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.

Near-surface temperature and moisture are key boundary conditions for simulating permafrost distribution, projecting its response to climate change, and evaluating the surface energy balance in alpine regions. However, in desertified permafrost zones of the Qinghai-Tibet Plateau (QTP), the observations remain sparse, and reported trends vary considerably among sites. This lack of consistent evidence limits the ability to represent microenvironmental processes in models and to predict their influence on permafrost stability. From September 2021 to August 2024, we conducted continuous observations at a desertified permafrost site on the central QTP, covering the vertical range from 150 cm above to 100 cm below the ground surface (boundary layer). Measurements included air and ground temperature, air humidity, soil moisture, wind speed, and net radiation. Results showed that the mean annual air temperature increased with decreasing height at a gradient of approximately 0.42 degrees C/m, while mean annual air humidity remained nearly constant at 56.8 +/- 1.1 % (150-0 cm). In the near-surface soil layer (0 similar to -10 cm), temperature rose by 3.6 +/- 0.1 degrees C and moisture decreased by 34.0 +/- 2.7 %. The mean annual ground temperature increased with depth at a rate of about 0.55 degrees C/m, whereas soil moisture decreased between -20 and -60 cm (52.86 %/m) and increased between -60 and -100 cm (56.30 %/m). Seasonal patterns showed marked difference: in the freezing season, the calculated total temperature increment within the boundary layer (1.91 degrees C) was 61 % lower than the observed value (4.88 degrees C), while in the thawing season, it was 58 % higher (4.38 degrees C > 2.77 degrees C). These results reveal strong vertical gradients and seasonal contrasts in thermal and moisture regimes, emphasizing the need to integrate coupled temperature-moisture processes into boundary layer parameterizations for cold-region environments. Improved representations can enhance permafrost modeling and inform infrastructure design in regions experiencing both warming and desertification.

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.

Infrastructure in northern regions is increasingly threatened by climate change, mainly due to permafrost thaw. Prediction of permafrost stability is essential for assessing the long-term stability of such infrastructure. A key aspect of geotechnical problems subject to climate change is addressing the surface energy balance (SEB). In this study, we evaluated three methodologies for applying surface boundary conditions in longterm thermal geotechnical analyses, including SEB heat flux, n-factors, and machine learning (ML) models by using ERA5-Land climate reanalysis data until 2100. We aimed to determine the most effective approach for accurately predicting ground surface temperatures for climate-resilient design of northern infrastructure. The evaluation results indicated that the ML-based approach outperformed both the SEB heat flux and n-factors methods, demonstrating significantly lower prediction errors. The feasibility of long-term thermal analysis of geotechnical problems using ML-predicted ground surface temperatures was then demonstrated through a permafrost case study in the community of Salluit in northern Canada, for which the thickness of the active layer and talik were calculated under moderate and extreme climate scenarios by the end of the 21st century. Finally, we discussed the application and limitations of surface boundary condition methodologies, such as the limited applicability of the n-factors in long-term analysis and the sensitivity of the SEB heat flux to inputs and thermal imbalance. The findings highlight the importance of selecting suitable boundary condition methodologies in enhancing the reliability of thermal geotechnical analyses 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.

Soil moisture is a vital parameter for a variety of applications including hydrological modelling and climate change studies, particularly in permafrost regions where freeze-thaw processes and complex terrain pose significant monitoring challenges. This study evaluates the accuracy of seven surface soil moisture (SSM) products (SMOS-IC, ESA CCI, AMSR2 LPRM, SMAP-L3, SMAP-L4, ERA5-Land, GLDAS-Noah) and three root-zone soil moisture (RZSM) products (SMAP-L4, ERA5-Land, GLDAS-Noah) using in situ observations from 19 stations in the permafrost region of the Heihe River Basin, China, from 2012 to 2020. Focusing on the thawing season (July-October), the analysis employs statistical metrics including Pearson correlation coefficient (R), unbiased root mean square error (ubRMSE), bias, and slope. Results indicate that SMAP-L3 and SMAP-L4 exhibit the highest SSM accuracy (R = 0.24 and 0.23, respectively) with low ubRMSE (0.037-0.038), while ERA5-Land shows the best RZSM correlation (R = 0.43) but may indicate the presence of systematic biases, nonlinear responses, or limitations in dynamic range, among other issues (slope = 0.01). Environmental factors such as precipitation, land surface temperature, and normalised difference vegetation index significantly influence accuracy. Spatial variability and scale mismatches highlight the need for improved land surface models and data assimilation. This study provides critical insights for selecting and refining soil moisture products to enhance hydrological and climate research in permafrost regions.

Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Correlations between the mechanical properties and surface scratch resistance of polylactic acid (PLA) are investigated via tensile and scratch tests on samples after degradation in soil for various times. The results show that the tensile yield strength of PLA is inversely proportional to the natural logarithm of the degradation time, and the scratch resistance and fracture toughness of PLA and the temperature rise near the indenter all increase and then decrease. The surface crystallinity of PLA also increases and then decreases, indicating that it and the scratch resistance are closely related. These findings provide useful information about how PLA behaves under degradation conditions. (c) 2025 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/).

Study region: The Tibetan Plateau (TP), China, contains the world's largest permafrost area outside the Polar Regions. Study focus: This study investigates the precipitation-induced advective heat flux (E-Pre), which represents the energy transfer resulting from the temperature difference between rainfall and soil. Observational data from three permafrost monitoring sites (Qumalai, Xidatan, and Tanggula) were combined with simulations from the Community Land Model version 5.0 (CLM5.0) to quantify E-Pre precipitation infiltration depth, and the probability of infiltration reaching the frozen soil layer. The analysis further examines how precipitation amount, soil texture, soil moisture, and freeze-thaw state jointly control infiltration processes and influence the soil thermal regime. New hydrological insights for the region: Infiltration depth varies with initial soil moisture and precipitation duration, from shallow retention to deep percolation. E-Pre is generally negative, with maximum cooling of-84.14 W m(-2) at QML,-73.24 W m(-2) at XDT, and -56.63 W m(-2) at TGL, but becomes positive during prolonged summer rainfall, reaching 45.43 W m(-2) at QML. Diurnal soil temperature variations shift E-Pre from cooling by day to reduced cooling or warming at night. Across the TP, mean infiltration depth is similar to 5 cm, higher in southeastern Tibet, with a regional mean E-Pre of-0.08 W m(-2). Warming effects are concentrated in the southeastern and central TP, while cooling dominates the arid west and high-elevation north.

Permafrost is undergoing widespread degradation affected by climate change and anthropogenic factors, leading to seasonal freezing and thawing exhibiting interannual, and fluctuating differences, thereby impacting the stability of local hydrological processes, ecosystems, and infrastructure. To capture this seasonal deformation, scholars have proposed various InSAR permafrost deformation models. However, due to spatial-temporal filtering smoothing high-frequency deformation and the presence of approximate assumptions in permafrost models, such differences are often difficult to accurately capture. Therefore, this paper applies an InSAR permafrost monitoring method based on moving average models and annual variations to detect freezing and thawing deformation in the Russian Novaya Zemlya region from 2017 to 2021 using Sentinel-1 data. Most of the study area's deformation rates remained between 10 and 10 mm/yr, while in key oil extraction areas, they reached -20 mm/yr. Seasonal deformation amplitudes were relatively stable in urban areas, but reached 90 mm in regions with extensive development of thermokarst lakes, showing a significant increasing trend. To validate the accuracy of the new method in capturing seasonal deformations, we used seasonal deformations obtained from different methods to retrieval the Active Layer Thickness (ALT), and compared them with field ALT measurement data. The results showed that the new method had a smaller RMSE and improved accuracy by 5% and 30% in two different ALT observation areas, respectively, compared to previous methods. Additionally, by combining the spatial characteristics of seasonal deformation amplitudes and ALT, we analyzed the impact of impermeable surfaces, confirming that human-induced surface hardening alters the feedback mechanism of perennial frozen soil to climate.