Shallow Landslide Hazard Research Articles

RegionGrow3D: A Deterministic Analysis for Characterizing Discrete Three‐Dimensional Landslide Source Areas on a Regional Scale

AbstractRegional‐scale characterization of shallow landslide hazards is important for reducing their destructive impact on society. These hazards are commonly characterized by (a) their location and likelihood using susceptibility maps, (b) landslide size and frequency using geomorphic scaling laws, and (c) the magnitude of disturbance required to cause landslides using initiation thresholds. Typically, this is accomplished through the use of inventories documenting the locations and triggering conditions of previous landslides. In the absence of comprehensive landslide inventories, physics‐based slope stability models can be used to estimate landslide initiation potential and provide plausible distributions of landslide characteristics for a range of environmental and forcing conditions. However, these models are sometimes limited in their ability to capture key mechanisms tied to discrete three‐dimensional (3D) landslide mechanics while possessing the computational efficiency required for broad‐scale application. In this study, the RegionGrow3D (RG3D) model is developed to broadly simulate the area, volume, and location of landslides on a regional scale (≥1,000 km2) using 3D, limit‐equilibrium (LE)‐based slope stability modeling. Furthermore, RG3D is incorporated into a susceptibility framework that quantifies landsliding uncertainty using a distribution of soil shear strengths and their associated probabilities, back‐calculated from inventoried landslides using 3D LE‐based landslide forensics. This framework is used to evaluate the influence of uncertainty tied to shear strength, rainfall scenarios, and antecedent soil moisture on potential landsliding and rainfall thresholds over a large region of the Oregon Coast Range, USA.

SLEM (Shallow Landslide Express Model): A Simplified Geo-Hydrological Model for Powerlines Geo-Hazard Assessment

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.

A GIS-Based Approach for Shallow Landslides Risk Assessment in the Giampilieri and Briga Catchments Areas (Sicily, Italy)

Shallow landslides pose a widely growing hazard and risk, globally and particularly in Mediterranean areas. The implementation of adequate adaptation and mitigation measures necessarily requires the development of practical and affordable methodologies and technologies for assessing the shallow landslides hazard and its territorial impact. The assessment of shallow landslide hazard maps involves two different and sequential steps: the susceptibility and the runout analysis, respectively, aimed at the identification of the initiation and the propagation areas. This paper describes the application in the Giampilieri and Briga Villages area (Sicily, Italy) of a shallow landslide risk process at a basin scale with an innovative approach in the runout assessment segment. The runout analysis was conducted using specific GIS tools employing an empirical–geometric approach at a basin scale. The exposure and vulnerability values of the elements at risk were assigned using a qualitative and semi-quantitative approach, respectively. The results highlight the effectiveness of the procedure in producing consistent runout hazard and risk assessments in the valley areas where the more important and vulnerable exposed elements are located. This study contributes to addressing the public administration demand for valuable and user-friendly tools to manage and drive regional planning.

Electrical Resistivity Imaging for Characterizing the Shallow Landslide Hazard Site at Kutupalong Rohingya Refugee Camps of Ukhiya, Cox's Bazar, Bangladesh

Electrical Resistivity Imaging for Characterizing the Shallow Landslide Hazard Site at Kutupalong Rohingya Refugee Camps of Ukhiya, Cox's Bazar, Bangladesh

Hydrologic-geotechnical modelling of shallow landslide and flood hazards caused by heavy rainfall

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.

Multistage Triaxial Behavior of Shallow Landslide Hazard Site Soil of Rangamati Sadar Area, Bangladesh

Multistage Triaxial Behavior of Shallow Landslide Hazard Site Soil of Rangamati Sadar Area, Bangladesh

Towards a National-Scale Dataset of Geotechnical and Hydrological Soil Parameters for Shallow Landslide Modeling

One of the main constraints in assessing shallow landslide hazards through physically based models is the need to characterize the geotechnical parameters of the involved materials. Indeed, the quantity and quality of input data are closely related to the reliability of the results of every model used, therefore data acquisition is a critical and time-consuming step in every research activity. In this perspective, we reviewed all official certificates of tests performed through 30 years at the Geotechnics Laboratory of the Earth Science Department (University of Firenze, Firenze, Italy), compiling a dataset in which 380 points are accurately geolocated and provide information about one or more geotechnical parameters used in slope stability modeling. All tests performed in the past (in the framework of previous research programs, agreements of cooperation, or to support didactic activities) were gathered, homogenized, digitalized, and geotagged. The dataset is based on both on-site tests and laboratory tests, it accounts for 40 attributes, among which 13 are descriptive (e.g., lithology or location) and 27 may be of direct interest in slope stability modeling as input parameters. The dataset is made openly available and can be useful for scientists or practitioners committed to landslide modeling.

Shallow landslides and vegetation at the catchment scale: A perspective

Shallow, rainfall-triggered landslides are an important catchment process that affect the rate and calibre of sediment within river networks and create a significant hazard, particularly when shallow landslides transform into rapidly moving debris flows. Forests and trees modify the magnitude and rate of shallow landsliding and have been used by land managers for centuries to mitigate their effects. We understand that at the tree and slope scale root reinforcement provides a significant role in stabilising slopes, but at the catchment scale root reinforcement models only partially explain where shallow landslides are likely to occur due to the complexity of subsurface material properties and hydrology. The challenge of scaling from slopes to catchments (from 1-D to 2-D) reflects the scale gap between geomorphic process understanding and modelling, and temporal evolution of material properties. Hence, our understanding does not, as yet, provide the necessary tools to allow vegetation to be targeted most effectively for landslide reduction. This paper aims to provide a perspective on the science underpinning the challenges land and catchment managers face in trying to reduce shallow landslide hazard, manage catchment sediment budgets, and develop tools for catchment targeting of vegetation. We use our understanding of rainfall-triggered shallow landslides in New Zealand and how vegetation has been used as a tool to reduce their incidence to demonstrate key points.

Four-Year Monitoring Study of Shallow Landslide Hazards Based on Hydrological Measurements in a Weathered Granite Soil Slope in South Korea

To build a comprehensive understanding of long-term hydro-mechanical processes that lead to shallow landslide hazards, this study explicitly monitored the volumetric water content (VWC) and rainfall amount for a weathered granite soil slope over a four year period. From the 12 operational landslide monitoring stations installed across South Korea, the Songnisan station was selected as the study site. VWC sensors were placed in the subsurface with a grid-like arrangement at depths of 0.5 and 1.0 m. Shallow landslide hazards were evaluated by applying an infinite slope stability model that adopted a previously proposed unified effective stress concept. By analyzing the variations in the monitored VWC values, the derived matric suctions and suction stresses, and the calculated factor of safety values, we were able to obtain numerous valuable insights. In particular, the seasonal effects of drainage and evapotranspiration on the slope moisture conditions and slope stability were addressed. Preliminary test results indicated that continuous rainfall successfully represented the derived matric suction conditions at a depth of 1.0 m in the lower slope, although this was not the case for the upper and middle slopes. The significance of a future study on cumulative field monitoring data from various sites in different geological conditions is highlighted.

On the rainfall induced deep-seated and shallow landslide hazard in Taiwan

Abstract 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.

Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution

As the frequency and intensity of heavy rainfall increase, the frequency of extreme rainfall-induced landslides also increases. Thus, the importance of accurate assessment of extreme rainfall-induced landslide hazard increases. Landslide hazard assessment requires estimations of two components: spatial probability and temporal probability. While various approaches have been successfully used to estimate spatial landslide susceptibility, fewer studies have addressed temporal probability and, consequently, a commonly accepted method does not exist. Prior approaches have estimated temporal probability using frequency analysis of past landslides or landslide triggering rainfall events. Hence, a large amount of information was required: sufficiently complete historical data on recurrent landslides and repetitive rainfall events. However, in many cases, it is difficult to obtain such complete historical data. Therefore, this study developed a new approach that can be applied to an area where incomplete data are available or where nonrepetitive landslide events have occurred. To evaluate the temporal probability of landslide occurrence, the developed approach adopted extreme value analysis using the Gumbel distribution. The exceedance probability of a rainfall threshold was evaluated, using the Gumbel model, with 72-h antecedent rainfall threshold. This probability was then considered to be the temporal probability of landslide occurrence. The temporal probability of landslides was then integrated with landslide susceptibility results from a multi-layer perceptron model. Consequently, the landslide hazards for different future time periods, from 1 to 200 years, were estimated.

A coupled hydrological-geotechnical framework for forecasting shallow landslide hazard—a case study in Halong City, Vietnam

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.

A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility

This study developed and verified a new hybrid machine learning model, named random forest machine (RFM), for the spatial prediction of shallow landslides. RFM is a hybridization of two state-of-the-art machine learning algorithms, random forest classifier (RFC) and support vector machine (SVM), in which RFC is used to generate subsets from training data and SVM is used to build decision functions for these subsets. To construct and verify the hybrid RFM model, a shallow landslide database of the Lang Son area (northern Vietnam) was prepared. The database consisted of 101 shallow landslide polygons and 14 conditioning factors. The relevance of these factors for shallow landslide susceptibility modeling was assessed using the ReliefF method. Experimental results pointed out that the proposed RFM can help to achieve the desired prediction with an F1 score of roughly 0.96. The performance of the RFM was better than those of benchmark approaches, including the SVM, RFC, and logistic regression. Thus, the newly developed RFM is a promising tool to help local authorities in shallow landslide hazard mitigations.

Regional rainfall threshold maps drawn through multivariate geostatistical techniques for shallow landslide hazard zonation

The Empirical Rainfall Thresholds (ERTs) for shallow landslide initiation are commonly devised worldwide mostly to be implemented within landslide early warning systems. Nonetheless, since the pioneering works on ERTs in the 1980s, only meteorological variables - that are cumulated E or intensity I and duration D values of rainfalls that are likely to trigger landslides - have been used to predict landslide occurrence, even though they are characterized by a large uncertainty. Over time, many efforts have been devoted to constrain ERT to geo-morphological characters of the landslide locations but, since nowadays, they did not get to a sound new method to derive ERT and strengthen its ability to forecast future rainfall-induced landslide. In this study, local geo-morphological characters have been taken into account by means of the co-kriging technique to constrain the E and D mean values of a regional ERT and their confidence intervals. The study area, where the proposed method was trained, is the hilly side of the Abruzzo region (Italy). Here, 62 shallow landslides have been analyzed in the time span of 2013–2017 by collecting 62 (D,E) pairs related to the rainfalls that were likely to trigger them. The relevant geo-morphological features for the considered territory have been selected through the principal component analysis. Then, the Multi-Collocated Co-Kriging technique, through ISATIS Geovariances software, has been applied to derive the spatial variability structures of E and D values conditioned by the selected geo-morphological parameters. Therefore, threshold values of E and D and their confidence intervals have been calculated generating a new shape of regional ERT, consisting of maps of continuous estimated threshold values of (D,E) and confidence interval values suitable for being used in early warning systems for shallow landslide initiation.

Preliminary analysis on the impacts of the rhizosphere on occurrence of rainfall-induced shallow landslides

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.

Building physically based models for assessing rainfall-induced shallow landslide hazard at catchment scale: case study of the Sorrento Peninsula (Italy)

The assessment of rainfall-induced shallow landslide hazards at the catchment scale poses a significant challenge. Traditional empirical approaches for landslide hazard assessment often assume that conditions having caused failure in the past will not change in the future. This assumption may not hold in a climate change scenario. Physically based models (PBMs) therefore represent the natural approach to include changing climate effects. PBMs would in principle require the combination of a three-dimensional (3-D) mechanical and water-flow model. However, a full 3-D finite element model at the catchment scale, with relatively small elements required to capture the pore-water pressure gradients, would have a significant computational cost. For this reason, simplifications to the mechanical (i.e., infinite slope) and water-flow models (i.e., one-dimensional or hybrid 3-D) are introduced, often based on a priori assumptions and not corroborated by experimental evidence. The paper presents a methodology to build a PBM in a bottom-up fashion based on geological surveys and geotechnical investigation. The PBM is initially set as simple as possible and then moved to a higher level of complexity if the model is not capable of simulating past landslide events. The approach is presented for the case study of Sorrento Peninsula and two main landslide events recorded during the winter of 1996–1997.

Shallow landslide susceptibility assessment in granitic rocks using GIS-based statistical methods: the contribution of the weathering grade map

Shallow landslides (i.e., slide, flow, and complex) are widespread around the world, affecting the soil mantle and upper regolith as a result of the weathering of granitic bedrock, and periodically cause enormous social and economic damages. Shallow landslide hazards are predominantly due to the scarcity of warning signs during the pre-failure stage, high velocities reached in the post-failure phase, and an increase in mobilized volumes caused by the entrapment of material in the downhill path of the phenomena. Owing to the abovementioned aspects, susceptibility assessment of shallow landslides in weathered granitic rocks is a relevant issue for land use planning and design purposes. This study proposes a three-step methodology for the susceptibility assessment of these phenomena. The methodology has been tested and validated at the 1:10,000 scale over a 30.4-km2 area in southern Italy, where weathered granitic rocks are periodically affected by shallow landslides. This methodology is divided into three successive steps: step 1 consists of database creation, with an emphasis on the weathering grade map (including five weathering classes, from class II to class VI, each one characterized by comparable mechanical behavior), and steps 2 and 3 focus respectively on susceptibility map calibration and validation through statistical analyses. The area under the ROC curve (AUC) shows values ranging from 0.95 in step 2 (calibration) to 0.88 in step 3 (validation) and is a testament to the good overall predictive accuracy of the methodology. The obtained results demonstrate both the effectiveness and the consistency of the proposed methodology in performing susceptibility mapping of shallow landslides in weathered granitic rocks, as well as the important role played by the weathering grade map.

Shallow landslide hazard assessment using high-resolution DEM for application to road alignment design.

Shallow landslide hazard assessment using high-resolution DEM for application to road alignment design.

Topographical factor-based shallow landslide hazard assessment: a case of Dayi area of Guizhou Province in China

BackgroundThree groups of factors related to topography, geology and hydrology have influence on the triggering of shallow landslides in soil material. In this paper a single representative factor (T-factor) for the topography is proposed, which can be used to define threshold values for the possibility of shallow soil slides. This study was carried out in the Dayi area, Guizhou Province, China. During a heavy rainfall event on June 6, 2011, 230 shallow soilslides were triggered. In some slopes, no shallow landslides were triggered even though some of the topographical factors point to unstable high probability.ResultsWe isolated and analyzed the influence of the topography on the triggering of shallow landslides in catchments with almost identical hydrological and geological conditions and propose a new T-factor as a topographical indicator which is a combination of the slope angle, the upslope contributing area, the cross-section, and the free-face of a (potential) shallow landslide. Higher T-factor values are related to higher probabilities of shallow landsliding. The probability assessment with the topographical factor T was successfully validated in other areas of the USA and Japan.ConclusionsWithout information on the geological and rainfall conditions, a quick and primary prediction of shallow landsliding using the topographical factor T is proposed. Additionally a new R-factor is proposed as a rainfall indicator, which is a combination of the 1 hour rainfall and cumulative rainfall before the landslide event and the annual rainfall. Higher R-factor values are generally related to higher probabilities of shallow landsliding. The primary probability factor P, which is the combination of T and R, gives a final indication of the probability of shallow landsliding.

Dynamic assessment of rainfall-induced shallow landslide hazard

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.