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Landslides often happen where they have already occurred in the past. The potential of landslides to reduce or enhance conditions for further landsliding has long been recognized and has often been reported, but the mechanisms and spatial and temporal scales of these processes have previously received little specific attention. Despite a preponderance of qualitative and anecdotal evidence, analysis has been limited. As a result, there is little consensus on the meaning of terms such as landslide repetition, recurrence, and reactivation. This source of confusion is evident when such terms are also used to describe systems where landsliding is prevalent but unrelated to landslide history. Recent findings, partly based on a rare multi-temporal landslide inventory for an area in Italy, show that the impacts of earlier landslides affect a substantial fraction of landslides, that landslides following earlier landslides differ from those that do not, and that accounting for the effect of previous landslides can improve susceptibility assessments. These findings await confirmation in other landslide-prone landscapes but show that consecutive landsliding deserves more attention, which requires consistent terminology. No such terminology is presently available, and we therefore propose it in this manuscript. We use the term "uncorrelated landsliding" to describe situations where landslides are common, but where a correlation with environmental variables such as terrain steepness is not implied. We propose "correlated landsliding" to describe situations where landslides are common and correlations with environmental variables exist, and "path-dependent landsliding" to describe situations where causal relations exist between consecutive landslides, for instance, when landslides occur at the scarp of previous landslides. These are situations where past landslides impact future landslides. Within the path-dependent category, we distinguish three subcategories based on the spatial distance between earlier and later landslides: "reactivation" or "continuation" if essentially the same material recommences or continues to slide, "local activation" if an earlier slide causes changes in a local hillslope that cause a later slide, and "remote activation" if an earlier slide causes changes elsewhere in the landscape that cause a later landslide. We use this proposed set of terms to outline some prominent knowledge gaps and potential research questions. [ABSTRACT FROM AUTHOR]

This contribution tests the added value of including landslide path dependency in statistically based landslide susceptibility modelling. A conventional pixel-based landslide susceptibility model was compared with a model that includes landslide path dependency and with a purely path-dependent landslide susceptibility model. To quantify path dependency among landslides, we used a space–time clustering (STC) measure derived from Ripley's space–time K function implemented on a point-based multi-temporal landslide inventory from the Collazzone study area in central Italy. We found that the values of STC obey an exponential-decay curve with a characteristic timescale of 17 years and characteristic spatial scale of 60 m. This exponential space–time decay of the effect of a previous landslide on landslide susceptibility was used as the landslide path-dependency component of susceptibility models. We found that the performance of the conventional landslide susceptibility model improved considerably when adding the effect of landslide path dependency. In fact, even the purely path-dependent landslide susceptibility model turned out to perform better than the conventional landslide susceptibility model. The conventional plus path-dependent and path-dependent landslide susceptibility model and their resulting maps are dynamic and change over time, unlike conventional landslide susceptibility maps. [ABSTRACT FROM AUTHOR]

This contribution tests the added value of including landslide path dependency in statistically-based landslide susceptibility modelling. A conventional pixel-based landslide susceptibility model was compared with a model that includes landslide path dependency, and with a purely path dependent landslide susceptibility model. To quantify path dependency among landslides, we used a Space-Time Clustering (STC) measure derived from Ripley's space-time K function implemented on a point-based multi-temporal landslide inventory from the Collazzone study area in central Italy. We found that the values of STC obey an exponential decay curve with characteristic time scale of 17 years, and characteristic space scale of 60 meters. This exponential space-time decay of the effect of a previous landslide on landslide susceptibility was used as the landslide path dependency component of susceptibility models. We found that the performance of the conventional landslide susceptibility model improved considerably when adding the effect of landslide path dependency. In fact, even the purely path dependent landslide susceptibility model turned out to perform better than the conventional landslide susceptibility model. The conventional plus path dependent and path dependent landslide susceptibility model and their resulted maps are dynamic and change over time unlike conventional landslide susceptibility maps. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landslides cause major environmental damage, economic losses and casualties. Although susceptibility to landsliding is usually considered an exclusively location-specific phenomenon, indications exist that landslide history co-determines susceptibility to future landslides. In this contribution, we quantified the role of landslide path dependency (the effect of landslides on landslides) using a multi-temporal landslide inventory from Italy. The fraction of landslides following earlier landslides in the same location exhibited an exponential decay, with susceptibility increasing 15-fold right after an initial landslide, and returning to pre-landslide values after about 25 years. We investigated the role of the geometry and location of a previous landslide for the occurrence of follow-up landslides. Larger landslides are more likely to cause follow-up landslides. Also landslide shape, topographic wetness index, the vertical distance to the nearest channel network, the absolute profile curvature and relative slope position of an earlier landslide, however, are important in predicting whether a follow-up landslide occurs. Combined in a binary logistic model, these attributes correctly predict 60% of times whether a landslide will be followed-up. These findings open the way for time-variant mapping of susceptibility to landslides, by including the effect of the spatio-temporal history of landsliding on susceptibility. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]