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

Hazard assessment for regional typhoon-triggered landslides by using physically-based model – a case study from southeastern China

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

Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India

The number of rainfall-induced landslides and the resulting casualties are increasing worldwide. Efficient Landslide Early Warning Systems (LEWS) are the best way to reduce the risk due to such events, but the number of operational LEWS is still limited. A new data-driven approach for spatio-temporal landslide forecasting on a regional scale is proposed, integrating Landslide Susceptibility Maps (LSMs) using RF algorithm and probabilistic hydro-meteorological thresholds, considering both rainfall severity and antecedent soil wetness. The proposed method is also compared with two deterministic process-based approaches: Transient Rainfall Infiltration and Grid-based Regional Slope Stability (TRIGRS) and SHALSTAB, considering the spatial variability in soil thickness and properties, along with the rainfall data. The quantitative comparison is carried out for two test areas in the Western Ghats of India (Idukki and Wayanad), for two different spatial resolutions. The efficiency and area under the curve (AUC) values from a receiver operating characteristic curve (ROC) were used to evaluate the performance of different models. The results for Idukki indicate that the efficiency values of the data-driven approach were improved by 4.67 % by using fine resolution DEM (digital elevation model) of 12.5 m resolution, while in the case of TRIGRS and SHALSTAB models, the improvements were 3.39 % and 1.83 %, respectively. For Wayanad, the improvement in efficiencies was further lesser, 2.59 % in the case of data-driven model, and 0.95 % and 0.73 % in the cases of TRIGRS and SHALSTAB, respectively. The maximum efficiency and AUC values were obtained by the data-driven model for both regions, with a spatial resolution of 12.5 m. The maximum efficiency values were obtained as 81.21 % and 83.33 % for Idukki and Wayanad, respectively, and the corresponding AUC values were 0.92 and 0.93. The results indicate that the model proposed in this study, with data-driven approach performs better than the process-based approaches and can bypass the complexities involved in modeling.

The importance of unsaturated soil properties in the development of slope susceptibility map for Old Alluvium in Singapore

Slope failures caused by rainfall are a regular occurrence in residual soils. As a result of climate change, increased precipitation is anticipated; preventing slope failures due to rain is therefore essential. One typical technique for pinpointing regions at risk for slope failures is the use of a “slope susceptibility map.” Because the groundwater table is usually deep, many slopes in residual soil are typically unsaturated. Soil-water characteristic curve, permeability function, and unsaturated shear strength are the essential unsaturated soil parameters that should be factored into the creation of a slope susceptibility map. This research involved the Old Alluvium region in Singapore as a case study. Transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model was used to establish pore-water pressure distributions over this region. Scoops3D was utilized to include the pore-water pressures calculated by TRIGRS for evaluating slope stability in three dimensions. To assess the reliability of the developed slope susceptibility map, two-dimensional (2-D) numerical analyses were performed on a subset of historically unstable residual soil slopes at the Bidadari site. Minimum safety factors determined via numerical analyses of the slopes under study agreed well with those determined via the slope susceptibility map.

Accelerating Effect of Vegetation on the Instability of Rainfall-Induced Shallow Landslides

Rainfall-induced shallow landslides are widespread throughout the world, and vegetation is frequently utilized to control them. However, in recent years, shallow landslides have continued to frequently occur during the rainy season on the vegetated slopes of the Loess Plateau in China. To better probe this phenomenon, we considered vegetation cover in the sensitivity analysis of landslide hazards and used the transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model to quantitatively describe the impacts of different types of vegetation cover on slope stability. Based on the rainfall information for landslide events, the spatiotemporal distributions of the pore water pressure and the factor of safety of the vegetated slopes were inverted under the driving changes in the soil properties under different vegetation types, and the average prediction accuracy reached 79.88%. It was found that there was a strong positive correlation between the cumulative precipitation and the proportion of landslide-prone areas in woodland covered by tall trees, grassland covered by shrubs and grasses, and cultivated land. The highest landslide susceptibility, which has the greatest potential to hasten the occurrence of rainfall-induced landslides, is found in woodland with tall trees. Therefore, this paper proposes the promoting relationship between vegetation and landslide erosion, which provides a new scientific perspective on watershed management to prevent shallow landslide disasters and manage and develop watershed vegetation.

Analysis of Maximum-Rainfall-Infiltration-Induced Slope Stability Using the Transient Rainfall Infiltration and Grid-based Regional Slope-stability Model in Cililin, West Java, Indonesia

DOI:10.17014/ijog.9.2.263-278Landslide is one type of geological disasters that frequently occurs during the rainy season. Rainfall infiltration can cause soil saturation that increases the positive pore water pressure, disturbing the slope stability. Therefore, knowledge of future landslide-triggering rainfall is required for mitigation efforts and reducing the risk of landslide hazards. This paper presents slope-stability modeling in the Cililin area using the well-established infinite slope model called the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS). The modeling used the rainfall data obtained from the statistical analysis of the maximum daily rainfall by using the Gumbel distribution. The present study applied six scenarios in the modeling. Scenario I is the initial condition without rainfall, showing the slope stability influenced by topography, slope, and soil characteristics. TRIGRS modeling involves rainfall infiltration in scenarios II, III, IV, V, and VI. The maximum rainfalls used in the modeling are 66, 76, 101, 120, and 132 mm/d, showing that rainfall infiltration affected the slope stability. The result indicates that rainfall triggered an increase and expansion of the area distributions critical to the slope stability.

Combining rainfall-induced shallow landslides and subsequent debris flows for hazard chain prediction

Landslides, debris flows, and other destructive natural hazards induced by heavy rainfall in mountainous regions are sometimes not independent but combined to form a disaster chain. Based on the integral link between the triggering of the landslide and the subsequent debris flow, we propose an approach that combines the Transient Rainfall Infiltration and Grid-Based Regional Slope Stability (TRIGRS) model and the Rapid Mass Movements Simulation (RAMMS) model to achieve hourly hazard prediction. The results indicate that the TRIGRS model performed well in predicting the spatial distribution of the shallow landslides, with a success rate of 81.86%. Thus, it is reasonable to use it as the initial input for debris flow simulations. The relationship between the landslide area and the accumulated rainfall obtained using the TRIGRS model is a power-law relationship, which provides a reference for regions that lack rainfall data to predict the material source of a debris flow. The coupled model was found to have a good accuracy of 76.77% in simulating the debris flow. This was close to the debris flow simulation based on the interpreted landslides, and it still produced reasonable results and a more practical value. Furthermore, the proposed coupled model can dynamically predict disasters by the hour based on actual rainfall events. Therefore, the results of this study help provide a more complete hazard prediction picture for rainfall-induced landslide-debris flow hazards in mountainous regions.

Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS

The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model has been widely used to define rainfall thresholds for triggering shallow landslides. In this study, the rainfall intensity(I)-duration(D) thresholds for multiple slope units of an area in Pu’an County, Guizhou Province, China were defined based on TRIGRS. Given that TRIGRS is used to simulate the slope stability under the conditions of a given increasing sequence of I-D data, if the slope reaches instability at I = a, D = b, it will also become unstable in the case of I = a, D > b or I > a, D = b. To explore the effect of these I-D data with the same I or D values on the definition of I-D thresholds and the best method to exclude these data, two screening methods were used to exclude the I-D data that caused instability in the TRIGTS simulation. First, I-D data with the same I values when D values are greater than a certain limit value were excluded. Second, several D values were selected to exclude I-D data with the same I values for a slope unit. Then, an I value was selected to exclude I-D data with the same D values. After screening, two different I-D thresholds were defined. The comparison with the thresholds defined without screening shows that the I-D data with the same I or D values will reduce the accuracy of thresholds. Moreover, the second screening method can entirely exclude these data.

MAT.TRIGRS (V1.0): A new open-source tool for predicting spatiotemporal distribution of rainfall-induced landslides

In this work, a new TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model based on Matlab® is proposed, named MAT.TRIGRS (V1.0), which can be applied to research of spatiotemporal distribution of rainfall-induced landslides. It is characterized by simple data processing, straightforward setting of script parameters, high computation efficiency and convenient operation. A test on the effectiveness of this tool is conducted using the data of the 2019 rainfall-induced landslide in the Shuicheng area, Guizhou Province, China. The data of drilling, terrain, hydrological and mechanical properties of the study area are prepared and converted into TIF grid files. Based on the MAT.TRIGRS model, variations of water head depth and slope instability with time are calculated. The results show that compared with TRIGRS version 2, MAT.TRIGRS is more convenient and friendly in data processing and operation. The comparative experiments prove that the computation efficiency of the new tool increases by 40%. It can be used to predict spatial-temporal distribution of rainfall-induced landslides and contribute to the design and implementation of the landslide early warning system (LEWS).

The Spatial Model using TRIGRS to determine Rainfall-Induced Landslides in Banjarnegara, Central Java, Indonesia

Severe landslides followed by debris flow were recorded to have occurred on 12 December 2014 and discovered to have ruined infrastructures and buried hundreds of peoples in Karangkobar subdistrict of Banjarnegara district, Central Java. There was, however, a high rainfall of up to 200 mm per day for two days before the disaster. Therefore, this research was conducted to predict and assess the landslide area using Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) version 2.0 model to calculate the pore water pressure and safety factor (FS) during rainfall infiltration. The TRIGRS model focused on spatial analysis. The data used as input for this analysis include the DEM, geological and geotechnical properties, infiltration variables, and rainfall intensity. Meanwhile, the FS value was observed to be lowest at the initial condition before rainfall infiltration by ranging between 1 and 1.2 and distributed at the steep slope area near Jemblung. The results were validated through the back analysis of a reference landslide event and the instability in the area was confirmed to be initiated in the 3 three hours of rainfall while the hazards area occurs majorly at the steep slopes with slope angles greater than 30o after 24 hours. The simulation results showed the steep slope area with an inclination angle greater than 30o is susceptible to failure during the rainfall infiltration due to FS < 1.2 while some locations with steep slopes were likely not to fail as indicated by FS >1.2. This study generally concluded that the TRIGRS was able to predict the location of the failure when compared with the results from the field observation of the landslide occurrences.

Improving Spatial Landslide Prediction with 3D Slope Stability Analysis and Genetic Algorithm Optimization: Application to the Oltrepò Pavese

In this study, we compare infinite slope and the three-dimensional stability analysis performed by SCOOPS 3D (software to analyze three-dimensional slope stability throughout a digital landscape). SCOOPS 3D is a model proposed by the U. S. Geological Survey (USGS), the potentialities of which have still not been investigated sufficiently. The comparison between infinite slope and 3D slope stability analysis is carried out using the same hydrological analysis, which is performed with TRIGRS (transient rainfall infiltration and grid-based regional slope-stability model)—another model proposed by USGS. The SCOOPS 3D model requires definition of a series of numerical parameters that can have a significant impact on its own performance, for a given set of physical properties. In the study, we calibrate these numerical parameters through a multi-objective optimization based on genetic algorithms to maximize the model predictability performance in terms of statistics of the receiver operating characteristics (ROC) confusion matrix. This comparison is carried out through an application on a real case study, a catchment in the Oltrepò Pavese (Italy), in which the areas of triggered landslides were accurately monitored during an extreme rainfall on 27–28 April 2009. Results show that the SCOOPS 3D model performs better than the 1D infinite slope stability analysis, as the ROC True Skill Statistic increases from 0.09 to 0.37. In comparison to other studies, we find the 1D model performs worse, likely for the availability of less detailed geological data. On the other side, for the 3D model we find even better results than the two other studies present to date in the scientific literature. This is to be attributed to the optimization process we proposed, which allows to have a greater gain of performance passing from the 1D to the 3D simulation, in comparison to the above-mentioned studies, where no optimization has been applied. Thus, our study contributes to improving the performances of landslide models, which still remain subject to many uncertainty factors.

Developing Real-Time Nowcasting System for Regional Landslide Hazard Assessment under Extreme Rainfall Events

In this research, a real-time nowcasting system for regional landslide-hazard assessment under extreme-rainfall conditions was established by integrating a real-time rainfall data retrieving system, a landslide-susceptibility analysis program (TRISHAL), and a real-time display system to show the stability of regional slopes in real time and provide an alert index under rainstorm conditions for disaster prevention and mitigation. The regional hydrogeological parameters were calibrated using a reverse-optimization analysis based on an RGA (Real-coded Genetic Algorithm) of the optimization techniques and an improved version of the TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability) model. The 2009 landslide event in the Xiaolin area of Taiwan, associated with Typhoon Morakot, was used to test the real-time regional landslide-susceptibility system. The system-testing results showed that the system configuration was feasible for practical applications concerning disaster prevention and mitigation.

Incorporating the Effects of Complex Soil Layering and Thickness Local Variability into Distributed Landslide Susceptibility Assessments

Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.

Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model

In Norway, shallow landslides are generally triggered by intense rainfall and/or snowmelt events. However, the interaction of hydrometeorological processes (e.g., precipitation and snowmelt) acting at different time scales, and the local variations of the terrain conditions (e.g., thickness of the surficial cover) are complex and often unknown. With the aim of better defining the triggering conditions of shallow landslides at a regional scale we used the physically based model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope stability) in an area located in upper Gudbrandsdalen valley in South-Eastern Norway. We performed numerical simulations to reconstruct two scenarios that triggered many landslides in the study area on 10 June 2011 and 22 May 2013. A large part of the work was dedicated to the parameterization of the numerical model. The initial soil-hydraulic conditions and the spatial variation of the surficial cover thickness have been evaluated applying different methods. To fully evaluate the accuracy of the model, ROC (Receiver Operating Characteristic) curves have been obtained comparing the safety factor maps with the source areas in the two periods of analysis. The results of the numerical simulations show the high susceptibility of the study area to the occurrence of shallow landslides and emphasize the importance of a proper model calibration for improving the reliability.

Landslide Vulnerability Mapping for Landslide Victim Relocation

some victims of landslides must be relocated if the location affected by a landslide impossible to be occupied again. The relocation site of the Sijeruk-Banjarnegara landslide victims had experienced a landslide again, due to the inappropriate location selection. It was necessary to mapping the landslide vulnerability for the selection of relocation sites so that the new location must safe from landslides. Settlements that were still located in the high-risk landslide were as the danger the occupants. The selection of relocation sites must be appropriate and the risk of landslides was low. This study aims to produce a vulnerability landslide map so that land suitability can be obtained for the development of settlements by referring to the threat of landslide hazards. Slope stability modelling uses the TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) program version 2.0. The principles are modelling the effect of rainfall on regional slope stability. The data needed in modelling was digital elevation model (DEM), soil data index properties, soil engineering properties, and rainfall. The modelling results show that the safety factor values in the study were ranged from 0.8 to 10. Safe numbers below 1.2 means are prone to landslides. Slope stability map can be used to determine the location of the relocation of landslide victims.

Sensing and monitoring for assessment of rainfall-induced slope failures in residual soil

Real-time monitoring can be used to assess the effect of climatic conditions on slope stability. However, this can be costly. There is therefore need for a methodology to select critical slopes for stability monitoring using comprehensive field instrumentation throughout the year. In this study, the Transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model was used to generate a slope susceptibility map (SSM) of Singapore. This model was selected as it can incorporate unsaturated soil properties in spatial analyses of water infiltration and factor of safety (FoS) calculations. Available studies on establishing SSMs by incorporating unsaturated soil mechanics principles are limited and most focus on mountainous areas. This paper presents relatively new research on the development of a SSM for a densely populated area like Singapore. The study area was limited to residual soil from Bukit Timah Granite in Singapore. TRIGRS helped to determine zones that are highly susceptible to failure; the instrumentation can be used to monitor high-risk areas during dry and rainy periods. A selected slope was instrumented with tensiometers, moisture sensors, piezometers and a rain gauge, all connected to a data logger for real-time monitoring. The changes in pore-water pressure and soil moisture with time during wet and dry periods were used for slope stability analyses. FoS variations over time can be used to assess slope stability, especially during periods of heavy rainfall.

Estimation of Rainfall-Induced Landslides Using the TRIGRS Model

Rainfall-induced landslides have become the biggest threat in the Indian Himalayas and their increasing frequency has led to serious calamities. Several models have been built using various rainfall characteristics to determine the minimum rainfall amount for landslide occurrences. The utilisation of such models depends on the quality of available landslide and rainfall data. However, these models do not consider the effect of local soil, geology, hydrology and topography, which varies spatially. This study is to analyse the triggering process for shallow landslides using physical-based models for the Indian Himalayan region. This research focuses on the utilisation and dependability of physical models in the Kalimpong area of Darjeeling Himalayas, India. The approach utilised the transient rainfall infiltration and grid-based regional slope-stability (TRIGRS) model, which is a widely used model in assessing the variations in pore water pressure and determining the change in the factor of safety. TRIGRS uses an infinite slope model to calculate the change in the factor of safety for every pixel. Moreover, TRIGRS is used to compare historical rainfall scenarios with available landslide database. This study selected the rainfall event from 30th June to 1st July 2015 as input for calibration because the amount of rainfall in this period was higher than the monthly average and caused 18 landslides. TRIGRS depicted variations in the factor of safety with duration before, during and after the heavy rainfall event in 2015. This study further analysed the landslide event and evaluated the predictive capability using receiver operating characteristics. The model was able to successfully predict 71.65% of stable pixels after the landslide event, however, the availability of more datasets such as hourly rainfall, accurate time of landslide event would further improve the results. The results from this study could be replicated and used in other unstable Indian Himalayan regions to establish an operational landslide early warning system.

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions

Landslides in Kerala, India, have been shown to be preceded not only by critical rainfall over a short period but also a much longer period of elevated pore pressure. Such rainfall-triggered landslides are difficult to monitor due to a lack of adequate data on the locations of failures and precipitation. Here, a method is presented using Transient Rainfall Infiltration and Grid-based Regional Slope stability (TRIGRS) as a tool to model the relationship between critical rainfall and antecedent pore pressure as they relate to slope stability, which can be useful for hazard assessment in sparse data regions. This is demonstrated by parameterizing the model with a combination of regional data sources, remote sensing, and temporal back-analysis based on two known failure events (June 2004 and July 2007). Ranges of possible geotechnical and hydraulic parameters were obtained from various local and regional sources, and soil thickness was modeled as a function of slope angle. Rainfall was estimated using satellite microwave radiometry data. For back-analysis, combinations of cohesion, friction angle, and water table depth were then tested in TRIGRS using trial and error until the predicted and observed failure times coincided for the two failure events. While the spatial prediction accuracy of the model is low and multiple solution sets are expected to exist, the results confirm that information regarding the critical pre-failure conditions and stability changes over time can be derived despite data-poor circumstances. Future studies can be undertaken extending this method to characterize many parameter combinations and incorporate more failure cases to develop probabilistic early-warning thresholds.

Three-dimensional, time-dependent modeling of rainfall-induced landslides over a digital landscape: a case study

Physically based approaches for the regional assessment of slope stability using digital elevation model (DEM) topography usually consist of one-dimensional (1D) descriptions and often include more simplifying assumptions than realistic, three-dimensional (3D) analyses. We investigated a new application of the well-known, publicly available software TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model) in combination with Scoops3D to analyze 3D slope stability throughout a digital landscape in a time-dependent fashion, typically not implemented in three-dimensional models. TRIGRS simulated the dynamic hydraulic conditions within slopes induced by a rainstorm, and Scoops3D used the resulting pore water pressure for 3D stability assessment. We applied this approach to the July 2011 landslide event at Mount Umyeon, South Korea, and compared the results with the landslide initiation locations reported for this rainfall event. We described soil depth in the study area by three different simple models. Stability maps, obtained by the 1D (TRIGRS only) and 3D (TRIGRS and Scoops3D) time-dependent approaches, were compared with observations to assess the timing and locations of unstable sites via a synthetic index, previously developed specifically for dealing with point landslide locations. We highlight the performance of the 3D approach versus the 1D method represented by TRIGRS alone, as well as the consistency of the time dependence of the results obtained using the combined approach with the observations.

Comparing the performance of TRIGRS and TiVaSS in spatial and temporal prediction of rainfall-induced shallow landslides

This study compares the performance of transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model and time-variant slope stability (TiVaSS) model in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. This study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( $${\text{LR}}_{\text{class}}$$ index), which was developed for addressing point-like landslide locations. Moreover, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30 to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.