LISA (“Landslide Investigation and Simulation Archive”) is a database containing the observations collected across over 50 downscaled physical simulations of landslides. These experiments were performed over 7 years using a landslide simulator in Politecnico di Milano. Downscaled landslide simulations offer the opportunity to study the relationships among the landslide triggering factors in controlled conditions. The experiments considered different settings in terms of slope angle, rainfall intensity and soil characteristics. Furthermore, several tools were implied to monitor the evolution of water infiltration and failure development throughout the duration of the tests, including a Time Domain Reflectometer, tensiometers, Arduino soil moisture probes and optical fibres. To ensure water balance, superficial runoff was also collected, while Electrical Resistivity Tomography was used to monitor infiltration. Superficial deformations were assessed using photogrammetric techniques with optical cameras. Triggering factors linked to climate change were also explored, such as snow melting and wildfires, in terms of slopes constituted by burnt soil. Having so much information organized in terms of a database can be relevant for many aims. By way of example, these experimental data can be used to test and validate slope stability models and to define rainfall thresholds.
How to cite: Scaioli, A., Panzeri, L., Corti, M., Zanzi, L., Arosio, D., Azadeh, H., Papini, M., and Longoni, L.: A new database of landslide simulator experiments: LISA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12358, https://doi.org/10.5194/egusphere-egu25-12358, 2025.
Large mountainous areas of Campania (Italy) are frequently subject to rainfall-induced landslides, which sometimes cause heavy damage to buildings and infrastructure. Specifically, landslides are triggered on steep slopes covered with unsaturated air-fall pyroclastic deposits from eruptions of Vesuvius resting upon fractured limestone bedrock. The characteristics of these phenomena, their wide diffusion, and the difficulty of predicting their exact time and place of occurrence, which strongly depend on local factors, make the recourse to structural risk mitigation interventions rarely feasible. Hence, landslide early warning systems (LEWS) are the most effective way to mitigate the associated risk. Currently, the operating LEWS are based on empirical thresholds based only on precipitation information (e.g., intensity and duration of precipitation), but they give rise to numerous false and even some missed alarms. The inclusion of antecedent hydrologic information prior to rainfall events improves the predictive performance of hazard assessment tools and is here applied to the definition of hydrometeorological thresholds to be implemented in LEWS.
A novel methodology is proposed to define the hydrometeorological thresholds for large areas, considering the uncertainties linked to the spatial variability of geomorphological and meteorological factors. The proposed methodology is applied to the north-facing side (an area of approximately 80 km2) of the Partenio Mountains, a carbonate massif in Campania (Italy), frequently hit by rainfall-induced debris flows involving the pyroclastic deposits mantling the steep slopes.
As it often happens for geohazard inventories, the available dataset is too scarce to allow carrying out significant statistical analyses. Therefore, a 500-year long synthetic dataset of the hydrological response to precipitation of a reference slope with known geometry and a homogeneous soil layer with known properties is generated, providing the values of root-zone soil moisture and aquifer water level. Specifically, a stochastic NSRP rainfall generator is coupled with a previously developed physically based model of the flows through the unsaturated deposit and its hydraulic connection to a perched aquifer forming during the rainy season. The slope stability is evaluated under the infinite slope hypothesis, which allows the identification of landslide events. To define an operational LEWS for the whole study area, the effects on slope stability of the uncertainty related to the spatial variability of the slope morphological features, soil hydraulic and geotechnical properties is introduced. Similarly, the uncertainty of the meteorological and hydrological variables used for the definition of the 3D thresholds (rainfall depth, root-zone soil moisture and aquifer water level) is also considered, to mimic the effects of spatially variable quantities observed only in few sparse points. Consequently, the synthetic dataset is perturbed, superimosing Normal-distributed random fluctuations on the hydrometeorological variables and on the calculated values of the factor of safety.
The effect of uncertainty on the operational predictive performance shows the robustness of the hydrometeorological thresholds. Moreover, this result is confirmed by the application of the obtained thresholds to available data of occurred landslides, and measured rainfall and soil moisture in the north-facing part of Partenio Massif in the period 2002-2020.
How to cite: Greco, R., Marino, P., Roman Quintero, D. C., Abdullah, A., and Santonastaso, G. F.: 3D hydrometeorological thresholds for early warning of rainfall-induced landslides in Campania (Italy): application to Partenio massif, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4344, https://doi.org/10.5194/egusphere-egu25-4344, 2025.
The undertaking of stability analysis and impact range prediction of rainfall-induced shallow landslides at the regional scale is of great significance for landslides' early warning and prevention. The existing deterministic physical models for landslides that consider the effect of rainfall rarely consider the kinematics process after landslide destabilization when conducting regional hazard assessments. Thus, the Regional Shallow Landslide Hazards Rapid Assessment Model (RSLHRA) considering dynamic processes is proposed. This model considers the spatiotemporal instantaneous variation characteristics of surface runoff and subsurface wetting front under rainfall conditions, coupled with three-dimensional stability calculation methods to determine unstable units, and predicts the movement characteristics of landslides through dynamic models, achieving rapid assessment of regional-scale landslide hazard. To illustrate, the rainfall-induced regional Clustered shallow landslides that occurred in Guidong County, Hunan Province, China in 2021 were assessed using the RSLHRA model. The results show that soil permeability coefficient, cohesion, and internal friction angle are the most important input parameters of the RSLHRA model; The model can accurately capture the spatiotemporal distribution characteristics of shallow landslides induced by a rainfall process. By comparing the predicted area of the model with the actual occurrence area of the landslide, the accuracy of the model prediction can reach 60-70%. In addition, due to the use of a meshless numerical simulation method suitable for fluid motion analysis under the assumption of depth averaging and incompressibility, the computational efficiency of the model in predicting the kinematics of unstable landslides and debris flows has increased by 20 times compared to other models. The proposed model is expected to provide theoretical and technical support for regional landslide risk prevention and early warning.
Keywords: Landslides Hazard, Raid Assessment, Regional Cluster, Landslides Stability, Rainfall Infiltration, Landslides kinematics
How to cite: Liu, L., Wang, J., Pei, L., Liang, X., Yan, J., Chen, Y., Zhang, Y., and Xiao, L.: Rapid Hazard Assessment Model for the Extreme Rainfall-induced Regional Clustered Shallow Landslides, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3521, https://doi.org/10.5194/egusphere-egu25-3521, 2025.
Physically-based models for rainfall-triggered landslides enhance understanding of the interactions between rainfall, soil hydrology, and slope stability. Pre-event landslide modeling presents significant challenges, primarily due to uncertainties in estimating landslide volumes, which depend on the complex geometries of natural and basal sliding surfaces. Furthermore, physically-based distributed models often face challenges in acquiring datasets that are both spatially and temporally comprehensive.
This study introduces a methodology leveraging recent advancements in remote sensing technologies, which offer promising non-contact solutions for estimating landslide characteristics. A key focus is on calculating soil thickness, a critical parameter influencing mobilized soil weight and the factor of safety (FS) for physically based modeling. We integrate InSAR data from the European Ground Motion Service (EGMS), which provides freely accessible, continental-scale ground motion and displacement rate observations over stable targets (the so-called persistent scatterers, or PS), generally identified with man-made infrastructures or rock outcrops, with the mass conservation method. This method assumes minimal changes in the sliding base geometry during the observed deformation period, linking the rate of landslide thickness change to the spatial variation of the vertical deformation mean yearly velocity, enabling soil thickness estimation and sliding geometry definition. The experiment involved selecting landslides with a minimum number of PS falling on their surface, then setting up the system of differential linear equations applied to the selected PS targets. Tikhonov regularization was employed to overcome ill-posedness, and the equations were solved by finite difference methods implemented in Matlab. The Tikhonov regularization introduces a smoothing parameter which assigns a weight to the Laplacian term of the thickness model. The methodology is being tested in a case study area within the Friuli-Venezia Giulia region, in Italy, known for well-documented shallow landslides in the Italian Landslide Inventory (IFFI).
Preliminary results demonstrate that the soil thickness and sliding geometry can be retrieved with reasonable accuracy, although measurements are highly sensitive to the choice of the smoothing parameter used in the regularization process.
How to cite: Yurdakul, E., Arnone, E., Nardi, F., Refice, A., Annis, A., Bras, R. L., and Capolongo, D.: Exploiting EGMS data in a thickness inversion methodology to enhance shallow landslide assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7226, https://doi.org/10.5194/egusphere-egu25-7226, 2025.
Protection forests play a critical role in mitigating surface landslides and controlling hydrological processes, yet their identification and assessment remain a challenge in forest and land management. This study, conducted as part of the PRIN-PNRR MILETO project, introduces in Italy a novel procedure for identifying protection forests using a deterministic statistical approach tailored to surface landslides in Italy. The SlideForMAP software forms the core of this methodology, integrating key inputs on soil and vegetation characteristics to assess landslide susceptibility. By explicitly incorporating the role of vegetation, the software offers a refined analysis of areas prone to landslides. Computationally efficient, the method supports the evaluation of extensive regions, facilitating applications at a regional scale.
In southern Italy the MILETO project has implemented this methodology to map and evaluate protection forests though case studies. These areas, often characterized by steep terrain and varying climatic conditions, are particularly prone to hydrogeological hazards like landslides. The project focuses on linking hydrological and soil stability models with vegetation dynamics, a key determinant in mitigating landslide risk.
These outputs provide actionable insights for forest and land managers. The hazard maps enable planners to pinpoint locations where protection forests mitigate landslide risks most effectively, while heat maps highlight areas for intervention to enhance forest functionality. This systematic approach bridges the gap between theoretical modeling and practical forest management, supporting sustainable landscape practices and disaster risk reduction approaches. By focusing on direct protection forest detection, this case study in southern Italy contributes to integrating environmental modelling and geospatial data to create a robust framework for safeguarding vulnerable regions.
How to cite: Giadrossich, F., Trotta, S., Ngo, H. M., Sanesi, G., Scotti, R., Raffaella, L., Di Prima, S., and Cohen, D.: Mapping Direct Protection Forests Using SlideForMAP Software: A Case Study from the MILETO Project in Southern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19546, https://doi.org/10.5194/egusphere-egu25-19546, 2025.
Frequently occurring rainfall-induced landslides in pyroclastic soil deposits of Campania (southern Italy) are very threatening for the local community and the infrastructure. The major factor behind the triggering of such landslides is assuredly the rainfall. However, there are several other hydrological processes which are responsible for predisposing the slopes to failure. The study area is Partenio massif of Campania, where slopes are covered with coarse grained loose pyroclastic soils deposited in alternate layers of ashes and pumices laying on densely fractured limestone bedrock. The assessment of landslide triggering considers both static and dynamic factors. The former account for landslide susceptibility assessment, while the latter are responsible for the assessment of time-dependent landslide hazard. Several studies reported in literature explored various methodologies for the assessment of landslide susceptibility. However, landslide hazard assessment still needs attention especially in terms of reliably predicting triggering location and its probability under dynamically varying conditions.
Landslide susceptibility is evaluated with a probabilistic approach based on available historical precipitation records, considering only slope inclination and soil thickness as geomorphological controlling factors. In fact, owing to the characteristics of the considered area, the rest of the features influencing the landslide susceptibility like geo-lithology, geomorphology, vegetation and soil characteristics were assumed to be homogenous. The slopes of the area have been thus grouped in eight classes according to their inclination and soil thickness. The response of the slopes to precipitation is assessed by applying an 1D model of unsaturated flow and slope stability to the eight slope classes, considering the hourly rainfall recorded in 22 years (2002-23) by ten rain gauges around the study area. Slope susceptibility is evaluated as the historical (static) probability of landslide occurrence, based on the number of predicted slope failures from model simulations. Susceptibility mapping is carried out based on slope units, which are assigned to a slope class according to their inclination and thickness and are associated to the nearest rain gauge.
Landslide hazard is also assessed with a probabilistic approach, based on Bayes’ theorem, by integrating susceptibility with dynamic controls, i.e., triggering rainfall and antecedent rootzone soil moisture. Landslide triggering hazard is evaluated as the dynamic conditional probability, i.e. based on the number of failures for each slope class and for given event rain depth and antecedent soil moisture conditions. Hazard mapping is finally carried out based on slope unit susceptibility, and dynamic controls derived from the simulations with the nearest rain gauge data.
The obtained maps were tested by comparing them with actual reported landslides. Specifically, the susceptibility map well agrees with the locations of landslides recorded between 1999 and 2022. The operational applicability of the proposed hazard mapping was carried out by replacing the modelled antecedent conditions with those obtained from ERA5-Land. The dynamic triggering probability maps well identify the dates and the zones where landslides have been reported.
How to cite: Abdullah, A., Marino, P., Roman Quintero, D. C., and Greco, R.: Probabilistic mapping of susceptibility and hazard of rainfall-triggered landslides in pyroclastic slopes of Campania (Italy)., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5089, https://doi.org/10.5194/egusphere-egu25-5089, 2025.
For many years, shallow landslides have been considered not to be related to bedrock geology. However, our experience clearly suggests that shallow landslides occur more frequently on specific bedrock types. This is because subsurface water migration behavior is strongly dependent on soil structures, most of which are derived from rock weathering. There are at least four types of surface structures that are prone to shallow landslides:
1) Some types of rocks are weathered with well-defined weathering front, which provides a common landslide model. In this case, porosity and permeability have high contrast between the soil layer and the bedrock, and therefore infiltrating rainwater commonly forms groundwater table within the soil and the resultant pore pressure build up causes shear failure. Piping erosion along the boundary might proceed and lead to the initiation of landslide. Vapor-phase crystallized ignimbrite, mudstone and gruss of granitoid form such structure.
2) Unwelded ignimbrite is weathered to become finer than the underlying fresh materials. Volcanic glass grains of unwelded ignimbrite interact with filtrating water and become finer than the original fresh one, forming a capillary barrier at the weathering front. Halloysite, which forms within the soil, are washed away by the groundwater and clogged in narrower spaces of pores to form clay bands, which prohibit downward water filtration. Weight increase of surface soil due to perched water on the clay bands and on the capillary barrier initiate landslide along with the suction decrease.
3) Surface materials that consist of dense rock blocks and soil have been prone to shallow landslides. Hornfels and spheroidally weathered granitoid form such surface materials. Subsurface flow washes away finer fraction to leave rock framework with open spaces, which might be collapsed and subsequent pressure buildup may cause landslide.
4) Horizontal impermeable beds overlain by permeable beds prohibits downward filtration of rainwater that comes through the permeable beds and the water moves laterally along the boundary and flows out of the slope. Such water flow out often induces landslide of surface soil. Tempestite that deposited on lower shoreface forms such structure.
How to cite: Chigira, M.: Geological background of shallow landslides induced by rainstorms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7581, https://doi.org/10.5194/egusphere-egu25-7581, 2025.
A wide literature and research interest focus on mapping shallow landslides, investigating their triggering factors, evaluating connected hazards and providing policies for risk mitigation, using a variety of methods. Many researchers also explored the role of weathering processes as predisposing factors of landslides, but most of them applied this approach to deep mass movements or did not consider soils from a pedological point of view, thus not taking into account their intrinsic juxtaposition of different pedogenic horizons and their spatial variability. Interesting results based on the basic concept of soil profile, well-known to soil scientists but often neglected by geologists or engineers, were mainly obtained in soils developed on pyroclastic materials, frequently affected by flow-like landslides, and more limitedly in other soil types. The ongoing project “SOIL SHADES – SOIL features and pedogenic processes as predisposing factors of SHAllow landsliDES”, funded by Next Generation EU, National Recovery and Resilience Plan (PNRR) of Italy, M4.C2.1.1., National Research Programme (PNR)–Research Projects of Significant National Interest (PRIN), aims at filling this gap. In its framework, we applied an integrated multidisciplinary, multi-analytical and multiscale approach in a pilot catchment (Turbolo Stream) of Calabria, southern Italy. For its geological-geomorphological, pedological and environmental features, this basin can be considered representative of other drainage basins in several Mediterranean and mid-latitude regions. Field surveys and aerial photo interpretation allowed us to provide an inventory of landslides in that pilot area and select some benchmark soil profiles able to catch the local pedodiversity, different lithologies and geomorphological features, where shallow movements occurred. At some of these sites, remote and proximal sensing investigations, such as electromagnetic induction (EMI), electrical resistivity tomography (ERT) and drone-based 3D topography acquisition were carried out to map the soil spatial variability from the landslide scar to the toe of its body and from the topographic surface to the depth of the potential failure surface. Results of the geophysical surveys were consistent with the soil profile depths and/or with the presence of relevant morphological changes already described along the profiles. Twenty-six soil samples collected from 6 soil profiles were morphologically described (color, pedogenic structure, skeletal rock fragments, clay coatings, nodules, etc.) and analyzed in laboratory to measure physical and chemical properties (particle size distribution, organic carbon content, pH, electrical conductivity, cation exchange capacity, soluble salts, etc.), while the micromorphological analysis was carried out only on selected horizons. Geotechnical analyses to obtain bulk density, Atterberg limits, shear strength, cohesion and internal friction angle were performed on the same samples, where applicable. Major data showed clear changes of pedological and geotechnical properties across the soil profile, thus supporting a prominent role of soil-formation processes on the modification of the original properties of the parent materials, as potential predisposing factors of shallow landslides. Nonetheless, the different soil types did not display homogenous behavior and mutual relationships from top to bottom or between specific pedological and geomechanical data, suggesting a complex interplay between parent rocks, pedogenesis and other morphodynamic processes recorded at the soil profile scale.
How to cite: Scarciglia, F., Conforti, M., Borrelli, L., Ceravolo, E., Cofone, G., Ietto, F., Perri, F., Ruocco, P., Terribile, F., and Vingiani, S.: The role of soil features and pedogenic processes as potential factors of shallow landslides. A catchment-scale multidisciplinary approach in the frame of the Project “Soil Shades”, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12238, https://doi.org/10.5194/egusphere-egu25-12238, 2025.
Shallow loess landslides typically occur under the influence of rainfall and irrigation, where hydrodynamic processes significantly affect soil strength and stability by altering particle gradation, soluble salt content, and mineral dissolution. Loess, characterized by high porosity and low density, is highly susceptible to structural changes under water infiltration. These microstructural changes not only exacerbate the collapsibility of loess but also weaken its strength, thereby increasing the risk of landslides. However, most existing studies focus on infiltration tests conducted in laboratories using collected samples, lacking long-term monitoring of the microstructural properties of in-situ loess slopes. As a result, these studies fail to ensure that their findings accurately reflect the actual conditions of natural slopes.
To address this gap, this study conducts long-term monitoring of a typical loess slope in the field, with regular artificial irrigation and natural rainfall recording, alongside borehole sampling. Using techniques such as Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), Laser Granulometry, and Particle Flow Code (PFC), this research systematically examines the effects of water infiltration on loess microstructure, particle migration, and mineral dissolution. It also explores the spatial and temporal evolution of loess properties at different depths. To date, two sets of loess samples have been used to establish a quantitative relationship between water infiltration and microstructural characteristics (e.g., porosity, mineral dissolution rate, and particle migration rate). The results indicate that during long-term infiltration, the content of cemented minerals in the shallow soil decreases, fine particles are lost, and the pore structure evolves toward a single large-pore form. Furthermore, PFC-based simulations reveal the weakening process of soil strength under water infiltration, providing an in-depth analysis of the particle-level mechanisms underlying strength degradation. This study offers a theoretical basis for the design and optimization of monitoring and early warning systems for loess landslides.
How to cite: Wang, X., Zeng, R., and Wei, Z.: Effect of Microstructural Evolution of Loess under Infiltration on Soil Strength, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20170, https://doi.org/10.5194/egusphere-egu25-20170, 2025.
The reasonableness of landslide risk analysis results for forest slopes poses a significant challenge due to the reliability of environmental data and the complex factors affecting the stability of large-scale forest slopes. This study introduces a novel approach to assessing the impact of critical factors on landslide risk analysis for forest slopes. The factors examined include topsoil thickness, soil strength, hydrological conditions, and vegetation. We utilize the TRIGRS program, a widely used tool in geotechnical engineering, to analyze the safety factor of a large forest slope covering an area of 100 hectares, situated at elevations ranging from 800 to 900 meters in the mountainous region of Kaohsiung, Taiwan. Some areas of the site experienced shallow landslides due to heavy rainfall in 2009. The shallow soil at the forest slope consists mainly of silty sands and clayey materials mixed with decomposed slate. Multiple regression analysis is used to evaluate the sensitivity of these critical factors to the landslide risk analysis results. The critical factors include six independent variables: soil cohesion (c), soil friction angle (f), root cohesion (cR), coefficient of hydraulic conductivity (Ks), air entry value on soil-water retention curve (a), and topsoil thickness (Z).
The research findings emphasize the significant role of topsoil thickness and tree root reinforcement in analyzing landslide risks on large-scale forest slopes. Reliable soil strength is crucial for these assessments, while hydrological soil parameters are less important. These findings provide a valuable reference for evaluating landslide risks in extensive forested areas. Notably, the study also highlights the necessity of obtaining trustworthy field data to improve the accuracy of landslide risk assessments. Furthermore, the results underscore the practical implications for future field applications, offering valuable insights for those involved in environmental risk management.
How to cite: Fan, C.-C., Yang, K.-M., and Tseng, W.-T.: The assessment of critical factors in the landslide risk analysis of forest slopes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1360, https://doi.org/10.5194/egusphere-egu25-1360, 2025.
Geo-hydrological hazards induced by rainfall in small catchments, such as landslides, debris flows and flash floods, represent serious risks to infrastructure and human worldwide. These phenomena are typically triggered by periods of heavy rain, with geomorphological features and antecedent soil and groundwater storage of the catchment as contributing factors(Bogaard & Greco, 2018). The assessment of water balance at the catchment scale may help to highlight the role played by different hydrological processes on the occurrence of these geohazards. In southern Italy's Campania region, steep slopes are covered by loose granular deposits covering a karstic limestone bedrock, making them particularly prone to shallow landslides. Over the past few decades, this region has indeed experienced some catastrophic landslides triggered by rainfall(Greco et al., 2021).
In this study, the water balance of landslide-prone catchments in Campania is modelled with a simplified lumped hydrological approach, based on the Budyko framework, by exploiting data from both meteorological and hydrological sources. Ground-based and satellite data between 2002-2022 have been considered for meteorological and geographic factors. The data include precipitation and stream water level obtained from the Multi-Risk Functional Center of the Civil Protection of Campania Region, and the actual evapotranspiration data sourced from TERRA Climate (Ning et al., 2024). Moreover, the groundwater recharge is estimated by using the Turc formulation, which is effective for semi-arid and temperate climates(Allocca et al., 2014), while stream runoff is derived from observed water levels of stream by Civil Protection website of Campania region.
The results provide insights into the interactions between precipitation, evapotranspiration, groundwater recharge, infiltration capacity of soil and stream runoff in the study area. Comparing the recorded landslide occurrences recorded (Calvello and Pecoraro, 2018; Peruccacci et al., 2023) with the water balance highlights the value of hydrological information in landslide hazard assessment.
How to cite: Aleem, M., Marino, P., and Greco, R.: Water Balance Assessment of Catchments in Pyroclastic-Covered landslide prone areas of Campania (Italy): A Budyko model application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5130, https://doi.org/10.5194/egusphere-egu25-5130, 2025.
Climate change induced the rise of extreme rainfall, resulting in an increase in the frequency and magnitude of landslides. Hence, a novel temporal modeling of rainfall-induced landslides incorporating both the dynamic nature of rainfall patterns and the slope failure mechanism was proposed. The proposed approach consists of three steps: (1) analysis of a critical continuous rainfall (CCR) using a physical-based model, (2) obtaining the cumulative distribution function of generalized extreme value distribution via the annual maximum rainfall series, and (3) analysis of temporal probability map. The result of the CCR map was validated with the 2018 landslide event in a small area of Hiroshima Prefecture, Japan. The result shows that the CCR map is highly reliable, with an AUC of 71.3%. The proportion of temporal probability >0.5 under the nonstationary model is greater than approximately 1.7, 1.9, 2.0, and 2.3 times the stationary model for the periods of 5, 10, 20, and 50 years, respectively. This indicates that the temporal probability increases according to a longer time period due to climate change-induced increased trend of extreme rainfall. The proposed approach can also be utilized to obtain the landslide temporal probability map for areas lacking landslide inventory.
How to cite: Nguyen, H.-H.-D., Nguyen, T.-N., Pham, M.-V., Song, C.-H., and Kim, Y.-T.: Temporal Modeling of Rainfall-Triggered Landslides: A Hybrid Approach Combining Physically-Based Modeling and Extreme Value Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5401, https://doi.org/10.5194/egusphere-egu25-5401, 2025.
In recent decades, climate change has increased slope instability in crop fields and agricultural land; the aim of this research is to identify the most suitable management practices for water retention in Vineyard as well as the less prone to soil erosion and shallow landslides. This study is part of the UNDER-VINE project, the areas of study are located in Oltrepò Pavese, a sector of the northern Appennines in Northern Italy.
In order to identify the proneness of the soils with different management practices (grass cover, legume-based mixture, cereal-based mixture, between and under-the-row mulching) to shallow landslides, the local data concerning several properties of the terrain (soil friction angle, slope angle, soil effective cohesion, root reinforcement provided by plant roots in the soil, soil unit weight, depth below ground level in which a potential sliding surface could develop and suction stress) were collected, field measurements and historical data were also taken into account. After that, the same data were used to calculate the safety factor (SF) formula for every cell of the digital elevation model, with one meter of resolution.
To accomplish that, a probabilistic model has been used, with the realization of a python script that takes for every parameter a value from a given range, than it calculates the SF for every cell. The outcome is a series of raster images showing the variation of the SF within the different sites.
Finally, the model should make it possible to understand which types of land use are most susceptible to slope instability, and whether the different management practices used can lead to a reduction in these phenomena.
How to cite: Giganti, M., Gambarani, A., Giarola, A., Meisina, C., and Bordoni, M.: Analysis of susceptibility to shallow slope instability for different soil management practices with a probabilistic model approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11017, https://doi.org/10.5194/egusphere-egu25-11017, 2025.
Rainfall-induced shallow landslides affect transport infrastructure by reducing serviceability and increasing road maintenance costs. These impacts are likely to become more severe with climate change. The aim of this research was to develop a framework to assess the spatio-temporal stability of slopes along transport corridors for decision support. The developed approach couples daily soil water balance with a widely used physically-based model: Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS). Performance of the framework was evaluated along the Mukamira – Kabaya national road that crosses a rugged topography in Nyabihu, Northwestern Rwanda. Spatio-temporal analyses conducted on landslide inventory in the road corridor indicated that the road has been susceptible to shallow landslides originating both from roadcuts and elsewhere in the road corridor. The observed temporal increase in landslides density in the corridor underscores an escalating threat to the road. Consequently, incorporating temporal variability of landslide predictors into future projection can assist to better understand prospective landslide activity in the road corridor, and the subsequent impact on the road. Applying a water balance model as input into TRIGRS provides a more realistic temporal assessment of initial soil moisture conditions and, in turn, a more relevant evaluation of triggering rainfall magnitudes. Using historic weather data, the framework showed capabilities of tracking variations of stability conditions of the roadside and forecasting the road sections that could potentially become blocked by road cut shallow instabilities. The framework highlights road sections exposed to distinct hazards, e.g. debris slides or debris flows paths. For landslide risk assessment, using historical data it was possible to link observed landslide occurrences to soil moisture and triggering rainfall conditions. It was also possible to estimate probable mobilised volumes of debris to be deposited on the road. In addition, the framework enabled evaluation of deteriorated shear strength of road cut materials on future projections of stability. For existing roads, the framework provides an important contribution to enable road asset managers to develop effective and economic maintenance plans. For the development of new roads, this framework can assist with the optimisation of alignment and cutslope morphology.
How to cite: Niyigena, C., Smith, A., Dijkstra, T., and Rwabuhungu, D.: A spatio-temporal framework for modelling shallow landslides along mountainous transport corridors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13597, https://doi.org/10.5194/egusphere-egu25-13597, 2025.
Shallow landslides pose significant hazards globally, particularly in regions with steep topography and susceptible geological conditions. These landslides are often triggered by intense rainfall or rapid snowmelt, and the understanding of their spatial and temporal dynamics is essential for hazard assessment and risk mitigation, especially in the context of climate change.
This study develops a statistically-based spatiotemporal model using Generalized Additive Models (GAMs) to evaluate shallow landslide susceptibility in the Orba basin(595 km2), Northern Italy. The model integrates static predictors such as slope and lithology with dynamic rainfall descriptors, particularly maximum rainfall intensity and antecedent cumulative rainfall, with the aim of finding a failure probability associated with certain values of antecedent cumulative and maximum rainfall intensity. Values for the rainfall descriptors are derived from a copula analysis that allows to estimate these parameters for defined return periods. In this way, both the spatial and the temporal components are included within the analyses.
Results highlight the nonlinear influence of cumulative rainfall on slope stability, consistent with suction stress theory, and the irrelevant effect of extreme rainfall intensities beyond a threshold. Susceptibility matrices derived from the model enable time-dependent assessments at the slope unit scale, offering valuable tools for early warning systems and climate change scenario analyses. In particular, the probabilistic methods using copula modelling allowed for the quantification of landslide susceptibility associated with specific return periods. Also, the deterministic and probabilistic analyses of future climate scenarios under varying RCP pathways revealed complex temporal trends in landslide susceptibility, demonstrating the significant impact of climate change on slope stability.
How to cite: Fumagalli, M., Frattini, P., and Crosta, G. B.: A probabilistic approach to model spatio-temporal landslide susceptibility, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20613, https://doi.org/10.5194/egusphere-egu25-20613, 2025.
Initiation of rainfall-induced landslides is intricately linked to hydrological conditions, mainly soil water content (SWC), which directly reflects precipitation intensity and patterns. Initiation may occur only on areas that are susceptible to the movement, i.e., the so-called conditionally stable areas. Existing methods delineate unconditionally and conditionally stable areas in “partially saturated” soils based on topography, mechanical properties, and a steady state wetness index (WI) or depth of groundwater level.
This study presents a methodology that delineates conditionally stable areas under fully unsaturated soil water conditions, i.e., in the absence of groundwater. In particular, the methodology identifies (i) the ‘partially-saturated’ conditionally stable areas previously mentioned in terms of groundwater level or positive pressure head, and (ii) an ‘unsaturated’ conditionally stable areas, assessed in terms of SWC or negative pressure head. This is obtained computing the factor of safety (FoS) by using two equations of the infinite slope model, which account for both saturated and unsaturated soil conditions. The region delineation ultimately depends on the spatial heterogeneity of topographic and hydro-mechanical properties of the terrain. Finally, for the conditionally stable areas, both ‘partially saturated’ and ‘unsaturated,’ we derive critical maps of landslide initiation, either in terms of SWC or pressure head, respectively. In order to provide efficient and easy-to-interpret maps, the methodology generates Homogeneous Soil Units (HSUs) where each unit is represented by a unique combination of slope and hydro-mechanical properties of the terrain. A unique critical value of SWC or pressure head will result for each HSU at a given hypothetical failure surface, i.e., soil depth.
We apply the methodology over the Friuli Venezia Giulia region, Italy, and central Puerto Rico, where thousands of shallow landslides were triggered by Hurricane Maria in September 2017.
This research received funding from European Union NextGenerationEU – National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investiment 1.1 -PRIN 2022 – 2022ZC2522 - CUP G53D23001400006.
How to cite: Thomas, J., Yurdakul, E., Soylu, E., Noto, L., Bras, R., and Arnone, E.: Delineating conditionally stable areas and critical soil water content maps for initiation of rainfall-induced landslides , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7288, https://doi.org/10.5194/egusphere-egu25-7288, 2025.
Predicting the spatial and temporal evolution of landslides is still one of the greatest challenges in landslide research. This is mainly due to the heterogeneous and complex interplay of landslide conditioning and triggering factors, which can lead to non-linear temporal and kinematic responses. Despite the growing literature demonstrating that hydrological antecedent conditions play a role in landslide acceleration, most landslide early warning systems (LEWS) often use only rainfall thresholds as the main triggering parameter. Therefore, the development of hydrometeorological threshold models that take into account pore water pressure data, antecedent hydrological conditions, and physiographic characteristics of slopes offers a great opportunity to improve existing LEWS. However, the investigation of slope dynamics and hydrometeorological thresholds requires an accurate, high-resolution data set. For this reason, the University of Vienna has initiated a long-term monitoring project (NoeSLIDE) that aims to obtain long-term in-situ surface and subsurface data (e.g. precipitation, piezometric levels, volumetric water content, vertical displacement) of several slopes in the region of Lower Austria.
In this study, the hydromechanical behaviour of a selected slope, the Hofermühle landslide, is investigated. We use an integrated approach combining field investigations, soil analysis, remote sensing, time series analysis (e.g. PASTAS) and numerical modelling to: (1) characterise the mechanical behaviour of the slope, (2) estimate snowpack and snowmelt rates, (3) understand and simulate the response and timing of groundwater, and thus porewater pressure, to rainfall and snowmelt, and (4) analyse the response of the slope to changes in porewater pressure to determine the critical hydro-meteorological conditions that lead to landslide accelerations. The preliminary results indicate that the studied landslide accelerates mainly in winter and spring and shows a heterogeneous spatial response to rainfall and snowmelt, which is largely influenced by its complex lithologic and hydrologic conditions. Furthermore, although changes in pore water pressure are the main driving mechanism for landslide acceleration, dry antecedent conditions and seasonal preferential flow patterns are also crucial for this process and need to be considered. This study provides useful information for disaster risk reduction as it is a further step towards a better understanding of the complex behaviour of landslides in Lower Austria.
How to cite: Jiménez Donato, Y. A., Bogaard, T., Carraro, E., Marr, P., Kanta, R., and Glade, T.: Investigating the role of pore water pressure and antecedent conditions in landslide acceleration: Insights from long-term monitoring in Lower Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20098, https://doi.org/10.5194/egusphere-egu25-20098, 2025.
In this work some representative soil profiles located in a pilot river catchment in northern Calabria, Southern Italy, were studied with the aim to understand the role of soil features on the stability of slopes and trigger factors of shallow landslides. The Turbolo Stream catchment was chosen as pilot area, as representative of many other geographic areas based on its geological and environmental features. This basin has an extension of about 30 km^2 and exhibits an important lithological, geomorphological and pedological variability. The main soil types range from highly mature soils (Alfisols) to poorly differentiated soils (Inceptisols and Entisols). Previous works investigated the landslide susceptibility in this basin by analyzing geological and geomorphological predisposing factors. However, the intrinsic properties of the soils that can trigger superficial landslides were not considered. The pedogenesis of the parent material leads to its differentiation into soil horizons and a varying spatial distribution of rheological properties for each horizon. The variation of these properties along the profile can potentially generate weak layers that become detachment surfaces once the limit equilibrium of the slope is overcome. The project “SOIL SHADES – SOIL features and pedogenic processes as predisposing factors of SHAllow landsliDES”, funded by Next Generation EU, National Recovery and Resilience Plan (PNRR) of Italy, M4.C2.1.1., National Research Programme (PNR)–Research Projects of Significant National Interest (PRIN), brings together numerous direct and indirect methodologies, trying to address this research question. Proximal and remote sensing techniques were coupled with field description and sampling of soil profiles, located on landslide scars or close to them, for specific laboratory analyses. The investigated soil profiles are six, developed on Paleozoic-Cretaceous crystalline rocks and Neogene deposits, and. For all profiles, individual horizons were sampled, and both pedological (chemical and physical) and geotechnical analyses were performed. In addition, the observation of soil thin sections under a polarizing optical microscope enabled to detect soil micromorphological features, especially those that may affect the physical properties of the horizons (porosity, clay coatings etc.). Although no clear relationships were detected between each pedological and geotechnical property, because of an inhomogeneous behavior of the parameters measured across each profile or between different profiles, some interesting results were obtained. Among chemical data, electrical conductivity (EC) and the sodium absorption ratio (SAR), the latter calculated from soluble salts measured through ion chromatography, enabled to classify the soil horizons of the studied profiles in terms of dispersivity, according to the classification chart proposed by Rengasamy and co-authors. Only three profiles out of six fall within the classes of potentially dispersive soils and dispersive soils, whereas the others are non-dispersive. This suggests that clay dispersivity may slightly contribute to trigger shallow landslides but is not the dominant control factor. The shear resistance, determined in situ through the vane test, showed higher values, as expected, in more mature and well-structured soil profiles, although bulk density values are not always consistent. This suggests that parent materials, degree of pedogenesis and the intrinsic soil spatial variability influence geomechanical parameters at different extents.
How to cite: Ceravolo, E., Conforti, M., Vingiani, S., Borrelli, L., Cofone, G., Ietto, F., Perri, F., Ruocco, P., Terribile, F., and Scarciglia, F.: Integration of pedological and geotechnical analyses at soil profile scale to assess shallow landslide susceptibility in a pilot catchment of Calabria, southern Italy. Results from the Project “Soil Shades”, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12101, https://doi.org/10.5194/egusphere-egu25-12101, 2025.
Shallow landslides are mass movements capable of causing severe damage to infrastructures and loss of lives. Similarly to other weather-driven geological processes, the spatial analysis of either hazard or susceptibility to shallow landslides by means of data-driven methods often involve two types of factors. Stable factors, such as geomorphological variables, represent the predisposing conditions for landslide occurrence. These factors, almost constant over time, are predominantly used in susceptibility assessments to identify areas potentially prone to landslides, irrespective of meteorological conditions. On the other hand, triggering factors, which are typically associated with highly dynamic variables, are also usually analyzed as they influence frequency and magnitude of landslide phenomena, hence being essential for hazard mapping. Heavy rainfalls may be regarded as the main triggering factor for shallow landsliding.
Rainfall is not a regular phenomenon and it is characterized by high spatial variability, particularly in mountainous regions, hence evaluating its spatial-temporal distribution represents a quite hard task, despite the availability of long-term meteorological stations records.
The primary objective of this study is to evaluate different interpolation methods for spatializing daily rainfall data to support shallow landslide hazard mapping. The study focuses on the Alpi Apuane region located in northern Tuscany (Italy), characterized by complex topography rising sharply few kilometers near the Ligurian sea coast. Daily precipitation data, collected over nearly seventy years, were obtained from various meteorological networks operating within the study area.
Different spatialization methods were selected to facilitate automated computation of the available large station dataset, such as the Inverse distance weighted interpolation, as well as different kriging methods, including the use of elevation data as a secondary variable for precipitation mapping.
The performance of the different methods was assessed for a set of significant precipitation days and involving an iterative process for random validation subsets selection.
Considering that landslides often occur in inaccessible areas and are generally poorly reported, their occurrence dates in landslide inventories are either frequently missing or uncertain.
In order to mitigate this issue, an inventory of shallow landslides was created for the study area through the visual interpretation of a multitemporal set of orthorectified aerial photographs. The available images used for landslide mapping span the period from 1954 to 2021. The acquisition of these aerial images was not temporally constant, the intervals between the acquisition range from 24 to 2 years, with an average value of 6 years. The last two decades (2003-2021) instead are characterized by a regular acquisition of aerial images of about 3 years. For each landslide, the triggering period was defined by the time interval between two consecutive image acquisition dates t(n) and t(n+1), the latter representing the oldest image where the landslide was recognized. The pre-landslide period was defined to correspond to the time interval preceding t(n), i.e. the youngest image acquired before landslide triggering. The computed daily precipitation maps were used for the analysis of intense rainfall events occurred during both pre-landslide and triggering periods, enabling the assessment of triggering daily precipitation associated to the landslide areas of the multitemporal inventory.
How to cite: Oliveira, E. R., D’Addario, E., Masoni, G., Pippi, M., and Disperati, L.: Daily rainfall data spatialization for the analysis of shallow landslide triggering conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18060, https://doi.org/10.5194/egusphere-egu25-18060, 2025.
