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. 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

LANDSLIDE RISK MANAGEMENT USING THE MATHEMATICAL MODEL TRIGRS: Gestão de riscos a deslizamentos de terra utilizando o modelo matemático TRIGRS

Landslides are recurrent events in Brazil, usually triggered by intense rainfall. When these events occur in urban areas, they end up becoming disasters due to economic damage, social impact, and loss of human life. The identification and monitoring of landslide-prone areas are crucial to avoid fatalities. Therefore, the aims of this work are a temporal analysis of the Factor of Safety variation in Campos do Jordão, using the mathematical model TRIGRS. During the analyzed period, two heavy rainfall events were recorded in the area and triggered landslides. The results show TRIGRS efficiency in correctly identify landslide-prone areas and its applicability to become a useful tool for urban planning and early warning systems

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, Fs) of variably saturated hillslopes. Much of this work has focused on a deterministic characterization of hillslope stability. Yet, simulated Fs values are subject to considerable uncertainty due to our inability to characterize accurately the soil mantles 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 indicates their inability to accurately characterize hillslope stability

Spatial transferability of the physically based model TRIGRS using parameter ensembles

The development of better, more reliable and more efficient susceptibility assessments for shallow landslides is becoming increasingly important. Physically based models are well-suited for this, due to their high predictive capability. However, their demands for large, high-resolution and detailed input datasets make them very time-consuming and costly methods. This study investigates if a spatially transferable model calibration can be created with the use of parameter ensembles and with this alleviate the time-consuming calibration process of these methods. To investigate this, the study compares the calibration of the model TRIGRS in two different study areas. The first study area was taken from a previous study where the dynamic physically based model TRIGRS was calibrated for the Laternser valley in Vorarlberg, Austria. The calibrated parameter ensemble and its performance from this previous study are compared with a calibrated parameter ensemble of the model TRIGRS for the Passeier valley in South Tyrol, Italy. The comparison showed very similar model performance and large similarities in the calibrated geotechnical parameter values of the best model runs in both study areas. There is a subset of calibrated geotechnical parameter values that can be used successfully in both study areas and potentially other study areas with similar lithological characteristics. For the hydraulic parameters, the study did not find a transferable parameter subset. These parameters seem to be more sensitive to different soil types. Additionally, the results of the study also showed the importance of the inclusion of detailed information on the timing of landslide initiation in the calibration of the model

Numerical modeling of unsaturated layered soil for rainfall-induced shallow landslides

In this paper, a pioneer study on numerical modeling of rainfall-induced shallow landslides in unsaturated layered soil using the variably saturated flow equation is presented. To model the shallow landslides, the infinite slope stability analysis coupled with the hydrological model with the consideration of the fluctuation of time-dependent pore water pressure and Gardner equation for soil water characteristic curve was developed. A linearization process for the nonlinear Richards equation to deal with groundwater flow in unsaturated layered soil is derived using the Gardner model. To solve one-dimensional flow in the unsaturated zone of layered soil profiles, flux conservation and the continuity of pressure potential at the interface between two consecutive layers are considered in the numerical discretization of the finite difference method. The validity of the proposed model is established in three numerical problems by comparing the results with the analytical and other numerical solutions. Application examples have also been conducted. Obtained results demonstrate that the fluctuation of pore water pressure in unsaturated layered soil dominates slope stability of landslides and the lowest factor of safety may occur at the interface between two consecutive layers. The findings observed in this study are a fundamental contribution to environmental protection engineering for landslides in areas with higher occurrence and vulnerability to extreme precipitation

Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability

AbstractRainfall intensity-duration landslide-triggering thresholds have become widespread for the development of landslide early warning systems. Thresholds can be in principle determined using rainfall event datasets of three types: (a) rainfall events associated with landslides (triggering rainfall) only, (b) rainfall events not associated with landslides (non-triggering rainfall) only, (c) both triggering and non-triggering rainfall. In this paper, through Monte Carlo simulation, we compare these three possible approaches based on the following statistical properties: robustness, sampling variation, and performance. It is found that methods based only on triggering rainfall can be the worst with respect to those three investigated properties. Methods based on both triggering and non-triggering rainfall perform the best, as they could be built to provide the best trade-off between correct and wrong predictions; they are also robust, but still require a quite large sample to sufficiently limit the sampling variation of the threshold parameters. On the other side, methods based on non-triggering rainfall only, which are mostly overlooked in the literature, imply good robustness and low sampling variation, and performances that can often be acceptable and better than thresholds derived from only triggering events. To use solely triggering rainfall—which is the most common practice in the literature—yields to thresholds with the worse statistical properties, except when there is a clear separation between triggering and non-triggering events. Based on these results, it can be stated that methods based only on non-triggering rainfall deserve wider attention. Methods for threshold identification based on only non-triggering rainfall may have the practical advantage that can be in principle used where limited information on landslide occurrence is available (newly instrumented areas). The fact that relatively large samples (about 200 landslides events) are needed for a sufficiently precise estimation of threshold parameters when using triggering rainfall suggests that threshold determination in future applications may start from identifying thresholds from non-triggering events only, and then move to methods considering also the triggering events as landslide information starts to become more available