Occurrence Of Shallow Landslides Research Articles

A comparative study of slope stability analysis via Geo5 using IoT framework

In mountainous regions, landslides pose significant and frequent threats, causing extensive damage due to their destructive nature and frequency, therefore determining their likely occurrence sites and environmental factors is essential for hazard assessment. The infinite slope approach characterizes slope stability using a factor of safety (FOS) to assess the likelihood of slope failure and is frequently used to estimate the occurrence of shallow landslides on soil and regolith-covered slopes. Various methods have been used to evaluate and spread uncertainty across such models. Slope failure is a significant geological occurrence brought on by topography and weather, which result in a variety of ground movements. Engineers must plan and implement measures to mitigate hazards, safeguarding both lives and property from potential risks and dangers by using an adequate stabilizing solution. Technology and software advancements have made it simpler than ever to handle challenging issues that used to require a lot of time in every profession. Over the past decade, software utilization in civil engineering has surged. GEO 5 is a versatile program gaining prominence, aiding in the resolution of diverse geotechnical challenges. Through the installation of IoT cameras in various sand and clay areas along the bank of the Mahandi River in Odisha, we have gathered the data necessary to develop the region in this case. a select selection of which we have chosen for our research. In this study, slope stability-related modules have been carefully examined and used for the analysis of slope stability. Using the GEO5 program, the geometry of the issues was established, and the study took into account several stability optimization techniques. Additionally, the cost factor of various reinforcing techniques was calculated and contrasted.

A Hydrodynamic‐Based Physical Unified Modeling for Simulating Shallow Landslide Local Failures, Mass Release and Debris Flow Run‐Out Extent Behavior

AbstractAccurate prediction of shallow landslide occurrence and the subsequent motion range after transformation into debris flows is crucial for reducing disaster‐induced losses. The use of Cellular Automata‐based (CA) hydrodynamic models has seen increasing application in predicting landslides, debris flows, and other related hazards. However, previous CA‐based models have primarily focused on the motion and evolution process of debris flows, lacking detailed description about the dynamic instability associated with shallow landslides. In this study, we propose a comprehensive CA‐based model that is based on existing theories and improved models for simulating hydrological processes, sliding surface identification algorithms, threshold‐based mechanical interactions, material entrainment, and deposition. The model was applied to the Yindongzi (YDZ) landslides in Sichuan, China. The accuracy of the model was validated through comparisons with field investigation results and calculations based on shallow water equations. This model enables efficient and rapid prediction of shallow landslide occurrence time, volume, spatial distribution, and runout distance. Evaluation of the model’s predictive performance reveals an error range of −23.12% to +44.26% for YDZ landslides. Moreover, the influence of different shallow landslide failure patterns on the deposition and erosion of debris flows was analyzed. The results indicate that the failure patterns of shallow landslides significantly affect the deposition and entrainment capacity of debris flows. This study provides a novel approach for predicting the occurrence of shallow landslides and the subsequent motion, entrainment, and deposition after transformation into debris flows.

Advancing landslide early warning systems through saturation monitoring and prediction

Landslides most commonly occur in the rainy season, resulting in damage to infrastructure and human lives. An early prediction framework for landslides would clearly help to mitigate damage. In this work, a prediction framework for shallow landslide initiation was developed and validated with a real case study. To assess the reliability of the prediction framework, back-analysis of a landslide that occurred in Malin village, Maharashtra, India on July 2014 was performed. Relations of landslide stability with soil saturation were established through a physically based approach using GeoStudio software. A leaky barrel algorithm was developed for the study location to monitor the effect of rainfall' on soil saturation. Simulation results of landslide stability were compared with the rainfall–soil saturation algorithm based on the leaky barrel. The presented framework was found to have good predictability of shallow landslide occurrence and is therefore recommended for real-time monitoring of landslide-prone locations.

Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions

During the next few decades, changes in rainfall frequency and magnitude are expected to have major impacts on landscape evolution, social, and economic aspects of human society. We focus on seasonal rainfall variations by the end of the twenty-first century to define affected landslide-prone areas, future landslide alerts and the impact of landslides on landscape development in the juncture of the Alpine, Pannonian, and Mediterranean region. A moderate and a worst-case climate scenario from CMIP5 global climate simulations were considered to determine the impact of rainfall on the two most common types of landslides in region, shallow and deep-seated landslides. The observed changes in the occurrence of shallow landslides are significant, especially in the winter months, where we can expect more landslide-prone areas compared to the baseline period. Shallow landslides will have a greater impact on the landscape in spring and summer than deep-seated landslides, especially in vineyards.

Modelling rainfall-induced landslides at a regional scale, a machine learning based approach

AbstractIn Italy, rainfall represents the most common triggering factor for landslides; thus, many Italian Regional Departments of Civil Protection are setting up warning systems based on rainfall recordings. Common methods are mainly based on empirical relationships that provide the rainfall thresholds above which the occurrence of landslide phenomena is likely to be expected. In recent years, the use of machine learning approaches has gained popularity in landslide susceptibility analysis and prediction. To support the operational early warning system of Liguria Civil Protection Department for landslides hazard, we propose the implementation of a polynomial Kernel regularized least squares regression (KRLS) algorithm, for predicting the daily occurrence of shallow landslides in the five Alert Zones in Liguria (North Western Italy). The model provides an estimate of the number of landslides associated with the set of three different hydrological features, also used for the Hydrological Assessment procedure: the soil moisture, the accumulated precipitation over 12 h and the precipitation peak over 3 h. Results of the model are converted to an Alert Scenario of landslide occurrence, based on the magnitude of the expected event and identified according to the National and Regional legislation (Regional Civil Protection guidelines D.G.R. n. 1116, 23/12/2020). The performance of the predictive model (e.g. accuracy of 93%) is deemed satisfactory and the methodology is considered a valuable support to the operational early warning system of Liguria Civil Protection Department. The choice of predictive variables allows, in future development, the values obtained from historical data to be replaced by those obtained from meteorological forecast models, introducing the use of the developed model in the operational forecasting chain.

Impact of Vegetation Differences on Shallow Landslides: A Case Study in Aso, Japan

Climate change has increased the frequency and scale of heavy rainfall, increasing the risk of shallow landslides due to heavy rainfall. In recent years, ecosystem-based disaster risk reduction (Eco-DRR) has attracted attention as one way to reduce disaster risks. Vegetation is known to increase soil strength through its root system and reduce the risk of shallow landslides. To reduce the risk of shallow landslides using vegetation, it is necessary to quantitatively evaluate the effects that vegetation has on shallow landslides. In this study, we constructed a generalized linear model (GLM) and random forest (RF) model to quantitatively evaluate the impact of differences in the vegetation, such as grasslands and forests, on the occurrence of shallow landslides using statistical methods. The model that resulted in the lowest AIC in the GLM included elevation, slope angle, slope aspect, undulation, TWI, geology, and vegetation as primary factors, and the hourly rainfall as a trigger factor. The slope angle, undulation, and hourly rainfall were selected as significant explanatory variables that contribute positively to shallow landslides. On the other hand, elevation and TWI were selected as significant explanatory variables that contribute negatively to shallow landslides. Significant differences were observed among multiple categories of vegetation. The probability of shallow landslide in secondary grasslands was approximately three times that of coniferous and broadleaf forests, and approximately nine times that of broadleaf secondary forests. The landslide probability of shrubs was approximately four times that of coniferous and broadleaf forests, and approximately ten times that of broadleaf secondary forests. The results of constructing the RF model showed that the importance was highest for the hourly rainfall, followed by geology, then elevation. AUC values for the GLM and RF model were 0.91 and 0.95, respectively, indicating that highly accurate models were constructed. We quantitatively showed the impact of differences in vegetation on shallow landslides. The knowledge obtained in this study will be essential for considering appropriate vegetation management to reduce the risk of future shallow landslides.

Predisposing Factors for Shallow Landslides in Alpine and Hilly/Apennines Environments: A Case Study from Piemonte, Italy

Landslides are the most common natural hazard in the Piemonte region (northwestern Italy). This study is focused on shallow landslides caused by the sliding of the surficial detrital-colluvial cover caused by rainfall and characterized by a sudden and fast evolution. This study investigates shallow landslide events compared with variables considered as main predisposing qualitative factors (lithology, pedology and land use) to obtain a zonation of shallow landslide susceptibility in a GIS environment. Additionally, wildfire occurrence is also evaluated as a further predisposing factor for shallow landslide initiation. The resulting susceptibility map shows a strong correlation between the first three variables and shallow landslide occurrence, while it shows a negligible, or very localized, relationship with wildfire occurrence. Through the intersection of the predisposing factors with the landslide data points, a map of homogeneous zones is obtained; each identified zone is characterized by uniform lithological, soil-type, and land-use characteristics. The shallow landslide density occurrence is computed for each zone, resulting in a four-range susceptibility map. The resulting susceptibility zones can be used to define and evaluate the hazard linked to shallow landslide events for civil protection and regional planning purposes.

Estimation of Shallow Landslide Susceptibility Incorporating the Impacts of Vegetation on Slope Stability

This study aimed to develop a physical-based approach for predicting the spatial likelihood of shallow landslides at the regional scale in a transition zone with extreme topography. Shallow landslide susceptibility study in an area with diverse vegetation types as well as distinctive geographic factors (such as steep terrain, fractured rocks, and joints) that dominate the occurrence of shallow landslides is challenging. This article presents a novel methodology for comprehensively assessing shallow landslide susceptibility, taking into account both the positive and negative impacts of plants. This includes considering the positive effects of vegetation canopy interception and plant root reinforcement, as well as the negative effects of plant gravity loading and preferential flow of root systems. This approach was applied to simulate the regional-scale shallow landslide susceptibility in the Dadu River Basin, a transition zone with rapidly changing terrain, uplifting from the Sichuan Plain to the Qinghai–Tibet Plateau. The research findings suggest that: (1) The proposed methodology is effective and capable of assessing shallow landslide susceptibility in the study area; (2) the proposed model performs better than the traditional pseudo-static analysis method (TPSA) model, with 9.93% higher accuracy and 5.59% higher area under the curve; and (3) when the ratio of vegetation weight loads to unstable soil mass weight is high, an increase in vegetation biomass tends to be advantageous for slope stability. The study also mapped the spatial distribution of shallow landslide susceptibility in the study area, which can be used in disaster prevention, mitigation, and risk management.

Temporal prediction of shallow landslides exploiting soil saturation degree derived by ERA5-Land products

ERA5-Land service has been released recently as an integral and operational component of Copernicus Climate Change Service. Within its set of climatological and atmospheric parameters, it provides soil moisture estimates at different soil depths, represeting an important tool for retrieving saturation degree for predicting natural hazards as shallow landslides. This paper represents an innovative attempt aiming to exploit the use of saturation degree derived from ERA5-Land soil moisture products in a data-driven model to predict the daily probability of occurence of shallow landslides. The study was carried out by investigating a multi-temporal inventory of shallow landslides occurred in Oltrepò Pavese (northern Italy). The achieved results follow: (i) ERA5-Land-derived saturation degree reconstructs well field trends measured in the study area until 1 m from ground; (ii) in agreement with the typical sliding surfaces depth, saturation degree values obtained since ERA5-Land 28–100 cm layer represent a significant predictor for the estimation of temporal probability of occurrence of shallow landslides, able especially to reduce overestimation of triggering events; (iii) saturation degree estimated by ERA5-Land 28–100 cm layer allows to detect soil hydrological conditions leading to triggering in the study area, represented by saturation degree in this layer close to complete saturation. Even if other works of research are required in different geological and geomorphological settings, this study demonstrates that ERA5-Land-derived saturation degree could be implemented to identify triggering conditions and to develop prediction methods of shallow landslides, thanks also to its free availability and constantly updating with a delay of 5 days.

Preliminary insights from hydrological field monitoring for the evaluation of landslide triggering conditions over large areas

Rainfall-induced landslides represent a major threat to human activities, and thus an improved understanding of their triggering mechanisms is needed. The paper reports some preliminary inferences on this topic, based on the data recorded over a 2-year period by a multi-parametric monitoring station located on one of the slopes of the Monterosso catchment (Cinque Terre, north-western Italy). This catchment has experienced multiple, concurrent shallow landslides after intense rainfall events. After defining a soil hydraulic model through data interpretation and numerical simulations, slope stability analyses were performed to elucidate several aspects related to shallow landslide occurrence. Both long-term climate conditions and single rainfall events were simulated via physically based approaches. The findings from these simulations enabled us to assume the pattern of infiltration and quantify the impact of soil hydraulic behavior on landslide triggering conditions. In this regard, various analyses were carried out on the same triggering event both at local scale and in the overall catchment, with a view to highlighting the role of initial soil moisture and soil hysteretic behavior in slope stability.

The nurse-plant effect under the dislodgement stress of landslides

While the mitigating effects of trees on shallow landslide occurrence are well recognised, the impact of landslides on tree community structure and tree-tree interactions have received much less research attention. The structures of tree communities before and after landslides were compared in a 25-ha subtropical forest plot. Tree-tree interactions were examined by analysing the pre- and post-landslide spatial point patterns of large (DBH ≥ 20 cm) and small (1 cm ≤ DBH < 20 cm) tree cohorts. In landslide scarps, 35 (34%) of 104 large trees and 467 (13%) of 3.072 small trees survived. Large (L) and small (S) tree cohorts were paired together for spatial analyses, including pre-landslide (PL) (LPL-SPL), surviving (S) (LS-SS), and missing (M) large-small tree paired cohorts (LM-SM). We randomly selected trees from the pre-landslide tree cohorts to create two virtual paired cohorts, the L34%-S13% and L66%-S87% paired cohorts, whose population sizes were identical to the field-observed LS-SS and LM-SM paired cohorts respectively, but with random spatial patterns. Post-landslide survival rates of trees increased monotonically with DBH. Large trees dislodged by landslides scarcely reduced small-tree survival. Evidence for this included: (i) the distance from small trees to the nearest large trees of the LM-SM paired cohort did not differ significantly from that of the virtual L66%-S87% paired cohort; (ii) survival rates of small trees near LM individuals did not differ significantly from those without large trees nearby. Surviving large trees had positive effects on the survival of small trees, indicated by: (i) the distance from small trees to the nearest large trees of the LS-SS paired cohort was significantly lower than that of the virtual L34%-S13% paired cohort; (ii) SS individuals clumped around LS individuals, whereas the virtual L34%-S13% spatial relationship was random. Large trees prevent landslide dislodgement of adjacent small trees through the nurse-plant effect. Our study suggests that landslide damage in sloping forests may be reduced simply by constantly maintaining a critical density of large trees.

Deep learning forecast of rainfall-induced shallow landslides

Rainfall triggered landslides occur in all mountain ranges posing threats to people and the environment. Given the projected climate changes, the risk posed by landslides is expected to increase, and the ability to anticipate their occurrence is key for effective risk reduction. Empirical thresholds and physically-based models are used to anticipate the short-term occurrence of rainfall-induced shallow landslides. But, evidence suggests that they may not be effective for operational forecasting over large areas. We propose a deep-learning based strategy to link rainfall to landslide occurrence. We inform and test the system with rainfall and landslide data available for the last 20 years in Italy. Our results indicate that it is possible to anticipate effectively the occurrence of rainfall-induced landslides over large areas, and that their location and timing are controlled primarily by the precipitation, opening to the possibility of operational landslide forecasting based on rainfall measurements and quantitative meteorological forecasts.

Exploring the Potential of Portable Spectroscopic Techniques for the Biochemical Characterization of Roots in Shallow Landslides

In the present work, Raman, Fourier Transform Infrared (FTIR) and elemental Laser-Induced Breakdown Spectroscopy (LIBS) spectroscopic techniques were used for the assessment of the influence of plant root composition towards shallow landslide occurrence. For this purpose, analyses were directly carried out on root samples collected from chestnut forests of the Garfagnana basin (northern Apennines, Italy) in different areas devoid and affected by shallow landslides due to frequent heavy rain events. Results have highlighted a correlation between the biochemical constituents of wooden roots and the sampling areas. In particular, different content of lignin/cellulose, as well as minerals nutrients, have been detected in roots collected where shallow landslides occurred, with respect to more stable areas. The results achieved are in line with the scientific literature which has demonstrated the link between the chemical composition of roots with their mechanical properties and, in particular, tensile strength and cohesion. Finally, portable spectroscopic instrumentations were employed without the need for either any sample preparation for Raman and LIBS spectroscopy or minimal preparation for FTIR spectroscopy. This novel and fast approach has allowed achieving information on the content of the major constituents of the root cell, such as cellulose and lignin, as well as their mineral nutrients. This approach could be reasonably included among the vegetation protection actions towards instability, as well as for the evaluation of shallow landslide susceptibility, combining geological, vegetational and biochemical parameters with sustainability.

Modeling probabilistic-based reliability assessment of gridded rainfall thresholds for shallow landslide occurrence due to the uncertainty of rainfall in time and space

Abstract This study aims to model a probabilistic-based reliability assessment of the gridded rainfall thresholds for shallow landslide occurrence (RA_GRTE_LS) to quantify the effect of the uncertainty of rainfall in time and space on the rainfall thresholds under consideration of local soil properties. The proposed RA_GRTE_LS model is developed by coupling the uncertainty analysis with the logistic regression equation using a significant number of the landslide-derived rainfall thresholds of the specific warning times. The 30 historical gridded hourly rainstorms at 10 study grids in the study area (Jhuokou River watershed) are used in 1,000 simulations of rainfall-induced shallow landslides under an assumption of the soil layer of 310 cm. The results reveal that the shallow landslide in the study area probably occurs at the time step of less than the 36th hour around the bottom of the soil layer (about 275 cm) during a rainstorm; also, using the proposed RA_GRTE_LS model, the resulting rainfall thresholds and quantified reliabilities, especially for the warning time of less than 18 h, exhibit a sizeable varying trend in space due to the variations in rainfall and soil properties; accordingly, the short-term rainfall thresholds for shallow landslide occurrence could be locally determined under acceptable reliability.

Effect of Rainfall Intensity on Landslide Initiation: Flume Tests and Numerical Analysis

This paper seeks to investigate the effect of rainfall intensity on the occurrence of shallow landslides by means of a series of flume tests. Coarse-grained material was used to build a slope, and several rainfall events with an intensity of either 40 mm/h, 70 mm/h, or 100 mm/h were simulated to initiate slope failure. A set of pore water pressure and moisture content sensors was installed in the slope to monitor changes in the water conditions during each test. Different initial moisture contents of 5% and 10% of the soil mass were used to better understand the effect of moisture on slope stability during rainfall. It was found that the slope failed when intensities of 70 mm/h and 100 mm/h were used; however, no failure was observed with a rainfall intensity of 40 mm/h. The failure patterns were found to be similar, with progressive slides occurring as more water infiltrated the slope. A numerical procedure to estimate the factor of safety over the period of the rainfall event was proposed and validated against the laboratory data. The results of the numerical analysis yielded the failure time, which was close to the time observed in the flume tests.

Rainfall-induced debris flows and shallow landslides in Ribeira Valley, Brazil: Main characteristics and inventory mapping

Debris flows are one of Brazil's most frequent mass movement processes, triggered by extreme rainfall events and initial volume provided by shallow landslides. Despite the recurrence of catastrophic occurrences, Brazil still lacks basic data containing the main characteristics of previous events. In this way, this research aimed to make a morphometric characterization of the event and to provide debris-flow and shallow landslides inventories. For the morphometric analysis, a Digital Elevation Model (DEM) was used. For the inventory map of debris flow runout and shallow landslides scars, a post-event free access image from Google Earth Pro and satellite images from RapidEye were used. The results show that debris flows had two main flows that affected different areas of the city of Itaóca. Also, one single shallow landslide contributed as initial volume to the debris flows that reach the city downtown, demonstrating the importance of entrainment. Shallow landslides analysis shows its concentration in slopes between 20.1 – 30°, with orientation South and Southeast, elevation between 600 – 800m, and in concave curvatures. The results helped to better understand debris flows in Brazil, highlighting their relationship with the occurrence of shallow landslides as one of the main triggering factors. Those data are crucial to mitigation action of possible new events.

A Simple Method for Predicting Rainfall-Induced Shallow Landslides

A method of practical interest is presented in this study for predicting shallow landslide triggering due to expected rainfall scenarios. The proposed approach is based on some closed-form expressions derived using physically-based models and requires a limited number of material parameters obtained from conventional tests. Two typical triggering mechanisms are considered: in the first case, a landslide is caused by a reduction in suction due to rain infiltration in unsaturated soils; in the second one, a slope failure occurs due to the formation of a water table with consequent generation of positive pore water pressures within the slope. A simple criterion is also provided to establish which triggering mechanism occurs. For each considered mechanism, a rainfall threshold curve is derived, which is formally similar to several empirical intensity-duration relationships proposed in the literature for predicting the occurrence of shallow landslides. However, unlike these empirical relationships, the present threshold curves explicitly depend on basic parameters such as slope geometry, soil properties, and pore water pressure existing at the depth of the potential slip surface before the rainfall. The proposed approach is also applied to well-documented case studies to assess its predictive capacity.

Rainfall Threshold for Shallow Landslide Triggering Due to Rising Water Table

In the present study, a simple-to-use method is proposed for a preliminary prediction of the occurrence of shallow landslides (generally, with a thickness of 1–2 m) due to rainfall. This method can be used when a water table forms within the slope or the existing groundwater level rises due to rain infiltration, resulting in an increase in the pore water pressure. A relationship is also provided to establish when these conditions occur and the method can consequently be used. The proposed method combines a simplified solution to evaluate the change in pore water pressure within the slope due to infiltration, with the simple scheme of infinite slope to calculate a critical value of the pore water pressure that determines the incipient failure condition of the slope. In this way, a threshold curve can be also determined to readily assess whether a rainfall event with expected intensity and duration is capable of causing a slope failure at a given depth, where the initial pore water pressure is known. The method is completely analytical and only requires a few parameters as input data, which in addition can be obtained from conventional tests. A well-documented case study is considered to show how the method can be used for routine applications.

Modeling snowmelt influence on shallow landslides in Tartano valley, Italian Alps

Shallow landslides (SLs) are rapid soil mass movements, typically occurring in the mountain areas, involving the most superficial soil layers up to 5 to 10 m in depth. Damages, and casualties due to shallow landslides are recorded globally, and in literature a variety of models to study landslides have been implemented hitherto. Often times, shallow landslides occur in the wake of snowfall events, when sudden temperature increase triggers fast snow thaw, and soil moisture increases thereby. Several models studied the influence of intensity, and duration of rainfall upon shallow landslides, but the effect of snow melt in spring/summer was little considered so far. Thus, we developed a simple but robust, and parameter-wise parsimonious model, that mimics the triggering mechanism of SLs, accounting for the combined effect of precipitation duration and intensity, and snowmelt at thaw. The model is here applied to the case study of the high altitude Tartano basin, paradigmatic of SLs in the Alps of Lombardia. Our results showed that about 26 % of the Tartano basin slopes display unstable conditions. Using a traditional (i.e. rainfall-based) approach, the occurrence of shallow landslides was predicted in ca. 19 % of the basin, mainly during storms in October and November. In contrast, when snowmelt was included, the model was able to mimic potential SLs even during April and May, when snow melt rate is the highest, and may increase SLs triggering potential, to ca. 26 % of the treated area. With better spatial and temporal description of slope failure as achieved here, validated against observed failures, a public authority may be prepared to implement emergency plans, to prevent injuries, causalities, and damages to infrastructures even during springtime, when shallow landslides may occur in response to fast snowmelt, even during dry, clear sky days, and with scarce/null precipitation.

Introducing SlideforMAP: a probabilistic finite slope approach for modelling shallow-landslide probability in forested situations

Abstract. Shallow landslides pose a risk to infrastructure and residential areas. Therefore, we developed SlideforMAP, a probabilistic model that allows for a regional assessment of shallow-landslide probability while considering the effect of different scenarios of forest cover, forest management and rainfall intensity. SlideforMAP uses a probabilistic approach by distributing hypothetical landslides to uniformly randomized coordinates in a 2D space. The surface areas for these hypothetical landslides are derived from a distribution function calibrated on observed events. For each generated landslide, SlideforMAP calculates a factor of safety using the limit equilibrium approach. Relevant soil parameters are assigned to the generated landslides from log-normal distributions based on mean and standard deviation values representative of the study area. The computation of the degree of soil saturation is implemented using a stationary flow approach and the topographic wetness index. The root reinforcement is computed by root proximity and root strength derived from single-tree-detection data. The ratio of unstable landslides to the number of generated landslides, per raster cell, is calculated and used as an index for landslide probability. We performed a calibration of SlideforMAP for three test areas in Switzerland with a reliable landslide inventory by randomly generating 1000 combinations of model parameters and then maximizing the area under the curve (AUC) of the receiver operation curve. The test areas are located in mountainous areas ranging from 0.5–7.5 km2 with mean slope gradients from 18–28∘. The density of inventoried historical landslides varies from 5–59 slides km−2. AUC values between 0.64 and 0.93 with the implementation of single-tree detection indicated a good model performance. A qualitative sensitivity analysis indicated that the most relevant parameters for accurate modelling of shallow-landslide probability are the soil thickness, soil cohesion and the precipitation intensity / transmissivity ratio. Furthermore, we show that the inclusion of single-tree detection improves overall model performance compared to assumptions of uniform vegetation. In conclusion, our study shows that the approach used in SlideforMAP can reproduce observed shallow-landslide occurrence at a catchment scale.