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Most regional landslide warning systems utilize empirically derived rainfall thresholds that are difficult to improve without recalibration to additional landslide events. To address this limitation, we explored the use of synthetic rainfall to generate thousands of possible storm patterns and coupled them with a physics‐based hydrology and slope stability model for various antecedent soil saturation scenarios to analyze pore water pressure and factor of safety metrics. We used these metrics to generate two‐tiered alert thresholds that can be employed to assess shallow landslide potential for any given combination of storm and antecedent wetness. When applied to the San Francisco Bay region (CA, USA), the results are consistent with events that caused widespread landsliding. Our deterministic modeling approach, which accounts for plausible ranges in soil hydraulic and mechanical properties, can inform the development of the next generation of warning systems for rainfall‐induced landsliding. Plain Language Summary: Providing accurate warning in advance of rainfall‐induced landsliding can help reduce loss of life and property but requires knowledge of hillslope wetness and future rainfall conditions to forecast when widespread landsliding is likely. However, typical landslide warning systems consider only the rainfall characteristics (e.g., rainfall amount, duration, and/or intensity) that were measured during past landslide events. These are used to define a set of rainfall conditions, or a threshold, for which landsliding should be expected within a given area. When a rainfall threshold does not perform well for subsequent landsliding events, it can be difficult to diagnose the problem because hillslope wetness is not considered. We present an approach to develop landslide thresholds that accounts for both rainfall and hillslope wetness. The results are consistent with historical and recent widespread landsliding events in our study region and, more importantly, can be related to measurable properties of hillslopes. This more direct approach could revolutionize the development of the next generation of landslide warning systems. Key Points: Thresholds for rainfall‐induced landsliding of soil‐mantled hillslopes identified with physics‐based hydrologic and slope stability modelingLibrary of modeled infiltration events based on variations in rainfall and antecedent soil saturation facilitates timely hazard assessmentDeterministic modeling approaches that account for parameter variability can inform the next generation of landslide warning systems [ABSTRACT FROM AUTHOR]

Yang, J., 2018. Landslide damage from extreme rainstorm geological accumulation layers within plain river basins. In: Ashraf, M.A. and Chowdhury, A.J.K. (eds.), Coastal Ecosystem Responses to Human and Climatic Changes throughout Asia. To provide the basis of an early warning and detection for landslide disasters and to provide decision support for local governments in disaster prevention and mitigation, this research studied landslide damage assessment from the geological accumulation layer in a plain river basin under conditions of an extreme rainstorm. Based on an analysis of the relationship between landslide geology and rainfall and a damage model of landslides under conditions of extreme rainfall, the landslide damage assessment described in this article used the SINMAP model. The layer of geological accumulation in the Wenchuan–Diexi River section in the upper reaches of the Minjiang River was the study area. Spatial distribution characteristics and the main factors inducing a landslide disaster were studied, and a danger-evaluation model for landslides in that region was established for landslide assessment research and for applying that research. Based on the SINMAP model, the effects of different extreme rainstorm events on the stability of regional slopes were analyzed. The results of a stability evaluation for landslides show that the area has an unstable zone of 4339.8 km2, which accounts for 64.49% of the total area. The unstable area in this evaluation was basically consistent with the distribution of an existing landslide, which shows that the model can adequately evaluate slope stability and landslide potential from the layer of geological accumulation in this area. [ABSTRACT FROM AUTHOR]

The article focuses on the Taroko National Park in Taiwan where the wet slopes and earthquakes have made landslides common. Niels Hovius, a geomorphologist and his colleagues have made it a landscape dynamics observatory by installing various instruments. It is noted that monitoring landslides will help the researchers understand what triggers them and how landscapes recover. Landslide researcher Robert Emberson has found that landslides in Taiwan are sources of Carbon dioxide.

The main purpose of this study was to establish a data-driven approach and assess its potential for shallow landslide susceptibility mapping of the Shennongjia Forestry District, China. For the data processing, Fisher segmenting was used for the classification of topographical factors (gradient, relief amplitude, plan curvature, and normalized difference vegetation index) and the improved Otsu method was used to determine the buffer length thresholds of the locational factors (proximity to roads, rivers and faults). The computations show that these two objective methods help to avoid the uncertainty caused by the subjective judgments and the obtained thresholds are more consistent with actual conditions. Considering the different data types, the locational and topographical factors were evaluated by fuzzy comprehensive evaluation while the geological factors were evaluated by frequency index, and then the three separate evaluations were combined using radar chart analysis. The validation result shows that the susceptibility map based on a data-driven approach has a high accuracy of prediction and this approach will be salutary for risk mitigation and land-use management in mountainous regions. [ABSTRACT FROM AUTHOR]

Landslides and other geological disasters occur frequently and often cause high financial and humanitarian cost. The real-time, early-warning monitoring of landslides has important significance in reducing casualties and property losses. In this paper, by taking the high initial precision and high sensitivity advantage of FBG, an FBG strain sensor is designed combining FBGs with inclinometer. The sensor was regarded as a cantilever beam with one end fixed. According to the anisotropic material properties of the inclinometer, a theoretical formula between the FBG wavelength and the deflection of the sensor was established using the elastic mechanics principle. Accuracy of the formula established had been verified through laboratory calibration testing and model slope monitoring experiments. The displacement of landslide could be calculated by the established theoretical formula using the changing values of FBG central wavelength obtained by the demodulation instrument remotely. Results showed that the maximum error at different heights was 9.09%; the average of the maximum error was 6.35%, and its corresponding variance was 2.12; the minimum error was 4.18%; the average of the minimum error was 5.99%, and its corresponding variance was 0.50. The maximum error of the theoretical and the measured displacement decrease gradually, and the variance of the error also decreases gradually. This indicates that the theoretical results are more and more reliable. It also shows that the sensor and the theoretical formula established in this paper can be used for remote, real-time, high precision and early warning monitoring of the slope. [ABSTRACT FROM AUTHOR]

Seismically-induced landslides can be detrimental to urban communities, often resulting in significant damage and repair costs, blockage of lifeline connection routes and utilities, environmental impacts, and potential for loss of life. Consistent, reliable hazard maps can assist agencies to efficiently allocate limited resources to prepare for these events. This paper presents methodology for evaluating and mapping seismically-induced landslide hazards across a large area utilizing performance-based design strategies. This approach scales site-specific seismic hazard curve analysis techniques to a regional scale evaluation by combining generally available data, including: previous landslide inventories, lidar and photogrammetric topographic data, geologic mapping, NEHRP site classifications based on shear wave velocity (VS30) measurements, and seismic hazard curves for the analysis. These maps can be combined with maps generated for other hazards (e.g., liquefaction) for a fully probabilistic, multi-hazard evaluation and risk assessment. To demonstrate the methodology, a series of landslide hazard maps showing the probabilities of exceeding different thresholds of movement (e.g., 0.1, 0.3, and 1.0 m) were generated for western Oregon. The study area contains weak, wet soils that experience land sliding regularly even without significant seismic activity. The maps show reasonable agreement with landslide inventory and susceptibility maps. [ABSTRACT FROM AUTHOR]

Complete landslide inventories are rarely available. The objectives of this study were to (i) elaborate the influence of incomplete landslide inventories on statistical landslide susceptibility models and to (ii) propose suitable modelling strategies that can reduce the effects of inventory-based incompleteness. In this context, we examined whether the application of a novel statistical approach, namely mixed-effects models, enables predictions that are less influenced by such inventory-based errors. The study was conducted for (i) an area located in eastern Austria and (ii) a synthetically generated data set. The applied methodology consisted of a simulation of two different inventory-based biases and an in-depth evaluation of subsequent modelling results. Inventory-based errors were simulated by gradually removing landslide data within forests and selected municipalities. The resulting differently biased inventories were introduced into logistic regression models while we considered the effects of including or excluding predictors that are directly related to the respective inventory-based bias. Mixed-effects logistic regression was used to account for variation that was due to an inventory-based incompleteness. The results show that most erroneous predictions, but highest predictive performances, were obtained from models generated with highly incomplete inventories and predictors that were able to directly describe the respective incompleteness. An exclusion of such bias-describing predictors led to systematically confounded relationships. The application of mixed-effects models proved valuable to produce predictions that were least affected by inventory-based errors. This paper highlights that the degree of inventory-based incompleteness is only one of several aspects that determine how an inventory-based bias may propagate into the final results. We propose a four-step procedure to deal with incomplete inventories in the context of statistical landslide susceptibility modelling. [ABSTRACT FROM AUTHOR]

In this study, in order to map storm event-induced landslide susceptibility for central Taiwan, remote-sensing data were used to interpret landslides using a geographical information system (GIS). Discriminant analysis was used to establish rules that can successfully classify landslide and nonlandslide slopes. GIS-based slope units were used as the terrain subdivision to determine landslide susceptibility. A set of causative factors and a triggering factor were used to build a landslide prediction model. Data for shallow landslides triggered by three crucial rainfall events in July 2001, July 2004, and June 2006 were selected for developing the model. Among the factors used, the total accumulated rainfall is found to be a key factor in the interpretation of landslides. The landslide susceptibility maps were built for the Guoshin region and validated using predicting samples and data from a subsequent storm event, the 2008 Sinlaku typhoon. Results show that the developed model is capable of predicting the shallow landslide distribution induced by heavy rainfall in the study area. [ABSTRACT FROM AUTHOR]

Seismic shaking can cause slope instability in otherwise relatively stable permafrost terrains. In addition, rapid ice melting in low-permeability fine-grained soils can lead to excess pore-water pressure build-up and cause instability in slopes even at small angles. This study addresses the active-layer detachment (ALD) slope instability hazard and develops a systematic risk assessment framework for existing and future linear infrastructures, such as energy pipelines, bridges, and roads traversing permafrost regions. Mild slopes, with average gradient of 7°, are considered in this study as the most representative of actual field conditions. The potential for earthquake-triggered ALD is analytically quantified. State-of-the-art ALD morphological statistics for northern Canada are combined with seismic slope stability analyses to determine ( i) the probability of linear infrastructure exposure to permanent ground deformations (PGDs) caused by ALD and ( ii) the extent of the potential PGD that the linear infrastructure may be subjected to. The Monte Carlo technique is applied to simulate and assess the sensitivity of the model to parameters such as earthquake magnitude and source-to-site distance. Findings from this study can be used to evaluate the vulnerability of linear infrastructures exposed to the ALD hazard. [ABSTRACT FROM AUTHOR]

This paper presents a new region-based preparatory factor, total flux ( TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides. [ABSTRACT FROM AUTHOR]

Fifty-five descending images from the ENVISAT satellite were processed using the small baseline subset (SBAS) method to derive the spatial and temporal ground deformation of the Bailong River Basin between 2003 and 2010. The basin is one of the most severely landslide- and debris flow-affected areas of China. As a result, 104 sites with high deformation areas were identified. Interferometric Synthetic Aperture Radar (InSAR) analysis was combined with landslide inventory data and field surveys, and anomalous areas were classified into three main types: landslide; debris; and subsidence. Displacement rates up to 35 mm/yr were evaluated away from the sensor along a line-of-sight (LOS) direction. The results gained should allow a more accurate prediction and monitoring of landslides, debris, and subsidence; further, they demonstrate the capability of the SBAS method to analyze any displacement effect and identify dangerous and uninhabitable areas in the basin. The small baseline subset method can thus contribute to the prediction and prevention of geohazards in the area. [ABSTRACT FROM AUTHOR]

The dynamic monitoring of landslides in engineering geology has focused on the correlation among landslide stability, rainwater infiltration, and subsurface hydrogeology. However, the understanding of this complicated correlation is still poor and inadequate. Thus, in this study, we investigated a typical landslide in southwestern China via time-lapse electrical resistivity tomography (TLERT) in November 2013 and August 2014. We studied landslide mechanisms based on the spatiotemporal characteristics of surface water infiltration and flow within the landslide body. Combined with borehole data, inverted resistivity models accurately defined the interface between Quaternary sediments and bedrock. Preferential flow pathways attributed to fracture zones and fissures were also delineated. In addition, we found that surface water permeates through these pathways into the slipping mass and drains away as fissure water in the fractured bedrock, probably causing the weakly weathered layer to gradually soften and erode, eventually leading to a landslide. Clearly, TLERT dynamic monitoring can provide precursory information of critical sliding and can be used in landslide stability analysis and prediction. [ABSTRACT FROM AUTHOR]

This work focuses on the development of a combined statistical-mechanical approach to predict changes in landslide displacement rates from observed changes in rainfall amounts. The forecasting tool FLAME (Forecasting Landslides Accelerations induced by Meteorological Events) associates (1) a statistical impulse response (IR) model to simulate the changes in landslide rates by computing a transfer function between the input signal (e.g. rainfall) and the output signal (e.g. displacement) and (2) a simple 1D mechanical (MA) model (e.g. viscoplastic rheology) to take into account changes in pore water pressure. The models have been applied to forecast the displacement rates at the Super-Sauze landslide (South East France). The performance of different combinations of models (IR model alone, MA model alone and a combination of the IR and MA models) is evaluated against observed changes in pore water pressures and displacement rates at the study site. Results indicate that the three models are able to reproduce the displacement pattern in the general kinematic regime (succession of acceleration and deceleration phases); conversely, extreme kinematic regimes such as fluidization of part of the landslide mass are not being reproduced. The approach constitutes however a robust tool to predict changes in displacement rates from rainfall or groundwater time series. [ABSTRACT FROM AUTHOR]

Logistic regression studies which assess landslide susceptibility are widely available in the literature. However, a global review of these studies to synthesise and compare the results does not exist. There are currently no guidelines for the selection of covariates to be used in logistic regression analysis, and as such, the covariates selected vary widely between studies. An inventory of significant covariates associated with landsliding produced from the full set of such studies globally would be a useful aid to the selection of covariates in future logistic regression studies. Thus, studies using logistic regression for landslide susceptibility estimation published in the literature were collated, and a database was created of the significant factors affecting the generation of landslides. The database records the paper the data were taken from, the year of publication, the approximate longitude and latitude of the study area, the trigger method (where appropriate) and the most dominant type of landslides occurring in the study area. The significant and non-significant (at the 95 % confidence level) covariates were recorded, as well as their coefficient, statistical significance and unit of measurement. The most common statistically significant covariate used in landslide logistic regression was slope, followed by aspect. The significant covariates related to landsliding varied for earthquake-induced landslides compared to rainfall-induced landslides, and between landslide type. More importantly, the full range of covariates used was identified along with their frequencies of inclusion. The analysis showed that there needs to be more clarity and consistency in the methodology for selecting covariates for logistic regression analysis and in the metrics included when presenting the results. Several recommendations for future studies were given. [ABSTRACT FROM AUTHOR]

Landslides are a common type of mass movement that occur in large numbers and have significant effects on topography. In Turkey, landslides rank second after earthquakes among natural disasters that cause widespread loss of life and property. This study investigates the monitoring of the Taşkent Landslide (Konya, Turkey), which has considerably affected the local topography. The landslide in Taşkent had an adverse effect on transportation to nearby towns and villages. Initial studies were conducted using the global navigation satellite system (GNSS) measurement scale; these studies are currently on-going. Since 2011, studies using terrestrial laser scanning (TLS), which is one of the latest ground-positioning technologies, have also been launched. This article presents the results of the TLS and GNSS approaches used to monitor, characterise and model an active landslide. It includes the comparison and analysis of a point cloud, three-dimensional model, profile and change in volume mass acquired in different time periods in one landslide area. The point cloud data, obtained from two different time periods using the TLS technique, and supported by the results of GNSS analysis, were analysed via different methods. The TLS revealed that up to 4 m of material was displaced at the site of the landslide, and it revealed detailed information about how the landslide occurred. The volume of the mass that moved as a result of the landslide was determined, and movements that occurred in the geological structure were monitored. These studies showed that the TLS point cloud method can be effectively used in the monitoring of landslides that occur in areas that are large, difficult to reach, rugged or mountainous. [ABSTRACT FROM AUTHOR]

A debris-flow hazard assessment was conducted for the Medano Creek drainage basin, at Great Sand Dunes National Park and Preserve, following the 2,400 ha Medano Fire in 2010. Debris-flow probability and volume predictions were made using empirical regression models. Model parameters include burn severity, rainfall intensity, topographic characteristics, and soil properties. Model results provided park resource managers with information on potential sub-basin-specific hazards to park visitors, roads, and campsites. Monitoring of the first significant rainfall events following the fire and the resulting debris-flow responses provided means for model validation. Of the three probability models utilized, two predicted high probability of debris-flow occurrence for all sub-basins that produced debris flows, with numerous false positives, while the third failed to predict high probability in any of the sub-basins. The volume model predicted volumes within approximately one order of magnitude higher than those measured. [ABSTRACT FROM AUTHOR]

An ensemble algorithm of data mining decision tree (DT)-based CHi-squared Automatic Interaction Detection (CHAID) is widely used for prediction analysis in variety of applications. CHAID as a multivariate method has an automatic classification capacity to analyze large numbers of landslide conditioning factors. Moreover, it results two or more nodes for each independent variable, where every node contains numbers of presence or absence of landslides (dependent variable). Other DT methods such as Quick, Unbiased, Efficient Statistic Tree (QUEST) and Classification and Regression Trees (CRT) are not able to produce multi branches based tree. Thus, the main objective of this paper is to use CHAID method to perform the best classification fit for each conditioning factors, then, combined it with logistic regression (LR) to find the corresponding coefficients of best fitting function that assess the optimal terminal nodes. In the first step, a landslide inventory map with 296 landslide locations were extracted from various sources over the Pohang-Kyeong Joo catchment (South Korea). Then, the inventory was randomly split into two datasets, 70 % was used for training the models, and the remaining 30 % was used for validation purpose. Thirteen landslide conditioning factors were used for the susceptibility modeling. Then, CHAID was applied and revealed that some conditioning factors such as altitude, soil drain, soil texture and TWI, as terminal nodes and reflected the best classification fit. Then, a proposed ensemble technique was applied and the interpretations of the coefficients showed that the relationship between the decision tree branch nodes distance from drain, soil drain, and TWI, respectively, leads to better consequences assessment of landslides in the current study area. The validation results showed that both success and prediction rates, 75 and 79 %, respectively. This study proved the efficiency and reliability of ensemble DT and LR model in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

The prediction of landslide movement acceleration is a complex problem, among others identified for deep-seated landslides, and represents a crucial step for risk assessment. Within the scope of this problem, the objective of this paper is to explore a modelling method that enables the study of landslide function and facilitates displacement predictions based on a limited data set. An inverse modelling approach is proposed for predicting the temporal evolution of landslide movement based on rainfall and displacement velocities. Initially, the hydrogeology of the studied landslides was conceptualised based on correlative analyses. Subsequently, we applied an inverse model with a Gaussian-exponential transfer function to reproduce the displacements. This method was tested on the Grand Ilet (GI) and Mare-à-Poule-d'Eau (HB) landslides on Reunion Island in the Indian Ocean. We show that the behaviour of landslides can be modelled by inverse models with a bimodal transfer function using a Gaussian-exponential impulse response. The cumulative displacements over 7 years of modelling (2 years of calibration period for GI, and 4 years for HB) were reproduced with an RMSE above 0.9. The characteristics of the bimodal transfer function are directly related to the hydrogeological functioning demonstrated by the correlative analyses: the rapid reaction of a landslide can be associated with the effect of a preferential flow path on groundwater level variations. Thus, this study shows that the inverse model using a Gaussian-exponential transfer function is a powerful tool for predicting deep-seated landslide movements and for studying how they function. Beyond modelling displacements, our approach effectively demonstrates its ability to contribute relevant data for conceptualising the sliding mechanisms and hydrogeology of landslides. [ABSTRACT FROM AUTHOR]

The main objective of this study is to better understand and quantify the consequences for landslide susceptibility assessment caused by (i) the discrimination (or not) of landslide typology and (ii) the use of different predisposing factor combinations. The study area for this research was Lajedo parish (Flores Island, Azores—Portugal). For the landslide susceptibility modeling, 12 predisposing factors and a historical landslide inventory with a total of 474 individual landslide rupture areas were used as inputs, and the Information Value method was then applied. It was concluded that susceptibility models developed specifically for each landslide typology achieve better results when compared to the model developed for the total inventory, which suffers from a bias caused by the strong spatial abundance of one landslide typology. A total of 4095 susceptibility models were tested for each typology, and the best models were selected according to their goodness of fit. The best model for both falls and slides has seven predisposing factors, some of which do not correspond to the factors that have the best individual discriminatory capabilities. The number of expected and observed unique terrain conditions for each model allowed us to conclude that with the successive addition of predisposing factors, there is an inability of the territory to generate new observed unique terrain conditions. This consequence was directly related to the inability to increase the goodness of fit of the computed models. For each landslide typology, the predictive capacity of the best susceptibility model was assessed by computing the Prediction Rate Curves and the Area Under the Curve. [ABSTRACT FROM AUTHOR]