Implementing landslide path dependency in landslide susceptibility modelling

Landslide susceptibility modelling—a crucial step towards the assessment of landslide hazard and risk—has hitherto not included the local, transient effects of previous landslides on susceptibility. In this contribution, we implement such transient effects, which we term “landslide path dependency”, for the first time. Two landslide path dependency variables are used to characterise transient effects: a variable reflecting how likely it is that an earlier landslide will have a follow-up landslide and a variable reflecting the decay of transient effects over time. These two landslide path dependency variables are considered in addition to a large set of conditioning attributes conventionally used in landslide susceptibility. Three logistic regression models were trained and tested fitted to landslide occurrence data from a multi-temporal landslide inventory: (1) a model with only conventional variables, (2) a model with conventional plus landslide path dependency variables, and (3) a model with only landslide path dependency variables. We compare the model performances, differences in the number, coefficient and significance of the selected variables, and the differences in the resulting susceptibility maps. Although the landslide path dependency variables are highly significant and have impacts on the importance of other variables, the performance of the models and the susceptibility maps do not substantially differ between conventional and conventional plus path dependent models. The path dependent landslide susceptibility model, with only two explanatory variables, has lower model performance, and differently patterned susceptibility map than the two other models. A simple landslide susceptibility model using only DEM-derived variables and landslide path dependency variables performs better than the path dependent landslide susceptibility model, and almost as well as the model with conventional plus landslide path dependency variables—while avoiding the need for hard-to-measure variables such as land use or lithology. Although the predictive power of landslide path dependency variables is lower than those of the most important conventional variables, our findings provide a clear incentive to further explore landslide path dependency effects and their potential role in landslide susceptibility modelling.     

Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory

Landslides are a major category of natural disasters, causing loss of lives, livelihoods and property. The critical roles played by triggering (such as extreme rainfall and earthquakes), and intrinsic factors (such as slope steepness, soil properties and lithology) have previously successfully been recognized and quantified using a variety of qualitative, quantitative and hybrid methods in a wide range of study sites. However, available data typically do not allow to investigate the effect that earlier landslides have on intrinsic factors and hence on follow-up landslides. Therefore, existing methods cannot account for the potentially complex susceptibility changes caused by landslide events. In this study, we used a substantially different alternative approach to shed light on the potential effect of earlier landslides using a multi-temporal dataset of landslide occurrence containing 17 time slices. Spatial overlap and the time interval between landslides play key roles in our work. We quantified the degree to which landslides preferentially occur in locations where landslides occurred previously, how long such an effect is noticeable, and how landslides are spatially associated over time. We also investigated whether overlap with previous landslides causes differences in landslide geometric properties. We found that overlap among landslides demonstrates a clear legacy effect (path dependency) that has influence on the landslide affected area. Landslides appear to cause greater susceptibility for follow-up landslides over a period of about 10  years. Follow-up landslides are on average larger and rounder than landslides that do not follow earlier slides. The effect of earlier landslides on follow-up landslides has implications for understanding of the landslides evolution and the assessment of landslide susceptibility.     

Rockfall hazard assessment along a road in the Sorrento Peninsula,Campania, southern Italy

Rockfalls are common in the steep and vertical slopes of the Campania carbonate massifs and ridges, and frequently represent the main threat to the anthropogenic environment, potentially damaging urban areas, scattered houses, roads, etc. Despite the generally limited volumes involved, the high velocity of movement (from few to tens of metres per second) poses rockfalls among the most dangerous natural hazards to man. Evaluating the rockfall hazard is not an easy task, due to the high number of involved factors, and particularly to the difficulty in determining the properties of the rock mass. In this paper, we illustrate the assessment of the rockfall hazard along a small area of the Sorrento Peninsula (Campania region, southern Italy). Choice of the site was determined by the presence of a road heavily frequented by vehicles. In the area, we have carried out detailed field surveys and software simulations that allow generating simple rockfall hazard maps. Over twenty measurement stations for geo-mechanical characterization of the rock mass have been distributed along a 400-m-long slope of Mount Vico Alvano. Following the internationally established standards for the acquisition of rock mass parameters, the main kinematics have been recognized, and the discontinuity families leading to the different failures identified. After carrying out field experiments by artificially releasing a number of unstable blocks on the rock cliff, the rockfall trajectories along the slope were modelled using 2-D and 3-D programs for rockfall analysis. The results were exploited to evaluate the rockfall hazard along the threatened element at risk.     

Tier-based approaches for landslide susceptibility assessment in Europe

In the framework of the European Soil Thematic Strategy and the associated proposal of a Framework Directive on the protection and sustainable use of soil, landslides were recognised as a soil threat requiring specific strategies for priority area identification, spatial hazard assessment and management. This contribution outlines the general specifications for nested, Tier-based geographical landslide zonings at small spatial scales to identify priority areas susceptible to landslides (Tier 1) and to perform quantitative susceptibility evaluations within these (Tier 2). A heuristic, synoptic-scale Tier 1 assessment exploiting a reduced set of geoenvironmental factors derived from common pan-European data sources is proposed for the European Union and adjacent countries. Evaluation of the susceptibility estimate with national-level landslide inventory data suggests that a zonation of Europe according to, e.g. morphology and climate, and performing separate susceptibility assessments per zone could give more reliable results. To improve the Tier 1 assessment, a geomorphological terrain zoning and landslide typology differentiation are then applied for France. A multivariate landslide susceptibility assessment using additional information on landslide conditioning and triggering factors, together with a historical catalogue of landslides, is proposed for Tier 2 analysis. An approach is tested for priority areas in Italy using small administrative mapping units, allowing for relating socioeconomic census data with landslide susceptibility, which is mandatory for decision making regarding the adoption of landslide prevention and mitigation measures. The paper concludes with recommendations on further work to harmonise European landslide susceptibility assessments in the context of the European Soil Thematic Strategy.     

GIS techniques and statistical models in evaluating landslide hazard

Geographical Information Systems (GIS) and numerical cartography may greatly facilitate the development and use of statistical models for the assessment of regional landslide hazard. From a small drainage basin located in Central Italy, relevant geological and geomorphological factors were collected and processed by applying GIS technology. In particular, modules were used which both generate high-fidelity digital terrain models and automatically partition the terrain into main slope-units. The resulting information was then analysed by discriminant analysis which enabled landslide hazard and risk to be evaluated in each slope-unit. Although not lacking drawbacks, the method proved to be a feasible and cost-effective approach to landslide susceptibility assessment and mapping.     

Rock fall hazard along the railway corridor to Jerusalem,Israel, in the Soreq and Refaim valleys

We evaluate rock fall hazard along the railway corridor to Jerusalem, Israel, in the Soreq and Refaim valleys. For the purpose, we use a combination of historical information on past rock fall events, field surveys aided by the interpretation of aerial photographs, and numerical rock fall modeling. Historical information indicates that on July 11, 1927 an m _L 6.2 Dead-Sea transform earthquake caused rock falls in the studied area. The seismically induced rock falls damaged the railway tracks. Field observations revealed that the source area for the 1927 failures was located in the Aminadav formation, at the contact with the Moza formation. At the stratigraphic contact, rock blocks 10⁰–10¹ m³ in size are formed as a result of tensile stresses and associated fracturing in the dolomite of the Aminadav formation, combined with continuous creep of the blocks on the marl of the underlying Moza formation. We use topographical, geological, and geomorphological information to calibrate a three-dimensional numerical simulation of rock falls in the studied area. We use the results of the numerical modeling, and additional independent information, to assess rock fall hazard and the associated risk in the Soreq and Refaim valleys. Results indicate that in the studied area, rock fall risk to the railway line to Jerusalem is due primarily to Dead-Sea transform earthquakes, with m _L  > 6. We identify nine sections of the railway line where rock fall risk exists, for a total length of 2.5 km. We further note that seismically induced rock falls can produce damage to the road network in the studied area, make it difficult or impossible for earthquake casualties to reach hospitals in Jerusalem. We conclude offering recommendations on how to mitigate the risk posed by earthquake-induced rock falls in the studied area.     

Temporal correlations and clustering of landslides

This paper examines temporal correlations and temporal clustering of a proxy historical landslide time series, 2255 reported landslides 1951–2002, for an area in the Emilia‐Romagna Region, Italy. Landslide intensity is measured by the number of reported landslides in a day (D_L) and in an ‘event’ (S_event) of consecutive days with landsliding. The non‐zero values in both time series D_L and S_event are unequally spaced in time, and have heavy‐tailed frequency‐size distributions. To examine temporal correlations, we use power‐spectral analysis (Lomb periodogram) and surrogate data analysis, confronting our original D_L and S_event time series with 1000 shuffled (uncorrelated) versions. We conclude that the landslide intensity series D_L has strong temporal correlations and S_event has likely temporal correlations. To examine temporal clustering in D_L and S_event, we consider extremes over different landslide intensity thresholds. We first examine the statistical distribution of interextreme occurrence times, τ, and find Weibull distributions with parameter γ << 1·0 [D_L] and γ < 1·0 [S_event]; thus D_L and S_event each have temporal correlations, but S_event to a lesser degree. We next examine correlations between successive interextreme occurrence times, τ. Using autocorrelation analysis applied to τ, combined with surrogate data analysis, we find for D_L linear correlations in τ, but for S_event inconclusive results. However, using Kendall's rank correlation analysis we find for both D_L and S_event the series of τ are strongly correlated. Finally, we apply Fano Factor analysis, finding for both D_L and S_event the timings of extremes over a given threshold exhibit a fractal structure and are clustered in time. In this paper, we provide a framework for examining time series where the non‐zero values are strongly unequally spaced and heavy‐tailed, particularly important in the Earth Sciences due to their common occurrence, and find that landslide intensity time series exhibit temporal correlations and clustering. Many landslide models currently are designed under the assumption that landslides are uncorrelated in time, which we show is false. Copyright © 2010 John Wiley & Sons, Ltd.     

Regional hydrological thresholds for landslides and floods in the Tiber River Basin (central Italy)

 The definition of landslide warning thresholds, based on the analysis of hydrological data, is proposed. In the Tiber River Basin of central Italy historical information on landslides and floods, for the period 1918–1990, was available from a nationwide bibliographical and archive inventory on geohydrological catastrophes. Hydrological data were obtained from mean daily discharge records at various gauging stations within the basin. Several hundred hydrological events, broadly defined as a series of consecutive days having mean daily discharge exceeding a predefined value, were identified. Hydrological parameters obtained from the discharge records were used to rank the events according to their probability to trigger mass movements or inundations and to define regional thresholds for the occurrence of landslides and floods. The proposed approach, not lacking limitations, has conceptual and operational advantages, among which is the possibility of using historical information on geohydrological catastrophes. Received: 20 November 1996 · Accepted: 25 June 1997