Climate change is reshaping the risk landscape for natural gas pipelines, with landslides emerging as a major driver of technological accidents triggered by natural hazards (Natech events). Conventional Natech risk models rarely incorporate climate-sensitive parameters such as groundwater levels and soil moisture, limiting their capacity to capture evolving threats. This study develops a probabilistic model that explicitly links climate-driven landslide susceptibility to pipeline vulnerability, providing a quantitative basis for assessing pipeline failure probability under different emission projection scenarios. Using Monte Carlo simulations across five regions in China, the results show that under high-emission pathways (SSP5-8.5), pipeline failure probability in summer increases dramatically. For example, from 0.320 to 0.943 in Xinjiang, 0.112 to 0.220 in Sichuan, and 0.087 to 0.188 in Hainan. In cold regions, winter failure probability more than doubles, rising from 0.206 to 0.501 in Heilongjiang and from 0.235 to 0.488 in Beijing. These shifts reveal an overall increase in risk, intensification of seasonal contrasts, and, in some areas, a reconfiguration of high-risk periods. Sensitivity analysis highlights groundwater levels and soil moisture as the dominant drivers, with regional differences shaped by precipitation regimes, permafrost thaw, and typhoon impacts. Building on these insights, this study proposes an AI-based condition-monitoring framework that integrates real-time climate and geotechnical data to support adaptive early warning and safety management.

Study region The eastern Qilian Mountains, located on the northeastern margin of the Tibetan Plateau, span elevations from similar to 2600 to 5300 m around the Menyuan area. It is characterized by cold, alpine climatic conditions and hosts both permafrost and seasonally frozen ground, which are highly sensitive to climate change and have important hydrological and ecological implications. Study focus This study develops an enhanced multi-temporal InSAR framework to monitor frozen ground dynamics in the eastern Qilian Mountains using Sentinel-1 data from 2014 to 2024, with a particular focus on the permafrost-seasonally frozen ground transition zone around Menyuan. It addresses key challenges in permafrost monitoring by implementing a co-seismic deformation separation model, a Common Scene Stack (CSS)-based atmospheric correction method, and a time-series decomposition model with linearly varying annual amplitude to capture evolving freeze-thaw behavior under climate change. New hydrological insights for the region The results reveal clear hydrological and thermal contrasts between permafrost and seasonally frozen ground. Seasonally frozen ground exhibits higher seasonal deformation amplitudes, more rapid interannual changes, and shorter thermal response lags compared to permafrost, reflecting its more dynamic hydrothermal regime. The estimated freeze-thaw layer thickness ranges from 0 to 5.3 m, with thinning trends in seasonally frozen ground at lower elevations and slight thickening of active layers in high-elevation permafrost. These findings highlight ongoing frozen ground degradation and provide new insights into subsurface water-energy interactions and long-term cryospheric responses to climate warming in alpine environments.

The thermal stability of permafrost, a foundation for engineering infrastructure in cold regions, is increasingly threatened by the dual stressors of climate change and anthropogenic disturbance. This study investigates the dynamics of the crushed rock revetted embankment at the Kunlun Mountain Section of the Qinghai-Tibet Railway, systematically investigating the coupled impacts of climate warming and engineering activities on permafrost thermal stability using borehole temperature monitoring data (2008-2024) and climatic parameter analysis. Results show that under climate-driven effects, the study area experienced an air temperature increase of 0.2 degrees C per decade over the 2015-2024. Concurrently, the mean annual air thawing degree-days (TDD) rose by 13.8 degrees C center dot d/a, leading to active-layer thickening at a rate of 3.8 cm center dot a- 1at natural ground sites. From 2008 to 2024, the active layer had thickened by 0.7-0.8 m. At the embankment toe (BH 5), the active-layer thickening rate (3.3 cm center dot a- 1) was 25 % lower than that at the natural ground borehole (3.8 cm center dot a- 1); correspondingly, the underlying permafrost temperature increase rate at the toe (0.3 degrees C per decade) was lower than that at the natural borehole (0.5-0.6 degrees C per decade). Permafrost warming rates decreased with depth. Shallow layers (above -2 m) were significantly influenced by climate, with warming rates of 0.3-0.6 degrees C per decade. In contrast, deep layers (below -10 m) showed warming rates converging with the background atmospheric temperature trend (0.2 degrees C per decade). Thermal regime disturbance was most pronounced at horizontal distances of 3.0-5.0 m from the embankment. Nevertheless, the crushed-rock revetment maintained a permafrost table 0.6 m shallower than that of natural ground, confirming its thermal diode effect (facilitating convective cooling in winter), which partially offset climate warming impacts. This study provides critical empirical data and validates the cooling mechanism of crushed-rock revetment, which is essential for predicting the long-term thermal stability and informing adaptive maintenance strategies for railway infrastructure in warming permafrost regions.

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.

Ecosystem carbon use efficiency (CUE) is a key indicator of an ecosystem's capacity to function as a carbon sink. While previous studies have predominantly focused on how climate and resource availability affect CUE through physiological processes during the growing season, the role of canopy structure in regulating carbon and energy exchange, especially its interactions with winter climate processes and nitrogen use efficiency (NUE) in shaping ecosystem CUE in semi-arid grasslands, remains insufficiently understood. Here, we conducted a 5-year snow manipulation experiment in a temperate grassland to investigate the effects of deepened snow on ecosystem CUE. We measured ecosystem carbon fluxes, soil nitrogen concentration, species biomass, plants' nitrogen concentration, canopy height and cover and species composition. We found that deepened snow increased soil nitrogen availability, while the concurrent rise in soil moisture facilitated nutrient acquisition and utilization. Together, these changes supported greater biomass accumulation per unit of nitrogen uptake, thereby enhancing NUE. In addition, deepened snow favoured the dominance of C3 grasses, which generally exhibit higher NUE and greater height than C3 forbs, providing a second pathway that further elevated community-level NUE. The enhanced NUE, through both physiological efficiency and compositional shifts, promoted biomass production and facilitated the development of larger canopy volumes. Larger canopy volumes under deepened snow increased gross primary production through improved light interception, while the associated increase in autotrophic maintenance respiration was moderated by higher NUE. Besides, denser canopies reduced understorey temperatures throughout the day, particularly at night, thereby suppressing heterotrophic respiration. Ultimately, deepened snow increased ecosystem CUE by enhancing carbon uptake while limiting respiratory carbon losses. Synthesis. These findings demonstrated the crucial role of biophysical processes associated with canopy structure and NUE in regulating ecosystem CUE, which has been largely overlooked in previous studies. We also highlight the importance of winter processes in shaping carbon sequestration dynamics and their potential to modulate future grassland responses to climate change.

Against the backdrop of global warming, the increasing spatiotemporal variability in precipitation patterns has intensified the frequency and risk of dry-wet abrupt alternation (DWAA) events in semi-arid regions. This study investigates the Hailar River Basin in northern China (1980-2019) and develops the Soil Moisture Concentration Index (SMCI) using daily soil moisture (SM) data simulated by the VIC hydrological model. A high-resolution temporal framework is introduced to detect DWAA events and evaluate the impact of precipitation pattern variations on dry-wet transitions in the basin. The results indicate: (1) Annual precipitation in the basin has significantly increased (0.47 mm y(-1) in the south, P < 0.05), while precipitation intensity follows a gradient pattern, increasing in the upstream (3.65 mm d1 y1) and decreasing in the downstream (-2.34 mm y(-1)). Additionally, the number of dry days and short-duration, high-intensity precipitation events has risen; (2) Soil moisture (SM) data simulated by the VIC model effectively capture DWAA events, showing significantly higher | SMCI| values downstream than upstream (P < 0.05) and indicating more intense dry-wet transitions in the downstream region. Furthermore, 78 % of the area exhibits an increasing trend in |SMCI|(1980-2019), with dry-to-wet transition events occurring more frequently than wet-to-dry events. For instance, in 2013, the maximum coverage area reached 48 % in a single day; (3) The random forest model highlights the spatial heterogeneity of DWAA driving factors: upstream water yield is the dominant factor, whereas downstream variations are closely associated with precipitation intensity (R-2 = 0.76) and the frequency of heavy rainfall days. Permafrost degradation and land use changes further heighten hydrological sensitivity in the downstream region. This study offers a transferable methodological framework for understanding extreme hydrological events and reveals that the driving mechanisms of DWAA are spatially heterogeneous, shifting from being dominated by terrestrial factors in the headwaters to meteorological factors downstream-a finding with significant implications for water resource management in other large, heterogeneous semi-arid basins.

Thawing permafrost, driven by climate change and anthropogenic activities, presents escalating challenges for infrastructure in northern regions. Traditional design strategies focused on maintaining frozen ground are increasingly unsustainable due to accelerated warming and extreme weather events. This paper introduces a Thaw Settlement Evaluation Framework for assessing thaw settlement at regional and site-specific scales. The framework comprises (1) regional thaw settlement hazard assessments to identify zones prone to thaw settlement and (2) site-specific thaw strain estimations for quantitative settlement predictions. By consolidating established methodologies, such as thaw hazard indices and empirical thaw strain estimation tools, the framework provides adaptable workflows tailored to the specific needs and data availability of the project. Key outputs include thaw settlement hazard levels, thaw strain estimation, and stress-strain characterization, which together offer actionable insights to support infrastructure planning, design, and maintenance in permafrost regions. By consolidating resources and streamlining decision-making, the framework equips practitioners, planners, and decision-makers with practical guidance to address challenges of thawing permafrost.

Canopy reflectance (CR) models describe the transfer and interaction of radiation from the soil background to the canopy layer and play a vital role in the retrieval of biophysical variables. However, few efforts have focused on estimating soil background scattering operators, resulting in uncertainties in CR modelling, especially over sloping terrain. This study developed a canopy reflectance model for simulating CR over sloping terrain, which combines the general spectral vector (GSV) model, the PROSPECT model, and 4SAIL model coupled with topography (GSV-PROSAILT). The canopy reflectance simulated by GSV-PROSAILT was validated against two datasets: discrete anisotropic radiative transfer (DART) simulations and remote sensing observations. A comparison with DART simulations under various conditions revealed that the GSV-PROSAILT model captures terrain-induced CR distortion with high accuracy (red band: coefficient of determination $\lpar {\rm R 2} \rpar = 0.731$(R2)=0.731, root-mean-square error (RMSE) = 0.007; near infrared (NIR) band: $\rm R2 = 0.8319$R2=0.8319, RMSE = 0.0098). The results of remote sensing observation verification revealed that the GSV-PROSAILT model can be successfully used in CR modelling. These validations confirmed the performance of GSV-PROSAILT in soil and canopy reflectance modelling over sloping terrain, indicating that it can provide a potential tool for biophysical variable retrieval over mountainous areas.

Flash floods are often responsible for deaths and damage to infrastructure. The objective of this work is to create a data-driven model to understand how predisposing factors influence the spatial variation of the triggering factor (rainfall intensity) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database. We extracted the accumulated precipitation from the Copernicus database by considering two days of duration. The analysed predisposing factors for flooding were extracted considering the whole basin where each occurrence is located. These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. The median of SPI, mean elevation and the area of the basin are the top three most relevant predisposing factors interpreted by the model for defining the rainfall input for flash flooding in mainland Portugal.