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Powerlines are strategic infrastructures for the Italian electro-energetic network, and natural threats represent a potential risk that may influence their operativity and functionality. Geo-hydrological hazards triggered by heavy rainfall, such as shallow landslides, have historically affected electrical infrastructure networks, causing pylon failures and extensive blackouts. In this work, an application of the reworked version of the model proposed by Borga et al. and Tarolli et al. for rainfall-induced shallow landslide hazard assessment is presented. The revised model is called SLEM (Shallow Landslide Express Model) and is designed to merge in a closed-from equation the infinite slope stability with a simplified hydrogeological model. SLEM was written in Python language to automatise the parameter calculations, and a new strategy for evaluating the Dynamic Contributing Area (DCA) and its dependence on the initial soil moisture condition was included. The model was tested for the case study basin of Trebbia River, in the Emilia-Romagna region (Italy) which in the recent past experienced severe episodes of geo-hydrological hazards. The critical rainfall ratio (rcrit) able to trigger slope instability prediction was validated against the available local rainfall threshold curves, showing good performance skills. The rainfall return time (TR) was calculated from rcrit identifying the most hazardous area across the Trebbia basin with respect to the position of powerlines. TR was interpreted as an index of the magnitude of the geo-hydrological events considering the hypothesis of iso-frequency with precipitation. Thanks to its fast computing, the critical rainfall conditions, the temporal recurrence and the location of the most vulnerable powerlines are identified by the model. SLEM is designed to carry out risk analysis useful for defining infrastructure resilience plans and for implementing mitigation strategies against geo-hazards. [ABSTRACT FROM AUTHOR]

Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers. [ABSTRACT FROM AUTHOR]

The southern Apuan Alps (Italy) are prone to rainfall-induced landslides. A first attempt to calculate rainfall thresholds was made in 2006 using non-statistical and repeatable methods for the 1975–2002 period. This research aims to update, validate, and compare the results of that attempt through different statistical approaches. Furthermore, a new dataset of rainfall and landslides from 2008 to 2016 was collected and analyzed by reconstructing the rainfall events via an automatic procedure. To obtain the rainfall thresholds in terms of the duration–intensity relationship, we applied three different statistical methods for the first time in this area: logistic regression (LR), quantile regression (QR), and least-squares linear fit (LSQ). The updated rainfall thresholds, obtained through statistical methods and related to the 1975–2002 dataset, resulted in little difference from the ones obtained with non-statistical methods and have similar efficiency values among themselves. The best one is provided by the LR, with a landslide probability of 0.55 (efficiency of 89.8%). The new rainfall thresholds, calculated by applying the three statistical methods on the dataset from 2008–2016, are similar to the 1975–2002 ones, except for the LR threshold, which exhibits a higher slope. This result confirms the validity of the thresholds obtained with the old database. [ABSTRACT FROM AUTHOR]

Landslide hazard assessment is an important component of risk management and land-use planning. This study aims to investigate the application of a physically-based model named after the fast shallow landslide assessment model (FSLAM) to rainfall-triggered landslide hazard assessment. In August 2015, a total of 123 landslides induced by Typhoon Soudelor in Wenzhou City, southeastern China, was taken as an example. Five input raster files (elevation, soil types, vegetation, antecedent rainfall, event rainfall) and two parameter files regarding soil properties and vegetation were determined. Considering the randomness and uncertainty of soil and vegetation parameters on the regional scale, FSLAM model computes the probability of failure (PoF) by using random parameters inputs. Finally, the landslide hazard map was generated for the study area to reflect the landslide risk. The results showed that FSLAM could accurately capture the effect of rainfall on PoF of slopes, and more than 70% of the landslide were identified in very high/high hazard zones. The accuracy of the receiver operating characteristic (ROC) reached 0.720, which was higher than that of the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (0.620). Regarding the computational time, FSLAM had better efficiency, and the consuming time was 1/25 compared with TRIGRS. [ABSTRACT FROM AUTHOR]

In this work, we evaluated the influence of root structure on shallow landslide distribution. Root density measurements were acquired in the field and the corresponding root cohesion was estimated. Data were acquired from 150 hillslope deposit trenches dug in areas either devoid or affected by shallow landslides within the Garfagnana Valley (northern Tuscany, Italy). Results highlighted a correlation between the root reinforcement and the location of measurement sites. Namely, lower root density was detected within shallow landslides, with respect to neighboring areas. Root area ratio (RAR) data allowed us to estimate root cohesion by the application of the revised version of the Wu and Waldron Model. Then, we propose a new method for the assimilation of the lateral root reinforcement into the infinite slope model and the limit equilibrium approach by introducing the equivalent root cohesion parameter. The results fall within the range of root cohesion values adopted in most of the physically based shallow landslide susceptibility models known in the literature (mean values ranging between ca. 2 and 3 kPa). Moreover, the results are in line with the scientific literature that has demonstrated the link between root mechanical properties, spatial variability of root reinforcement, and shallow landslide locations. [ABSTRACT FROM AUTHOR]

Landslides triggered by heavy rainfall pose significant threats to human settlements and infrastructure in temperate and equatorial climate regions. This study focuses on the development of the Open Landslide Project (OLP), an open source landslide inventory aimed at facilitating geostatistical analyses and landslide risk management. Using a multidisciplinary approach and open source, multisatellite imagery data, more than 3000 landslides triggered by the extreme rainfall of autumn 2019 in northwestern Italy were systematically mapped. The inventory creation process followed well-defined criteria and underwent rigorous validation to ensure accuracy and reliability. The dataset's suitability was confirmed through multivariate correlation and Double Pareto probably density function. The OLP inventory effectiveness in assessing landslide risks was proved by the development of a landslide susceptibility model using binary logistic regression. The analysis of rainfall and lithology revealed that regions with lower rainfall levels experienced a higher occurrence of landslides compared to areas with higher peak rainfall. This was attributed to the response of the lithological composition to rainfalls. The findings of this research contribute to the understanding and management of landslide risks in anthropized climate regions. The OLP has proven to be a valuable resource for future geostatistical analysis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Shallow landslides in Souk Ahras region, NE Algeria, occur usually after prolonged rainfall events. Three localities which are Zaarouria, Hammam Tassa, and Mechroha were chosen to study these landslides and the conditions of their occurrences. They have been observed to affect mainly agricultural lands, main supplies, and the road network. This paper aims to find the relationship between the rainfall, the soil engineering properties, the geology, and the geomorphology. A number of field trips were organized to the study areas where landslide masses were described and mapped as well as some field measurement such as natural moisture content and sampling. In the laboratory, we establish gain size distribution, Atterberg limits, density, and effective cohesion and internal friction. Then we established the unsaturated soil property functions such as the soil water characteristic curve (SWCC) and the hydraulic function. The stability computation was undertaken using, GeoStudio software 2020, a coupled analysis where the Seep/W simulates the seepage and the Slope/W for the safety factor. The results show that the sector study containing: silty sand (SM), well graded sand with silt and gravel (SW-SM), and clayey sand with Gravel (SC) while hydraulic conductivity varies between 4.5. 10−6 for 4.10−5 and 10−3 m/s. The factor of safety varies between 0.8, 1.16, and 0.8. In conclusion, our study confirms the field observations as shallow landslides have been numerous in mars 2019 when rainfall reaches 110 mm. Moreover, high permeability soils, in Mechrouha, show a more dramatic decrease in the factor of safety as the 100 = -mm rainfall amount is reached while it decreases gradually for the other two less permeable soils of Zaarouria and Hammam Tassa. [ABSTRACT FROM AUTHOR]

Slope instability occurrences as damaging shallow-landslides in the residual soil around mountains has been widely studied with geophysical, geotechnical and hydrogeological techniques but relating soil electrical resistivity to hydraulic conductivity for characterisation of lithology inducing of these landslides is not common. In this study, we used Electrical Resistivity Tomography (ERT) data and Hydraulic Conductivity (HC) data obtained from soil samples collected within 1–4.5 m depth in the borehole to assess the characteristics of soil that can induce landslide in the study location. The HC data were derived empirically from Beyer, Kozeny-Carman and Slitcher formula which were validated with HC obtained from laboratory experiment. The Empirical Derived Hydraulic Conductivities (EDHC) were correlated with the soil resistivity. The result shows a strong correlation between soil resistivity and HC with regression values of R2 = 0.9702, R2 = 0.9153 and R2 = 0.9232 for Beyer, Kozeny-Carman and Slitcher formula, respectively. The ERT model revealed a possible sliding surface between two contrasting resistive top material and underneath conductive materials at about 4 m depth. The HC assessment result corroborated the ERT model result because high and low-HC values were obtained in the borehole soil samples within 0–4 m and > 4 m depths from EDHC, respectively. The low-HC zone below 4 m depth was responsible for the occurrences of the shallow-landslides in the study. Key points: Relationship between soil electrical resistivity and hydraulic conductivity were examined for assessment of lithology inducing shallow-landslide. Soil electrical resistivity delineated a boundary between two contrasting resistive and conductive lithologies at 4 m depth. Strong correlation ranged between R2 = 0.9153 to 0.9702 were observed between soil resistivity and hydraulic conductivity for the soil samples examined. The conductive lithology with low hydraulic conductivity below 4 m depth was responsible for the occurrences of the shallow-landslides. [ABSTRACT FROM AUTHOR]

Fujian Province is one of the most frequent areas of landslide disasters in China, so the study of landslide susceptibility in this region is of great significance. Based on the mass landslides that occurred in Sanming, Fujian in 2019, the study used DEM data and aerial imagery to sort out the area, elevation, lithology, and road or building proximity of 131 landslides, and calculated the slope, profile curvature, and plan curvature of the landslides after improving the elevation matrix according to the geometry of landslides. Normalized landslide frequency was used to calculate the contribution of each causative factor to landslide disasters, so as to establish logistic regression models of landslide susceptibility on different scales. It was found that the model based on the 500 m * 500 m grid was the most suitable for evaluating landslide susceptibility. However, the logical regression model is deficient, not only the number of samples should reach more than 50, but also the incidence rate should be at least 30–70%, in order to avoid the model being not significant caused of the small number of samples and the sample imbalance, resulting in too many underestimated or overestimated samples. [ABSTRACT FROM AUTHOR]

A 6.6-Mw earthquake struck the Iburi region of Hokkaido, Japan, in 2018, triggering massive landslides. Most of these landslides were shallow and occurred mostly in the Atsuma and Abira towns. Ta-c and Ta-d tephra layers have been found in the Towa landslide at Atsuma from the Tarumai volcano, while Ta-d, En-a, and Spfa-1 tephra layers have been found in the Mizuho landslide at Abira from the Tarumai and Eniwa volcanos, as well as the Shikotsu caldera. Field observations from previous studies revealed that the sliding layers were located in the Ta-d and En-a layers at the Towa and Mizuho landslides, respectively. Unlike previous research on earthquake-induced landslides, which were investigated using mechanical properties, this study investigates the characteristics of physical properties, saturated permeability properties, and content of clay minerals on sliding and surrounding tephra layers. Results from this study reveal that the physical properties of sliding layers from two landslides demonstrated the same characteristics: non-plastic soil with a low density of soil particles, void ratio, and dry density; these characteristics could influence earthquake-induced landslides. It also reveals a relationship between the plasticity chart and the age of tephra materials, including the relationship between the weathering process and density of soil particles and the dissimilarity in characteristics of saturated permeability properties in tephra materials. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to compare the performance of Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), and Bayesian Logistic Regression (BLR) algorithms for landslide susceptibility modeling in the Yozidar-Degaga region, Iran. Initially, a distribution map with 175 landslides and 175 non-landslide locations was prepared and the data were classified into a ratio of 80% and 20% for training and model validation, respectively. Based on Information Gain Ratio (IGR) technique, 13 derived factors from topographic data, land cover and rainfall were selected for modeling. Then, the SVM, SGD, and BLR algorithms were selected based on size of the data and required accuracy of the output, to learn and prepare landslide susceptibility maps. Statistical criteria were employed to evaluate the models for both training and validation datasets. Finally, the performance of these models was evaluated by the area under the receiver operating curve (AUC). The results showed that SVM algorithm (AUC = 0.920) performed better than SGD (AUC = 0.918) and BLR (AUC = 0.918) algorithms. Therefore, the SVM model can be suggested as a useful tool for better management of landslide-affected areas in the study area. In this study, all three models (SVM, SGD and BLR) were implemented in WEKA 3.6.9 software environment to prepare landslide susceptibility maps. [ABSTRACT FROM AUTHOR]

By utilizing the Green-Ampt infiltration equation and the infinite slope stability model, a method for analyzing shallow slope failures caused by rainfall is developed. With rainfall intensity, soil characteristics, and topography, the modified Green-Ampt infiltration equation is used to estimate the rainfall infiltration capacity and depth of infiltration in a given slope. Assigning the calculated depth of infiltration as the depth of slip surface, the factor of safety of the slope is obtained through the infinite slope stability model. A time-series visualization map of the space-time varying factor of safety is generated when the method is implemented with the aid of Geographic Information System (GIS) software. The model is applied and validated with the landslides that occurred during the October 2019 Typhoon Hagibis in Marumori, Japan. The model results show good agreement with the reported time and depths of failure, and the analysis of the spatial distribution of predicted failures yielded receiver operating characteristic-area under the curve (ROC-AUC) value of 0.90. The applicability of the model can be extended for post-analysis, real-time, or projected assessment of slope stability, depending on the nature of input rainfall data (e.g., historical, real-time, forecast, hypothetical). [ABSTRACT FROM AUTHOR]

The existence of shallow landslide brings huge threats to the human lives and economic development, as the Lang County, Southeastern Tibet prone to landslide. Landslide susceptibility mapping (LSM) is considered as the key for the prevention of hazard. The primary goal of the present study is to assess and compare four models: classification and regression tree, gradient boosting decision tree (GBDT), adaptive boosting-decision tree and random forest for the performance of landslide susceptibility modeling. Firstly, a landslide inventory map consisting of 229 historical shallow landslide locations was prepared and the same number of non-landslide points was determined by k-means clustering. Secondly, 12 conditioning factors were considered in the landslide susceptibility modeling. The prediction performance of the four models were estimated by fivefold cross validation and relative operating characteristic curve (ROC), area under the ROC curve (AUC) and statistical measures. The results showed that the GBDT performed best in the training and validation dataset, with the highest prediction capability (AUC = 0.986 and 0.940), highest accuracy value (95.3% and 88.1%) and highest kappa index (0.904 and 0.772), respectively. Therefore, the GBDT was considered to be the most suitable model and applied to the whole study area for LSM. The results of this study also demonstrate that the performance can be enhanced with the use of ensemble learning. The sampling strategy of non-landslide points can be improved by combining with clustering analysis which are more reasonable. [ABSTRACT FROM AUTHOR]

Destructive landslides were triggered by the 6.7 Mw Eastern Iburi earthquake that struck southern Hokkaido, Japan, on 6 September 2018. In this study, we carried out 1-D inversion analysis of one of the shallow landslides near the epicenter using a Bing debris-flow model. At this site, the slope failure comprised cover soil with an initial down-slope length of ~ 80 m and a thickness of ~ 7 m on a slope with < 20° dip. The landslide moved southeastward with a run-out distance of ~ 100 m. Inversion analysis of the post-failure deposit geometry was conducted with the Markov Chain Monte Carlo method (MCMC) to optimize the Bingham rheological parameters of the debris. The analysis reproduced several features of the deposit geometry with a yield stress of ~ 1500 Pa and dynamic viscosity of 800–3000 Pa s. The results suggest that the shallow landslide can be approximated by the flow of a viscoplastic fluid with high-mobility debris and a maximum frontal velocity of 6–9 m/s, with a flow duration of 2–4 min. [ABSTRACT FROM AUTHOR]

Slopes covered with unsaturated shallow pyroclastic deposits, lying upon fractured limestone bedrock, are widespread in the mountains around Naples (southern Italy). Rainfall infiltration, reducing soil suction, eventually triggers shallow landslides. While drastic reduction of suction is unanimously recognized as the triggering mechanism, there is still debate about the hydrological processes controlling slope drainage and causing the establishment of landslide predisposing conditions. Field observations at the slope of Cervinara suggested that temporary storage of water in a perched aquifer, in the upper part of the fractured bedrock, may affect the leakage through the soil-bedrock interface. Hence, a physically based model, coupling flows in the unsaturated soil cover and in the perched aquifer, has been applied to three large rainfall events which occurred in December 1999 (when a landslide was triggered), January 2009, and November 2012. The results highlight that the different responses of soil and aquifer to precipitations, related not only to rainfall event characteristics (i.e., duration and mean intensity) but also to the initial conditions of the slope, determined by antecedent precipitations, can play a prominent role in the triggering of landslides. In fact, further simulations with synthetic rainfall events and different initial conditions provide a possible interpretation of the triggering of the landslide of December 1999, indicating that a soil profile with dry conditions at the base and a low level in the perched aquifer, typical of late autumn, can impede the drainage of infiltrating water through the soil-bedrock interface, thus favoring the build-up of pore pressure within the soil cover. [ABSTRACT FROM AUTHOR]

Shallow landslides have posed significant threats to humans around the world. In order to reduce landslide disaster risk, the effectiveness of early warning systems and hazard zonation work needs to be improved. This research attempted to couple a landslide simulation model (LS-RAPID model) and a hydrological model (Rainfall-Runoff-Inundation (RRI) model) to exploit the advantages of each model for simulating and predicting landslide hazard (location and timing). The pilot area is a small catchment where a shallow landslide happened in July 2015 after 2 days of heavy rain. The landslide buried 3 houses and killed 8 people in Cao Thang Ward, Halong City, Vietnam. A soil sample was collected from the sliding surface and tested using an undrained ring-shear apparatus ICL-2 in the undrained condition. A thickness map of the potential sliding material was interpolated from the relationship between the depth of sandy soil layer and slope (based on 12 soil drill locations) and updated through field surveys in the study area. Different pore water pressure ratio scenarios were applied in the LS-RAPID model to simulate in 3D the initiation and motion of the rapid shallow landslide to create different hazard maps. The subsurface water level was monitored at two locations on the top of the shallow landslide. Based on the observed subsurface water and rainfall data, the RRI model was calibrated and then integrated with the LS-RAPID scenarios to generate Risk Index maps. The simulation results from the newly proposed coupled hydrological-geotechnical framework were compared with those from the observed landslide hazard and showed the reliability to predict the spatial and temporal occurrence of landslide hazard. This could be very useful for supporting decision-makers in rainfall-induced landslide hazard early warning and land use planning. [ABSTRACT FROM AUTHOR]

On 2 September 2018, an intense rainstorm swept Mengdong Town in Yunnan Province of China, inducing a serious landslide and debris flow disaster with 10 deaths and 11 missing. Image interpretation, field survey, and slope stability analysis were used to examine the characteristics and initiation mechanism of this hazard. A total of 1774 landslide scars were identified, the area of which occupied 8.26% of the study region. Due to the spatial inhomogeneity of precipitation, these scars mainly concentrated along valleys in the lower part of the study area. Besides, well-forested hillslopes were more prone to landslides, indicating the limited role of trees in stabilizing slopes in extreme rainfall events. The initiation of landslides is mainly attributed to the week cohesion of the saturated clayey sand beneath the root zone, where the tensile resistance of roots was absent, and the increase of positive pore water pressure. Additionally, 288 landslide scars were situated adjacent to roads, demonstrating that the road construction activity had intensified the landslide disaster. Owing to the relatively low mobility of landslides, a considerable portion of landslide debris deposited on the valley floor in smaller watersheds (< 6.03 hm2), while the remaining portion entered high-order channels. In these channels, where stream power was relatively large, the woody debris and soil carried by landslides were entrained by streamflow, and debris flows were formed. Moreover, the magnitude of debris flow was amplified by the vertical and lateral erosion of the stream channel. [ABSTRACT FROM AUTHOR]

Rainfall-induced diffuse shallow landslides are one of the most critical natural hazards as they often evolve into highly destructive flow slides and debris flows. Vegetation is recognised to play a key role in landslide occurrence and is frequently invoked as a potential remedial measure for slope stabilisation at the catchment scale. The beneficial action of vegetation is generally associated with mechanical (root anchoring) and hydrological (suction generated by root water uptake) effects. There is indeed a third effect that has thus far been little explored. The rhizosphere, the portion of soil directly affected by plant roots, is characterised by hydraulic conductivity higher than the underlying soil horizons. This significantly affects hillslope hydrology by promoting lateral diversion of rainwater. This paper presents a case study in Scotland where the rhizosphere is demonstrated to play a major role in controlling shallow landslides. Field investigation and laboratory testing were carried out to characterise the hydraulic conductivity of the rhizosphere and deeper horizons. In turn, this formed the basis for the development of a physically based model for the slope. The model was first validated against its capability to simulate failure of two historical landslides and then exploited to demonstrate the beneficial effect of the rhizosphere. The lesson learned from this study is that shallow landslide hazard can be mitigated by enhancing the capacity of the rhizosphere to act as a natural lateral drainage. This implies that plants with root-system architecture that enhances lateral subsurface flow should be privileged when designing vegetation-based remedial measures. [ABSTRACT FROM AUTHOR]

In mechanism studies on sudden sediment source areas (SSAs), such as those of shallow landslides, area-specific soil strength information at the soil layer between saturated and unsaturated soil is an important factor. Although various soil-testing methods have been developed, it is difficult to measure soil strength directly due to local variation. Therefore, the purpose of this study is to estimate SSAs of shallow landslides by considering the soil strength attributes reflected in a simple subsurface hydrologic concept (SHC). The selected study area is a granitic region in Jinbu, eastern South Korea, where shallow landslides occurred at more than 1200 locations in 2006. To estimate SSAs, soil strength was generated using several methods (i.e., direct shear tests [DSTs], Monte Carlo-simulated direct shear tests [MSDTs], and triaxial compression test [TCTs]). In addition, to investigate the effects of soil attributes in saturated/unsaturated soil on the SHC, soil depth was surveyed at a small hillslope site within the large study area. A topographically driven physical shallow landslide stability model (SHALSTAB) was utilized to investigate the performance based on the various soil strength attributes, and the results were analyzed using receiver operating characteristic (ROC) analyses. In the evaluation using the three soil strength measures (Case I), the accuracy assessment of the estimated SSAs was relatively low (range of 0.45–0.71). Using the soil strength attribute reflecting SHC (Case II), the results showed a reasonable range of accuracy (0.84–0.85). In addition, using the effects of soil strength reflected by SHC and average soil depth (Case III) also showed good predictive accuracy (0.81–0.82), but it was lower than that of Case II. This indicates that combining the hydraulic properties related to soil strength facilitates more accurate identification of SSAs causing geomorphic surface changes. [ABSTRACT FROM AUTHOR]

Physically-based models are reliable techniques for landslide susceptibility assessment, therefore, several models have been proposed in the literature. In this study, two different hydrological models (FSLAM and TRIGRS) and two slope stability calculation methods (infinite slope and limit equilibrium method with spherical failure surface in SCOOPS3D) were compared. The rainfall-triggered shallow landslides that occurred in Kaptanpaşa, Rize (Turkey) in 2017 were selected as a case study. The main focus is investigating the impact of landslide morphology on the success of the slope stability models. For this purpose, landslides in the inventory were grouped according to their morphological characteristics, such as the landslide depth, length, width and their ratios, and their compatibility with model assumptions (e.g. the infinite slope assumption). Then, the model performances in different landslide types were evaluated separately. To calibrate the model parameters a new time-saving, semi-automated calibration strategy, based on a sensitivity analysis, is proposed. Concretely, stability parameters c ′ ∅ ′ Z were calibrated via a MATLAB code by considering the area under the curve (AUC) and extreme conditions: saturated and initial conditions. Overall, SCOOPS3D is deduced to have better performance than infinite slope analysis. However, the accuracy of SCOOPS3D is revealed to decrease for landslides with a high ratio of length/depth and width/depth. From the comparison of hydrological models, FSLAM yielded more plausible results than TRIGRS with the available data. It is demonstrated that the morphological features of the landslides significantly influence the performance of the physically-based stability models. Furthermore, it is highlighted that the resolution of the Digital Elevation Model in comparison to the width and length of the landslides in an inventory can be influential in the performance of the physical model. 10 m resolution of DEM is deemed to be suitable for the study area judging the predictive performance in small landslides. • Landslide morphology affects physically-based landslide susceptibility assessments. • A new calibration strategy using stability parameters c ′ ∅ ′ Z via a MATLAB code. • Validation using 1025 shallow landslides triggered by rainfall in Rize,Turkey, 2017. • TRIGRS, FSLAM and SCOOPS3D are compared for landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]

• Multihazard approach considering rainfall-induced landslide and flooding. • Regional-scale, spatio-temporal simulation of slope stability and flood inundation. • Applied to the October 2019 typhoon event in Marumori, Japan. Landslides and flooding are two of the most common hazards associated with heavy rainfall. Most studies, however, tackle the occurrence of these hazards separately or independently. Therefore, this study introduces a catchment-scale, multihazard approach that unifies the assessment of both phenomena through a combination of hydrological and geotechnical principles. By incorporating surface flow, infiltration, and slope stability analyses, the spatio-temporal response of both landslides and flooding during intense or heavy rainfall is characterized. To improve computational efficiency, a hybrid approach, consisting of the zero- and local-inertia flow models, is introduced to approximate the surface flow. Additionally, rainfall infiltration is estimated using the one-dimensional Green-Ampt model, and the results are incorporated in a deterministic slope stability analysis whose failure mechanism is generated from the forces arising from the effects of surface flow and the downward propagation of water along the assumed failure surface. The model is applied in the catchment of Marumori Town, Japan which suffered numerous landslide and flooding disasters during Typhoon Hagibis in 2019. The results indicate that the proposed model successfully simulates the hydrologic and geotechnical response of the catchment showing the distribution of both unstable slopes and inundated areas indicative of landslide and flooding hazards. The model's capability to identify the timing and location of potential landslide and flood-prone areas could provide a practical framework for a unified early warning system, enhancing emergency planning and response efforts during heavy rainfall. [ABSTRACT FROM AUTHOR]

Landslide zoning is crucial for disaster prevention and reduction in areas of rainfall-triggered landslides. A shallow-layer landslide assessment model was established for Zhouqu area, simulation of the slope stability of extensive area was applied to predict the potential landslide fairly, and evaluation of the migration and conversion laws were performed to determine quantitatively the effect of rainfall on landslide under different rainfall warning levels. The results show that all 169 recorded landslide scars were classified as lower stability-to-defended by SINMAP model, and the susceptible landslide distribution in low-lying area is consistent with that in areas with relatively high topographic precipitation. With the rainfall intensity increases from 10 to 56 mm/h, the landslide and rainfall were found to exhibit a growing power function relation; the potentially unstable region is always the highest, highlighting a critical problem. Moreover, as the basically stable region and potentially unstable region gradually transition into the unstable region and extremely unstable region, respectively, the landslide potential increases gradually, and the hazard level becomes increasingly aggravated; consequently, the potential threat is more serious. When the hourly rainfall exceeds 20 mm, the slope stability is gradually weakened in the stable region, which is gradually migrating into the unstable region, thus triggering landslides, debris slides and flows. When the hourly rainfall exceeds 32 mm, the hazard degree changes qualitatively, caused by the quantitative change, and the geological hazard degree increases significantly. The study can provide a scientific basis for landslide zoning, monitoring, and early warning in the Zhouqu area as well as other alpine gully area. [ABSTRACT FROM AUTHOR]

The assessment of rainfall-induced shallow landslide hazards is a significant issue in the Three Gorges Reservoir area in China due to the rapid development of land in the past two decades. In this study, a probabilistic analysis method that combines TRIGRS and the point-estimate method for evaluating the hazards of shallow landslides have been proposed under the condition of rainfall over a large area. TRIGRS provides the transient infiltration model to analyze the pore water pressure during a rainfall. The point-estimate method is used to analyze the uncertainty of the soil parameters, which is performed in the geographic information system (GIS). In this paper, we use this method to evaluate the hazards of shallow landslides in Badong County, Three Gorges Reservoir, under two different types of rainfall intensity, and the results are compared with the field investigation. The results showed that the distribution of the hazard map is consistent with the observed landslides. To some extent, the distribution of the hazard map reflects the spatial and temporal distribution of the shallow landslide caused by rainfall. [ABSTRACT FROM AUTHOR]

This study ascertains landslide impact under climate change. The disaster impact under climate change was estimated by calculating the variation in safety factor (FS value). The result shows less landslide impact for the late 21st century (2075 - 2099) than in the late 20th century (1979 - 2003). The relationship between rainfall characteristics and landslide susceptibility was then clarified. A total of 40 out of 82 typhoon events from the late 20th century and 40 out of 88 events from the late 21st century were selected for estimating landslide impact. The landslide ratio in each event is calculated to find the variation in landslide occurrence between the late 20th and the late 21st century. The result proved that landslide occurrence is highly dependent on the rainfall characteristics. Landslides occurred earlier during each rainfall event for the late 21st century, which is consistent with rainfall characteristics, e.g., high intensity and short duration, for the late 21st century. [ABSTRACT FROM AUTHOR]

Process-based models are widely used for rainfall-induced shallow landslide forecasting. Previous studies have successfully applied the U.S. Geological Survey's Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model (Baum et al. 2002) to compute infiltration-driven changes in the hillslopes' factor of safety on small scales (i.e., tens of square kilometers). Soil data input for such models are difficult to obtain across larger regions. This work describes a novel methodology for the application of TRIGRS over broad areas with relatively uniform hydrogeological properties. The study area is a 550-km region in Central Italy covered by post-orogenic Quaternary sediments. Due to the lack of field data, we assigned mechanical and hydrological property values through a statistical analysis based on literature review of soils matching the local lithologies. We calibrated the model using rainfall data from 25 historical rainfall events that triggered landslides. We compared the variation of pressure head and factor of safety with the landslide occurrence to identify the best fitting input conditions. Using calibrated inputs and a soil depth model, we ran TRIGRS for the study area. Receiver operating characteristic (ROC) analysis, comparing the model's output with a shallow landslide inventory, shows that TRIGRS effectively simulated the instability conditions in the post-orogenic complex during historical rainfall scenarios. The implication of this work is that rainfall-induced landslides over large regions may be predicted by a deterministic model, even where data on geotechnical and hydraulic properties as well as temporal changes in topography or subsurface conditions are not available. [ABSTRACT FROM AUTHOR]

Many rainfall-induced landslides are reported to be shallow. Therefore, when regional slope stability analysis, or landslide hazard mapping is carried out, simple approaches, such as the infinite slope model, are often used. However, the infinite slope model is known to underestimate the factor of safety due to the absence of boundary effects. More sophisticated methods that account for the boundary effects at the toe and head of the landslide are much more computationally expensive. In this paper upper bound limit analysis (UBLA) is presented with a novel failure mechanism which consists of a translational parallelogram in the middle slope and two log-spiral components at the slope crest and slope toe to capture the boundary effect. The new approach is derived for a full range of pore water pressure conditions and validated by finite element limit analyses. For shallow landslides the translational UBLA is found to outperform the conventional log-spiral UBLA. The results of a large parametric study using the translational UBLA are then used to develop a novel analytical shallow landslide model which retains the simplicity of the traditional infinite slope model, but yet improves accuracy considerably, making this an attractive alternative for routine analysis such as landslide hazard mapping. [ABSTRACT FROM AUTHOR]

• A new ecological indicator system for shallow landslide analysis is proposed. • Ecological indicators are summarized by effects of plant on shallow slope stability. • Ecological indicators are reorganized by fundamental ecological element categories. • Applicability of the new ecological indicator system is suggested by a typical case. Ecological factors are important for shallow landslide risk assessment. Various ecological indicators have been previously proposed and studied. However, because the functional mechanisms of plants on shallow slope stability are very complex, a well-organized ecological indicator system that can reflect the effects of ecology on shallow slope stability is still absent, which in turn restrains the consideration of ecological factors in shallow landslide analysis. This paper proposes a new ecological indicator system for shallow landslide analysis based on a comprehensive survey of existing ecological indicators. Firstly, existing ecological indicators are summarized with regarded to the three major types of effects of vegetation on shallow slope stability (i.e., geotechnical mechanics effect, soil-hydrological effect, and interception and stemflow effect), so that the functional mechanisms of plants on slope stability are reflected. Then, all ecological indicators are grouped into the three fundamental ecological element categories (i.e., ecological attribute, ecological structure and ecological function) to constitute the new ecological indicator system. This re-organization, by eliminating redundancy, makes the new ecological indicator system more well-structured and more applicatory in practice. Finally, some issues regarding the application of the new indicator system are discussed. It is particularly recommended to consider the possible negative effects, the applicable spatial scale of specific ecological indicators, and the preferred application of ecological structure indicators. The new ecological indicator system is expected to facilitate the consideration of ecological factors in landslide risk assessment. [ABSTRACT FROM AUTHOR]

Heavy summer rainfall induces significant soil erosion and shallow landslide activity on the loess hillslopes of the Xining Basin at the northeast margin of the Qinghai-Tibet Plateau. This study examines the mechanical effects of five native shrubs that can be used to reduce shallow landslide activity. We measured single root tensile resistance and shear resistance, root anatomical structure and direct shear and triaxial shear for soil without roots and five rootsoil composite systems. Results show that Atriplex canescens (Pursh) Nutt. possessed the strongest roots, followed by Caragana korshinskii Kom., Zygophyllum xanthoxylon (Bunge) Maxim., Nitraria tangutorum Bobr. and Lycium chinense Mill. Single root strength and shear resistance relationships with root diameter are characterized by power or exponential relations, consistent with the Mohr-Coulomb law. Root mechanical strength reflects their anatomical structure, especially the percentage of phloem and xylem cells, and the degree and speed of periderm lignifications. The cohesion force of root-soil composite systems is notably higher than that of soil without roots, with increasing amplitudes of cohesion force for A. canescens, C. korshinskii, Z. xanthoxylon, N. tangutorum and L. chinense of 75.9%, 75.1%, 36.2%, 24.6% and 17.0 % respectively. When subjected to shear forces, the soil without root samples show much greater lateral deformation than the root-soil composite systems, reflecting the restraining effects of roots. Findings from this paper indicate that efforts to reduce shallow landslides in this region by enhancing root reinforcement will be achieved most effectively using A. canescens and C. korshinskii. [ABSTRACT FROM AUTHOR]

The main purpose of this study is to develop a new hazard evaluation technique considering the current limitations, particularly for shallow landslides. For this purpose, the Buyukkoy catchment area, located in the East Black Sea Region in the east of Rize province and the south of Cayeli district, was selected as the study area. The investigations were executed in four different stages. These were (1) preparation of a temporal shallow landslide inventory of the study area, (2) assessment of conditioning factors in the catchment, (3) susceptibility analyses and (4) hazard evaluations and mapping. A total of 251 shallow landslides in the period of 1955-2007 were recognised using different data sources. A 'Sampling Circle' approach was proposed to define shallow landslide initiation in the mapping units in susceptibility evaluations. To accomplish the susceptibility analyses, the method of artificial neural networks was implemented. According to the performance analyses conducted using the training and testing datasets, the prediction and generalisation capacities of the models were found to be very high. To transform the susceptibility values into hazard rates, a new approach with a new equation was developed, taking into account the behaviour of the responsible triggering factor over time in the study area. In the proposed equation, the threshold value of the triggering factor and the recurrence interval are the independent variables. This unique property of the suggested equation allows the execution of more flexible and more dynamic hazard assessments. Finally, using the proposed technique, shallow landslide initiation hazard maps of the Buyukkoy catchment area for the return periods of 1, 2, 5, 10, 50 and 100 years were produced. [ABSTRACT FROM AUTHOR]

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area. [ABSTRACT FROM AUTHOR]

Due to vibrant tectonic activity in the Taiwan area, the rock formations are highly fractured. The weathered formations or colluviums are susceptible to landslides due to the dynamic change in river morphology. The impact of climate change makes the landslide hazard more significant over the past decade. Focusing on the slate formation region in the upstreams of the Tachia River, Wu River, and Chuoshui River in Taiwan, this study explored the behavior of deep-seated and shallow landslides. In order to more accurately distinguish the deep-seated and shallow landslides, this study adopts the SHALSTAB model with the consideration of slope angle. And then, based on the classified deep-seated and shallow landslide inventories, the landslide susceptibility models, i.e., Logistic Regression and Support Vector Machine models, were created. These susceptibility models were developed for different watershed and in different scales; their performances were evaluated and compared, including the AUC values and the control factor weightings. A random sampling logic was proposed to increase the analysis efficiency, and results show a promising performance. The developed models in this study can more properly and efficiently generate susceptibility maps for landslide hazard assessment and mitigation. • Landslide susceptibility was predicted by logistic regression (LR) and supporting vector machines (SVM) in a catchment scale. • The SHALSTAB model, with the identification rate based slope angle thresholds, was used to classify the landslides to shallow landslides and deep-seated landslides. • The landslide susceptibility models of shallow and deep-seated landslides were established and discussed. • The scale effect of the rainfall induced landslide susceptibility were studied by different landslide susceptibility models. [ABSTRACT FROM AUTHOR]

In recent years, precipitation patterns in Korea have shifted to be characterized as short and intense rainfalls. In consideration of shallow landslide initiations primarily governed by heavy rainfalls at short-time scales that diminish drainage effects, the concept of critical continuous rainfall is proposed as a single-rainfall-variable threshold for shallow landslide forecasting. To generate a critical continuous rainfall map for hillslope areas in a city of Korea (Busan), this study designed and applied a systematic modeling process. As a preparatory stage, input datasets of geo-hydraulic properties and geotechnical properties were assembled using estimation techniques based on experiment data of field samples. The inherent and fixed critical continuous rainfall values for hillslope areas in Busan were derived through one-dimensional infiltration analysis coupled with infinite slope stability calculations. As a result of a detailed analysis of historical rainfall records in a case study area over a period of 11 years, three false forecasting cases were recorded, whereas all landslide-triggering rainfall events were correctly captured with no missed forecasting cases. The results of the case study indicate that the proposed critical continuous rainfall may be useful as an effective and straightforward indicator for forecasting the initiation of shallow landslides. [ABSTRACT FROM AUTHOR]

Shallow landslides are an increasing concern in Italy and worldwide because of the frequent association with vegetation management. As vegetation cover plays a fundamental role in slope stability, we developed a GIS-based model to evaluate the influence of plant roots on slope safety, and also included a landslide susceptibility map. The GIS-based model, 4SLIDE, is a physically based predictor for shallow landslides that combines geological, topographical, and hydrogeological data. The 4SLIDE combines the infinite slope model, TOPMODEL (for the estimation of the saturated water level), and a vegetation root strength model, which facilitates prediction of locations that are more susceptible for shallow landslides as a function of forest cover. The aim is to define the spatial distribution of Factor of Safety (FS) in steep-forested areas. The GIS-based model 4SLIDE was tested in a forest mountain watershed located in the Sila Greca (Cosenza, Calabria, South Italy) where almost 93% of the area is covered by forest. The sensitive ROC analysis (Receiver Operating Characteristic) indicates that the model has good predictive capability in identifying the areas sensitive to shallow landslides. The localization of areas at risk of landslides plays an important role in land management activities because landslides are among the most costly and dangerous hazards. [ABSTRACT FROM AUTHOR]