Rainfall-induced Landslide Hazard Research Articles

Assessing framework of rainfall-induced landslide hazard considering spatiotemporal asymmetry in extreme precipitation indices under climate change

Assessing framework of rainfall-induced landslide hazard considering spatiotemporal asymmetry in extreme precipitation indices under climate change

Hydro-mechanical analysis of slope stability using anisotropic constitutive model

This study introduces a novel and robust numerical approach to analyzing rainfall-induced landslide hazards, incorporating coupled hydro-mechanical behavior, anisotropic properties of unsaturated geomaterials, and soil–atmosphere interaction. The advanced hyperbolic Mohr-Coulomb model with anisotropy is adopted for mechanical responses, while generalized Darcy’s law is employed for hydraulic behaviors. This approach allows for anisotropic specification of hydromechanical properties like stiffness, strength, and intrinsic permeability. Atmospheric boundary conditions enable analysis of soil–atmosphere interactions, encompassing infiltration and runoff during extreme rainfall. Parametric analyses were conducted to evaluate slope safety factors under various anisotropic angles.

An Integration of Logistic Regression and Geographic Information System for Development of a Landslide Hazard Index to Land Use: A Case Study in Pingtung County in Southern Taiwan

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

Sequential probabilistic back analyses of spatially varying soil parameters and slope reliability prediction under rainfall

Accurately predicting slope reliability under a rainfall/rainstorm event is an important prerequisite for preventing rainfall-induced landslide hazards. However, the predicted probability of slope failure under the rainfall/rainstorm event is often larger than the observed frequency of slope instability. The spatial variability of multiple soil parameters was rarely accounted for. To address this issue, this paper proposes an efficient sequential probabilistic back analyses approach for learning multiple spatially varying soil parameters using Bayesian Updating with Subset simulation (BUS) method. Two survival records of a real slope in India (i.e., the slope stays stable before the rainfall and the slope keeps stable after a 57-day weak rainfall) are successively used in the sequential probabilistic back analyses of soil parameters. The results indicate that the proposed sequential probabilistic back analyses approach can effectively update the distributions of multiple spatially variable soil parameters by the fusion of slope survival records. More accurate statistics of soil parameters can be obtained when additional slope survival records are used in the probabilistic back analyses. Furthermore, two slope failure records under a 3-day heavy rainfall event and a rainfall event ranging from May 1, 2016 to June 30, 2016 in Chibo, India are, respectively, used to predict the slope reliability and further validate the effectiveness of the proposed approach. The predicted probabilities of slope failure under the target rainfall events are well consistent with the actual observation frequency. The proposed approach can provide a powerful and versatile tool for determining the statistics of soil parameters and early warning of landslide hazards under the future rainfall events.

Investigation of landslide hazard areas in the municipality of Cunha (Brazil) and climate projections from 2024 to 2040

Climate change can modify the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In Brazil, an example of a location with a high occurrence of landslides and with few studies on the subject is the municipality of Cunha, located in the State of São Paulo. In this way, the main aims of this study are twofold: 1) to identify and analyze areas susceptible to landslides and rainfall-induced landslide hazard in the municipality of Cunha; and 2) by using climate projections from the regional model Eta-HadGEM2-ES under RCP 4.5, to predict new extreme events and possible new landslides in this municipality, from 2024 to 2040. The susceptibility map has been prepared using selected physical-biotic environment elements whereas the hazard map has been produced using precipitation data. Using Eta-HadGEM2-ES climate projections under RCP 4.5, for precipitation events, results project the occurrence of 49 days with landslides for the period between 2024 and 2040. The outcomes of this study are considered of extreme relevance for the local community, public authorities, and decision-makers, to raise awareness regarding future urban planning strategies to prevent and mitigate the effects of catastrophes arising from natural hazard-induced disasters.

Hazard Assessment of Rainfall–Induced Landslide Considering the Synergistic Effect of Natural Factors and Human Activities

Landslide hazard assessment is essential for determining the probability of landslide occurrence in a specific spatial and temporal range. The hazard assessment of potential landslides could support landslide disaster early warning and disaster prevention decisions, which have important guiding significance for urban construction and sustainable development. Due to the lack of consideration of the synergistic effect of multiple factors and geographic scene heterogeneity, the accuracy of existing landslide hazard assessment methods still needs to be improved, and the interpretability and applicability of existing models still need to be improved. In this paper, we propose a landslide hazard assessment method considering the synergistic effect of multiple factors, including natural factors and human activities, and the heterogeneity of geographic scenes. On this basis, we carry out experimental verification on rainfall–induced landslides in Dehong Prefecture, Yunnan Province, China. Firstly, rainfall–induced landslide hazards’ characteristics and impact factors are analyzed and classified. The whole study area is divided into some homogeneous sub–regions using regional dynamic constraint clustering based on the similarity of underlying environmental variables. Then, considering the spatial autocorrelation between various landslide conditioning and trigger factors, a local weighted random forest model is developed to evaluate the rainfall–induced landslide hazards comprehensively. Experimental results show that the proposed method has higher accuracy and interpretability than the existing representative methods and can provide useful references for preventing landslide hazards.

Earthquake and rainfall-induced landslide hazard assessment of Kutupalong Rohingya camp using meteorological and geological information

Earthquake and rainfall-induced landslide hazard assessment of Kutupalong Rohingya camp using meteorological and geological information

Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold

Rainfall-induced landslide hazard warning, which refers to the prediction of the spatial-temporal probability of landslide occurrence in a certain area under the conditions of continuous rainfall processes, can be established based on landslide susceptibility mapping and critical rainfall threshold calculations. However, it is difficult to determine appropriate machine learning models for mapping landslide susceptibility. Additionally, it is significant to consider the influences of early effective rainfall on landslide instability in the critical rainfall threshold methods. Furthermore, the uncertainties of the critical rainfall threshold values generated by different calculation methods have not been well explored. To overcome these three drawbacks, first, frequency ratio analysis-based logistic regression (LR), support vector machine (SVM) and random forest (RF) models are adopted to predict landslide susceptibility for machine learning model comparison. Second, three different types of critical rainfall threshold methods, namely, cumulative effective rainfall-duration (EE-D), effective rainfall intensity-duration (EI-D) and cumulative effective rainfall-effective rainfall intensity (EE-EI) models, are proposed to calculate the temporal probabilities of landslide occurrence under rainfall conditions based on the concept of effective rainfall. The accuracies and uncertainties of these three critical rainfall threshold methods are discussed. Finally, the landslide susceptibility maps and the critical rainfall threshold values are coupled to predict the rainfall-induced landslide hazards. Xunwu County in China is selected as the study area, and several rainfall-induced landslides are used as the test samples of the proposed landslide hazard warning model. The results show that the RF model has remarkably higher susceptibility prediction accuracy than the SVM and LR models, and the prediction performance of the temporal probabilities of landslide occurrence using the EI-D values are higher than those of EE-D and EE-EI values. Furthermore, rainfall-induced landslide hazard warning is effectively implemented based on the coupling of the susceptibility map and EI-D model.

The Impact of Probability Density Functions Assessment on Model Performance for Slope Stability Analysis

The development of forecasting models for the evaluation of potential slope instability after rainfall events represents an important issue for the scientific community. This topic has received considerable impetus due to the climate change effect on territories, as several studies demonstrate that an increase in global warming can significantly influence the landslide activity and stability conditions of natural and artificial slopes. A consolidated approach in evaluating rainfall-induced landslide hazard is based on the integration of rainfall forecasts and physically based (PB) predictive models through deterministic laws. However, considering the complex nature of the processes and the high variability of the random quantities involved, probabilistic approaches are recommended in order to obtain reliable predictions. A crucial aspect of the stochastic approach is represented by the definition of appropriate probability density functions (pdfs) to model the uncertainty of the input variables as this may have an important effect on the evaluation of the probability of failure (PoF). The role of the pdf definition on reliability analysis is discussed through a comparison of PoF maps generated using Monte Carlo (MC) simulations performed over a study area located in the Umbria region of central Italy. The study revealed that the use of uniform pdfs for the random input variables, often considered when a detailed geotechnical characterization for the soil is not available, could be inappropriate.

Landslide Susceptibility Mapping of Central and Western Greece, Combining NGI and WoE Methods, with Remote Sensing and Ground Truth Data

The exploitation of remote sensing techniques has substantially improved pre- and post- disaster landslide management over the last decade. A variety of landslide susceptibility methods exists, with capabilities and limitations related to scale and spatial accuracy issues, as well as data availability. The Interferometric Synthetic Aperture Radar (InSAR) capabilities have significantly contributed to the detection, monitoring, and mapping of landslide phenomena. The present study aims to point out the contribution of InSAR data in landslide detection and to evaluate two different scale landslide models by comparing a heuristic to a statistical method for the rainfall-induced landslide hazard assessment. Aiming to include areas with both high and low landslide occurrence frequencies, the study area covers a large part of the Aetolia–Acarnania and Evritania prefectures, Central and Western Greece. The landslide susceptibility product provided from the weights of evidence (WoE) method proved more accurate, benefitting from the expert opinion and the landslide inventory. On the other hand, the Norwegian Geological Institute (NGI) methodology has the edge on its immediate implementation, with minimum data requirements. Finally, it was proved that using sequential SAR image acquisitions gives the benefit of an updated landslide inventory, resulting in the generation of, on request, updated landslide susceptibility maps.

Correction to: Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis

Correction to: Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis

The relationship of human activities and rainfall-induced landslide and debris flow hazards in Central China

Human activities have been recognized as one of the significant influencing factors on natural hazard, yet studies of quantitative relationship between human activities and natural hazard have been limited. The relations of human activities and hazards (landslide and debris flow) induced by heavy rainfall are quantitatively analyzed in this study and suggestions are provided for the integrated hazard mitigation management in the study area of Central China. The results show that human activities index (HAI) is an effective indicator to characterize spatiotemporal changes of human activities. The relations of rainfall-induced landslide, debris flow hazards and human activities are significant as shown in spatial distribution. The probability of rainfall-induced landslide, debris flow hazards is highly correlated with the intensity of human activities and the changes of HAI. However, in terms of temporal relations, human activities have more direct impact on rainfall-induced landslide hazard than on rainfall-induced debris flow hazard, as the frequency of landslide hazard shows a 2-year delay with HAI changing, in general, according to the cross-wavelet transform analysis. In comparison, no significant correlating period is shown between the changing human activities and the frequency of rainfall-induced debris flow hazard. Specifically, there are three types of time relationship between the changing human activities and rainfall-induced landslide, debris flow hazards in the affected counties: (1) concurrent changing, accounting for 30% of landslide and 18% of debris flow; (2) lagging relationship, accounting for 54% of landslide and 30% of debris flow; (3) no direct relationship, accounting for 16% of landslide and 52% of debris flow. The results in this study suggest that HAI is a simple and effective index for the local governments to manage human impacts on rainfall-induced landslide and debris flow hazards, and integrated prevention measures are suggested to be executed when HAI reaches 0.072. In addition, the types of human activities also impact landslide, as the ratio of Economy factor to Land use factor (E/L) is significantly (P < 0.05) higher in counties with concurrent relationship of changing human activities and frequency of rainfall-induced landslide hazard. The results should assist the policy-making for integrated mitigation of rainfall-induced landslide and debris flow hazards at local/regional level, while balancing economic development and natural hazard.

Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis

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).

Preliminary analysis of a rainfall-induced landslide hazard chain in Enshi City, Hubei Province, China in July 2020

Preliminary analysis of a rainfall-induced landslide hazard chain in Enshi City, Hubei Province, China in July 2020

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.

Rainfall-induced landslide identification using numerical modelling: A southern Chile case

Worldwide, Rainfall-Induced Landslide (RIL) affects the population with significant socioeconomic damage and high economic losses in developing countries. Nowadays, RIL hazard has gained attention from the scientific community in developing countries where low instrumental is available for identification and monitoring. However, RIL-prone zones identification is a complex task, mostly in zones with an increase of extreme hydrometeorological events due to climate change. We propose a new coupled methodology to identify RIL-prone zones based on slope instability (SI) during an extreme hydrometeorological event. We consider the Northern Slope of the Biobío River (NSBR, centered at ~36°S, 72°W) in the south of Chile, a densely populated area with historic RIL events. A one-way coupled system was carried out using the Weather Research and Forecasting (WRF) model to a hydrological model over an experimental area of 172 km2. Hydrological model results were used to calculate the factor of safety (FS) evaluating their temporal variations. Our results were compared with in-situ observations and remote sensing of slope-stability to evaluate their performance based on previous studies with good performance. Our results show that areas with low FS values represented shallow RIL and hyper-concentrated flows. Therefore, our approach could identify RIL-prone areas without the need for extensive in-situ information, which will reduce resources. Finally, our methodology allows implementation in countries with scarce or non-existent resources for the instrumental monitoring of hillslopes.

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.

Rainfall-induced Landslide Hazard Analysis Based on GIS in Yanbian Region, China

This paper mainly studies landslide hazard in Yanbian region，8 landslide factors of soil moisture content, hydrogeology, land use type, slope, slope aspect, fault, river and rainfall are analyzed in the area of Yanbian region. Using the analytic hierarchy process (AHP), the relative weights of 8 landslide factors were obtained, and the internal weights of each factor were determined by the data; the rainfall factors were then divided into three categories such as average annual rainfall, average flood season rainfall, and the maximum average rainfall. And the comparison of the rainfall factors was made. Finally based on the results of these weights to the remote sensing technology(RS) and geographic information system(GIS) technology combined to evaluate its area, we are mapping the risk assessment area for landslide hazard in Yanbian region. According to the map analysis, figure in the dangerous hierarchies result in accordance with the historical landslides is better. At the same time. landslides and hazard area of statistical data analysis showed that the AHP method combined with GIS technology is suitable for promoting the Yanbian regional landslide hazard assessment.

Rainfall-Induced Landslide Hazard Assessment along NH 1D from Ganiwan to Gumri (J &amp; K), India

Rainfall-Induced Landslide Hazard Assessment along NH 1D from Ganiwan to Gumri (J &amp; K), India

A probabilistic model for rainfall—induced shallow landslide prediction at the regional scale

This paper presents a new probabilistic physically-based computational model (called PG_TRIGRS) for the probabilistic analysis of rainfall-induced landslide hazard at a regional scale. The model is based on the deterministic approach implemented in the original TRIGRS code, developed by Baum et al. (USGS Open File Report 02–424, 2002) and Baum et al. (USGS Open File Report 08–1159, 2008). Its key innovative features are: (a) the application of Ordinary Kriging for the estimation of the spatial distributions of the first two statistical moments of the probability density functions of the relevant soil properties over the entire area, based on limited available information gathered from available information from limited site investigation campaigns, and (b) the use of Rosenblueth’s Point Estimate method as a more efficient alternative to the classical Monte Carlo method for the reliability analysis performed at the single-cell level to obtain the probability of failure associated to a given rainfall event. The application of the PG_TRIGRS code to a selected study area located in the Umbria Region for different idealized but realistic rainfall scenarios has demonstrated the computational efficiency and the accuracy of the proposed methodology, assessed by comparing predicted landslide densities with available field observations reported by the IFFI project. In particular, while the model might fail to identify all individual landslide events, its predictions are remarkably good in identifying the areas of higher landslide density.