TRIGRS Model Research Articles

Analysis of occurrence potential of the earth/debris flow and shallow landslides using the TRIGRS model (Case study: babolrood Basin, Mazandaran)

Analysis of occurrence potential of the earth/debris flow and shallow landslides using the TRIGRS model (Case study: babolrood Basin, Mazandaran)

A Heuristic Method to Evaluate the Effect of Soil Tillage on Slope Stability: A Pilot Case in Central Italy

Among the various predisposing factors of rainfall-induced shallow landslides, land use is constantly evolving, being linked to human activities. Between different land uses, improper agricultural practices can have a negative impact on slope stability. Indeed, unsustainable soil tillage can modify the mechanical properties of the soils, leading to a possible increase of the instability phenomena. However, the effects of soil tillage on slope stability are poorly investigated. To address this topic, the PG_TRIGRS model (a probabilistic, geostatistic-based extension of TRIGRS) was applied to a cultivated, landslide-prone area in central Italy, thoroughly studied and periodically monitored through systematic image analysis and field surveys. A heuristic approach was adopted to quantitatively evaluate the effect of soil tillage on the mechanical properties of the soil: after a first run of the model with unbiased parameters, the slope stability analysis was carried out assuming several percentages of reduction of the effective soil cohesion to mimic an increasing impact of soil tillage on the strength conditions. Then, a comparison between observed landslides and the spatial distribution of the probability of failure derived from the application of PG_TRIGRS was carried out. A back analysis with contingency matrix and skill scores was adopted to search for the best compromise between correct and incorrect model outcomes. The results show that soil tillage caused a 20 to 30% reduction in soil cohesion in the analyzed area.

Regional back analysis of landslide events using TRIGRS model and rainfall threshold: an approach to estimate landslide hazard for Kodagu, India

Regional back analysis of landslide events using TRIGRS model and rainfall threshold: an approach to estimate landslide hazard for Kodagu, India

Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

Regional scale debris flow susceptibility is widely evaluated by statistical methods. However, the initiation mechanism of debris flow is not considered, which leads to the neglect of the microtopographic characteristics. To address this problem, we established three novel combined models by introducing the physical model into statistical methods. The integrating models consists of two parts, the statistical models and the TRIGRS model. The eventual results obtained with the integrating model consider both the prediction result of the statistical method for debris flow susceptibility and the mechanism of debris flow initiation. To test the feasibility of the integrating model, three representative statistical models, the analytic hierarchy process (AHP), Shannon entropy (Entropy) and support vector machine (SVM) were selected to evaluate debris flow susceptibility in Yongji County of Jilin Province, China. The results demonstrate that the performance of the integrated models is significantly better than that of the single statistical model, especially in the local areas. The integrating models (AHP-TR, Entropy-TR, SVM-TR) can generate higher quality debris flow susceptibility maps (DFSMs) than the single model, which clearly reflect the scope and boundaries of the areas which are most prone to debris flow and identify the flat land and valleys between adjacent high-prone areas. It also reduces the overprediction generated by the physical model. In general, combining the statistical methods with the TRIGRS model can maximize the strengths of these models and avoid their weaknesses and obtain the effect of 1 + 1 > 2.

Assessing the Effectiveness of TRIGRS for Predicting Unstable Areas in a Tropical Mountain Basin (Colombian Andes)

Some physically based landslide models analyse pore water pressure changes due to rainfall infiltration and its effects on slope stability. The physically-based model TRIGRS has been successfully used in rainfall-induced shallow landslide assessments in different studies around the world; nevertheless, evaluating its performance in tropical mountain terrains, such as the Colombian Andes, is necessary. In this study, the TRIGRS model was applied to the La Arenosa basin (San Carlos, Colombia) and ROC (receiver operating characteristic) analysis was used to assess its effectiveness (performance) at predicting areas susceptible to shallow landslides in this tropical mountainous area. The results were compared with those obtained using the SHIA_Landslide (Simulacion Hidrologica Abierta, or SHIA, in Spanish) and SHALSTAB models in the same case study. The three models performed well, especially TRIGRS and SHIA_Landslide. The predictive results using TRIGRS were thoroughly analysed, describing the effect of the slope angle and its relationship with the estimated soil depth on the variation of the pressure head and the factor of safety (FS) during the simulated rainfall event. The high dependence of FS on soil thickness demonstrated that defining this variable must be carefully accomplished. The results suggest that TRIGRS can be a valuable tool in tropical mountain terrains, such as the Colombian Andes basins, and it can be useful despite the lack of data and the high parameter uncertainty that is common in many study areas.

Evaluation of shallow landslide susceptibility and Factor of Safety variation using the TRIGRS model, Serra do Mar Mountain Range, Brazil

Mathematical models on physical bases were developed to predict which areas are susceptible to landslides. The TRIGRS (Transient Rainfall Infiltration and Grid-based Regional) model also offers the possibility of identifying the depth where the Factor of Safety (Fs) reaches values lower than one, thus constituting hypothetical rupture surfaces. The present study aims to assess the variation of the Fs according to depth and the susceptibility of slopes using the TRIGRS model in a basin in the Serra do Mar Mountain Range (Brazil) impacted by landslides in 1967, based on geotechnical and hydrological values collected both from the existing literature and field generated data. Two stability scenarios were generated through the model, with geotechnical and hydrological values available in the literature being used for the first one, and field values collected in the area itself for the second. The scenarios were validated by means of two indexes, Scar Concentration (SC) and Landslide Potential (LP), both taken from the 1967 scar mapping. In validation (SC index), both of them scenarios, albeit with different spatial distributions, presented the same values, that is, 67% (Fs ≤ 1) and 33% (Fs > 1), which indicates a high percentage of correct results. The results pointed to a rupture surface ranging between 2.00 m and 2.50 m. Due to the satisfactory results obtained, TRIGRS may be used by the public authorities interested in defining and monitoring areas of susceptibility and hazard to landslides.

Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models

The stability evaluation of rainfall-induced landslides using a physical determination model supports disaster prevention, but it is mostly applied to the area with few landslides, and there is a lack of quantitative study on rainfall and landslide stability. This paper combined the Scoops3D model with the TRIGRS model (3D) to predict the shallow landslide spatiotemporal distribution and compared the simulation results with those of the TRIGRS model alone (1D), aiming to obtain more accurate assessment results. At the same time, the relationship between landslide stability and accumulative rainfall was quantitatively fitted to improve the real-time landslide prediction system. We applied the 1D and 3D models to the July 21, 2013 group-occurring landslide event (976 shallow landslides) in the Niangniangba basin, China. The required geotechnical parameters of both models were acquired by field and laboratory tests. We calculated the pressure head over time using the TRIGRS model based on practical rainfall data and predicted the shallow landslide stability using the Scoops3D model according to the resulting piezometric surface. We compared the landslide stability spatial distributions of the two models under initial and saturated conditions with the landslide catalogue. The success rate of landslides predicted by 3D model is 4.20% higher than 1D model. A composite index to quantitatively evaluate both models’ performances indicates over-prediction by the 1D model in the stable region, while the 3D model more effectively predicts shallow landslides with a smaller unstable region. The relationship between instability proportion and accumulative rainfall in the 1D and 3D model can be represented by y=24.57x0.18 and y=11.59x0.33, respectively. The 3D model shows more conservative result, and the rainfall threshold analysis proposed in this paper can provide reference for the time of most landslides in the case of insufficient data, which is an important indicator for disaster early warning and prediction.

The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models

The objective of this study was to use physically based models to carry out a back-analysis of the set of factors that may have influenced slope instability and the consequent development of 65 landslides in the Bengalar Stream basin, located in the Northern Region of Sao Jose dos Campos, Sao Paulo State, Brazil, associated with rainfall between March 7 and 8, 2016. Unlike other models, the FS FIORI model used in this study allowed extra variables to be added to the model that can influence hillslope stability and is associated with land use and land cover (LULC) variability. Analysis of intense short-term and accumulated long-term rainfall influence on slope instability was possible via a TRIGRS model. A comparative analysis was also carried out between a static model (FS FIORI) and a transient model (TRIGRS) which considered the factor of safety and pore pressure to be a function of precipitation and infiltration rates. Despite the differences in their hydrological components, both models were shown to present relatively similar and demonstrated stability rates coherence, according to the characteristics of each model. The FS FIORI model only classified 1.3% of the entire basin as unstable (FS ≤ 1), whereas the TRIGRS model classified 4.5% and 2.9% of the entire basin as unstable in scenarios 1 and 2, respectively. The validity and the accuracy of each model were tested via a receiver operating characteristic (ROC) curve and an area under the curve (AUC). AUC values were: 0.6552 for the FS FIORI model, and 0.7238 and 0.7186 for scenarios 1 and 2 of the TRIGRS model, respectively. The models performed well, with values considered to be acceptable. These results demonstrate an advancement in slope stability modeling studies, including conditioning factors associated with LULC for slope stability calculations.

Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin

ABSTRACT Physically-based distributed models are implemented for landslide susceptibility and hazard assessment around the world. Probabilistic methodologies are considered appropriate to study and quantify the uncertainties derived from the input parameters of these models. In this paper, three sets of Monte Carlo simulations, each one with 10,000 iterations, were applied for a slope stability analysis in a small basin of Envigado (Colombia), using the TRIGRS model, to characterise the uncertainty in the landslide assessment. Different parameters to determine the minimum number of realizations required to ensure a small variation in the failure probability were proposed and analyzed. The quality of the landslide susceptibility assessment was studied. Unexpected and probably erroneous results that may be common in the maps generated using this and other similar methodologies were identified and explained. Additionally, the distribution of the factor of safety was calculated for different grid cells of the basin, showing that the probability density function with the best adjustment to the frequency histogram of the factor of safety can vary between grid cells. The assumption of a normal distribution for the factor of safety would be inappropriate and would lead to miscalculations in this case study.

Risk Assessment of Shallow Landslides for Infrastructure Projects: Analysis of the Case of a Landslide in the El Cabuyal Zone

In this paper an assessment of the hazard, the vulnerability and the risk associated with shallow landslides in a watershed of the Valle de Aburra is carried out. The TRIGRS model with a Monte Carlo simulation to estimate the probability of failure in the watershed using water table depth as the trigger factor is implemented, which allows the assessment of the hazard along with its annual probability of occurrence. Additionally, a quantitative model for physical vulnerability of structures which takes into account landslide intensity and resistance of exposed elements is used. The annual risk is estimated for a house of the study site as the product of the hazard and the damage costs due to the occurrence of the catastrophic event, which considers vulnerability. The methodology used focuses on the shallow landslide occurred on October 26, 2016 on a slope within the study site, which affected the house that is being analyzed.

A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils

Shallow landslides are widespread in different geological contexts and generally occur as multiple events over large areas. When these phenomena involve fine-grained soils, they may cause serious consequences—in terms of environmental and property damages—and thus their spatial forecasting becomes a relevant issue for land use planning and design purposes. The existing literature provides several methods for landslide susceptibility assessment, categorized in qualitative and quantitative methods. When dealing with analyses at large scale (1:5000), quantitative methods are generally preferred. In this paper, landslide susceptibility maps are produced for a study area prone to shallow landsliding, located in Catanzaro (southern Italy). To this aim, two quantitative methods are implemented: the statistical “information value method” and the deterministic “TRIGRS model.” The two approaches are compared by means of two indicators of the grade of correctness of the landslide susceptibility maps: the area under curve of the ROC curve, AUC, and the overestimation index, OI. The results of the analyses in terms of AUC values demonstrate the effectiveness and consistency of both methods in performing the susceptibility mapping of the study area. When the OI values are considered, the results provided by the deterministic model are slightly better than the ones resulting from the statistical analysis. This does not come as a surprise for the case study at hand and it can be ascribed to the availability, within the study area, of: a reliable database of soil properties, and an in-depth knowledge of the behaviour of the considered landslides.

A probabilistic model for evaluating the reliability of rainfall thresholds for shallow landslides based on uncertainties in rainfall characteristics and soil properties

This study aims to develop a probabilistic rainfall threshold estimation model for shallow landslides (PRTE_LS) in order to quantify its reliability while being affected by uncertainties in the rainfall characteristics and soil properties. The rainfall characteristics include the rainfall duration, depth and storm patterns in which their uncertainties result from temporal variation. The effective cohesion of soil, the unit weight of soil, the angle of internal friction, hydraulic conductivity and hydraulic diffusivity are the soil properties represented as the soil parameters in the TRIGRS model in which uncertainties are attributed to spatial variation. After analyzing the sensitivity of rainfall characteristics and TRIGRS parameters to the estimation of rainfall thresholds, the maximum rainfall intensity, storm pattern and soil parameters (soil cohesion, soil friction angle and total unit weight of soil) are retreated as uncertainty factors used in the model development. The proposed PRTE_LS model is used for the reliability assessment of the issued rainfall thresholds in the Jhoukou River watershed, southern Taiwan, to demonstrate its applicability. The results indicate that the corresponding exceedance probability (i.e., underestimated risk) approximates 0.1 on average. In other words, its reliability reaches 0.9. However, issued rainfall thresholds with high reliability might hardly achieve the goal of early warning because shallow landslides can possibly happen before the actual rainfall amount exceeds the threshold. Consequently, the proposed PRTE_LS model can modify issued rainfall thresholds with the specific occurrence probability under the critical safety factors and warning durations being considered. As a result, the proposed PRTE_LS can not only quantify the reliability of the issued rainfall thresholds, but its results can also be referred to in modifying the issued thresholds in order to enhance the early-warning performance.

Prediction of rainfall‐induced shallow landslides in the Loess Plateau, Yan'an, China, using the TRIGRS model

AbstractIn this work, a transient rainfall infiltration and grid‐based regional slope‐stability model (TRIGRS) was implemented in a case study of Yan'an City, Northwest China. In this area, widespread shallow landslides were triggered by the 12 July 2013 exceptional rainstorm event. A high‐resolution DEM, soil parameters from in‐situ and laboratory measurements, water table depths, the maximum depth of precipitation infiltration and rain‐gauge‐corrected precipitation of the event, were used as inputs in the TRIGRS model. Shallow landslides triggered on the same day were used to evaluate the modeling results. The summarized results are as follows: (i) The characteristics and distribution of thirty‐five shallow landslides triggered by the 12 July 2013 rainfall event were identified in the study area and all were classified as shallow landslides with the maximum depth, area and volume less than 3 m, 200 m2 and 1000 m3, respectively, (ii) Four intermediate factor of safety (FS) maps were generated using the TRIGRS model to represent the scenarios 6, 12, 18 and 24 hours after the storm event. The area with FS < 1 increased with the rainfall duration. The percentage of the area with FS < 1 was 0.2%, 3.3%, 3.8% and 5.1% for the four stages, respectively. Twenty‐four hours after the rainstorm, TRIGRS predicted that 1255 grid cells failed, which is consistent with the field data. (iii) TRIGRS generated more satisfactory results at a given precipitation threshold than SINMAP, which is ideal for landslide hazard zoning for land‐use planning at the regional scale. Comparison results showed that TRIGRS is more useful for landslide prediction for a certain precipitation threshold, also in the regional scale. (iv) Analysis of the responses of loess slope prone to slope failure after different precipitation scenarios revealed that loess slopes are particularly sensitive to extended periods of heavy precipitation. Copyright © 2016 John Wiley & Sons, Ltd.

Shallow landslide prediction in the Serra do Mar, São Paulo, Brazil

Abstract. Various methods are currently used in order to predict shallow landslides within the catchment scale. Among them, physically based models present advantages associated with the physical description of processes by means of mathematical equations. The main objective of this research is the prediction of shallow landslides using TRIGRS model, in a pilot catchment located at Serra do Mar mountain range, São Paulo State, southeastern Brazil. Susceptibility scenarios have been simulated taking into account different mechanical and hydrological values. These scenarios were analysed based on a landslide scars map from the January 1985 event, upon which two indexes were applied: Scars Concentration (SC – ratio between the number of cells with scars, in each class, and the total number of cells with scars within the catchment) and Landslide Potential (LP – ratio between the number of cells with scars, in each class, and the total number of cells in that same class). The results showed a significant agreement between the simulated scenarios and the scar's map. In unstable areas (SF≤1), the SC values exceeded 50% in all scenarios. Based on the results, the use of this model should be considered an important tool for shallow landslide prediction, especially in areas where mechanical and hydrological properties of the materials are not well known.