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.

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.

Thaw hazards in high-latitude and glaciated regions are becoming increasingly frequent because of global climate warming and human activities, posing significant threats to infrastructure stability and environmental sustainability. However, despite these risks, comprehensive investigations of thaw-hazard susceptibility in permafrost regions remain limited. Here, this gap is addressed by a systematic and long-term investigation of thaw hazards in China's Qinghai Province as a representative permafrost area. A detailed inventory of 534 thawhazard sites was developed based on remote sensing, field verification, and surveys by a UAV, providing critical data for susceptibility analysis. Eleven environmental factors influencing thaw hazards were identified and analyzed using information gain and Shapley additive explanation. By using the random forest model, a susceptibility map was generated, categorizing the study area into five susceptibility classes: very low, low, moderate, high, and very high. The key influencing factors include precipitation, permafrost type, temperature change rate, and human activity. The results reveal that 17.5 % of the permafrost region within the study area is classified as high to very high susceptibility, concentrated primarily near critical infrastructure such as the Qinghai-Tibet Railway, potentially posing significant risks to its structural stability. The random forest model shows robust predictive capability, achieving an accuracy of 0.906 and an area under the receiver operating characteristic curve of 0.965. These findings underscore the critical role of advanced modeling in understanding the spatial distribution and drivers of thaw hazards, offering actionable insights for hazard mitigation and infrastructure protection in permafrost regions under a changing climate.

Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere. This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau, China, as well as in the related ecological and hydrological processes. However, the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques. Thus, this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected (GRD) data, the polarization decomposition parameters of Sentinel-1A single-look complex (SLC) data, the normalized difference vegetation index (NDVI) based on Sentinel-2B data, and the topographic factors based on digital elevation model (DEM) data. By combining these parameters with a machine learning model, we established a feature selection rule. A cumulative importance threshold was derived for feature variables, and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination (R2) and the unbiased root mean square error (ubRMSE). The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion, and the SHapley Additive exPlanations (SHAP) method was used to analyze the importance of these variables. The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion. Compared to the unfiltered model, the optimal feature combination led to a 0.09 increase in R2 and a 0.7% reduction in ubRMSE. Ultimately, the optimized model achieved a R2 of 0.87 and an ubRMSE of 5.6%. Analysis revealed that soil particle size had significant impact on soil water retention capacity. The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable, demonstrating a significant positive correlation. Moreover, the microtopographical features of hummocks interfered with soil moisture estimation, indicating that such terrain effects warrant increased attention in future studies within the permafrost regions. The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau, but also exhibits high computational efficiency (with a relative time reduction of 18.5%), striking an excellent balance between accuracy and efficiency. This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data, offering critical insights for ecological conservation, water resource management, and climate change adaptation on the Qinghai-Xizang Plateau.

Floods are devastating natural disasters causing significant damage worldwide, especially in southern Latin America, where recurrent river floods lead to severe impacts. This study proposes an innovative flood modelling approach using a naive Bayes classifier to simulate flood extents at a regional scale, incorporating spatial and temporal variability. Using 12 features, including topography, soil properties, precipitation and discharge, the model was trained with multiple flood events, avoiding sampling limitations and evaluating optimal pre-processing strategies for continuous data. The predictive capacity resulted in high performance metrics, with temporal validation accuracy (AC) up to 0.98 and a critical success index (CSI) of 0.58, and spatial validation achieved an AC up to 0.97 and CSI of 0.56, outperforming the hydrodynamic model by 65%. A reduced model with significant features improved computational efficiency and achieved a CSI exceeding 0.60. This practical tool supports flood risk management and enhances resilience in vulnerable regions.

Permafrost is one of the crucial components of the cryosphere, covering about 25% of the global continental area. The active layer thickness (ALT), as the main site for heat and water exchange between permafrost and the external atmosphere, its changes significantly impact the carbon cycle, hydrological processes, ecosystems, and the safety of engineering structures in cold regions. This study constructs a Stefan CatBoost-ET (SCE) model through machine learning and Blending integration, leveraging multi-source remote sensing data, the Stefan equation, and measured ALT data to focus on the ALT in the Qinghai-Tibet Plateau (QTP). Additionally, the SCE model was verified via ten-fold cross-validation (MAE: 20.713 cm, RMSE: 32.680 cm, R2: 0.873, and MAPE: 0.104), and its inversion of QTP's ALT data from 1958 to 2022 revealed 1998 as a key turning point with a slow growth rate of 0.25 cm/a before 1998 and a significantly increased rate of 1.26 cm/a afterward. Finally, based on multiple model input factor analysis methods (SHAP, Pearson correlation, and Random Forest Importance), the study analyzed the ranking of key factors influencing ALT changes. Meanwhile, the importance of Stefan equation results in SCE model is verified. The research results of this paper have positive implications for eco-hydrology in the QTP region, and also provide valuable references for simulating the ALT of permafrost.

Soil organic carbon (SOC) in the active layer (0-2 m) of the Tibetan Plateau (TP) permafrost region is sensitive to climate change, with significant implications for the global carbon cycle. Environmental factors-including parent material, climate, vegetation, topography, soil, and human activities-inevitably drive SOC variations. However, vegetation and climate are likely the two most influential factors impacting SOC variations. To test this hypothesis, we conducted experiments using 31 environmental variables combined with the recursive feature elimination (RFE) algorithm. These experiments showed that RFE retained all vegetation variables [Land cover types (LCT), normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP)] as well as two climate variables [Moisture index (MI) and drought index (DI)], supporting our hypothesis. We then analyzed the relationship between SOC and the retained vegetation and climate variables using random forest (RF), Shapley additive explanations (SHAP), and GeoDetector models to quantify the independent and interactive drivers of SOC distribution and to identify the optimal conditions for SOC accumulation. The RF model explained 68% and 42% of the spatial variability in SOC at depths of 0-1 m and 1-2 m, respectively, with SOC stocks higher in the southeast and lower in the northwest. Additionally, SOC stock at 0-1 m was significantly higher (p 0.05). Spearman correlation coefficients results indicated that NDVI, LAI, GPP, and MI had highly significant positive correlations with SOC (p < 0.01), whereas DI had a highly significant negative correlation with SOC (p < 0.01). SHAP analysis revealed environmental thresholds for SOC variations, with notable shifts at NDVI (0.40), LAI (7), GPP (250 g C m(-)(2) year(-)(1)), MI (0.40), and DI (0.50). The spatial distribution of these thresholds aligns with the 400 mm equivalent precipitation line. Additionally, GeoDetector results emphasized that interactions between climate and vegetation factors enhance the explanatory power of individual variables on SOC variations. The swamp meadow type, with an NDVI range of 0.73-0.84, LAI range of 11.06-15.94, and MI range of 0.46-0.56, was identified as the most favorable environment for SOC accumulation. These findings are essential for balancing vegetation and climate conditions to sustain SOC levels and mitigate climate change-driven carbon release.

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0 degrees C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation-factors associated with energy balance-were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

Moisture accumulation within road pavements, particularly in unbound granular materials with or without thin sprayed seals, presents significant challenges in high-rainfall regions such as Queensland. This infiltration often leads to various forms of pavement distress, eventually causing irreversible damage to the pavement structure. The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation, air temperature, and relative humidity. This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data. Consequently, there is an increasing demand for advanced, technology-driven methodologies to predict moisture variations based on climatic inputs. Addressing this gap, the present study employs five traditional machine learning (ML) algorithms, K-nearest neighbors (KNN), regression trees, random forest, support vector machines (SVMs), and gaussian process regression (GPR), to forecast moisture levels within pavement layers over time, with varying algorithm complexities. Using data collected from an instrumented road in Brisbane, Australia, which includes pavement moisture and climatic factors, the study develops predictive models to forecast moisture content at future time steps. The approach incorporates current moisture content, rather than averaged values, along with seasonality (both daily and annual), and key climatic factors to predict next step moisture. Model performance is evaluated using R2, MSE, RMSE, and MAPE metrics. Results show that ML algorithms can reliably predict long-term moisture variations in pavements, provided optimal hyperparameters are selected for each algorithm. The best-performing algorithms include KNN (the number of neighbours equals to 15), medium regression tree, medium random forest, coarse SVM, and simple GPR, with medium random forest outperforming the others. The study also identifies the optimal hyperparameter combinations for each algorithm, offering significant advancements in moisture prediction tools for pavement technology.