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.

Four-Year Monitoring Study of Shallow Landslide Hazards Based on Hydrological Measurements in a Weathered Granite Soil Slope in South Korea

To build a comprehensive understanding of long-term hydro-mechanical processes that lead to shallow landslide hazards, this study explicitly monitored the volumetric water content (VWC) and rainfall amount for a weathered granite soil slope over a four year period. From the 12 operational landslide monitoring stations installed across South Korea, the Songnisan station was selected as the study site. VWC sensors were placed in the subsurface with a grid-like arrangement at depths of 0.5 and 1.0 m. Shallow landslide hazards were evaluated by applying an infinite slope stability model that adopted a previously proposed unified effective stress concept. By analyzing the variations in the monitored VWC values, the derived matric suctions and suction stresses, and the calculated factor of safety values, we were able to obtain numerous valuable insights. In particular, the seasonal effects of drainage and evapotranspiration on the slope moisture conditions and slope stability were addressed. Preliminary test results indicated that continuous rainfall successfully represented the derived matric suction conditions at a depth of 1.0 m in the lower slope, although this was not the case for the upper and middle slopes. The significance of a future study on cumulative field monitoring data from various sites in different geological conditions is highlighted.

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.

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.

A Data-Driven Surrogate Approach for the Temporal Stability Forecasting of Vegetation Covered Dikes

Climatic conditions and vegetation cover influence water flux in a dike, and potentially the dike stability. A comprehensive numerical simulation is computationally too expensive to be used for the near real-time analysis of a dike network. Therefore, this study investigates a random forest (RF) regressor to build a data-driven surrogate for a numerical model to forecast the temporal macro-stability of dikes. To that end, daily inputs and outputs of a ten-year coupled numerical simulation of an idealised dike (2009–2019) are used to create a synthetic data set, comprising features that can be observed from a dike surface, with the calculated factor of safety (FoS) as the target variable. The data set before 2018 is split into training and testing sets to build and train the RF. The predicted FoS is strongly correlated with the numerical FoS for data that belong to the test set (before 2018). However, the trained model shows lower performance for data in the evaluation set (after 2018) if further surface cracking occurs. This proof-of-concept shows that a data-driven surrogate can be used to determine dike stability for conditions similar to the training data, which could be used to identify vulnerable locations in a dike network for further examination.

Numerical Simulation of Na-Tech Cascading Disasters in a Large Oil Depot

The mechanism of natural-hazard-triggered technological (Na-tech) cascading disasters is complex, and the extent to which their damage is aggravated by various secondary events is difficult to quantify. This study selected a large oil depot and constructed a full-scale three-dimensional scene model based on the surrounding geographical environment. The discrete element method (DEM), finite element method (FEM) and finite volume method (FVM) were employed to conduct numerical simulations of the process and consequences of the following Na-tech disasters: heavy-rainfall-induced landslide → blocks impacting an oil transportation pipeline and breaking it → oil leaking, spreading and resulting in a vapor cloud explosion. According to the results, the maximum impact of the 1 m³ of sliding mass formed in the landslide on the pipeline was over 7 MN (meganewton), and the pipeline fractured completely when it was loaded with a contact force of only 1.44 MN. The numerical simulation methods revealed the mechanism of Na-tech cascading disasters in a large oil depot and quantified the consequences of each event in the cascading disasters.

Critical Continuous Rainfall Map for Forecasting Shallow Landslide Initiations in Busan, Korea

In recent years, precipitation patterns in Korea have shifted to be characterized as short and intense rainfalls. In consideration of shallow landslide initiations primarily governed by heavy rainfalls at short-time scales that diminish drainage effects, the concept of critical continuous rainfall is proposed as a single-rainfall-variable threshold for shallow landslide forecasting. To generate a critical continuous rainfall map for hillslope areas in a city of Korea (Busan), this study designed and applied a systematic modeling process. As a preparatory stage, input datasets of geo-hydraulic properties and geotechnical properties were assembled using estimation techniques based on experiment data of field samples. The inherent and fixed critical continuous rainfall values for hillslope areas in Busan were derived through one-dimensional infiltration analysis coupled with infinite slope stability calculations. As a result of a detailed analysis of historical rainfall records in a case study area over a period of 11 years, three false forecasting cases were recorded, whereas all landslide-triggering rainfall events were correctly captured with no missed forecasting cases. The results of the case study indicate that the proposed critical continuous rainfall may be useful as an effective and straightforward indicator for forecasting the initiation of shallow landslides.

Debris Flow Susceptibility Assessment Using the Integrated Random Forest Based Steady-State Infinite Slope Method: A Case Study in Changbai Mountain, China

Debris flow events often pose significant damage and are a threat to infrastructure and even livelihoods. Recent studies have mainly focused on determining the susceptibility of debris flow using deterministic or heuristic/probabilistic models. However, each type of model has its own significant advantages with some irreparable disadvantages. The random forest model, which is sensitive to the region where the terrain conditions are suitable for the occurrence of debris flow, was applied along with the steady-state infinite slope method, which is capable of describing the initiation mechanism of debris flow. In this manner, a random-forest-based steady-state infinite slope method was used to conduct susceptibility assessment of debris-flow at Changbai mountain area. Results showed that the assessment accuracy of the proposed random-forest-based steady-state infinite slope method reached 90.88%; however, the accuracy of just the random forest model or steady-state infinite slope method was only 88.48% or 60.45%, respectively. Compared with the single-model assessment results, the assessment accuracy of the proposed method improved by 2.4% and 30.43%, respectively. Meanwhile, the debris-flow-prone area of the proposed method was reduced. The random-forest-based steady-state infinite slope method inherited the excellent diagnostic performance of the random-forest models in the region where the debris flow disaster already occurred; meanwhile, this method further refined the debris-flow-prone area from the suitable terrain area based on physico-mechanical properties; thus, the performance of this method was better than those of the other two models.

Investigation on the Role of Water for the Stability of Shallow Landslides—Insights from Experimental Tests

Shallow soil slips are a significant hydrogeological hazard which could affect extended areas of the high-gradient mountainous landscape. Their triggering is highly dependent on the rainfall water infiltration and its further redistribution, as well as the characteristic properties of the soil itself. The complex interaction between those factors generates a considerable degree of uncertainty in the understanding of the governing processes. In this work, we take a small step further towards the untangling of those intricate relationships through observation. The results of a set of 20 downscaled shallow land mass failures are analysed through a principal component analysis and a further detailed look at the resulting parametric trends. Moreover, electrical resistivity tomography measurements are added up to the interpretation of experimental data, by providing a glimpse on the rainfall water infiltration process at the subsurface level. The outcome of this work implies that the coupled interaction between rainfall intensity, hydraulic conductivity and soil moisture gradient is governing the stability of soil and while rainfall intensity and duration are essential instability predictors, they must be integrated with antecedent moisture and site-specific characteristics. A tentative comparison of the dataset with existing rainfall thresholds for shallow landslide occurrence suggests the potential application of experimental tests for thresholds’ definition or validation under the appropriate dimensional analysis. A dimensional analysis indicated the interconnection of parameters intrinsic to the problem, and the significance of scale effects in performing a downscaled simulation of land mass failure.

Application of a Three-Dimensional Deterministic Model to Assess Potential Landslides, a Case Study: Antong Hot Spring Area in Hualien, Taiwan

This study proposes a landslide disaster assessment model combining a fully three-dimensional, physically-based landslide model with high precision of in situ survey data such as surface slip signs, geologic drilling results, underground water observation, and displacement monitoring results over time to perform distribution of potential landslide zones and the size of landslides (area and volume) in the Antong hot spring area in Hualien, Taiwan. The distribution of potential landslide zones in the study area was represented by slope stability safety factors. The results of the analysis showed that the toe of the slope and two upward slopes in the study area were potential landslide areas with safety factors of 1.37, 0.92, and 1.19, respectively. The 3D model analysis results indicated that a landslide could occur at a depth of 20 m at the toe of the slope. Monitoring results for 2015 and 2016 showed that the sliding depth at the toe of the slope was approximately 22.5 m; consequently, the error of landslide depth was only 2.5 m. The simulated results and in situ monitoring results were in good agreement. In addition, the simulated landslide volume was also compared with the results of an empirical equation commonly used in Taiwan to determine their differences. The landslide volumes estimated using the empirical equation were only approximately 38.5% in zone 1, 42.9% in zone 2, and 21.7% in zone 3 of that generated by the proposed model. The empirical equation was used to calculate the landslide volume according to the landslide area, which was subsequently converted into landslide depth. However, the obtained landslide depth was considerably lower than that derived from the in situ monitoring, implying that an empirical estimation approach may result in serious underestimation. Thus, the proposed model could predict landslide area and volume in advance to assist authorities in minimizing loss of life and property damage during a heavy rainfall event.

Empirical and Physically Based Thresholds for the Occurrence of Shallow Landslides in a Prone Area of Northern Italian Apennines

Rainfall thresholds define the conditions leading to the triggering of shallow landslides over wide areas. They can be empirical, which exploit past rainfall data and landslide inventories, or physicallybased, which integrate slope physical–hydrological modeling and stability analyses. In this work, a comparison between these two types of thresholds was performed, using data acquired in Oltrepò Pavese (Northern Italian Apennines), to evaluate their reliability. Empirical thresholds were reconstructed based on rainfalls and landslides triggering events collected from 2000 to 2018. The same rainfall events were implemented in a physicallybased model of a representative testsite, considering different antecedent pore-water pressures, chosen according to the analysis of hydrological monitoring data. Thresholds validation was performed, using an external dataset (August 1992–August 1997). Soil hydrological conditions have a primary role on predisposing or preventing slope failures. In Oltrepò Pavese area, cold and wet months are the most susceptible periods, due to the permanence of saturated or close-to-saturation soil conditions. The lower the pore-water pressure is at the beginning of an event, the higher the amount of rain required to trigger shallow failures is. physicallybased thresholds provide a better reliability in discriminating the events which could or could not trigger slope failures than empirical thresholds. The latter provide a significant number of false positives, due to neglecting the antecedent soil hydrological conditions. These results represent a fundamental basis for the choice of the best thresholds to be implemented in a reliable earlywarning system.

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.

Combining TRIGRS and DEBRIS-2D Models for the Simulation of a Rainfall Infiltration Induced Shallow Landslide and Subsequent Debris Flow

TRIGRS revealed the responses of the total pressure heads and factors of safety with a depth change under a rainfall infiltration occurring on the Daniao tribe’s hill. The depth distribution of the collapsed zone could be identified under the condition where the factors of safety Fs = 1, and the results could calculate the area and volume. Afterward, DEBRIS-2D used TRIGRS’s results to assess the hazard zone of the subsequent debris flow motion. In this study, the DTM variation analysis results from both of before and after the Daniao tribe’s landslide are used to validate TRIGRS’s simulation, the area and the volume of the collapse zone within 8% and 23% errors, respectively. The real disaster range was depicted from the aerial photo used to validate the hazard zone simulation of DEBRIS-2D within 25% errors. In spite of that, the hazard zone from the simulation still included the real disaster range. The combining method for a rainfall infiltration induced a shallow landslide and subsequent debris flow, which was well-matched on a real disaster range on the Daniao tribe’s hill. Therefore, we believe that the TRIGRS and DEBRIS-2D combining methods would provide a better solution for an assessment of a rainfall infiltration inducing shallow landslide and subsequent debris flow motion. TRIGRS could, therefore, provide the area and depth distribution of the collapsed zone, and DEBRIS-2D could use TRIGRS’s results for subsequent debris flow hazard assessment. Furthermore, these results would be of great help in the management of slope disaster prevention.

Hurricane Maria in the U.S. Caribbean: Disturbance Forces, Variation of Effects, and Implications for Future Storms

The impact of Hurricane Maria on the U.S. Caribbean was used to study the causes of remotely-sensed spatial variation in the effects of (1) vegetation index loss and (2) landslide occurrence. The vegetation index is a measure of canopy ‘greenness’, a combination of leaf chlorophyll, leaf area, canopy cover and structure. A generalized linear model was made for each kind of effect, using idealized maps of the hurricane forces, along with three landscape characteristics that were significantly associated. In each model, one of these characteristics was forest fragmentation, and another was a measure of disturbance-propensity. For the greenness loss model, the hurricane force was wind, the disturbance-propensity measure was initial greenness, and the third landscape characteristic was fraction forest cover. For the landslide occurrence model, the hurricane force was rain, the disturbance-propensity measure was amount of land slope, and the third landscape characteristic was soil clay content. The model of greenness loss had a pseudo R2 of 0.73 and showed the U.S. Caribbean lost 31% of its initial greenness from the hurricane, with 51% lost from the initial in the Luquillo Experimental Forest (LEF) from Hurricane Maria along with Hurricane Irma. More greenness disturbance was seen in areas with less wind sheltering, higher elevation and topographic sides. The model of landslide occurrence had a pseudo R2 of 0.53 and showed the U.S. Caribbean had 34% of its area and 52% of the LEF area with a landslide density of at least one in 1 km2 from Hurricane Maria. Four experiments with parameters from previous storms of wind speed, storm duration, rainfall, and forest structure over the same storm path and topographic landscape were run as examples of possible future scenarios. While intensity of the storm makes by far the largest scenario difference, forest fragmentation makes a sizable difference especially in vulnerable areas of high clay content or high wind susceptibility. This study showed the utility of simple hurricane force calculations connected with landscape characteristics and remote-sensing data to determine forest susceptibility to hurricane effects.

A multidimensional stability model for predicting shallow landslide size and shape across landscapes

The size of a shallow landslide is a fundamental control on both its hazard and geomorphic importance. Existing models are either unable to predict landslide size or are computationally intensive such that they cannot practically be applied across landscapes. We derive a model appropriate for natural slopes that is capable of predicting shallow landslide size but simple enough to be applied over entire watersheds. It accounts for lateral resistance by representing the forces acting on each margin of potential landslides using earth pressure theory and by representing root reinforcement as an exponential function of soil depth. We test our model's ability to predict failure of an observed landslide where the relevant parameters are well constrained by field data. The model predicts failure for the observed scar geometry and finds that larger or smaller conformal shapes are more stable. Numerical experiments demonstrate that friction on the boundaries of a potential landslide increases considerably the magnitude of lateral reinforcement, relative to that due to root cohesion alone. We find that there is a critical depth in both cohesive and cohesionless soils, resulting in a minimum size for failure, which is consistent with observed size-frequency distributions. Furthermore, the differential resistance on the boundaries of a potential landslide is responsible for a critical landslide shape which is longer than it is wide, consistent with observed aspect ratios. Finally, our results show that minimum size increases as approximately the square of failure surface depth, consistent with observed landslide depth-area data.