Recent climate warming has accelerated permafrost thaw and dynamics of thermokarst lakes (TLs) on the Tibetan Plateau (TP). Yet, owing to the lack of long-term monitoring of TLs, our understanding of lake evolution processes and their driving factors remains uncertain. Here, using the global surface water product and timeseries Landsat imagery, we identified 58,538 TLs (0.01-3 km2) and determined the primary occurrence year of lake changes from 1990 to 2022. Our results indicated that TLs on the TP are primarily located in the central inland region, over 82 % of lakes experienced area expansion, and only 15 % in the northwest show decrease in area. Annual number of lake expansion peaked in 2016, whereas lake shrinkage was most common in 2019. The calculated lake area errors, field investigations, and validation of lake disturbance time demonstrated high accuracy and consistency. We applied the optimal machine learning regression model to distinguish the different drivers for lake expansion and shrinkage. The topographic and climatic factors are primary drivers for lake expansion, while differences in evaporation trend and soil temperature trend might contribute to lake shrinkage. This study highlights the vulnerability of permafrost on the TP to climate change, which can contribute to carbon sequestration estimation and infrastructure maintenance.

Northern basins are projected to continue warming at rates higher than the global average, with the impacts of warming compounded by concomitant deglaciation, permafrost thaw and vegetation shifts. The Mackenzie River Basin drains headwaters in the glaciated Canadian Rockies to the Arctic Ocean and is mostly underlain by permafrost. Scenarios of future change in this basin were simulated using the MESH distributed hydrological-cryospheric land surface model. MESH was forced with bias-corrected, downscaled RCM forcings and parameterized with a deep subsurface profile, organic soils, and glaciers. The model, validated against discharge, snowpack, and permafrost observations, was used to simulate 21st century hydrology and permafrost dynamics under the RCP8.5 emissions scenario, incorporating projected land cover change applied at two discrete time steps (2021 and 2065). The findings indicate a rapid acceleration of permafrost thaw. By the 2080s, most of the basin will be devoid of permafrost. By late century, river discharges shift to earlier and higher peaks in response to projected increases in precipitation, temperature and snowmelt, despite increases in evapotranspiration from longer snow-free seasons. Baseflow discharges increase in winter, due to higher precipitation and increased basin connectivity from permafrost thaw resulting in enhanced groundwater flow. Subsurface moisture storage rises slightly but the liquid water fraction increases dramatically, increasing subsurface runoff and river discharge. Canadian Rockies' deglaciation reduces summer and annual discharge in the Athabasca and Peace headwaters. Downstream and northward of the mountain headwaters the direct impacts of climate change on river discharge dominate those of changing land cover and glaciers.

Climate change is driving permafrost thaw, releasing previously frozen resources, such as nitrogen, to the soil active layer. In low-nitrogen systems, like boreal peatlands, this novel nitrogen source may benefit plant productivity. However, other resource limitations (for example, light) may limit plant access to thaw-front nitrogen. We used a stable isotope experiment to explore variations in understory boreal plant species' ability to take up different forms of newly released nitrogen from permafrost thaw under different canopy covers. This experiment occurred in a peatland in the sporadic discontinuous permafrost zone of the Northwest Territories, Canada. We added N-15 labelled ammonium, nitrate, and the amino acid glycine at the thaw front (40 cm depth) at two sites differentiated by high and low canopy cover and determined uptake of N-15 in leaves of several common and abundant boreal plant species. We found that the probability of plant uptake of thaw-front nitrogen was significantly greater at low canopy cover sites; however, nitrogen form, plant species, and foliar N-mass had no effect. We further found that Rubus chamaemorus had the highest foliar N-mass followed by Rhododendron groenlandicum, Chamaedaphne calyculata, and Vaccinium vitis-idaea. Our results demonstrate that access to nitrogen released from permafrost thaw by boreal plants may be mediated by light availability. Understanding the variation in site response to permafrost thaw contributes to our understanding of how boreal peatlands will change with ongoing climate change.

Permafrost thawing is a critical climate tipping point, with catastrophic consequences. Existing stabilization methods rely on refrigerant-based systems, such as thermosyphons and active refrigeration, which are capital-intensive, energy-demanding, or increasingly ineffective in warming climates. Most infrastructure built on permafrost requires continuous heat removal from the foundation as the underlying permafrost becomes progressively unstable. To address these challenges, we demonstrate a fully biomass-derived cooling geotextile that can effectively mitigate permafrost thawing through scalable nanoprocessing via a roll-to-roll fabrication (1.3 mmin-1). The cooling geotextile features a hierarchical three-layer design: a strong woven biomass scaffold, a permeable nonwoven fiber network, and an optimized porous coating layer with micro- and nano-structures. When anchored to bare ground, it extracts heat to the cold sky, enhances albedo from similar to 30% to 96.3%, and establishes a thermal barrier between soil and air. Engineered for Arctic durability, it withstands strong winds, extreme cold, and freeze-thaw cycles, exceeding the American National Engineering Handbook requirements (tensile strength 1,682 kg; tear strength 191 kg; puncture strength 61 kg). Field tests in West Lafayette, IN (40 degrees 25 ' 21 '' N, 86 degrees 55 ' 12 '' W) reveal up to 25 degrees C soil cooling under 500 Wm-2 irradiance. Its lightweight (0.8 kgm-2) and rollable attributes enable scalable and fast localized deployment. Simulations predict up to 12 degrees C surface cooling during Arctic summer (2020-2050), preventing up to 40,000 km2 of permafrost from thawing. Completely derived from biomass, cooling geotextile ensures a low carbon footprint (0.7 kgm-2), positioning itself as a sustainable solution for reinforcing Arctic coastline, reconstructing thawing landscape, and restoring the environment.

Small modular reactors (SMRs) are an alternative for clean energy solutions in Canada's remote northern communities, owing to their safety, flexibility, and reduced capital requirements. Currently, these communities are heavily reliant on fossil fuels, and the transition to cleaner energy sources, such as SMRs, becomes imperative for Canada to achieve its ambitious net-zero emissions target by 2050. However, applying SMR technology in permafrost regions affected by climate change presents unique challenges. The degradation of permafrost can lead to significant deformations and settlements, which can result in increased maintenance expenses and reduced structural resilience of SMR infrastructure. In this paper, we studied the combined effect of climate nonstationarity in terms of ground surface temperature and heat dissipation from SMR reactor cores for the first time in two distinct locations in Canada's North: Salluit in Quebec and Inuvik in the Northwest Territories. It was shown that these combined effects can make significant changes to the ground thermal conditions within a radius of 15-20 m around the reactor core. The change in the ground thermal conditions poses a threat to the integrity of the permafrost table. The implementation of mitigation strategies is imperative to maintain the structural integrity of the nuclear infrastructure in permafrost regions. The thermal modeling presented in this study paves the way for the development of advanced coupled thermo-hydromechanical models to examine the impact of SMRs and climate nonstationarity on permafrost degradation.

Permafrost degradation on the Tibetan Plateau (TP) has triggered widespread retrogressive thaw slumps (RTSs), affecting hydrology, carbon sequestration and infrastructure stability. To date, there is still a lack of long-term monitoring of RTSs across the TP, the thaw dynamics and comprehensive driving factors remain unclear. Here, using time-series Landsat imagery and change detection algorithm, we identified RTSs on permafrost regions of the TP from 1986 to 2020. Existing RTSs inventories and high-resolution historical imagery were employed to verify the identified results, the temporal validation of RTSs disturbance pixels demonstrated a high accuracy. In the study area, a total of 3537 RTSs were identified, covering a total area of 5997 ha, representing a 26-fold increase since 1986, and 69.2 % of RTSs formed since 2010. Most RTSs are located on gentle slope (4-12 degrees) at elevations between 4500 m and 5300 m, with a tendency to form in alpine grassland and alpine meadow. Annual variations in RTSs area exhibited a significant positive correlation with minimum air temperature, mean land surface temperature, and annual thawing index, while it showing a significant negative correlation with the decrease in downward shortwave radiation. Spatially, RTSs were more common in areas with higher soil water content and shallower active layer. Landsat imagery captured the vast majority of RTSs on the TP and revealed interannual disturbance details, but the 30 m resolution remains inadequate for delineating the refined boundaries of some micro-scale (< 0.18 ha) RTSs. Detected RTSs disturbances on the TP will aid in hazard management and carbon feedback assessments, and our findings provide novel insights into the impacts of climate change and permafrost environments on RTSs formation.

Widespread changes to near-surface permafrost in northern ecosystems are occurring through gradual top-down thaw and more abrupt localized thermokarst development. Both thaw types are associated with a loss of ecosystem services, including soil hydrothermal and mechanical stability and long-term carbon storage. Here, we analyzed relationships between the vascular understory, basal moss layer, active layer thickness (ALT), and greenhouse gas fluxes along a thaw gradient from permafrost peat plateau to thaw bog in Interior Alaska. We used ALT to define four distinct stages of thaw: Stable, Early, Intermediate, and Advanced, and we identified key plant taxa that serve as reliable indicators of each stage. Advanced thaw, with a thicker active layer and more developed thermokarst features, was associated with increased abundance of graminoids and Sphagnum mosses but decreased plant species richness and ericoid abundance, as well as a substantial increase in methane emissions. Early thaw, characterized by active layer thickening without thermokarst development, coincided with decreased ericoid cover and plant species richness and an increase in CH4 emissions. Our findings suggest that early stages of thaw, prior to the formation of thermokarst features, are associated with distinct vegetation and soil moisture changes that lead to abrupt increases in methane emissions, which then are perpetuated through ground surface subsidence and collapse scar bog formation. Current modeling of permafrost peatlands will underestimate carbon emissions from thawing permafrost unless these linkages between plant community, nonlinear active layer dynamics, and carbon fluxes of emerging thaw features are integrated into modeling frameworks.

Global warming has led to permafrost thawing in mid-latitude alpine regions, resulting in greater availability of carbon (C) and nutrients in soils. However, how these changes will impact the functional genetic potential of permafrost soil microbiomes, and subsequently, how they will influence the microbially mediated feedback of mountain soils under climate change remains unknown. To help answer this question, we conducted a permafrost thawing experiment on the north-facing slope near the summit of Muot da Barba Peider (2979 m above sea level) in the Swiss Alps. Specifically, we transplanted permafrost soils from a depth of 160 cm to the active-layer topsoils (0-18 cm) and incubated the soils in situ for three years. Using shotgun metagenomics, we found that transplantation significantly altered the gene structure of the permafrost microbiome, with changes occurring in the short term (< one year) and remaining stable over time. Transplanted soils exhibited an enhanced functional genetic potential, particularly for genes related to Information storage and processing, Cellular processes and signaling and Metabolism functions, which suggests increased cellular processes and metabolism. Carbohydrate-active enzymes involved in the degradation of both labile (such as starch) and recalcitrant (such as lignin) C substrates were enriched in transplanted soils, indicating an enhanced C-degradation potential. Nitrogen (N)-cycling genes related to the degradation and synthesis of N compounds, denitrification, assimilation and dissimilatory nitrate reduction were overrepresented in the transplanted soil, pointing to enhanced N assimilation and transformation potential. Our study elucidates how the permafrost microbiome may functionally respond to warming in the European Alps. This research complements observations from Tibetan and Arctic regions, improving our understanding of functional changes in thawing permafrost globally.

Permafrost thaw can drastically alter dissolved organic matter (DOM) composition within fluvial networks, and simultaneously affect the microbial communities that degrade DOM. However, it is unclear how coupled thaw-induced change in DOM and microbes might affect microbial decomposition of permafrost-origin DOM (biodegradation), and therefore possible mineralization to carbon dioxide. Here, we use a series of incubations to explore how biodegradation varies with DOM and microbe source, and how microbial community composition changes following incubation with thaw-origin DOM. We undertake this work using leachates from different stratigraphic units across a series of retrogressive thaw slumps on the Peel Plateau, Canada, and microbial communities from upstream of, and draining, slumps. DOM composition and biodegradation varied by stratigraphic unit and across sites that were only tens of kilometers apart, but situated along different recessional fronts of the Laurentide Ice Sheet. Permafrost leachates from paleo-active layers were generally more biolabile than leachates from deeper, unmodified tills, and both were more labile than active layer leachates. Biodegradation also tended to be slightly greater for incubations inoculated with microbes from unimpacted stream water. These results emphasize that permafrost thaw-derived DOM composition and biolability will vary across stratigraphic, landscape, and regional scales, and that the composition of the recipient microbial community may play a role in determining immediate DOM fate.

Global warming accelerates permafrost degradation, compromising the reliability of critical infrastructure relied upon by over five million people daily. Additionally, permafrost thaw releases substantial methane emissions due to the thawing of swamps, further amplifying global warming and climate change and thus posing a significant threat to more than eight billion people worldwide. To mitigate this growing risk, policymakers and stakeholders need accurate predictions of permafrost thaw progression. Comprehensive physics-based permafrost models often require complex, location-specific fine-tuning, making them impractical for widespread use. Although simpler models with fewer input parameters offer convenience, they generally lack accuracy. Purely data-driven models also face limitations due to the spatial and temporal sparsity of observational data. This work develops a physics-informed machine learning framework to predict permafrost thaw rates. By integrating a physics-based model into machine learning, the framework significantly enhances the feature set, enabling models to train on higher-quality data. This approach improves permafrost thaw rate predictions, supporting more reliable decision-making for construction and infrastructure maintenance in permafrost-vulnerable regions, with a forecast horizon spanning several decades.