Rapid landslide motions control impact area, flow velocity, deposition pattern, and, in extreme cases, are a river-blocking hazard; therefore, reliable dynamic simulations are of direct importance to engineering-geological hazard assessments. Depth-averaged models provide an efficient framework for simulating large-scale mass movements, but conventional physics-informed neural networks (PINNs) remain challenged with regard to nonlinear flows, which can limit their applicability in landslide analysis. To address these limitations, this study develops a physics-informed residual convolutional network model (PI-RCN) for depth-averaged landslide dynamics. The proposed framework combines sequential residual learning with depth-wise separable convolutions (DSCs) and incorporates physics-based residuals, mass conservation, and hard constraints to preserve physical consistency during time marching. The model is evaluated using a 1+1D frictionless dam-break benchmark, a Hong Kong landslide, and the Yigong rock avalanche. Results show that PI-RCN accurately reproduces the benchmark flow evolution with substantially fewer trainable parameters than a baseline fully connected PINN. In the Hong Kong case, the model demonstrates improved convergence stability and optimization efficiency. In the Yigong case, PI-RCN reproduces the main spatiotemporal evolution and multi-stage velocity variation of a long-runout rock avalanche. These results suggest that PI-RCN provides a useful physics-informed framework for efficient and consistent landslide dynamic simulation.

Landslide runout models are essential tools for delineating potential inundation areas, assessing risks, and designing mitigation structures. Semi-empirical landslide runout models are widely used due to their simple parameterization and high computational efficiency, but their reliance solely on DEM-derived grid units for terrain representation often limits their ability to capture landslide propagation across multiple slopes. In this study, we propose a semi-empirical LAndslide Runout Model based on Slope Units (LARMSU) to simulate runout and assess susceptibility in potentially unstable regions. By introducing a slope unit coordinate system into the traditional DEM, the mass movement in complex terrain is separated into uphill and downhill. A propagation direction controller, based on the diffusion angle and Monte Carlo simulation, along with a propagation distance controller, grounded in the energy conservation law, are designed to facilitate runout simulation. We first conducted a sensitivity analysis of key parameters to establish recommended ranges and parameter-setting suggestions for different landslide material types. The model was then validated using two landslide cases with runout paths crossing multiple slopes and compared with Flow-R, r.avaflow, and RAMMS, demonstrating its advantages. By accounting for slope transitions along the runout path, our model achieves improved performance in landslide runout simulation in terrains with pronounced slope transitions, and enables more reliable delineation of affected areas for hazard assessment.

As human socioeconomic activities expand into complex and perilous mountainous areas, engineering projects in these areas face unprecedented landslide risks, particularly long-distance linear projects such as railways and highways. However, research on landslide risk specifically tailored to linear projects in mountainous area remains limited. A novel technical framework for kinematic-based seismic landslide risk assessment to linear projects is proposed, whose key components include seismic landslide hazard, distributed kinematic-based landslide simulation, linear project vulnerability. The focus is on extracting the landslide source points under seismic action and improving the seismic landslide hazard with distributed landslide dynamic simulation. The linear projects are “discretized” into a series of line segments or points to conduct vulnerability evaluation. Taking the mountainous Western Sichuan Railway as a case study, the seismic landslide hazard was improved, a factor index system for railway project vulnerability was constructed, and the seismic landslide risk assessment for railway projects was completed. The Luding-Kangding section of the Western Sichuan Railway faces a significantly higher seismic landslide risk. The total railway length with very high and high seismic landslide risk is 1.44 km, accounting for 2.01

Two-dimensional phase unwrapping (PU) of interferometric synthetic aperture radar (InSAR) data remains difficult when steep deformation gradients and multi-source disturbances violate the Itoh condition. This study proposes FPUNet, a Fourier-enhanced encoder–decoder for joint denoising and 2-D PU, in which frequency-domain global context modeling is combined with complementary multi-scale spatial aggregation and attention-based feature refinement. Specifically, the bottleneck cascades a Fourier Mixed Residual Block (FMRB), atrous spatial pyramid pooling (ASPP), and a convolutional block attention module (CBAM) to suppress noise while preserving deformation-related fringe structures. FPUNet is trained end-to-end on realistically simulated Sentinel-1 interferograms generated from Shuttle Radar Topography Mission (SRTM) digital elevation models using a physics-informed composite loss that enforces data fidelity, gradient consistency, spectral regularization, and selective rewrapping consistency. On a synthetic benchmark of 1800 test interferograms, FPUNet achieves an RMSE of 0.79 rad, improving over a plain U-Net (1.61 rad) and producing fewer large fringe-number errors than least-squares, SNAPHU, PUNet, and DLPU. Experiments on real Sentinel-1 data over the Datong mining area and the 2022 Menyuan and Luding earthquakes further indicate improved phase closure and rewrapping consistency, particularly in high-gradient coseismic fringes, supporting FPUNet as a robust PU module for InSAR deformation monitoring.

Faults are the primary drivers of earthquakes and exert a strong control on rupture mechanisms, earthquake magnitude, and the spatial distribution of coseismic landslides (CLs). However, how CL spatial distribution patterns vary with faulting style remains poorly constrained. Here, we compiled a catalog of CLs associated with 18 global major earthquakes (MW > 6.0) within continental regions since 1900 and explored the distribution patterns of CLs associated with the three major earthquake types: oblique-slip, dip-slip, and strike-slip. Our results reveal two distinct spatial distribution patterns of CLs: a hanging-wall distribution for oblique-slip and dip-slip earthquakes and a bell-shaped distribution for strike-slip earthquakes. The orientation of CLs is closely related to fault geometry and slip type. Specifically, in oblique-slip, strike-slip, and dip-slip earthquakes, CLs predominantly develop parallel, perpendicular, or perpendicular to the fault strike, respectively. In terms of slip rake, CLs are mainly aligned perpendicular, parallel, and parallel to the fault slip direction for oblique-slip, strike-slip, and dip-slip events, respectively. Importantly, the distribution patterns of CLs encode information about ground movement during an earthquake. While Peak Ground Acceleration (PGA) serves as an indicator of ground motion intensity, a comprehensive characterization of CLs—including their size and predominant movement direction—requires consideration of both the earthquake type and the local slope conditions.

The spatial distribution of seismic landslides is influenced by a wide range of factors. Understanding the relative importance of these factor is crucial for accurately predicting seismic landslide risks. However, most recently studies on factor importance have focused on individual earthquake events, making it difficult to identify overarching patterns and differences across multiple cases. This study focuses on the eastern margin of the Qinghai-Xizang Plateau and analyzes six earthquake-induced landslide events. Using the decisiveness (DC), we quantitatively assessed the absolute importance of 14 influencing factors: seismic intensity, seismic fault distance, geologic age, non-seismic fault distance, elevation, slope, aspect, geomorphology type, average annual precipitation, river distance, soil type, vegetation type, land use type, and distance to roads. These factors are categorized into six groups: seismic, geo-tectonic, topographic-geomorphic, meteorological-hydrological, soil-vegetation, and human-activity. The results show that: overall, seismic, geo-tectonic, topographic-geomorphic, and meteorological-hydrological factors have a greater influence on seismic landslide occurence, whereas soil-vegetation and human-activity factors are less significant. Moreover, the importance of individual factors varies with earthquake magnitude: for larger-scale earthquakes seismic and geo-tectonic factors dominates; for smaller-scale earthquakes, topographic and hydrological conditions are more influential. By ensuring consistency in the landslide and influencing factor datasets and employing an absolute importance assessment approach across multiple cases, this study provides a systematic analysis of the key drivers of seismic landslides. The findings offer valuable insights for seismic landslide risk assessment and mitigation strategies.

Thousands of landslides worldwide lead to significant casualties and property damage. A numerical model is crucial for simulating the runout of possible future landslides and generating reliable hazard zone maps. Two main methods can be adopted to simulate motion: one based on the Eulerian description and the other based on the Lagrangian description. Each description offers varying adaptability to simulating landslides. In this paper, we proposed a method of coupling the depth-averaged smoothed particle hydrodynamics (SPH) method and a finite volume scheme with the van Leer splitting (FV-VLS). For the solid phase, we utilized the depth-averaged SPH method to simulate the movement process. The SPH method can track the movement of key parts. In contrast, we employed the FV-VLS based on the Eulerian description to reduce the calculation amount. In 1-D dam break simulation, both the depth-averaged SPH and FV-VLS methods yielded similar results. The 1-D two-phase dam break simulation shows that buoyancy reduces friction and lateral solid stress, increases hydraulic stress, and generates a complex motion process. The Yigong landslide simulation indicates that solid fractions will affect the motion trajectory in the 2-D simulation. A smaller solid fraction will cause the landslide to affect a larger area.

The eastern Himalayan syntaxis (EHS) region is characterized by steep topography, strong tectonic activity, and strong disaster activity, and the disaster activity has a temporal correlation with the change of seasonal climate factors. Different from other disaster-prone areas, the disasters in the EHS area are more destructive and more susceptible to changes in climate factors. However, previous studies on disaster susceptibility in the region mainly focused on geohazard samples, regional topography, and geological conditions and seldom considered the characteristics of disaster activity and the characteristics of seasonal temperature and rainfall changes, which often led to an underestimation of disaster susceptibility in this region. Therefore, in this study, SBAS-InSAR technology and the GEE (Google Earth Engine) platform were used to identify 317 geological disasters and construct the spatial–temporal motion field and spatial-temporal background information of disasters in the last 5 years. Based on the random forest and optimized evaluation data model, the disaster susceptibility in the EHS area was quantitatively evaluated. The AUC value of the model was 0.89, indicating that the results have high reliability. The results showed that the high-susceptibility areas were mainly distributed in the riverbank and high-altitude areas. The SHAP model revealed that the factors of temperature, precipitation, slope, and elevation in the EHS area had a great negative influence on the disaster. The quantitative results of the influence of seasonal temperature and precipitation on disaster susceptibility showed that the area of susceptibility in summer was significantly higher than that in winter, and the area of high and very high susceptibility increased by about 13

Major earthquakes can cause extensive landsliding that poses a major threat to both property and human lives. In addition to co-seismically triggered ground failure, the earthquake-affected region remains vulnerable to landslides due to loosened and unstable materials and structures. Many researchers have studied landslide distributions and their controlling factors after earthquakes, but the function of ground motion is unclear. To investigate the connection in a strike-slip earthquake, we analyzed the 5 September 2022 Luding earthquake (Mw 6.6) in Sichuan Province, China. We interpreted remote-sensing images to obtain the landslide distribution before and after the earthquake, calculated surface deformation from D-InSAR data (pre- and post-earthquake), utilized a point-source model for the focal mechanism inversion, and then constructed a finite fault model for the rupture slip. There are clear differences in the landslide distributions on the two sides of the fault before and after the earthquake. The density of co-seismic landslides on the west side of the fault exceeded that on the east side. The patterns of surface deformation and ground motion indicated that the areas with larger deformation and motion were associated with more landslides. Furthermore, the landslide size decreased with distance from the fault. A new finding is that co-seismic landslides induced by strike-slip earthquakes result in high landslide concentration on both sides of the fault, while previous studies find that co-seismic landslides triggered by thrust earthquakes present a hanging wall concentrated distribution pattern. These findings contribute to a more comprehensive understanding of the connection between ground movement patterns and landslide distributions. Our research focused on the 5 September 2022 Luding earthquake in Sichuan Province, China. The study identified distinct disparities in the distribution of landslides on either side of the fault, both landslides that happen before, during, and shortly after an earthquake. The western side of the fault exhibited a higher density of landslides following seismic activity compared with the eastern side during the Luding earthquake. The areas experiencing more significant deformation and motion during the earthquake were more prone to landslides. Moreover, landslides induced by strike-slip earthquakes displayed high landslide concentrations on both sides of the fault. In contrast, landslides triggered by thrust earthquakes predominantly exhibited a concentrated hanging wall distribution pattern. The type of fault is a primary controller of the landslide distribution pattern More landslides occurred in areas with more deformation and greater ground motion

The development of artificial intelligence makes it possible to rapidly segment landslides. However, there are still some challenges in landslide segmentation based on remote sensing images, such as low segmentation accuracy, caused by similar features, inhomogeneous features, and blurred boundaries. To address these issues, we propose a novel deep learning model called AST-UNet in this paper. This model is based on structure of SwinUNet, attaching a channel Attention and spatial intersection (CASI) module as a parallel branch of the encoder, and a spatial detail enhancement (SDE) module in the skip connection. Specifically, (1) the spatial intersection module expands the spatial attention range, alleviating noise in the image and enhances the continuity of landslides in segmentation results; (2) the channel attention module refines the spatial attention weights by feature modeling in the channel dimension, improving the model’s ability to differentiate targets that closely resemble landslides; and (3) the spatial detail enhancement module increases the accuracy for landslide boundaries by strengthening the attention of the decoder to detailed features. We use the landslide data from the area of Luding, Sichuan to conduct experiments. The comparative analyses with state-of-the-art (SOTA) models, including FCN, UNet, DeepLab V3+, TransFuse, TranUNet, and SwinUNet, prove the superiority of our AST-UNet for landslide segmentation. The generalization of our model is also verified in the experiments. The proposed AST-UNet obtains an F1-score of 90.14%, mIoU of 83.45%, foreground IoU of 70.81%, and Hausdorff distance of 3.73, respectively, on the experimental datasets.

The key to seismic landslide risk identification resides in the accurate evaluation of seismic landslide hazards. The traditional evaluation models for seismic landslide hazard seldom consider the landslide dynamic runout process, leading to an underestimation of seismic landslide hazard. Therefore, a joint Newmark–Runout model based on landslide dynamic runout is proposed. According to the evaluation results of static seismic landslide hazard, the landslide source points can be extracted, and the landslide dynamic runout process is simulated to obtain the dynamic seismic landslide hazard. Finally, the static and dynamic seismic landslide hazards are fused to obtain an optimized seismic landslide hazard. In September 2022, a strong Ms6.8 earthquake occurred in the eastern Tibetan Plateau, triggering thousands of landslides. Taking the 2022 Luding earthquake-induced landslide as a sample, the function relationship between seismic slope displacement and landslide occurrence probability is statistically modeled, which partly improves the traditional Newmark model. The optimized seismic landslide hazard evaluation of the Luding earthquake area is conducted, and then, the seismic landslide risk identification is completed by taking roads and buildings as hazard-affected bodies. The results show that the length of the roads facing very high and high seismic landslide risks are 3.36 km and 15.66 km, respectively, and the buildings on the Moxi platform near the epicenter are less vulnerable to seismic landslides. The research findings can furnish critical scientific and technological support for swift earthquake relief operations.

The landslide dynamics model is one of the methods for evaluating landslide motion processes, contributing to disaster prevention and mitigation. With the advancement of science and technology, GIS has become the mainstream platform for landslide simulation. However, the three-dimensional movement of landslides is intricate, leading to a lack of methods for three-dimensional landslide numerical simulation on GIS platforms. In this paper, we propose a three-dimensional, two-phase landslide dynamics model. Through the proposed solution, three-dimensional modeling and numerical simulation of landslides can be achieved on GIS platforms. Simultaneously, drawing inspiration from the SPH kernel functions, we visualize the results of the three-dimensional model on the GIS platform. Simulation of the Yigong landslide demonstrates that our solution can realize three-dimensional landslide simulation on the GIS platform. Our model adeptly captures numerous details in the landslide motion process. However, constrained by the inherent limitations of the three-dimensional model, the model results are susceptible to numerical oscillations and diffusion, with the accuracy of the model being controlled by grid partitioning.

Urban parks create cooler microclimates, mitigating urban heat island effects, but diurnal variations in cooling remain unclear. We analyzed 97 parks within Beijing's fifth ring road using ECOSTRESS land surface temperature data to assess cooling from both maximum and cumulative perspectives. Results show that park cooling is stronger during the daytime, with the maximum median cooling area and efficiency at 51.50 ha and 3.56, respectively, compared to the nighttime minimum values of 30.21 ha and 1.97. Cumulative cooling is highest in the early afternoon (intensity 0.011, gradient 0.50 degrees C) and lowest at night (intensity 0.005, gradient 0.10 degrees C). Dominant factors influencing daytime cooling are park area, shape index, and blue-green landscapes. Specifically, park area is positively correlated with the cooling area, while water index is positively correlated with cumulative cooling indicators, and the impact of shape and vegetation indices on cooling is unstable. At night, landscape design has little effect, and poorly designed water bodies may cause warming; park area becomes the key factor, positively associated with both cooling area and cumulative cooling. The threshold for cooling efficiency in Beijing is 24 ha during the day, increasing to 29 ha at night. These findings provide valuable insights for sustainable urban park planning.

Ice–snow freezing may disrupt the growth condition and structure of forest vegetation, increasing combustible loads and thus triggering forest fires. China’s subtropical regions are rich in forest resources, but are often disturbed by ice–snow freezing, especially due to climate change. Clarifying the responsive areas and times of forest fires to ice-snow freezing in this region is of vital importance for local forest fire management. In this study, meteorological data from 2001 to 2019 were used to extract the precipitation and its duration during the freezing period in order to analyze the freezing condition of forest vegetation in subtropical China. To improve the accuracy of identifying forest fires, we extracted forest fire information year-by-year and month-by-month based on the moderate resolution imaging spectroradiometer (MODIS) active fire data (MOD14A2) using the enhanced vegetation index (EVI), and analyzed the forest fire clustering characteristics in the region using the Moran’s Index. Then, correlation analysis between forest fires and freezing precipitation was utilized to explore the responsive areas and periods of forest fires caused by ice–snow freezing. Our analysis shows the following: (1) during the period of 2001–2019, the ice–snow freezing of forest vegetation was more serious in Hunan, Jiangxi, Hubei, and Anhui provinces; (2) forest fires in subtropical China have shown a significant downward trend since 2008 and their degree of clustering has been reduced from 0.44 to 0.29; (3) forest fires in Hunan, Jiangxi, and Fujian provinces are greatly affected by ice–snow freezing, and their correlation coefficients are as high as 0.25, 0.25, and 0.32, respectively; and (4) heavy ice–snow freezing can increase forest combustibles and affect forest fire behavior in February and March. This research is valuable for forest fire management in subtropical China and could also provide a reference for other regions.

河流对岸坡的侵蚀作用是滑坡失稳的重要因素之一,特别是在中国黄土地区.因此,探讨河流水位的季节性变化对黄土边坡稳定性的影响规律,对早期黄土滑坡灾害预警具有重要作用.论文以甘肃省天水市清泉村滑坡为例,分析5年内沿岸边坡形变速率与季节性水位变化之间的关系.基于SBAS-InSAR技术获取滑坡时序形变信息,借助MNDWI与DEM获取边坡底部时序水位信息,并结合GPM降雨数据进行相关性分析,研究发现随着降雨增多、河流水位的上涨,清泉村滑坡的形变速率明显增大,在夏季时滑坡形变量增长较快,并且边坡形变相较于水位季节变化具有一定的滞后性;经相关性分析,得出水位与滑坡形变的相关性系数为0.46,降雨与滑坡形变的相关性系数为0.39,表明清泉村滑坡的形变速率与水位变化更相关.因此,河流的季节性变化对滑坡形变速率具有一定影响,进而造成河流对沿岸滑坡的侵蚀作用呈现出季节性变化.研究揭示的黄土滑坡与河流水位的季节性变化关系,对揭示河岸边坡的演化过程、滑坡识别与灾害防治具有重要意义.

To discover the complex hydro-mechanical evolution on Loess Plateau that accompany large-scale land reclamation project, we tracked changes of moisture, matric suction, anisotropic displacements and stresses in geological interfaces of natural loess and deep man-made fill during a comprehensive process of construction and the subsequent three years rest period. Results indicate that vertical stress increases during emplacement of thick fill, while the lateral earth pressure first increases and then gradually decreases with elapsed time. There was a non-linear increase in lateral earth pressure with depth of infill during construction. Whereas lateral earth pressure decreased with depth in lengthy rest period. The fill vertically compressesed and laterally extended during construction, while the geological interface of fill and loess laterally rebounded during construction pause. The primary vertical displacement occurred at the contact zone between the original loess slope and fill. The lateral earth pressure coefficient of infill is influenced by lateral compression and extension of contact zone during land reclamation process. It mirrors the observation that variations in lateral earth pressure with depth are dependent on deformation of natural slope materials. Interestingly, vertical stress and lateral earth pressure periodically fluctuated during rest period, correlating to creeping and shrinkage in loess influenced by moisture content change under different environmental relative humidity. The volumetric moisture contents in fill and contact zone significantly increased during construction. The moisture content of contact zone is 10% higher than the overlying higher density fill after construction, which is attribute to the process of matric suction equilibrium. Considering a constant moisture content, the ratio of historical mean principal stress to deviatoric stress in fill and interface are under the principal stress failure line during construction. However, water diffusion potentially threats the stability of this man-made geological structure.

Solar photovoltaic (PV) is favored by the market because of its clean and renewable characteristics. There are abundant solar resources in the tropical regions of China. It is important and necessary to carry out comprehensive analysis of rooftop PV projects for tropical regions for scientific policy-makings. Here, we select Nanning as a case study to analyze the optimal options for PV installation on different roof types and estimate the electricity generation potential of rooftop PVs and its additional returns. Our analysis shows that: 1) the annual optimal azimuth and tilt angle in Nanning are 245° and 32.5°, respectively; 2) the tilt angle in southwest orientation has more space for adjustment, while that in the opposite orientation should remain horizontal; 3) for flat roofs, being fixed at the annual optimal angles is practical, while for gable roofs, the east-west direction is favorable; 4) the total potential of rooftop PV projects in Nanning can reach 19.99 TWh/year, resolving 76.1% of the social electricity demand. This research is valuable for rooftop PV installation and optimization in the tropical regions of China, which also could provide reference for other regions.

为了解决传统原位土体强度测试探测深度浅与过度依赖经验公式等问题,提出了钻孔原位剪切测试系统构想,并设计了样机,整个测试系统由孔内切削子系统和孔内变径剪切子系统组成,前者能够在钻孔内任意位置锚固并切削土环,后者能够对土环进行剪切试验,并通过传感器记录剪切力和剪切位移;基于模型箱钻孔剪切试验和室内直剪试验,进一步检验了钻孔原位剪切测试系统的可靠性.研究结果表明:模型箱钻孔剪切试验的剪切面平行于剪切力的方向,应力-应变曲线符合基本规律;黄土在模型箱钻孔剪切试验和室内直剪试验中均出现应变软化现象,且黄土的峰值剪切强度随含水率升高而下降;在室内直剪试验中黄土发生了脆性破坏,而在钻孔剪切试验中黄土表现出塑性破坏,在相同的孔隙比、含水率与加载压力下,钻孔剪切试验的峰值强度比室内直剪试验大,原因在于室内直剪试验剪切面的应力分布不均匀,而钻孔原位剪切试验的剪切面受力稳定且保持不变,与室内直剪试验相比,钻孔原位剪切测试系统具有更高的测试准度.

The Sichuan-Tibet railway goes across the Upper Jinsha River, along which a large number of large historical landslides have occurred and dammed the river. Therefore, it is of great significance to investigate large potential landslides along the Jinsha River. In this paper, we inspect the deformation characteristics of a rapid landsliding area along the Jinsha River by using multi-temporal remote sensing, and analyzed its future development and risk to the Sichuan-Tibet railway. Surface deformations and damage features between January 2016 and October 2020 were obtained using multi-temporal InSAR and multi-temporal correlations of optical images, respectively. Deformation and failure signs obtained from the field investigation were highly consistent. Results showed that cumulative deformation of the landsliding area is more than 50 cm, and the landsliding area is undergoing an accelerated deformation stage. The external rainfall condition, water level, and water flow rate are important factors controlling the deformation. The increase of rainfall, the rise of water level, and faster flow rate will accelerate the deformation of slope. The geological conditions of the slope itself affect the deformation of landslide. Due to the enrichment of gently dipping gneiss and groundwater, the slope is more likely to slide along the slope. The Jinsha River continuously scours the concave bank of the slope, causing local collapses and forming local free surfaces. Numerical simulation results show that once the landsliding area fails, the landslide body may form a 4-km-long dammed lake, and the water level could rise about 200 m; the historic data shows that landslide dam may burst in 2–8 days after sliding. Therefore, strategies of landslide hazard mitigation in the study area should be particularly made for the coming rainy seasons to mitigate risks from the landsliding area.

2020年3月30日四川省西昌市森林火灾发生后,该地区的地质灾害显著增多,火灾对表层土壤产生一定影响,但目前对火灾影响土壤的物理力学性质的机制及致灾特征研究尚浅.以未遭受火灾区域和遭受火灾区域的表层土样为研究对象,进行了总有机碳、颗粒级配和X射线衍射分析,并结合室内相关力学试验研究了火灾前后土样的黏土矿物含量及力学强度特性.试验结果表明:①火灾发生后土壤有机质含量降低,导致不稳定的无机土壤裸露;土壤团粒结构稳定性降低,土壤中细小颗粒含量增高,细小颗粒容易阻塞土壤表层孔隙,不利于雨水或地面径流的下渗;②火灾的高温过程使得土壤中黏土矿物含量变化,高岭石(Kao)相对含量减少了10％,伊利石/蛭石混层增加了1％,伊利石(It)减少了20％,蛭石(V)增加了29％,变化最为明显;③火灾发生后土壤黏聚力(c)、内摩擦角(Φ)明显衰减,应变软化现象更为明显,这是土壤黏土矿物含量的变化,即蛭石矿物含量增高导致的.森林火灾会从颗粒粒径,矿物含量和强度参数3个方面显著影响土壤的物理力学性质,从而导致火烧迹地遭受泥石流、滑坡等地质灾害的风险增加.