Transient Rainfall Infiltration And Grid-based Regional Slope-stability Analysis Research Articles

Sensitivity Analysis of a Physically Based Model to Assess Rainfall-Triggered Shallow Landslides

Rainfall-triggered shallow landslides are one of the most frequent geomorphological processes in tropical mountainous terrains, and they deserve special attention due to their potential negative consequences in society. Such processes involve several variables including those regarding precipitation, terrain morphology, and hydraulic and shear strength parameters of saturated and unsaturated soils. A spatially distributed and physically-based model for transient rainfall infiltration and grid-based regional slope stability analysis (TRIGRS), was implemented. It was used to analyze the influence of the mentioned variables on the triggering of shallow landslides by rainfall. A robust sensitivity analysis was carried out to quantify the influence of parameter variation on the change of the factor of safety (Delta f_textrm{s}) for shallow landslides, including a one-parameter-at-a-time analysis of a unique cell representing the simplest model space. Four combinations of saturated/unsaturated models and finite/infinite infiltration models were analyzed. The interaction among the hydraulic parameters was analyzed through small-multiple plots to observe their influence on Delta f_textrm{s}. It was found that Delta f_textrm{s} is most sensitive to variation in the maximum depth at which the landslide can be triggered, the slope angle, the cohesion, and the friction angle. In addition, it was found that the parameters of the soil–water characteristic curve for unsaturated soils have little influence on Delta f_textrm{s}. Finally, the interactions among the remaining parameters determine the impact of each one of these on Delta f_textrm{s}. Thus, the results showed that when the baseline set of parameters is changed, the influence of each parameter is modified.

A Non-Linear, Time-Variant Approach to Simulate the Rainfall-Induced Slope Failure of an Unsaturated Soil Slope: A Case Study in Sapa, Vietnam

In this study, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis (TRIGRS), v2.1 program, and module SLOPE/W in the Geostudio package were adopted for assessing rainfall-induced slope failure. TRIGRS was developed by the United States Geological Survey to determine the time-varying groundwater table at the regional scale under rainfall infiltration. The program employs partial differential equations represented by one-dimensional vertical flow in homogeneous materials for unsaturated conditions. With the application of a simple runoff routing scheme combined with the mass balance between rainfall, infiltration, and runoff over the study area, the distribution of the transient pore-water pressures within the entire landscape was simulated considering both the surface and subsurface flow. Additionally, compared to the traditional two-dimensional approach, the topographical conditions were also considered during the groundwater simulation. For conducting the slope stability analysis, a typical cross-section was constructed based on the site description. The predicted water-tables at the observed time of failure of the typical section were extracted and used in SLOPE/W to conduct the time-dependent modelling of rainfall-induced slope failures. In this study, the non-linear method was employed for simulating unsaturated soil shear strength, and the stability of the slope was evaluated using Bishop’s simplified method. We applied the approach to the landslide event that occurred on August 5, 2019, in Sapa district, Lao Cai province, Vietnam. The event resulted in severe damage and blocked the road for days. The predicted results on the stability of the slope as the factor of safety were compared with the actual slope failure during the event. The results showed that, by inputting accurate data, the applied approach could provide valuable evidence about the time of the slope failure.

Bayesian analysis of the impact of rainfall data product on simulated slope failure for North Carolina locations

In the past decades, many different approaches have been developed in the literature to quantify the load-carrying capacity and geotechnical stability (or the Factor of Safety, F s) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated F s values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantle's properties (hydraulic, geotechnical and geomorphologic) and spatiotemporal variability of the moisture content of the hillslope interior. This is particularly true at larger spatial scales. Thus, uncertainty-incorporating analyses of physically based models of rain-induced landslides are rare in the literature. Such landslide modeling is typically conducted at the hillslope scale using gauge-based rainfall forcing data with rather poor spatiotemporal coverage. For regional landslide modeling, the specific advantages and/or disadvantages of gauge-only, radar-merged and satellite-based rainfall products are not clearly established. Here, we compare and evaluate the performance of the Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) model for three different rainfall products using 112 observed landslides in the period between 2004 and 2011 from the North Carolina Geological Survey database. Our study includes the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis Version 7 (TMPA V7), the North American Land Data Assimilation System Phase 2 (NLDAS-2) analysis, and the reference 'truth' Stage IV precipitation. TRIGRS model performance was rather inferior with the use of literature values of the geotechnical parameters and soil hydraulic properties from ROSETTA using soil textural and bulk density data from SSURGO (Soil Survey Geographic database). The performance of TRIGRS improved considerably after Bayesian estimation of the parameters with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm using Stage IV precipitation data. Hereto, we use a likelihood function that combines binary slope failure information from landslide event and 'null' periods using multivariate frequency distribution-based metrics such as the False Discovery and False Omission Rates. Our results demonstrate that the Stage IV-inferred TRIGRS parameter distributions generalize well to TMPA and NLDAS-2 precipitation data, particularly at sites with considerably larger TMPA and NLDAS-2 rainfall amounts during landslide events than null periods. TRIGRS model performance is then rather similar for all three rainfall products. At higher elevations, however, the TMPA and NLDAS-2 precipitation volumes are insufficient and their performance with the Stage IV-derived parameter distributions indicate their inability to accurately characterize hillslope stability.

복합재해 영향을 고려한 산불 후 산사태 잠재적 피해 위험도 분석

Compound disaster is the type that increases the impact affected by two or more hazard events, and attention to compound disaster and multi-hazards risk is growing due to potential damages which are difficult to predict. The objective of this study is to analyze the possible impacts of post-fire landslide scenario quantitatively by using TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis), a physics-based landslide model. In the case of wildfire, soil organic material and density are altered, and saturated hydraulic conductivity decrease because of soil exposed to high temperature. We have included the change of soil saturated hydraulic conductivity into the TRIGRS model through literature review. For a case study, we selected the area of 8km2 in Pyeongchang County. The landslide modeling process was calibrated before simulate the post-wildfire impact based on landslide inventory data to reduce uncertainty. As a result, the mean of the total factor of safety values in the case of landslide was 2.641 when rainfall duration is 1 hour with rainfall intensity of 100mm per day, while the mean value for the case of post-wildfire landslide was lower to 2.579, showing potential landslide occurrence areas appear more quickly in the compound disaster scenario. This study can be used to prevent potential losses caused by the compound disaster such as post-wildfire debris flow or landslides.

Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach

Abstract. Distributed models to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides are based on deterministic laws. These models extend spatially the static stability models adopted in geotechnical engineering, and adopt an infinite-slope geometry to balance the resisting and the driving forces acting on the sliding mass. An infiltration model is used to determine how rainfall changes pore-water conditions, modulating the local stability/instability conditions. A problem with the operation of the existing models lays in the difficulty in obtaining accurate values for the several variables that describe the material properties of the slopes. The problem is particularly severe when the models are applied over large areas, for which sufficient information on the geotechnical and hydrological conditions of the slopes is not generally available. To help solve the problem, we propose a probabilistic Monte Carlo approach to the distributed modeling of rainfall-induced shallow landslides. For this purpose, we have modified the transient rainfall infiltration and grid-based regional slope-stability analysis (TRIGRS) code. The new code (TRIGRS-P) adopts a probabilistic approach to compute, on a cell-by-cell basis, transient pore-pressure changes and related changes in the factor of safety due to rainfall infiltration. Infiltration is modeled using analytical solutions of partial differential equations describing one-dimensional vertical flow in isotropic, homogeneous materials. Both saturated and unsaturated soil conditions can be considered. TRIGRS-P copes with the natural variability inherent to the mechanical and hydrological properties of the slope materials by allowing values of the TRIGRS model input parameters to be sampled randomly from a given probability distribution. The range of variation and the mean value of the parameters can be determined by the usual methods used for preparing the TRIGRS input parameters. The outputs of several model runs obtained varying the input parameters are analyzed statistically, and compared to the original (deterministic) model output. The comparison suggests an improvement of the predictive power of the model of about 10% and 16% in two small test areas, that is, the Frontignano (Italy) and the Mukilteo (USA) areas. We discuss the computational requirements of TRIGRS-P to determine the potential use of the numerical model to forecast the spatial and temporal occurrence of rainfall-induced shallow landslides in very large areas, extending for several hundreds or thousands of square kilometers. Parallel execution of the code using a simple process distribution and the message passing interface (MPI) on multi-processor machines was successful, opening the possibly of testing the use of TRIGRS-P for the operational forecasting of rainfall-induced shallow landslides over large regions.

Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event

Abstract. This paper presents the results from the application of a regional, physically based stability model: Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis (TRIGRS) for a region on Woomyeon Mountain, Seoul, South Korea. This model couples an infinite-slope stability analysis with a one-dimensional analytical solution to predict the transient pore pressure response to the infiltration of rainfall. TRIGRS also adopts the geographic information system (GIS) framework for determining the whole behaviour of a slope. In this paper, we suggest an index for evaluating the results produced by the model. Particular attention is devoted to the prediction of routes of debris flow, using a runoff module. In this context, the paper compares observed landslide and debris flow events with those predicted by the TRIGRS model. The TRIGRS model, originally developed to predict shallow landslides, has been extended in this study for application to debris flows. The results predicted by the TRIGRS model are presented as safety factor (FS) maps corresponding to transient rainfall events, and in terms of debris flow paths using methods proposed by several researchers in hydrology. In order to quantify the effectiveness of the model, we proposed an index called LRclass (landslide ratio for each predicted FS class). The LRclass index is mainly applied in regions where the landslide scar area is not well defined (or is unknown), in order to avoid overestimation of the model results. The use of the TRIGRS routing module was proposed to predict the paths of debris flow, especially in areas where the rheological properties and erosion rates of the materials are difficult to obtain. Although an improvement in accuracy is needed, this module is very useful for preliminary spatio-temporal assessment over wide areas. In summary, the TRIGRS model is a powerful tool of use to decision makers for susceptibility mapping, particularly when linked with various advanced applications using GIS spatial functions.

Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale

Abstract. In the framework of landslide risk management, it appears relevant to assess, both in space and in time, the triggering of rainfall-induced shallow landslides, in order to prevent damages due to these kind of disasters. In this context, the use of real-time landslide early warning systems has been attracting more and more attention from the scientific community. This paper deals with the application, on a regional scale, of two physically-based stability models: SLIP (Shallow Landslides Instability Prediction) and TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis). A back analysis of some recent case-histories of soil slips which occurred in the territory of the central Emilian Apennine, Emilia Romagna Region (Northern Italy) is carried out and the main results are shown. The study area is described from geological and climatic viewpoints. The acquisition of geospatial information regarding the topography, the soil properties and the local landslide inventory is also explained. The paper outlines the main features of the SLIP model and the basic assumptions of TRIGRS. Particular attention is devoted to the discussion of the input data, which have been stored and managed through a Geographic Information System (GIS) platform. Results of the SLIP model on a regional scale, over a one year time interval, are finally presented. The results predicted by the SLIP model are analysed both in terms of safety factor (Fs) maps, corresponding to particular rainfall events, and in terms of time-varying percentage of unstable areas over the considered time interval. The paper compares observed landslide localizations with those predicted by the SLIP model. A further quantitative comparison between SLIP and TRIGRS, both applied to the most important event occurred during the analysed period, is presented. The limits of the SLIP model, mainly due to some restrictions of simplifying the physically based relationships, are analysed in detail. Although an improvement, in terms of spatial accuracy, is needed, thanks to the fast calculation and the satisfactory temporal prediction of landslides, the SLIP model applied on the study area shows certain potential as a landslides forecasting tool on a regional scale.

Evaluation of TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis)’s predictive skill for hurricane-triggered landslides: a case study in Macon County, North Carolina

The key to advancing the predictability of rainfall-triggered landslides is to use physically based slope-stability models that simulate the transient dynamical response of the subsurface moisture to spatiotemporal variability of rainfall in complex terrains. TRIGRS (transient rainfall infiltration and grid-based regional slope-stability analysis) is a USGS landslide prediction model, coded in Fortran, that accounts for the influences of hydrology, topography, and soil physics on slope stability. In this study, we quantitatively evaluate the spatiotemporal predictability of a Matlab version of TRIGRS (MaTRIGRS) in the Blue Ridge Mountains of Macon County, North Carolina where Hurricanes Ivan triggered widespread landslides in the 2004 hurricane season. High resolution digital elevation model (DEM) data (6-m LiDAR), USGS STATSGO soil database, and NOAA/NWS combined radar and gauge precipitation are used as inputs to the model. A local landslide inventory database from North Carolina Geological Survey is used to evaluate the MaTRIGRS’ predictive skill for the landslide locations and timing, identifying predictions within a 120-m radius of observed landslides over the 30-h period of Hurricane Ivan’s passage in September 2004. Results show that within a radius of 24 m from the landslide location about 67% of the landslide, observations could be successfully predicted but with a high false alarm ratio (90%). If the radius of observation is extended to 120 m, 98% of the landslides are detected with an 18% false alarm ratio. This study shows that MaTRIGRS demonstrates acceptable spatiotemporal predictive skill for landslide occurrences within a 120-m radius in space and a hurricane-event-duration (h) in time, offering the potential to serve as a landslide warning system in areas where accurate rainfall forecasts and detailed field data are available. The validation can be further improved with additional landslide information including the exact time of failure for each landslide and the landslide’s extent and run out length.

Analysis of time-varying rainfall infiltration induced landslide

A case study of rainstorm-induced landslide is modeled following the hourly rainfall time series from the stage of infiltration caused by induced slope movement and soil saturated to excess pore pressures—Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Analysis (TRIGRS). The grid-based landslide stability analysis was conducted to model the increased pore pressures and runoff in the study area under the specified rainfall conditions. The generated time variances of pore pressures help determine landslide characteristics and mechanisms under rainfall conditions. Inputs of soil properties and permeability parameters for landslide stability analysis in the study area were prepared by TRIGRS adopted for transient infiltration analysis. Results of the analyses show that under heavy rainfall conditions, the infiltrated slope is unstable and the time of debris masses movement initiated is correlated to the recorded time. In the initiated landslide, characteristics and effects are considered and reflected in the numerical modeling under combinations of topography, land use, climatic and geological conditions. Results reveal that there is a plane failure surface and a potential circular failure surface at the study site besides the rock topple failures in the crest. A grid-based slope-stability analysis incorporated with the GIS spatial functions is more advantageous than the traditional two-dimensional analysis for specified slope profiles to determine the whole behavior of a slope.