The Tibetan Plateau (TP), often referred to as the 'Asian Water Tower', plays a critical role in regulating the hydrological cycle and influencing global climate patterns. Its unique topography and climatic conditions result in pronounced seasonal freeze-thaw (FT) dynamics of the land surface, which are critical for understanding permafrost ecosystem responses to climate change. However, existing studies on FT dynamics over the TP are limited by either short observational periods or deficiency in accuracy, failing to capture the long-term FT processes comprehensively. This study presents a novel satellite-based approach for monitoring the FT dynamics over the TP from 1979 to 2022, utilizing passive microwave observations. We developed a new algorithm that integrates discriminant function algorithm (DFA) with a seasonal threshold algorithm (STA), employing the freeze-thaw index (FTI) as the classification variable to determine optimal FT thresholds. The strong performance of the algorithm was confirmed by in-situ validation, with an overall accuracy of 91.46%, a Kappa coefficient of 0.83, and an F1-score of 0.92, outperforming other remote sensing-derived FT products such as SMAP (OA = 89.44%, Kappa = 0.79, F1 = 0.89). Results reveal significant changes in surface freeze-thaw dynamics over the past four decades. Between 1988-2022, frozen days exhibited a significant decreasing trend of -0.19 daysyear(-)(1), primarily attributed to the delayed freeze onset (0.19 daysyear(-)(1)), while thaw onset showed no significant trend. Spatially, permafrost regions experienced a more pronounced decrease in frozen days and earlier thaw onset compared to seasonally frozen regions. Moreover, marked interannual trend differences in FT processes were observed across elevation gradients, with higher elevations showing more negative trends in frozen days and thaw onset. This study provides a reliable and up-to-date analysis of surface FT process changes over the TP, informed by long-term satellite-based observational perspectives. These analyses revealed marked spatial heterogeneity in surface FT dynamics across the TP region, underscoring the impacts of climate change on the cryosphere and hydrology.

This study assesses the stability of the Bei'an-Hei'he Highway (BHH), located near the southern limit of latitudinal permafrost in the Xiao Xing'anling Mountains, Northeast China, where permafrost degradation is intensifying under combined climatic and anthropogenic influences. Freeze-thaw-induced ground deformation and related periglacial hazards remain poorly quantified, limiting regional infrastructure resilience. We developed an integrated framework that fuses multi-source InSAR (ALOS, Sentinel-1, ALOS-2), unmanned aerial vehicle (UAV) photogrammetry, electrical resistivity tomography (ERT), and theoretical modeling to characterize cumulative deformation, evaluate present stability, and project future dynamics. Results reveal long-term deformation rates from -35 to +40 mm/yr within a 1-km buffer on each side of the BHH, with seasonal amplitudes up to 11 mm. Sentinel-1, with its 12-day revisit cycle, demonstrated superior capability for monitoring the Xing'an permafrost. Deformation patterns were primarily controlled by air temperature, while precipitation and the topographic wetness index enhanced spatial heterogeneity through thermo-hydrological coupling. Wavelet analysis identified a 334-day deformation cycle, lagging climate forcing by similar to 107 days due to the insulating effects of peat. Early-warning analysis classified 4.99 % of the highway length as high-risk (subsidence 10.91 mm/yr). The InSAR-based landslide prediction model achieved high accuracy (Area Under the Receiver Operating Characteristic (ROC) Curve, or AUC = 0.9486), validated through field surveys of subsidence, cracking, and slow-moving failures. The proposed 'past-present-future' framework demonstrates the potential of multi-sensor integration for permafrost monitoring and provides a transferable approach for assessing infrastructure stability in cold regions.

Here, we present the result of different models for active layer thickness (ALT) in an area of the Italian Central Alps where a few information about the ALT is present. Looking at a particular warm year (2018), we improved PERMACLIM, a model used to calculate the Ground Surface Temperature (GST) and applied two different versions of Stefan's equation to model the ALT. PERMACLIM was updated refining the temporal basis (daily respect the monthly means) of the air temperature and the snow cover. PERMACLIM was updated also to minimize the bias of the snow cover in summer months using the PlanetScope images. Moreover, the contribution of the solar radiation was added to the air temperature to improve the summer GST. The modelled GST showed a good calibration and, among the two versions of Stefan's equation, the first (ALT1) indicates a maximum active layer thickness of 7.5 m and showed a better accuracy with R2 of 0.93 and RMSE of 0.32 m. The model underlined also the importance of better definition of the thermal conductivity of the ground that can strongly influence the ALT.

Ground ice, cryostratigraphical and sediment analyses have been done on samples from 16 boreholes covering the different landforms in the lower part of the valley Longyeardalen, where the largest settlement in Svalbard, Longyearbyen, is located. This allows the production of the first ever top 1 m permafrost ice content map showing the spatial distribution of ground ice (excess ice content) for the Longyearbyen area based on the collected ground ice data and the quaternary geology map of the valley. The valley was infilled since deglaciation with up to 45 m of mainly alluvial sediment and marine mud, whereas colluvial and till deposits with thicknesses from less than 1 m to more than 7 m are dominating the hillsides surrounding the valley. Rock glaciers and ice cored moraines are the landforms with the highest ice content, with assumed over 20% excess ice in the top metre of permafrost. Till and solifluction material has a medium ice content with 10%-20% excess ice content, whereas colluvial deposits have a low ice content with 5%-10% excess ice content. These landforms all have an active layer thickness between 1.6 and 2.2 m. Alluvial deposits in the valley floor has the lowest ice content with 0%-2% excess ice content. Pore ice, suspended ice and reticulate cryostructures dominates the ground ice types, with layered, lenticular and porphyritic cryostructures also present. Marine sediments are widespread and only found in the lower parts of the valley beneath the marine limit. These findings are important to understand and to be prepared for increased landslide risk that is expected due climate warming thawing the top of permafrost and bringing more rainfall in the near future.

The changing Arctic climate is affecting groundwater flow and storage in supra-permafrost aquifers due to groundwater recharge changes and thaw-driven alterations to aquifer properties and connectivity. Changes to shallow subsurface hydrological processes can drive extensive ecological and biogeochemical changes in addition to potential surface hydrologic regime shifts. This study uses a pan-Arctic geospatial approach to classify shallow, unconfined Arctic aquifers (supra-permafrost aquifers) as topography-limited (TL) (characterized by low permeability, wet climate, and/or low slopes) or recharge-limited (high permeability, dry climate and/or steep slopes) based on the water table ratio framework. Under current conditions, the continuous and discontinuous permafrost zones were determined to be predominantly (65%) TL, with an average net decrease of 5.6% by the year 2100 under RCP2.6 and RCP8.5 conditions. This apparent stability masks local-scale heterogeneity, with change in aquifer function projected at dispersed locations throughout the Arctic, and in clustered hot spots in Siberia and the central Canadian Arctic. Coastal zones around the Arctic are more TL (94%) compared to the overall average, leaving them especially vulnerable to ocean-driven impacts on groundwater such as subsurface seawater intrusion or groundwater flooding. Arctic coasts in Siberia and eastern Canada are also particularly susceptible to water table rise due to high relative sea-level rise which may exceed the active layer thickness and result in substantive changes to saturation. Classification results are sensitive to input values, particularly hydraulic conductivity, which remains a source of uncertainty in the analysis. Despite the sparsity of Arctic data, the available open-source datasets provide valuable insight into broad spatiotemporal trends in aquifer function and highlight particularly vulnerable regions and geographic areas where uncertainty should drive additional data collection and study. These results provide new context for conceptualizing changes to shallow Arctic aquifers as the climate evolves in the 21st century.

Aim Alaska's boreal forest is experiencing increasingly severe fires, droughts, and pest attacks that may destabilize carbon sequestration. Our aim was to understand boreal forest resilience to changing wildfire regimes using remote-sensed datasets validated with ground-truthing (GT).Location Five recently burned boreal forest sites (2010-2019) near Fairbanks, Alaska.Methods We used four AVIRIS-NG hyperspectral image datasets (425 spectral bands at 5-nm intervals; 3.5 x 43 km average swath) imaged by NASA in 2017-2018 during the Arctic-Boreal Vulnerability Experiment (ABoVE). Spectral analysis included fire fuel loads and random forest (RF) models constructed from key bands to describe common pre- and postburned vegetation classes. Models were validated with 89 GT plots inside the AVIRIS scenes. GT included tree stem densities, understory cover, soil characteristics, radial growth of 51 spruce trees from cores, and visual damage assays of 668 conifers and deciduous trees.Results Spectral evidence of high fuel loads in 2017 pre-dated a 2019 wildfire. Post-GT local models described vegetation more accurately than pre-GT, but accuracy decreased when spectral rulesets were broadened to increase overall classification. Soil temperature, basal area, slope, elevation, and tree density varied widely; thaw depth, soil moisture, moss cover, and canopy height varied mainly by vegetation class. Invasive species and thermokarst were insignificant. Deciduous seedlings were abundant in postburned sites; however, conifer seedling densities were similar to unburned forest. Upland spruce radial growth showed earlier drought sensitivity than lowland spruce.Conclusion Spectral analysis revealed fire vulnerability in some areas; however, local and temporal spectral variation presented challenges to accurately classify vegetation in AVIRIS scenes. GT suggests that recovering forests near Fairbanks may lack sufficient conifer recruitment to replace existing stands. Sites with stable seasonal thaw may offset drought stress under global warming.

Permafrost degradation under climate warming plays a crucial role in hydrological and ecological processes, including the regional water cycle and terrestrial carbon balance. The Tibetan Plateau (TP), which contains the largest expanse of high-altitude permafrost globally, remains understudied in terms of how permafrost degradation affects surface water resources and regional carbon dynamics. Using permafrost simulation models and quantitative analysis, we assess the spatiotemporal impacts of permafrost degradation on surface water resources and carbon dynamics. In the inner endorheic regions of the TP, ground ice meltwater contributed 12.6% of the total lake volume increase from 2000 to 2020, accelerating lake expansion and affecting nearby infrastructure and ecosystems. Cryospheric meltwater accounted for 4.6% of total runoff in the source areas of the Yangtze, Yellow, Lancang, Yarlung Zangbo, and Nujiang Rivers in 2002-2018. This cryospheric meltwater contribution is projected to peak in the 2030s-2040s, followed by a decline, with potentially profound implications for downstream water availability. From 2000 to 2020, carbon sequestration of alpine grassland in permafrost regions is 1.05-1.29 Tg C a-1 in 2000-2020. This estimate is underestimated by approximately 35.5% to 48.1% without considering the impact of permafrost degradation. Top-down thawing of permafrost from 2002 to 2050 is projected to release 129.39 +/- 21.02 Tg C a-1 of thawed soil organic carbon (SOC), with 20.82 +/- 3.06 Tg C a-1 decomposed annually. Additionally, permafrost collapse and thermokarst lake are estimated to reduce ecosystem carbon sinks by 0.41 (0.29-0.52) Tg C a-1 in 2020. (c) 2025 The Authors. Published by Elsevier B.V. and Science China Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Suprapermafrost groundwater (SPG) plays a critical role in hydrological and ecological functioning of permafrost regions, yet its spatiotemporal dynamics and controlling mechanisms remain poorly understood on the Qinghai-Tibet Plateau (QTP). Here, we integrated in situ observations, geophysical surveys, and machine learning (ML) models (including XGBoost, LightGBM, and RandomForest) to investigate the seasonal variation, drivers, and projections of SPG dynamics in alpine meadow (AM) and alpine wet meadow (AWM) ecosystems. Results showed that SPG tables ranged from -1.1 to -0.1 m in AM and from -1.3 to -0.2 m in AWM during the warm season. SPG fluctuations were primarily driven by thaw depth (TD) and rainfall infiltration and exhibited similar seasonal patterns across both ecosystems. A greater TD was associated with a deeper SPG table, as deeper thawing expanded the unsaturated zone and enhanced vertical drainage, indicating an exponential relationship between TD and SPG table position, and a linear relationship with aquifer thickness. In contrast, rainfall infiltration increased shallow soil moisture and elevated SPG tables, with responses influenced by rainfall intensity, duration, and infiltration pathways. Spatial heterogeneity in SPG distribution was further shaped by vegetation structure and microtopographic variation. Furthermore, ML models projected that mean summer SPG table depths in the 2090s would increase by 0.06 m under SSP126 and 0.64 m under SSP585 in AWM ecosystems, and by 0.37 m under SSP126 and 0.87 m under SSP585 in AM ecosystems. These findings provide new insights into how climate warming affects hydrological processes in permafrost regions of the QTP.

In this study, the effect of near-field and far-field ground motions on the seismic response of the soil pile system is investigated. The forward directivity effect, which includes a large velocity pulse at the beginning of the velocity time history of the ground motion is the most damaging phenomenon observed in near-field ground motions. To investigate the effect of near-field and far-field ground motions on the seismic response of a soil-pile system, a three-dimensional model consisting of the two-layer soil, liquefiable sand layer over dense sand, and the pile is utilized. Modeling is conducted in FLAC 3D software. The P2P Sand constitutive model is selected for sandy soil. Three fault-normal near-field and three far-field ground motion records were applied to the model. The numerical results show that near field velocity pulses have a considerable effect on the system behavior and sudden huge displacement demands were observed. Also, during the near-field ground motions, the exceeded pore water pressure coefficient (Ru) increases so that liquefaction occurs in the upper loose sand layer. Due to the pulse-like ground motions, a pulse-like relative displacement is created in response to the pile. Meanwhile the relative displacement response of the pile is entirely different due to the energy distribution during the far-field ground motions.

Buried pipelines are essential for the safe and efficient transportation of energy products such as oil, gas, and various chemical fluids. However, these pipelines are highly vulnerable to ground movements caused by geohazards such as seismic faults, landslide, liquefaction-induced lateral spreading, and soil creep, which can result in potential pipeline failures such as leaks or explosions. Response prediction of buried pipelines under such movements is critical for ensuring structural integrity, mitigating environmental risks, and avoiding costly disruptions. As such, this study adopts a Physics-Informed Neural Networks (PINNs) approach, integrated with a transfer learning technique, to predict structural response (e.g., strain) of both unreinforced and reinforced steel pipes subjected to Permanent Ground Displacement (PGD). The PINN method offers a meshless, simulation-free alternative to traditional numerical methods such as Finite Element Method (FEM) and Finite Difference Method (FDM), while eliminating the need for training data, unlike conventional machine learning approaches. The analyses can provide useful information for in-service pipe integrity assessment and reinforcement, if needed. The accuracy of the predicted results is verified against Finite Element (FE) and Finite Difference (FD) methods, showcasing the capability of PINNs in accurately predicting displacement and strain fields in pipelines under geohazard-induced ground movement.