Rainfall-triggered Landslides Research Articles

Identification of inventory-based susceptibility models for assessing landslide probability: a case study of the Gaoping River Basin, Taiwan

ABSTRACTThis study describes the use of inventory-based landslide susceptibility index (LSI) models based on the selection of causative factors, functional relationships between factors and integration to a hazard-warning model. We first merged landslide inventory data of five typhoon-training events and obtained sets of data representing environmental conditions where landslides are likely to occur. These well-defined data sets are used to select five representative causative factors, i.e. the slope angle, rock strength, drainage, curvature and soil type. Four bivariate statistical model combinations were tested: the linear combination, geometric mean, and two other mixed combinations. As a result, the modulation effects between (i) rock strength and slope and (ii) drainage and curvature were intensified in mixed model 2 (MX2) through factor multiplication. The MX2 LSI was integrated with three multivariate landslide hazard-warning models and tested with four triggering factor rainfall parameter sets. Results lead to the conclusion that threshold models with terrain-influenced rainfall can better identify hazard-warning locations. Modulating factor combinations for the hazard warning can also mirror true environmental conditions, yielding more representative model results. This study improves the method for identifying LSI models for the application to rainfall-triggered landslide hazard models.

Landslide inventory, Teziutlán municipality, Puebla, México (1942–2015)

ABSTRACTThis article describes the spatial distribution of landslides in Teziutlán, Puebla, Mexico, which has been historically affected by mass movement processes. The most significant disaster associated with landslides in October 1999. Rainfall-triggered landslides and floods caused more than 100 deaths in Teziutlán and economic losses of US$233 million in Sierra Norte de Puebla. A multi-temporal landslide inventory map (1:25,000) for the period 1942–2015 was constructed by means of field observation and the analysis and interpretation of aerial photographs and satellite images. The inventory map includes 662 landslides and covers 163 km2. The total landslide area is in the order of 0.71 km2. Taking into account the scarp, channel and depositional area, the mean surface of the landslides is 1075 m2. The largest documented area was 17,512 m2. The smallest landslide area mapped was 24 m2. Most movements can be considered as having been small.

Landslide zoning analysis in Zhouqu under different rainfall warning levels

Landslide zoning is crucial for disaster prevention and reduction in areas of rainfall-triggered landslides. A shallow-layer landslide assessment model was established for Zhouqu area, simulation of the slope stability of extensive area was applied to predict the potential landslide fairly, and evaluation of the migration and conversion laws were performed to determine quantitatively the effect of rainfall on landslide under different rainfall warning levels. The results show that all 169 recorded landslide scars were classified as lower stability-to-defended by SINMAP model, and the susceptible landslide distribution in low-lying area is consistent with that in areas with relatively high topographic precipitation. With the rainfall intensity increases from 10 to 56 mm/h, the landslide and rainfall were found to exhibit a growing power function relation; the potentially unstable region is always the highest, highlighting a critical problem. Moreover, as the basically stable region and potentially unstable region gradually transition into the unstable region and extremely unstable region, respectively, the landslide potential increases gradually, and the hazard level becomes increasingly aggravated; consequently, the potential threat is more serious. When the hourly rainfall exceeds 20 mm, the slope stability is gradually weakened in the stable region, which is gradually migrating into the unstable region, thus triggering landslides, debris slides and flows. When the hourly rainfall exceeds 32 mm, the hazard degree changes qualitatively, caused by the quantitative change, and the geological hazard degree increases significantly. The study can provide a scientific basis for landslide zoning, monitoring, and early warning in the Zhouqu area as well as other alpine gully area.

A proposed cell model for multiple-occurrence regional landslide events: Implications for landslide susceptibility mapping

Multiple-occurrence regional landslide events (MORLEs) consist of hundreds to thousands of shallow landslides occurring more or less simultaneously within defined areas, ranging from tens to thousands of square kilometres. While MORLEs can be triggered by rainstorms and earthquakes, this paper is confined to those landslide events triggered by rainstorms. Globally, MORLEs occur in a range of geological settings in areas of moderate to steep slopes subject to intense rainstorms. Individual landslides in rainstorm-triggered events are dominantly small, shallow debris and earth flows, and debris and earth slides involving regolith or weathered bedrock.The model used to characterise these events assumes that energy distribution within the event area is represented on the land surface by a cell structure; with maximum energy expenditure within an identifiable core and rapid dissipation concentrically away from the centre. The version of the model presented here has been developed for rainfall-triggered landslide events. It proposes that rainfall intensity can be used to determine different critical landslide response zones within the cell (referred to as core, middle, and periphery zones). These zones are most readily distinguished by two conditions: the proportion of the slope that fails and the particular type of the slope stability factor that assumes dominance in determining specific sites of landslide occurrence. The latter condition means that the power of any slope stability factor to distinguish between stable and unstable sites varies throughout the affected area in accordance with the landslide response zones within the cell; certain factors critical for determining the location of landslide sites in one part of the event area have little influence in other parts of the event area. The implication is that landslide susceptibility maps (and subsequently derived mitigation measures) based on conventional slope stability factors may have only limited validity for many events.The overall ability to predict the impact of these events and consequently the development of effective mitigation measures is limited by the ability to predict the travel path, storm centre, and intensity range within the cell structure of extreme weather systems.

Sensitivity analysis and calibration of a dynamic physically based slope stability model

Abstract. Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological–geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 behavioural model runs with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

Comparison landslide-triggering rainfall threshold using satellite data: TRMM and GPM in South Bandung area

The number of landslides events in Bandung ranked fourth in Indonesia. The landslide is affected not only by the geographical character, but also by the rainfall intensity and duration. Thus, the information of the threshold of landslide triggering rainfall is important to be obtained. This study is aim to determine the threshold of rainfall in South Bandung using rainfall data provided by TRMM and GPM satellites. The satellite data is corrected by ground-observation data in three sites. The empirical method used in this study performs the relationship between the rainfall intensity and duration to the landslide event and the threshold curve obtained by lowering the regression line intensity-duration (ID) to lowest point rainfall in causing the landslide. The relationship of rainfall ID for landslide threshold is expressed in I= 2.3 e(-0.03*D) for TRMM data, while I= 7.20 e(-0.09*D) for GPM data. The result shows that the GPM data better than TRMM data and the analysis of measurement data showed a pattern corresponding to the observed values.

Rainfall-triggered slope instabilities under a changing climate: comparative study using historical and projected precipitation extremes

This study aims to quantitatively assess the impact of extreme precipitation events under current and future climate scenarios on landslides. Rainfall-triggered landslides are analyzed primarily using extreme precipitation estimates, derived using the so-called stationary assumption (i.e., statistics of extreme events will not vary significantly over a long period of time). However, extreme precipitation patterns have shown to vary substantially due to climate change, leading to unprecedented changes in the statistics of extremes. In this study, a nonstationary approach, applied to climate model simulations, is adopted to project the upper bound of future precipitation extremes. Future precipitation estimates are obtained from the coupled model intercomparison project phase 5 (CMIP5) simulations. Baseline (historical) and projected (future) precipitation extremes are obtained for a study area near Seattle, Washington. The precipitation patterns are integrated into a series of fully coupled two-dimensional stress – unsaturated flow finite element simulations. The responses of the baseline and projected models at a 7 day rainfall duration obtained for a 50 year recurrence interval are compared in terms of the local strength reduction factor, displacements, matric suctions, and suction stresses. The results indicate that the usage of historical rainfall data can lead to underestimations in the hydromechanical behavior of natural slopes where locally increased transient seepage rates occur from the upper bound of future extreme precipitation estimates.

Landslide prediction system for rainfall induced landslides in Slovenia (Masprem)

In this paper we introduce a landslide prediction System for modelling the probabilities of landslides through time in Slovenia (Masprem). The System to forecast rainfall induced landslides is based on the landslide susceptibility map, landslide triggering rainfall threshold values and the precipitation forecasting model. Through the integrated parameters a detailed framework of the System, from conceptual to operational phases, is shown. Using fuzzy logic the landslide prediction is calculated. Potential landslide areas are forecasted on a national scale (1: 250,000) and on a local scale (1: 25,000) for five selected municipalities where the exposure of inhabitants, buildings and different type of infrastructure is displayed, twice daily. Due to different rainfall patterns that govern landslide occurrences, the System for landslide prediction considers two different rainfall scenarios (M1 and M2). The landslides predicted by the two models are compared with a landslide inventory to validate the Outputs. In this study we highlight the rainfall event that lasted from the 9th to the 14th of September 2014 when abundant precipitation triggered over 800 slope failures around Slovenia and caused large material damage. Results show that antecedent rainfall plays an important role, according to the comparisons of the model (M1) where antecedent rainfall is not considered. Although in general the landslides areas are over-predicted and largely do not correspond to the landslide inventory, the overall Performance indicates that the system is able to capture the crucial factors in determining the landslide location. Additional calibration of input parameters and the landslide inventory as well as improved spatially distributed rainfall forecast data can further enhance the model's prediction.

Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis

Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.

Evaluation of different models in rainfall-triggered landslide susceptibility mapping: a case study in Chunan, southeast China

This study applied the information value model (IVM), logistic regression (LR), artificial neural networks (ANN) and support vector machine (SVM) to rainfall-triggered landslide susceptibility mapping for a typical study area in southeast China. Their capabilities under 42 modelling scenarios with different combination of 9 conditioning factors were tested and compared using the performance metric of area under receiver operating characteristic curve (AUC). The results showed that all the models could acceptably produce landslide susceptibility maps (LSMs) with the achieved AUC of 86.05, 85.89, 87.65 and 85.63% for model training, and that of 86.03, 85.7, 86.4 and 85.11% for model validation. Slightly, ANN was best, followed by LR, IVM and SVM. The LR showed a better generalization capability under the circumstances of changing combination of the conditioning factors, whereas the performance of ANN and SVM appeared to be sensitive to that. Although the comparable performance (i.e. AUC) could be achieved by all the models, the hazard zones classified from the produced LSMs exhibited considerable variation in spatial patterns. The IVM tended to overestimate the landslide susceptibility compared to others, and predicted a less area at very low-level hazard. The associated uncertainty caused by the modelling methods will lead to different management costs and risks being taken when applying the produced LSMs in geological engineering design and planning.

Development of a coupled hydrological-geotechnical framework for rainfall-induced landslides prediction

In this paper, we propose a new coupled hydrological-geotechnical model called CRESLIDE (Coupled Routing and Excess Storage and SLope-Infiltration-Distributed Equilibrium), which can alleviate the chronic flaws of landslides simulation and prediction. CRESLIDE is designed to improve the original landslides model (SLIDE) through the coupling of hydrological model (CREST) and to deliver an integrated system for predicting storm-triggered landslides. This coupled system is implemented and evaluated in Macon County, North Carolina, where Hurricane Ivan triggered widespread landslides in September 2004 during the hurricane season. Model simulations from CRESLIDE show its reliability to predict landslides occurrence (location and timing). Receiver Operating Characteristic (ROC) analysis demonstrates that the coupled system (CRESLIDE) has higher specificity (94.10%) and higher sensitivity (11.36%) compared to the original SLIDE model (specificity=93.32%, sensitivity=10.23%) and a well-known landslide model (TRIGRS, whose sensitivity is 6.98%). This improved predictive performance demonstrates the advantage of coupling hydrological and geotechnical models with a more realistic representation of infiltration. It warrants a better depiction of the spatial and temporal dependence of hydrological and geotechnical processes in the course of the rainfall-triggered landslide event. This kind of model integration is useful for landslides prediction and early warning.

Preliminary studies on the dynamic prediction method of rainfall-triggered landslide

Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorological-geotechnical early warning system GRAPES-LFM (GRAPES: Global and Regional Assimilation and PrEdiction System; LFM: Landslide Forecast Model), basing on the GRAPES model and the landslide predicting model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides. This integrated system is evaluated in Dehua County, Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP (National Centers for Environmental Prediction) FNL (Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety (FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.

Urbanisation and landslides: hazard drivers and better practices

Rapid unplanned urbanisation is driving increasing rainfall-triggered landslide risk in low-income communities in tropical developing countries. Conventional slope stabilisation techniques are often unaffordable and most disaster-risk-reduction funding is currently spent post-disaster. However, experience in the Caribbean has changed local engineering practice and World Bank policy, demonstrating that community-based surface water drainage is affordable and effective in mitigating urban landslides. New evidence presented in this paper identifies specific informal construction practices generating further landslide hazards and bioengineering schemes most effective for landslide mitigation. A dynamic hydrology–slope stability model is used to simulate the factor of safety response of nine slope classes (angle and soil strength) to progressive vegetation removal, slope cutting and loading, for six design storms. The effectiveness of 76 bioengineering schemes for improving stability is modelled. Key recommendations are that deforestation should be limited and slope cutting avoided as cutting is most detrimental to stability. Site-specific modelling is needed to identify where deep-rooting, lightweight trees might add stability, whereas grasses are beneficial in all locations.

Landslide characteristics and spatial distribution in the Rwenzori Mountains, Uganda

In many landslide-prone regions, data on landslide characteristics remain poor or inexistent. This is also the case for the Rwenzori Mountains, located on the border of Uganda and the DR Congo. There, landslides frequently occur and cause fatalities and substantial damage to private property and infrastructure. In this paper, we present the results of a field inventory performed in three representative study areas covering 114 km2. A total of 371 landslides were mapped and analyzed for their geomorphological characteristics and their spatial distribution. The average landslide areas varied from less than 0.3 ha in the gneiss-dominated highlands to >1 ha in the rift alluvium of the lowlands. Large landslides (>1.5 ha) are well represented while smaller landslides (<1.5 ha) are underrepresented. The degrees of completeness of the field inventories are comparable to those of similar historical landslide inventories. The diversity of potential mass movements in the Rwenzori is large and depends on the dominant lithological and topographic conditions. A dominance of shallow translational soil slides in gneiss and of deep rotational soil slides in the rift alluvium is observed. Slope angle is the main controlling topographic factor for landslides with the highest landslide concentrations for slope angles above 25–30° in the highlands and 10–15° in the lowlands. The undercutting of slopes by rivers and excavations for construction are important preparatory factors. Rainfall-triggered landslides are the most common in the area, however in the zones of influence of the last two major earthquakes (1966: Mw = 6.6 and 1994: Mw = 6.2), 12 co-seismic landslides were also observed.

선행강우를 고려한 산사태 유발 강우기준(ID curve) 분석

본 연구에서는 국내 산사태 발생을 예측하기 위하여 선행강우의 영향을 고려한 산사태 유발 강우기준(Intensity-Duration, ID curve)을 제안하였다. 1999년부터 2013년까지 국내에서 유발된 202개의 산사태에 대하여, 기상청 강우자료를 바탕으로 산사태 발생 시점 이전의 시강우량 데이터를 수집하고 분석하였다. 선행강우의 영향을 고려하기 위해 강우사상간 시간(Inter event time definition, IETD)을 6, 12, 24, 48, 72, 96시간으로 구분하고, 회귀분석을 통해 강우기준을 제안하였다. 국외의 산사태 유발강우기준과 제안된 유발강우기준을 비교하였으며, 선행강우에 대한 산사태 유발 강우기준의 변화를 분석하였다. 그 결과, 국내의 경우 비교적 낮은 강우강도에서 산사태가 유발되는 것으로 나타났으며, IETD가 증가할수록 산사태 유발강우기준의 기울기가 증가하는 경향이 나타났다. 따라서 단기간의 강우에 대해서는 산사태 유발강우기준(강우강도)이 높아지고, 장기간의 강우에 대해서는 낮아지는 것을 알 수 있었다. 2014년도에 국내에서 발생한 산사태 재해이력을 이용하여 검토한 결과, 본 연구에서 제안된 ID curve가 산사태 유발을 비교적 잘 예측할 수 있는 것으로 나타났다. This study was conducted to suggest a landslide triggering rainfall threshold (ID curve) for landslide prediction by considering the effect of antecedent rainfall. 202 rainfall data including domestic landslide and rainfall records were used in this study. In order to consider the effect of antecedent rainfall, rainfall data were analyzed by changing Inter Event Time Definition (IETD) and IETD based ID curve were presented by regression analysis. Compared to the findings of the previous studies, the presented ID curve has a tendency to predict the landslides occurring at a relatively low rainfall intensity. It is shown that the proposed ID curve is appropriate and realistic for predicting landslides through the validation of proposed ID curve using records of landslides in 2014. Based on this analysis, it is found that the longer IETD, the greater the effect of antecedent rainfall, and the steeper the gradient of ID curve. It is also found that the rainfall threshold (intensity) is higher for the short period rainfall and lower for the long period rainfall.

Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy

A statistical approach was employed to model the spatial distribution of rainfall-triggered landslides in two areas in Sicily (Italy) that occurred during the winter of 2004–2005. The investigated areas are located within the Belice River basin and extend for 38.5 and 10.3km2, respectively. A landslide inventory was established for both areas using two Google Earth images taken on October 25th 2004 and on March 18th 2005, to map slope failures activated or reactivated during this interval. Geographic Information Systems (GIS) were used to prepare 5m grids of the dependent variables (absence/presence of landslide) and independent variables (lithology and 13 DEM-derivatives). Multivariate Adaptive Regression Splines (MARS) were applied to model landslide susceptibility whereas receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate model performance. To evaluate the robustness of the whole procedure, we prepared 10 different samples of positive (landslide presence) and negative (landslide absence) cases for each area. Absences were selected through two different methods: (i) extraction from randomly distributed circles with a diameter corresponding to the mean width of the landslide source areas; and (ii) selection as randomly distributed individual grid cells. A comparison was also made between the predictive performances of models including and not including the lithology parameter.The models trained and tested on the same area demonstrated excellent to outstanding fit (AUC>0.8). On the other hand, predictive skill decreases when measured outside the calibration area, although most of the landslides occur where susceptibility is high and the overall model performance is acceptable (AUC>0.7). The results also showed that the accuracy of the landslide susceptibility models is higher when lithology is included in the statistical analysis. Models whose absences were selected using random circles showed a significantly better performance when learning and validation samples were extracted from the same area; whereas, conversely, no significant difference was observed when testing the models outside the training area.

Analysis of a rainfall-triggered landslide at rest and be thankful in Scotland

Weather and climate are key influences upon the triggering of landslides in Scotland. One of the primary factors influencing the landslide and debris flows occurrence is rainwater infiltration into an initially unsaturated slope. This leads to an increase in both degree of saturation and pore-water pressure and, consequently, to a decrease in the shear strength of the soil, which eventually triggers a landslide event. The A83 trunk road at the Rest and Be Thankful in Scotland is an area prone to risk from landslide and, because of climate change, this risk is going to become higher. An increased frequency of heavier rainfall has been observed in recent years and forecasts for the next decades are not more reassuring. In 2014, the biggest landslide event has occurred at the Rest and Be Thankful Pass. More than 2000 tonnes of landslip debris have been removed in efforts to reopen the route A83. Although the route is now open again, still many are the aspects related to the triggering of such event that have not been clarified yet. The aim of this paper is to address the problem of developing a simple numerical model to analyse rainfalltriggered shallow landslides.

The SESAMO early warning system for rainfall-triggered landslides

The development of Web-based information systems coupled with advanced monitoring systems could prove to be extremely useful in landslide risk management and mitigation. A new frontier in the field of rainfall-triggered landslides (RTLs) lies in the real-time modelling of the relationship between rainfall and slope stability; this requires an intensive monitoring of some key parameters that could be achieved through the use of modern and often low-cost technologies. This work describes an integrated information system for early warning of RTLs that has been deployed and tested, in a prototypal form, for an Italian pilot site. The core of the proposed system is a wireless sensor network collecting meteorological, hydrological and geotechnical data. Data provided by different sensors and transmitted to a Web-based platform are used by an opportunely designed artificial neural network performing a stability analysis in near real-time or in forecast modality. The system is able to predict whether and when landslides could occur, providing early warnings of potential slope failures. System infrastructure, designed on three interacting levels, encompasses a sensing level, integrating different Web-based sensors, a processing level, using Web standard interoperability services and specifically implemented algorithms, and, finally, a warning level, providing warning information through Web technologies.

Accounting for soil parameter uncertainty in a physically based and distributed approach for rainfall-triggered landslides

In this study we propose a probabilistic approach for coupled distributed hydrological-hillslope stability models that accounts for soil parameters uncertainty at basin scale. The geotechnical and soil retention curve parameters are treated as random variables across the basin and theoretical probability distributions of the Factor of Safety (FS) are estimated. The derived distributions are used to obtain the spatio-temporal dynamics of probability of failure, in terms of parameters uncertainty, conditioned to soil moisture dynamics. The framework has been implemented in the tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator-VEGetation Generator for Interactive Evolution)-Landslide model and applied to a basin in the Luquillo Experimental Forest (Puerto Rico) where shallow landslides are common. In particular, the methodology was used to evaluate how the spatial and temporal patterns of precipitation, whose variability is significant over the basin, affect the distribution of probability of failure, through event scale analyses. Results indicate that hyetographs where heavy precipitation is near the end of the event lead to the most critical conditions in terms of probability of failure. Copyright © 2015 John Wiley & Sons, Ltd.

Geologic, Geotechnical, and Geophysical Investigation of a Shallow Landslide, Eastern Kentucky

Shallow colluvial landslides are common in eastern Kentucky, as well as in the east-central Appalachian region. A geological, geotechnical, and geophysical investigation was carried out for a shallow colluvial landslide in Boyd County, KY. The purpose of this project was to assess the geologic conditions, extent, and behavior of a rainfall-triggered landslide in eastern Kentucky and to evaluate the use of electrical resistivity as a tool to characterize a shallow colluvial landslide. This study showed that 1) colluvial landslide movement is correlated to the rainfall and 2) inverted resistivity sections with distinct resistivity contrasts that correlated to landslide stratigraphy, depth of the failure surface, and groundwater regimes.