rapid warming in recent decades has triggered the formation of dozens of thermal collapse ponds across the fen palsa. The frequency of these features has more than doubled since 2000 CE, underscoring the degradation of underlying permafrost in response to changing climatic conditions. Continued warming is expected to cause the complete loss of the permafrost lens and alter ecosystem dynamics, disturbance regimes, and carbon and nutrient cycling in alpine systems throughout the Rocky Mountains. Full article

Permafrost thickness serves as a critical indicator of hydrogeological conditions in cold regions and significantly influences the safety of engineering infrastructure. Due to the combined effects of climate, ecology, and human activities, the thermal characteristics and spatial distribution of permafrost in the Greater and Lesser Khingan Mountains of Northeast China exhibit high complexity, rendering existing permafrost thickness estimation methods largely inapplicable in this region. We developed an integrated estimation framework that bridges the gap between limited deep ground temperature measurements and regional-scale mapping. To overcome the scarcity of deep borehole (>20m) data, a physical-statistical inversion method was employed to derive permafrost base depths from shallow borehole temperature profiles, thereby expanding the foundational dataset to 104 representative sites. Integrating these ground observations with satellite-derived products (e.g., MODIS NDVI) and auxiliary environmental covariates (e.g., DEM-based topography and gridded climatic data), a Random Forest algorithm (RF) was applied to generate a 1 km-resolution permafrost thickness distribution map across Northeast China with a classification accuracy of 0.74. The results indicate that the average permafrost thickness in the study area is 47.71 ± 10 m, exhibiting a spatial pattern of thicker in the north and west, thinner in the south and east, and greater in mountainous areas than in plains. The top three influencing factors of permafrost thickness are atmospheric precipitation, surface thawing degree days (TDDs), and topographic position index (TPI), revealing that the thickness of discontinuous permafrost in northeastern China is primarily governed by local factors such as soil moisture, represented by the thick permafrost existed under a small patch of ground surface. This study provides a new methodological framework for estimating permafrost thickness in regions with limited ground temperature gradient measurement in deep boreholes. Full article

As an important ecological obstacle prone to climatic changes, the Tibetan Plateau has been transformed by retreating glaciers, degrading permafrost, and deteriorating grasslands. Recent ecological remote sensing evaluations typically use medium-resolution and single-source optical imagery, highlight natural factors while ignoring human impacts, and encounter difficulties with time-focused interpretability and continuity within complex terrains. This research proposes a theory combining large language models with satellite embedding to holistically examine the ecology of the Tibetan Plateau between 2000 and 2024. We created an ecological satellite embedding (ESE) model applying self-supervised learning to integrate 12 ecological variables into combined space and time representations as of 2024, according to the Prithvi-Earth Observation (Prithvi-EO) foundational model involving low-rank adaptation (LoRA). GeoChat reasoning was applied to turn the embedded variables into a comprehensive representation feature (CRF). Field research demonstrated strong accuracy for the fraction of absorbed photosynthetically active radiation (FAPAR, R² = 0.9923) and aboveground biomass (AGB, R² = 0.8690). Space and temporal analyses demonstrated a general ecology-dependent enhancement accompanied by significant space-based clustering (Moran’s I = 0.50–0.80), hotspots in humid southeastern areas, major upward trends in vegetation indices and productivity metrics (p < 0.05), and higher shifts in transition regions. Despite the marginal degradation risk, the grassland carrying capacity has expanded extensively in the main farming regions. The comprehensible CRF schema identified three management areas: potential risk, enhancement potential, and stable conservation management. This transferable modular approach connects expert reasoning with data-driven modeling, presenting adaptable methods for assessing ecosystems in high-altitude, data-sparse environments, and practical ways to promote ecological management. Full article