Search Results (373)

The spatial representativeness of rain gauges is critical for accurately estimating rainfall that triggers landslides and for defining operational thresholds. This study evaluates the potential error in conventional rain-gauge-based methods for estimating landslide-triggering rainfall, using 548 landslide events across Italy from the e-ITALICA database, which reports the duration of each rainfall event and the location of the nearest available rain gauge. A radar-based assessment, using the Surface Rainfall Intensity (SRI) product (1 km² resolution) provided by the Italian Department of Civil Protection, quantified discrepancies between rainfall at landslide locations and at the nearest rain gauges. Seasonal analysis was performed, considering summer events (April–September), typically associated with convective and spatially variable rainfall, and winter events (October–March), generally more stratiform and uniform rainfall. Results indicate that the probability of large discrepancies increases with distance. Summer events show larger discrepancies at short distances compared to winter events, but seasonal distributions converge at larger distances. These findings provide useful insights into rain gauge representativeness in studies of rainfall-induced landslides. Full article

Given the escalating impacts of global climate change and extreme weather events, the accurate stability assessment of rainfall-induced landslides necessitates a comprehensive consideration of both seepage processes and the inherent spatial variability of soils. Traditional deterministic and two-dimensional (2D) analyses often fail to capture the multi-dimensional kinematic features of slope failures and the stochastic nature of soil heterogeneity, thereby leading to inaccurate risk assessments. This study proposes a three-dimensional (3D) slope reliability analysis framework. Within this framework, a 3D slope geometric model is constructed using GeoStudio 2025.1.0 software, and seepage analysis is conducted by the SEEP3D module. To account for soil spatial variability, the Karhunen–Loève (K-L) expansion method is employed to discretize key shear strength parameters (effective cohesion and effective angle of internal friction). The factor of safety (F_s) is evaluated using the 3D simplified Bishop method, which is then coupled with Monte Carlo simulations to determine the probability of failure (P_f). The results show that rainfall infiltration causes progressive dissipation of shallow matric suction and a significant rise in the groundwater table near the slope toe, resulting in reduced effective stress in the critical resistance zone. As rainfall intensity increases, the F_s decreases approximately linearly from 1.14 to 0.90, whereas the P_f increases nonlinearly from nearly 0 to 98.36%. Under the rainstorm condition, although the F_s remains above unity at 1.063, the corresponding P_f reaches 23%, indicating that deterministic evaluation based only on the F_s may underestimate the actual failure risk. The proposed framework provides a quantitative tool for evaluating rainfall-induced slope instability by integrating transient hydraulic response, three-dimensional spatial variability, and probabilistic reliability assessment. Full article

The power system is the backbone of the energy network, and overhead lines are its vital structures. Weather threats may jeopardise the reliability of lines and make them a weak link. In particular, heavy rainfall episodes can cause failures, especially in mountain areas. Current climate changes may exacerbate the effects on the ground, intensifying rainfall episodes and increasing the frequency of extreme events. In this context, debris flows triggered by rather intense precipitation and characterised by fast kinematics can destroy pylons and electric connections, affecting the infrastructures not only in the upper ridges but also downstream across the fan apex, where powerlines are much more distributed. This study presents an in-depth back-analysis of two debris flow events triggered in concomitance with a heavy cloudburst that occurred in Talamona (Sondrio Province, Italy) in July 2008 and in Campo Tartano (Sondrio Province, Italy) in April 2024. These events hit onsite powerlines, causing blackouts and showing the potential vulnerabilities of the local electricity system. An analysis of rainfall-induced landslide failure is carried out using the numerical model CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment) and MIST-DF (Modelling Impulsive Sediment Transport—Debris Flow) with the aim of reconstructing the dynamics of the first (i.e., Talamona) geo-hydrological event. Powerline vulnerability is also investigated against debris flow dynamics, discussing possible strategies to reduce pylon exposure and to increase the resilience of the local electro-energetic network. Since, under climate change scenarios, heavy rainfall episodes are projected to intensify, an alternative approach based on rainfall-threshold curves is presented and applied to both cases of study. The latter, already implemented for civil protection purposes, could be useful in early-warning procedures against potential debris flow hazards. For both methodologies, the findings from the study confirm the strength of the approaches and foster their application in different situations (back-analysis and early warning) to reduce powerlines’ geo-hydrological risks. Full article

Rainfall-induced landslide early warning systems require reliable estimation of soil moisture conditions. This study proposes a Soil Water Index (SWI) framework based on a three-stage TANK model. Through GLUE (Generalized Likelihood Uncertainty Estimation)-based behavioral parameter sampling and K-means clustering, SWI response characteristics were classified into two representative scenarios: slow drainage (Scenario 1) and fast drainage (Scenario 2). Two-stage thresholds—Watch (α = 0.40 × SWI_peak) and Warning (β = 0.70 × SWI_peak)—were established from SWI rise profile analysis at 500 m and 5 km resolutions, providing 20–27 and 4–5 h of lead time, respectively. Verification against the July 2025 heavy rainfall event across multiple resolutions and spatial extents yielded Hit Rates of 0.984–1.000, while FAR (False Alarm Ratio) remained structurally high (0.607–0.648 for grids sharing the rainfall field with occurrence sites). These findings confirm that SWI serves as an effective regional-scale necessary condition indicator for landslide-triggering moisture, but FAR reduction requires integration with slope susceptibility information. Full article

Sediment connectivity is a key indicator of whether eroded sediment can be efficiently transported within a catchment. Landslides are a major form of rainfall-induced erosion on the steep slopes of the Loess Plateau and contribute substantially to overall catchment sediment yield. However, evaluating the connectivity of landslide-derived sediment and its implications for sediment transport risk remains challenging. Therefore, field investigations were conducted in three watersheds (R1, R2, and R3) on the Loess Plateau to examine landslides triggered by rainstorms. We analyzed the characteristics of landslide erosion and its influencing factors, applied graph theory to investigate sediment connectivity after landslides occurred, and assessed the risk of sediment transport to the catchment outlet. The results showed that the landslide number densities in the catchments R1, R2, and R3 were 9, 155, and 214 km⁻², respectively. The average erosion intensities were 25,153, 53,074, and 172,153 t km⁻², respectively. The network analyses indicated that the locations of landslides within the catchments were primarily concentrated in areas with high transport networks and high sediment accessibility to the catchment outlets. The sediment connectivity index further showed that 59%, 43%, and 51% of landslides in the three watersheds, respectively, were at high risk of delivering sediment to the catchment outlet. Accordingly, measures such as slope drainage and gully dam construction may help reduce both landslide occurrence and sediment transport. These findings provide new insights into the transport risk of eroded sediment from a connectivity perspective, identify hotspot areas of sediment connectivity and landslide erosion, and support the targeted prevention and control of catchment erosion. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), and GIS-based spatial data processing. A total of 261 landslides were mapped, affecting approximately 19% of the study area. Slides constitute the dominant movement type (66.7%), followed by complex landslides, flows, and falls. Landslide distribution is strongly controlled by geological and morphometric factors: more than 80% of mapped phenomena occur in highly fractured granitic and gneissic rocks, over 70% lie within 500 m of faults, and more than 90% are located within 300 m of streams. Slope gradient (25–55°) and local relief (350–550 m) further contribute to slope instability patterns. The historical dataset documents 237 landslide-induced road damage events over 75 years, with a marked increase in occurrence since the early 2000s. Most damage events affected the SS18 road and frequently corresponded to reactivations of pre-existing landslides, highlighting the long-term persistence of slope instability and the seasonal influence of intense autumn–winter precipitation. Overall, the results demonstrate that landslide hazard in the Costa Viola is governed by the interplay between structural, lithological, geomorphic, and climatic factors, compounded by anthropogenic modifications along road corridors. The combined landslide inventory and historical database provide a robust basis for risk mitigation, identification of critical road sectors, and future susceptibility and predictive modelling to support effective territorial planning. Full article

As one of the most catastrophic geological hazards globally, landslides exhibit heightened risks due to their increasing frequency, destructive potential, and extensive spatial distribution. The primary objective of this study is to develop an integrated analytical framework to quantitatively evaluate the stability of variably shaped slopes under rainfall infiltration. The core hypothesis is that slope curvature significantly alters infiltration behavior and stress distribution, leading to morphology-dependent failure mechanisms. Employing Green-Ampt infiltration theory coupled with limit equilibrium analysis, we establish stability prediction models for three fundamental slope geometries (linear, concave, convex) under contrasting rainfall regimes (high-intensity vs. low-intensity precipitation). The derived analytical solutions reveal two critical phenomena: (1) progressive downward migration of the saturation front maintaining parallelism with slope surfaces during infiltration and (2) time-dependent stability deterioration following hyperbolic decay patterns. The proposed models are rigorously validated through numerical simulations employing finite element methods, which demonstrate remarkable congruence with theoretical predictions, showing safety factor discrepancies below 5% (ΔFs < 0.05). Particularly, concave slopes exhibit 18–22% faster destabilization rates compared to convex counterparts under equivalent rainfall conditions. The validated models elucidate the spatiotemporal evolution of matric suction and pore pressure distributions, providing quantitative insights into morphology-dependent failure thresholds. These findings advance predictive capabilities for rainfall-induced landslides through physics-based stability criteria, offering critical guidance for terrain-specific early warning systems and mitigation strategies in geohazard-prone regions. Full article

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability Analysis Tool (Scoops 3D) joint model can overcome the shortcomings of using a single TRIGRS model for hydrological analysis and a single Scoops 3D model for slope stability analysis. Landslide risk assessment based on expected economic loss, on the other hand, can overcome the issue of maintaining the risk level edge and sorting at the same level. In this paper, the TRIGRS model’s head pressures were put into the Scoops 3D model, with the southeast of Fangta, a town in Shaanxi province, China, as the study area. The relationship between the slope gradient and the number of grids in each stable grade was certified. The rainfall thresholds for landslides, based on both rainfall intensity and rainfall duration, were obtained by rerunning the TRIGRS-Scoops 3D joint model. The landslide range and land uses of each dangerous slope were determined by maximum likelihood classification, and then the expected economic loss was calculated. To verify the reliability of the TRIGRS-Scoops 3D joint model, the identified dangerous slopes were compared with the results from landslide susceptibility mapping. The results show that the unstable grids are concentrated within a slope gradient of 30° to 35°, and the landslide early warning levels are divided into Tier 3, Tier 2, and Tier 1 Warnings. The occurrence of shallow loess landslides is affected by both rainfall intensity and rainfall duration, and the combined effect should be considered in early warning. The distribution of both extreme susceptible grids and high susceptible grids across all 23 dangerous slopes demonstrates the reasonableness of the TRIGRS-Scoops 3D joint model. The landslide susceptible probability within some dangerous slopes exhibits spatial variability. The mapping relationship between the slope gradient and loess landslides is extremely complex. This paper can provide a theoretical basis for the early warning and risk management for rainfall-induced shallow loess landslides; the proposed method is also applicable to other regions with similar geological and meteorological conditions. Full article

Accurate prediction of landslide run-out distance is fundamental to hazard mapping, emergency planning, and risk-informed engineering design. However, many data-driven studies implicitly treat landslides as a homogeneous population and provide limited, physically interpretable insights into how geomorphic factors govern run-out behavior. To address these limitations, we propose a cluster-aware and explainable modeling framework to predict run-out distance L using four source-region and slope descriptors: crown–toe relief H, source area A, source volume V, and mean source-slope inclination $\theta$. The dataset consists of 10,159 rainfall-induced landslides compiled from official inventories and peer-reviewed literature. After standardizing predictors, the optimal number of clusters is determined using information criteria (AIC/BIC), followed by k-means clustering to identify distinct landslide regimes. We first benchmark Random Forest, eXtreme Gradient Boosting, CatBoost, and LightGBM on identical data splits without hyperparameter tuning, using $R^2$, RMSE, and MAE as performance metrics. LightGBM consistently outperforms the alternatives and is therefore selected as the base learner. Within each cluster, LightGBM is further optimized using the Alpha Evolution (AE) algorithm, with Particle Swarm Optimization and Bayesian Optimization serving as benchmarks. The resulting AE-LightGBM model achieves the highest predictive accuracy across clusters. Model interpretability is achieved using TreeSHAP, which decomposes predictions into cluster-specific baselines and additive contributions from H, A, V, and $\theta$. By integrating regime-sensitive learning with robust explainability, the proposed framework improves run-out distance prediction while providing transparent, physically meaningful insights to support scenario analysis and engineering decision-making. Full article

In July 2024, a major rainfall-induced landslide disaster occurred in Zixing county, Hunan Province, triggering more than 4000 landslides with a total area exceeding 21 km². The scale of this hazard underscores a critical need for long-term stability assessment of the affected slopes. While previous studies have primarily used optical remote sensing to map landslide distributions, quantitative evaluation of post-failure movement dynamics remains limited. This study developed an integrated monitoring framework that combines time-series SBAS-InSAR displacement measurements (using Sentinel-1 data from August 2024 to September 2025) with deep learning-based optical interpretation, rainfall analysis, and geological data. Our approach enables the quantitative, region-scale stability assessment of the Zixing landslide cluster one year after the initial event. Experimental results reveal sustained surface displacement with rates ranging from −30 to 30 mm/year, and localized displacements exceeding 40 mm/year. Notably, over 48% of the mapped landslides are classified as active or critically active, indicating widespread, ongoing instability. Correlation analysis further establishes precipitation as a key driver of accelerated movement. Beyond the Zixing case, this work provides a transferable methodology for assessing long-term post-disaster landslide behavior, offering direct value for regional hazard management and early-warning systems. Full article

The strength of Xigeda strata decreases significantly upon contact with water, and the shear strength between sandstone and mudstone layers is lower than that within the individual layers. Therefore, the interlayer dip angle plays an important role in determining the failure mode of rainfall-induced landslides. To investigate the effect of interlayer dip angle on the mechanical response of Xigeda sandstone–mudstone slopes under rainfall conditions, a total of five model slope tests were conducted. Different ratios of model materials were selected for the sandstone and mudstone, and artificial rainfall with intensities representative of the Panxi region was simulated using a calibrated rainfall device. A combination of photography and instrument measurements was employed to study the seepage field, deformation field, and slope failure characteristics at five interlayer dip angles. It is shown that when the interlayer dip angle is smaller than the slope angle, an increase in the interlayer dip angle accelerates the movement of the wetting front along the weak interlayer plane. At the same time, this increase shortens the time to the occurrence of abrupt displacement and increases the corresponding displacement magnitude, which makes slope failure prediction more challenging. The shoulders of all slopes experienced displacement earliest and exhibited the largest displacement amplitude. The slope failure mode transitioned from shallow surface sliding to interlayer sliding. When the interlayer dip angle surpassed the slope angle, the weak interlayer plane was no longer the dominant control surface. Slope stability was thereby moderately enhanced, with the failure mode shifting to through-layer sliding. Full article

Increasing frequency and intensity of extreme precipitation events, together with altered soil saturation dynamics, have significantly increased the occurrence of shallow landslides. These processes are closely linked to climate change and increasingly affect mountainous and hilly regions worldwide, where rainfall-induced pore pressure variations and transient infiltration govern slope instability. Despite growing recognition of climate-driven slope failures, most conventional geomechanical analyses still rely on static assumptions and simplified boundary conditions, which are insufficient to capture the pronounced temporal variability of hydro-climatic forcing. To address this gap, this study introduces a conceptual and methodological framework for a proactive landslide cadaster, developed within the Climate Adaptive Resilience Evaluation (CARE) framework. Rather than serving as a static inventory of past events, the proposed cadaster functions as a structured, updatable repository of climate–geomechanical parameters that directly support advanced landslide analyses. The core innovation lies in the formalization of the climate–geomechanical interface, which enables the transformation of climatic and hydrological variables into parameters directly applicable in geomechanical modeling. These parameters encompass climatic, hydrological, geomechanical, and thermo-hydraulic processes and are assigned to spatially referenced locations, complemented by documented landslide occurrences. Their spatial distribution forms a network of reference points that allows interpolation, continuous updating, and reuse across multiple analyses. In this way, the cadaster becomes a proactive, process-based data infrastructure, serving as the foundational input for scenario-based landslide susceptibility, hazard, and risk assessments within the CARE analytical workflow. The conceptual framework is illustrated through an example from Slovenia, focusing on the Visole area near Maribor, where selected data types and workflow steps are presented for demonstration purposes. Full article

In July 2024, a severe rainfall event struck Sichuan Province, Southwest China, triggering deadly landslides and causing significant societal impacts. This study investigates the spatiotemporal characteristics and underlying mechanisms of the event using high-resolution surface observations, radar reflectivity, and ERA5 reanalysis data. The rainfall exhibited distinct mesoscale organization, with two primary precipitation centers identified: subregion A located within the plateau-lain transitional zone of the western Sichuan Basin, and subregion B situated over the Chengdu Plain. Synoptic-scale analysis indicated that the rainfall developed under favorable large-scale atmospheric conditions, including a mid-tropospheric trough, a pronounced low-level jet, and a well-defined Southwest Vortex (SWV), which is a dominant lower-tropospheric circulation system in this region. The evolution of rainfall was closely tied to the initiation and subsequent eastward progression of the SWV. The rainfall-producing mesoscale convective system (MCS) first formed over subregion A at approximately 2300 BST (UTC + 8) on 19 July. Vorticity budget diagnostics revealed that vertical advection and low-level convergence significantly contributed to vortex intensification during this initial phase, closely associated with the orographic lifting of low-level airflow. Convective activity in subregion B commenced roughly four hours later, coinciding with the eastward propagation of the SWV, during which horizontal vorticity advection became the primary mechanism sustaining the vortex. After 1400 BST on 20 July, the SWV weakened significantly, leading to the dissipation of the MCS and the cessation of rainfall. Full article