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 (c_R), 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.