Comparing Landslide Hazard Maps

The objective of the method explained in this paper isto obtain a better insight in the decision rulesapplied by geomorphologists in the direct mapping oflandslide hazard. This can be obtained by forcinggeomorphologists to specify for each unit (polygon) intheir hazard map the criteria that they used toclassify the unit as high, medium or low hazard. Whenthis is done systemically for an entire area, it ispossible to analyze those criteria statistically, andto evaluate whether they can be grouped into generaldecision rules, or whether these criteria arecompletely site specific. The same area in the Alpagoregion in Italy was mapped at 1 : 5000 scale by threeteams of experts individually. The different methodsare presented and the results are compared.     

Seismic microzonation of Dehradun City using geophysical and geotechnical characteristics in the upper 30 m of soil column

The understanding of geotechnical characteristics of near-surface material is of fundamental interest in seismic microzonation. Shear wave velocity (Vs), one of the most important soil properties for soil response modeling, has been evaluated through seismic profiling using the multichannel analysis of surface waves in the city of Dehradun situated along the foothills of northwest Himalaya. Fifty sites in the city have been investigated with survey lines between 72 and 96 m in length. Multiple 1-D and interpolated 2-D profiles have been generated up to a depth of 30–40 m. The Vs were used in the SHAKE2000 software in combination with seismic input motion of the recent Chamoli earthquake to obtain site response and amplification spectra. The estimated Vs are higher in the northern part of the study area (i.e., 200–700 m/s from the surface to a depth of about 30 m) as compared to the south and southwestern parts of the city (i.e., 180–400 m/s for the same depth range). The response spectra suggest that spectral acceleration values for two-story structures are three to eight times higher than peak ground acceleration at bedrock. The analysis also suggests peak amplification at 3–4, 2–2.5, and 1–1.5 Hz in the northern, central, and south-southwestern parts of the city, respectively. The spatial distributions of Vs and spectral accelerations provide valuable information for the seismic microzonation in different parts of the urban area of Dehradun.     

Physically based dynamic run-out modelling for quantitative debris flow risk assessment: a case study in Tresenda,northern Italy

Quantitative landslide risk assessment requires information about the temporal, spatial and intensity probability of hazardous processes both regarding their initiation as well as their run-out. This is followed by an estimation of the physical consequences inflicted by the hazard, preferentially quantified in monetary values. For that purpose, deterministic hazard modelling has to be coupled with information about the value of the elements at risk and their vulnerability. Dynamic run-out models for debris flows are able to determine physical outputs (extension, depths, velocities, impact pressures) and to determine the zones where the elements at risk can suffer an impact. These results can then be applied for vulnerability and risk calculations. Debris flow risk has been assessed in the area of Tresenda in the Valtellina Valley (Lombardy Region, northern Italy). Three quantitative hazard scenarios for different return periods were prepared using available rainfall and geotechnical data. The numerical model FLO-2D was applied for the simulation of the debris flow propagation. The modelled hazard scenarios were consequently overlaid with the elements at risk, represented as building footprints. The expected physical damage to the buildings was estimated using vulnerability functions based on flow depth and impact pressure. A qualitative correlation between physical vulnerability and human losses was also proposed. To assess the uncertainties inherent in the analysis, six risk curves were obtained based on the maximum, average and minimum values and direct economic losses to the buildings were estimated, in the range of 0.25–7.7 million €, depending on the hazard scenario and vulnerability curve used.     

Qualitative landslide susceptibility assessment by multicriteria analysis: A case study from San Antonio del Sur, Guantánamo, Cuba

Geomorphological information can be combined with decision-support tools to assess landslide hazard and risk. A heuristic model was applied to a rural municipality in eastern Cuba. The study is based on a terrain mapping units (TMU) map, generated at 1:50,000 scale by interpretation of aerial photos, satellite images and field data. Information describing 603 terrain units was collected in a database. Landslide areas were mapped in detail to classify the different failure types and parts. Three major landslide regions are recognized in the study area: coastal hills with rockfalls, shallow debris flows and old rotational rockslides denudational slopes in limestone, with very large deep-seated rockslides related to tectonic activity and the Sierra de Caujerí scarp, with large rockslides. The Caujerí scarp presents the highest hazard, with recent landslides and various signs of active processes. The different landforms and the causative factors for landslides were analyzed and used to develop the heuristic model. The model is based on weights assigned by expert judgment and organized in a number of components such as slope angle, internal relief, slope shape, geological formation, active faults, distance to drainage, distance to springs, geomorphological subunits and existing landslide zones. From these variables a hierarchical heuristic model was applied in which three levels of weights were designed for classes, variables, and criteria. The model combines all weights into a single hazard value for each pixel of the landslide hazard map. The hazard map was then divided by two scales, one with three classes for disaster managers and one with 10 detailed hazard classes for technical staff. The range of weight values and the number of existing landslides is registered for each class. The resulting increasing landslide density with higher hazard classes indicates that the output map is reliable. The landslide hazard map was used in combination with existing information on buildings and infrastructure to prepare a qualitative risk map. The complete lack of historical landslide information and geotechnical data precludes the development of quantitative deterministic or probabilistic models.     

Parameterizing a physically based shallow landslide model in a data poor region

Shallow landslides and consequent debris flows are an increasing concern in the Western Ghats of Kerala, India. Their increased frequency has been associated with deforestation and unfavourable land‐use practices in cultivated areas. In order to evaluate the influence of vegetation on shallow slope failures a physically based, dynamic and distributed hydrological model (STARWARS) coupled with a probabilistic slope stability model (PROBSTAB) was applied to the upper Tikovil River basin (55·6 km²). It was tuned with the limited evidence of groundwater conditions during the monsoon season of 2005 and validated against observed landslide activity in the hydrological year 2001–2002. Given the data poor conditions in the region some modifications to the original model were in order, including the estimation of parameters on the basis of generalized information from secondary sources, pedo‐transfer functions, empirical equations and satellite remote sensing data. Despite the poor input, the model captured the general temporal and spatial pattern of instability in the area. Sensitivity analysis proved root cohesion, soil depth and angle of internal friction as the most dominant parameters influencing slope stability. The results indicate the importance of root cohesion in maintaining stability and the critical role of the management of rubber plantations in this. Interception and evapotranspiration showed little influence on the development of failure conditions. The study also highlights the importance of high resolution digital terrain models for the accurate mechanistic prediction of shallow landslide initiation. Copyright © 2009 John Wiley & Sons, Ltd.     

The Modelling Of Landslide Hazards Using Gis

Slope instability hazard assessment is based on theanalysis of the terrain conditions at sites whereslope failures occurred in the past. For the analysisof the causative factors the application of geographicinformation systems (GIS) is an essential tool in thedata analysis and the subsequent hazard assessment.Three scale levels of hazard mapping are defined – adirect experience-driven mapping at reconnaissancelevel, a statistical approach to determine thecausative factors in a quantitative susceptibilitymapping, and a methodology at large scale making use ofdeterministic models.     

Rockfall trajectory modeling combined with heuristic analysis for assessing the rockfall hazard along the Maratea SS18 coastal road (Basilicata,Southern Italy)

In this paper, a study aimed to assess the rockfall hazard along a portion of the SS18 coastal road, located in the coastal area of Maratea (Basilicata Region, Southern Italy), is presented. The relevance of this study derives from the location of the study area, because the SS18 is a strategic roads in a touristic area, and, since the hazard assessment was performed in 2004 within a project financed by the Viability Regional Department of Autonomous National Company of Roads (ANAS), from the possibility to validate the results by using real rockfall events occurred after 2004. The procedure for assessing the rockfall hazard was composed of four sequential analyses: (i) geomechanical and kinematic characterization of rock mass, (ii) implementation of Romana’s (1985) Slope Mass Rating (SMR) method for identifying the potential boulder release areas (rockfall initiation areas), (iii) determination of rockfall trajectories by using a 3D numerical model (ROTOMAP), (iv) calculation and mapping of the hazard index by combining three factors, i.e., (a) lithological features of outcropping materials on rock faces, (b) kinematic compatibility defined by simulating the rockfall trajectories, and (c) spatial distribution of occurred rockfall events. Finally, the proposed methodology was validated by combining the distribution of the hazard levels along the road with the location on the SS18 of the rockfall events occurred from 2004 to 2014.     

Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India

Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed.     

Frequency–size relation of shallow debris slides on cut slopes along a railroad corridor: A case study from Nilgiri hills, Southern India

The probability of landslide volume, V _L , is a key parameter in the quantitative hazard analysis. Several studies have demonstrated that the non-cumulative probability density, p(V _L ), of landslide volumes obeys almost invariably a negative power law scaling of p(V _L ) for landslides exceeding a threshold volume and a roll-over of small landslides. Some researchers attributed the observed roll-over to under-sampling of data, while others relate it to a geo-morphological (physical) property of landslides. We analyzed 15 sets of a complete landslide inventory containing shallow debris slides (2 ≤ V _L  ≤ 3.6 × 10³ m³) with sources located on cut slopes along a 17-km-long railroad corridor. The 15 datasets belong to individual years from 1992 to 2007. We obtained the non-cumulative probability densities of landslide volumes for each dataset and analyzed the distribution pattern. The results indicate that for some datasets the probability density exhibits a negative power law distribution for all ranges of volume, while for others, the negative power scaling exists only for a volume greater than 10 m³, with scaling exponent β varying between 0.96 and 2.4. When the spatial distribution of landslides were analyzed in relation to the terrain condition and triggering rainfall, we observed that the number of landslides and the range and the frequency of volumes vary according to the changes in local terrain condition and the amount of rainfall that trigger landslides. We conclude that the probability density distribution of landslide volumes has a dependency on the local morphology and rainfall intensity and the deviation of small landslides from power law, i.e., the roll-over is a “real effect” and not an artifact due to sampling discrepancies.     

Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation

This paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semi-quantitative model with ten indicator maps and a cell size of 90 × 90 m. The model was designed and implemented using spatial multi-criteria evaluation techniques in a GIS system. Each indicator was processed, analysed and standardised according to its contribution to hazard and vulnerability. The indicators were weighted using direct, pairwise comparison and rank-ordering weighting methods, and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region and administrative units at provincial and municipal levels. The Sierra Maestra mountain system was found to have the largest concentration of high landslide risk index values while the Nipe–Cristal–Baracoa system has the highest absolute values, although they are more dispersed. The results obtained allow designing an appropriated landslide risk mitigation plan at national level and to link the information to the national hurricane early warning system, allowing also warning and evacuation for landslide-prone areas.