Landslide Typology Research Articles

Pre-and post-landslide analyses for sustainable development in Kiarapandak area, Bogor, West Java, Indonesia

Abstract The landslide in Kiarapandak has destroyed several dwellings, impaired farming and agriculture, and critical land formation. Analysing the conditions before and after the landslide is essential for supporting sustainable development, protecting the community, and mitigating environmental damage. This investigation employs Google Earth imagery, site surveys, field observations, landslide typology, and vulnerability assessments. The research area, Kiarapandak, is categorized as a landslide-prone typology Zone B with a predominantly 21%–40% slope and more than 40%. The vulnerability level is high. In the critical land created by the landslide, there is a reduction in soil fertility due to surface stripping, increased erosion, and surface runoff, causing sedimentation and river flooding. Land rehabilitation efforts are necessary to improve the quality of land resources in the post-landslide area and support the ecosystem while generating economic value for affected landowners. An agroforestry system, with a vertical arrangement of species strata, can be implemented in the post-landslide location to serve ecological and financial functions and optimize land resource quality through fertilizer application.

Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China

Different types of landslides exhibit distinct relationships with environmental conditioning factors. Therefore, in regions where multiple types of landslides coexist, it is required to separate landslide types for landslide susceptibility mapping (LSM). In this paper, a landslide-prone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area (TGRA), China, was selected as the study area. 733 landslides were classified into three types: reservoir-affected landslides, non-reservoir-affected landslides, and rockfalls. Four landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models (logistic regression, random forest, support vector machine), and a Deep Learning (DL) model. After comparing the models using receiver operating characteristics (ROC), the landslide susceptibility indexes of three types landslides were acquired by the best performing model. These indexes were then used as input to generate the final map based on the Stacking method. The results revealed that DL model showed the best performance in LSM without considering landslide types, achieving an area under the curve (AUC) of 0.854 for testing and 0.922 for training. Moreover, when we separated the landslide types for LSM, the AUC improved by 0.026 for testing and 0.044 for training. Thus, this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps. These maps in turn, can be valuable tools for evaluating and mitigating landslide hazards.

Exploring the geomorphological adequacy of the landslide susceptibility maps: A test for different types of landslides in the Bidente river basin (northern Italy)

Landslide susceptibility modelling is a crucial tool for implementing effective strategies in landslide risk mitigation. A plethora of statistical methods is available for generating accurate prediction images; however, the reliability of these models in terms of geomorphological adequacy is often overlooked by scholars. This critical flaw may result in concealed prediction errors, undermining the trustworthiness of the obtained maps. A key aspect of evaluating the geomorphological soundness of these models lies in factor analysis, specifically considering the correlation of explanatory variables with the final susceptibility score rather than solely focusing on their impact on model accuracy.This study delves into research conducted in the Bidente river basin (Italy) that analyes results obtained from slide, flow, and complex susceptibility models using Weight of Evidence (WoE) and Multivariate Adaptive Regression Splines (MARS) statistical methods. The research critically examines each factor class's role in defining susceptibility scores for different landslide typologies. The comparison between susceptibility maps generated by WoE and MARS for each typology (slide = 0.78; flow = 0.85; complex: 0.79) (slide = 0.78; flow = 0.85; complex: 0.79)reveals good to excellent prediction skill, with MARS demonstrating a 5 % higher performance index.The study emphasises the importance of spatial relationships between variables and landslide occurrences, highlighting that individual classes of variables influence the final susceptibility score based on their combined role with other predictor classes. In particular, in this study, results highlight that lithotecnical and landform classification classes delimit the landslide domain, while topographic attributes (steepness, curvatures, SPI and TWI) modulate the score inside. The proposed approach offers insights into investigating the geomorphological adequacy of landslide prediction images, emphasising the significance of factor analysis in evaluating model reliability and uncovering potential errors in susceptibility maps.

Landslide Inventory and Disaster Management Based on Community Participation for Sustainability Environment in the Volcanic Area

Landslides are still an essential problem for areas with rough morphology. There are several ways to map landslide vulnerability that can be used to reduce disaster risk. To produce maps with high accuracy is one of the challenges. This study proposes a community-based landslide inventory mapping approach. Communities are important in providing information about disaster events and are essential to disaster management. The aims of this study are 1) to conduct field investigations for the community for landslide inventory; 2) to identify and analyze the role of the community in disaster risk reduction for a sustainable environment. The method used in this research is using participatory mapping (P-GIS) and focus group discussion. The community is participatively asked to reconstruct past landslides. Community-based landslide data inventory using geospatial technology in the form of remote sensing imagery as support. The results of community participation showed that there were 138 landslides with translational and rotational landslide typologies. There is a community role in reducing the risk of landslides through the Taji Village Disaster Preparedness Community (SIBAT). The management model for land management carried out by the community is based on the principles of environmental sustainability in the form of easy application, local-based materials, local-based culture, and local-based knowledge, and provides sufficient economic income.

Investigating the Effects of Cell Size in Statistical Landslide Susceptibility Modelling for Different Landslide Typologies: A Test in Central–Northern Sicily

Optimally sizing grid cells is a relevant research issue in landslide susceptibility evaluation. In fact, the size of the adopted mapping units influences several aspects spanning from statistical (the number of positive/negative cases and prevalence and resolution/precision trade-off) and purely geomorphological (the representativeness of the mapping units and the diagnostic areas) to cartographic (the suitability of the obtained prediction images for the final users) topics. In this paper, the results of landslide susceptibility modelling in a 343 km2 catchment for three different types of landslides (rotational/translational slides, slope flows and local flows) using different pixel-size mapping units (5, 8, 10, 16 and 32 m) are compared and discussed. The obtained results show that the higher-resolution model (5 m) did not produce the best performance for any of the landslide typologies. The model with 8 m sized pixels displayed the optimal threshold size for slides and slope flows. In contrast, for local flows, an increasing trend of model prediction accuracy was reached with 32 m pixels, which was a higher value than that presented using 8 m pixels. The variable importance analysis demonstrated that the better performance of the 8 m cells was due to their effectiveness in capturing morphological conditions which favour slope instability (profile curvature and middle and high ridges).

Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China

Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into “very high” or “high” susceptibility zones, while “very low” or “low” susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5~5.4%.

Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India

Landslide susceptibility modeling is complex as it involves different types of landslides and diverse interests of the end-user. Developing mitigation strategies for the landslides depends on their typology. Therefore, a landslide susceptibility based on different types should be more appealing than a susceptibility model based on a single inventory set. In this research, susceptibility models are generated considering the different types of landslides. Prior to the development of the model, we analyzed landslide inventory for understanding the complexity and scope of alternative landslide susceptibility mapping. We conducted this work by examining a case study of Kalimpong region (Himalayas), characterized by different types of landslides. The landslide inventory was analyzed based on its differential attributes, such as movement types, state of activity, material type, distribution, style, and failure mechanism. From the landslide inventory, debris slides, rockslides, and rockfalls were identified to generate two landslide susceptibility models using deep learning algorithms. The findings showed high accuracy for both models (above 0.90), although the spatial agreement is highly varied among the models.

Activity maps of multi-source mudslides from the Daunia Apennines (Apulia, southern Italy)

Multi-source mudslides are common in the Daunia Apennines, i.e., the Apulian sector of the Southern Apennines of Italy, and play an important role in the historical evolution of the landscape, with significant impact on the social and economic activities of the area. They also represent the most relevant geological hazard along the front of the Apennine Chain and expose to a considerable risk the local population and infrastructures. The outer sector of the Daunia Apennines is part of the foreland thrust belt system, made up of fissured fine-grained materials, belonging to the Daunia tectonic unit formations. Concerning landslide typology, the most frequent slope movements are represented by flows and composite mudslides, the latter typically starting as slumps evolving from flows. This paper deals with the production of landslide activity maps of three case studies of composite mudslides from the front of the Daunia Apennines. The maps were produced integrating traditional interpretation of multi-year aerial photograph coverage and field surveys with the available digital elevation models to analyze surface morphology. Each landslide activity map corresponds to multi-temporal landslide inventories compiled for the last 15–20 years. This paper outlined that in the last 20 years the front of the Daunia Apennines underwent a significant reactivation of slope failure phenomena after years of quiescence, or of limited activity. The three case studies clearly confirm that: (i) the active portions of the mudslides are located inside or near preexisting slope failures, and (ii) the spatial distribution of instability phenomena is strictly dependent upon the presence of older, larger and dormant, phenomena. Landslide activity maps are an important tool for the evaluation of landslide susceptibility and hazard in the study area, and for quantitative geomorphological analyses it aimed at understanding the long-term geomorphological evolution of this portion of the Southern Apennines. Moreover, we argue that high-quality multi-temporal inventories could have positive effects on all derivative products and analyses, including erosion studies and landscape modeling, susceptibility and hazard assessments, and risk evaluations as well.

Interaction of alongslope and downslope processes in the Alentejo Margin (SW Iberia) – Implications on slope stability

An integrated analysis of multibeam bathymetry and single- and multichannel seismic records were used to image the morpho-stratigraphy of the Alentejo Margin (Southwest Portuguese Continental Margin). The complex interaction of several alongslope and downslope processes in the area leads to the formation of various bottom current driven depositional and erosive features (moat) as well as gravity-driven features (gullies and landslides) in the Alentejo Margin. These processes feed each other contributing for the active sedimentary pattern on the area. Sines Contourite Drift (SCD) is a ~2311 km2 sedimentary feature with a perimeter of 303.9 km, 98 km length and 35 km width.Landslides occur either on steep and on gentle slopes between ~200 and 3200 mwd (meters water depth) in the study area, which has a total extent of ~85 km × 82 km. Scar concentration is higher in the middle- and lower-slope, whereas the thickest debris deposits are found in the slope basin area - Lebre Basin (LB). Translational landslides, with planar failure planes parallel to the slope surface are the main landslide typology verified in the study area. Several triggering and pre-conditioning factors, resulting from the interaction of alongslope and downslope processes, contribute for varied landslides scar concentration. Steep-slope and high sedimentation rates favour sediments under-consolidation, promoting excess pore water pressure and weak layers formation.In this paper, we show that the interaction of bottom currents with pre-existing tectonic structures promotes the complex interaction of both alongslope and downslope processes thus promoting an active and diversified geomorphological evolution and generalised slope instability.

Implications of Landslide Typology and Predisposing Factor Combinations for Probabilistic Landslide Susceptibility Models: A Case Study in Lajedo Parish (Flores Island, Azores—Portugal)

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.

Introducing a New Inventory of Large Martian Landslides

AbstractLandslides have been observed in different terrestrial environments and also on planets, satellites, and asteroids. Long runout landslides are strongly dependent on the initial mass position, material and slope path properties, topographic relief, and presence of volatiles. Therefore, landslides represent a means for the description of rock properties and environment of deposition prevailing at the time of occurrence and may assist in understanding the geological and climatological history of the planetary surfaces. Concerning Mars, previous studies have concentrated on Valles Marineris, where among the largest and longest landslides have been observed. Using different imagery, we present and analyze an original database of 3,118 Martian landslides of deposit area greater than 0.1 km2 throughout the planet between 60°N and 60°S, resulting in a data set far richer than previously done. After a distinction is made between different typologies of landslides, their position and the statistical distribution of their geometrical properties are examined. Large landslides cluster along the Noctis Labyrinthus‐Valles Marineris‐Margaritifer Terra system. Rock avalanches within craters are widespread, but no significant large landslides have been found at latitudes higher than 40°S and 46°N. The magnitude‐frequency distribution follows a power law with scaling exponent ranging between 1.02 and 1.57, for the entire data set and varies according to the geomorphological settings, the landslide typology, and mobility. A volume‐area power law relationship (exponent: 1.12–1.24) is proposed, based on the reconstruction of 222 landslide geometries, and compared to those for similar terrestrial landslides (1.39). Similarities with respect to terrestrial landslide, distribution with respect to impact craters and impact energy, and cryosphere extent are also discussed.

Global Scale Analysis of Martian Landslide Mobility and Paleoenvironmental Clues

AbstractThe mobility of landslides on Mars is studied based on a database of 3,118 events. To establish the volume of the landslides for the whole data set based on the deposit area, a new volume‐area relationship based on a representative data set of 222 landslides is used. By plotting the H/L ratio between fall height H and runout L versus volume, the landslide mobility is analyzed and compared with existing empirical relationships for Martian and terrestrial landslides. By analyzing the mobility in terms of normalized residuals, that is, the relative deviation of the H/L ratio from the data set best‐fit line, mobility is found to depend on both the landslide location on Mars and the landslide typology. This allows us to identify four different types of high‐mobility (hypermobile) landslides. Three classes of high‐mobility landslides are associated respectively to meteoroid impact, the Olympus Mons aureoles, and landslides with Toreva‐block failure style, and their mobility can be explained by the peculiar flow mechanics. The fourth class includes landslides associated with isolated craters, those in the regions wetted by the putative Oceanus Borealis, and the ones at high latitudes. We suggest that the common factor behind all the hypermobile landslides of this fourth kind is the presence of ice. This is confirmed by our data showing that landslides increase in mobility with latitude. The latitudinal trend mirrors the distribution of ice as detected by radar, neutron probes, and the presence of glacial and layered ejecta morphologies. Because the overall landslide distribution supports the presence of ice‐lubricated conditions, two ice lubrication models are presented showing how ice melting within or underneath the landslides could enhance mobility. By proper analysis in terms of apparent friction residuals, we find that the mobility of landslides in Valles Marineris with the largest landslide concentration is lower than average. We explain this circumstance partly from the smaller role of ice in equatorial Valles Marineris and partly because the collapses from high slope relief imply high‐speed impact with the floor valley confinement, loss of momentum, and decrease in mobility. Environmental consequences imply that the present subsurface ice distribution may have been persistent throughout the Amazonian period.

Weathering characterization for landslides modeling in granitoid rock masses of the Capo Vaticano promontory (Calabria, Italy)

This work focuses on developing multidisciplinary researches concerning weathering profiles related to landscape evolution of the Capo Vaticano promontory on the Calabria Tyrrhenian side (southern Italy). In this area, the tectonic uplift, occurred at least since Pleistocene, together with the Mediterranean climatic conditions, is the main cause of deep weathering and denudation processes. The latter occurred on the outcropping rocks of the crystalline-metamorphic basement, made up of weathered granitoids, in turn belonging to the Monte Poro granitoid complex (intermediate to felsic plutonic rocks covered by Cenozoic sedimentary successions). Field observations coupled to borehole explorations, geophysical surveys, and minero-petrographical analyses allowed the characterization of the granitoid outcrops typical of the studied area in terms of kind and degree of slope instability. This characterization was based on suitable correlations verified between several factors as weathering degree, elastic properties of rocks, and discontinuity features. Weathering profiles are mainly composed by rock masses varying from completely weathered rock with corestones of highly weathered rock (classes IV–V) to slightly weathered rocks (class II). The weathered rocks are involved in several landslide typologies such as debris flow (frequency 48.5%), translational slide (frequency 33.3%), and minor rock fall and rotational slide (frequency 9%). The achieved data allowed the establishment of a general correlation between weathering degree and type of slope instability. Debris flow-type instabilities are predominant on the steeper slopes, involving very poor rock masses ascribed to the shallowest portions of the weathering class IV. Translational slides are less widespread than the previous ones and often involve a mixture of soil and highly weathered rocks. Rotational slides are more frequently close to the top of the slopes, where the thicknesses of more weathered rocks increase, and involve mainly rock masses belonging to the weathering classes IV and V. Rock falls mostly occur on the vertical escarpments of the road cuts and are controlled by the characteristics of the main discontinuities. The assessment of rock mass rating and slope mass rating, based on the application of the discontinuity data, allowed respectively an evaluation of the quality of rock masses and of the susceptibility of rock slopes to failure. The comparison between the last one and the real stability conditions along the cut slopes shows a good correspondence. Finally, the geological strength index system was also applied for the estimation of rock mass properties. The achieved results give a worthy support for a better understanding of the relationship between the distribution of landslides and the geological features related to different weathering degrees. Therefore, they can provide a reliable tool to evaluate the potential stability conditions of the rock slopes in the studied area and a general reference framework for the study of weathering processes in other regions with similar geological features.

Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java

There are different approaches and techniques for landslide susceptibility mapping. However, no agreement has been reached in both the procedure and the use of specific controlling factors employed in the landslide susceptibility mapping. Each model has its own assumption, and the result may differ from place to place. Different landslide controlling factors and the completeness of landslide inventory may also affect the different result. Incomplete landslide inventory may produce significance error in the interpretation of the relationship between landslide and controlling factor. Comparing landslide susceptibility models using complete inventory is essential in order to identify the most realistic landslide susceptibility approach applied typically in the tropical region Indonesia. Purwosari area, Java, which has total 182 landslides occurred from 1979 to 2011, was selected as study area to evaluate three data-driven landslide susceptibility models, i.e., weight of evidence, logistic regression, and artificial neural network. Landslide in the study area is usually affected by rainfall and anthropogenic activities. The landslide typology consists of shallow translational and rotational slide. The elevation, slope, aspect, plan curvature, profile curvature, stream power index, topographic wetness index, distance to river, land use, and distance to road were selected as landslide controlling factors for the analysis. Considering the accuracy and the precision evaluations, the weight of evidence represents considerably the most realistic prediction capacities (79%) when comparing with the logistic regression (72%) and artificial neural network (71%). The linear model shows more powerful result than the nonlinear models because it fits to the area where complete landslide inventory is available, the landscape is not varied, and the occurence of landslide is evenly distributed to the class of controlling factor.

Landslide phenomena in weathered granitoid rocks of the Fabrizia surroundings (Serre Massif, southern Italy)

This work shows the preliminary data on the weathered granitoids and related landslide phenomena in the area surrounding Fabrizia, Serre Massif (southern Calabria). Fabrizia lies on an old erosion surface composed of basement rocks made up of weathered granitoids, in turn belonging to the Serre Massif granitoid complex, and overlain by thin eluvial-colluvial deposits. The weathered profiles of the granitoid complex have been studied through a visual description, following both qualitative and quantitative criteria. Weathered profiles are mainly composed by rocky masses varying from completely weathered to highly or moderately weathered terms, characterized by Schmidt Hammer values ranging from 0-20 (class V) to 21-43 (class IV-III). The slopes of the Fabrizia surroundings are characterized by widespread mass movements. Typical landslide typologies are rockfalls for the fresher rocks (class III) and translational slides for the more weathered rocks (classes IV and V). The shallow horizons composed both by thin eluvial-colluvial deposits and by more weathered granitoids (class V-VI), may generate dangerous earth flows, debris flow or debris avalanches under intense and extensive rainfalls.

Rockfall runout, Mount Cimone area, Emilia-Romagna Region, Italy

Numerous mass movements of different typology characterize both mountainous and piedmont sectors of the Emilia-Romagna Region (Apennine chain, North Italy). Although a less spatially frequent landslide typology within the region, rock falls represent severe threats to buildings, roads and persons due to their high propagation velocity. This paper presents an extract of the Emilia-Romagna regional map of the rock fall runout areas at a scale of 1:25,000. The analysis of rock fall runout areas was based upon a three-dimensional morphological method (TDM). The zone presented in the Main Map encompasses the area surrounding Mount Cimone, in the Emilia-Romagna Region. The proposed regional map of rockfall runout is noteworthy for planning actions and strategies aimed at the prevention and reduction of landslide risk at a regional scale.

A probabilistic approach for landslide hazard analysis

We present a general framework for probabilistic landslide hazard analysis. With respect to other quantitative hazard assessment approaches, this probabilistic landslide hazard analysis has the advantage to provide hazard curves and maps, and to be applicable to all typologies of landslides, if necessary accounting for both their onset and transit probability.The method quantifies, for a given slope location, the exceedance probability of being affected by a landslide with a specific local intensity within a reference time interval, i.e. the hazard curve, under the common assumption that landslides behave as a Poisson process. Hazard maps are calculated, reducing the hazard curve to single values by choosing a fixed probability of exceedance following standards or regulation requirements. The method is based on the assessment of a landslide onset frequency, a runout frequency for long-runout landslides, and the local definition of landslide intensity, which can be expressed through different parameters, according to landslide typology. For long runout landslides, the runout and spatially-varying intensity and uncertainty are considered.Hazard curves and maps play a fundamental role in the design and dimensioning of mitigation structures, in land planning and in the definition of risk and hazard management policies. Starting from the general framework, we apply the methodology for rockfall hazard analysis, and we test it in an area affected by the Christchurch 2011 earthquake, New Zealand, which triggered a large number of rockfalls, killing five people.

Susceptibility assessment to different types of landslides in the coastal cliffs of Lourinhã (Central Portugal)

The coastal zone of Lourinhã (located in Central Portugal) is characterized by beach–cliff systems, where beaches are narrow and cliffs have notorious slope instability. These cliffs evolve by different types of landslides, which are one of the main sources of natural hazard and risk in this coastal region.In this work, aerial photo interpretation and a systematic field survey were performed in order to obtain an inventory of landslides of the following types: rotational slides, translational slides and debris flows. The entire coast was then split into 261 terrain mapping units. For each unit, landslide predisposing factors were derived and classified: cliff elevation, slope angle (maximum, mean and standard deviation), potential solar radiation, slope curvature (profile and plan), lithological units and geologic structure. The predictive susceptibility models were computed for each type of landslide using a bi-variate statistical method — the Information Value Method. The degree of fit and the predictive capacity of the models were assessed using the Effectiveness Ratio, the standard Receiver Operator Characteristic curves and the respective Area Under Curve.Results show that each landslide typology occurs in particular terrain conditions. Individual susceptibility models evidence better predictive capacity than susceptibility model for total landslides.

GIS application for regional assessment of seismically induced slope failures in the Sierra Nevada Range, South Spain, along the Padul Fault

Complete rupture of the Padul Fault represents one of the largest plausible earthquakes in the Sierra Nevada Range, one of the most seismically active regions of Spain. We performed a regional assessment of earthquake-triggered slope instabilities in the western part of the range to determine the most likely types of failures from such an earthquake in the region and suggest where such failures have a higher likelihood of occurring. These results are broadly useful for management of regional life-lines and future development. First, a slope-instability inventory of the Sierra Nevada was produced to identify the most common instability types. Subsequently, the Newmark’s sliding rigid-block methodology, implemented in a geographic information system, was used to obtain the distribution of Newmark displacements in the area considering a M w 6.6 earthquake on the Padul Fault. The Newmark displacements were then compared to the distribution of the inventoried slope instabilities to identify the areas where seismicity could reactivate old slope instabilities or generate new ones, and to identify the involved landslide typology. The most likely seismically induced slope instabilities in the Sierra Nevada are rock falls and rock slides. These types of instabilities could be triggered by Newmark displacements of 2 cm or less.

The slope aspect: A predisposing factor for landsliding?

The influence of slope aspect on the distribution of landslides was studied in the Milia and Roglio basins in Tuscany, Italy. For each basin, the new Tuscany region landslide inventory that was initiated in 2010 was used. The landslides were split into separate datasets based on their prevailing movement typology. To assess the results that were obtained from the different slope aspect values, maps of the lithology, slope angle, distances to streams, and distances to tectonic lineaments were included in the bivariate statistical analysis as comparison terms. For each basin, all of the geo-environmental factor maps were compared with the different landslide typologies with GIS software. Pearson's Chi2 (χ2) coefficient was used to test the degree of spatial association between each predictor variable and landslide type. In addition, Cramer's V test was used to quantify the strength of the degree of association. Next, a conditional analysis was applied to all of the possible combinations that occurred between the slope aspect and other landslide-predisposing factors. Overall, the slope aspect significantly affected the distribution of superficial landslide types, but apparently not that of other landslide types.