The giant prehistoric Seymareh landslide in the Zagros Mountains (Iran) is one of the largest known landslides on the Earth’s surface. The debris with an estimated volume of 44 km3 dammed two rivers, generating three lakes, that persisted for about 3 ka after the event. The post-overflow morphodynamics, characterized by an accelerated and intense stream network erosion, obliterated most of the primary landforms, such as ridges and blocks on the debris surface, making it difficult for scientists to interpret the emplacement kinematics of the landslide. In this regard, a novel spatial statistical approach is proposed here to zone the landslide debris in primary (original) and secondary (modified) regions which are, respectively, attributed to the original shape of the landslide debris and the one reshaped by fluvial erosion. The zonal computation combines the density classes of the mapped primary (ridge and blocks) and secondary (gullies) landforms, according to assumed conditions for representativeness of primary and secondary zones. For validating the model, 62 soil samples taken from the debris surface were classified according to the Unified Soil Classification System standard, and the field density measurements were performed in 28 sites. Based on the classification results, six types of soils were detected, among which 68% of them were ML. The ML samples were aggregated into five subgroups based on their relative proximity, and for each subgroup, four permeability tests were performed. The permeability results demonstrate that the high permeability values are associated with secondary zones, while low values with primary ones, thus confirming the zonation proposed by the statistical approach. The study of the spatial arrangement of the kinematic evidence on the primary landforms allowed to deduce that the landslide was a double-step single event, which infilled a paleo-valley enclosed by two anticline folds. During the emplacement, a part of the debris dissipated its energy over passing the anticlines with divergent directions, NW and NE, while the rest swept back into the Seymareh paleo-valley into the SE direction. The proposed approach represents a promising tool for the detection of primary landforms to assess the emplacement kinematics of landslides.
Different soil cover saturation has a significant effect in influencing slope stability conditions of weathered covers under earthquake-induced shaking. Here we analyze the Montecilfone, Italy (2018), case history, an Mw 5.1 earthquake that revealed an exceptionality in the spatial distribution of the surveyed earthquake-induced shallow landslides. This feature can be justified as intense rainfall occurred in the epicentral area before the seismic event, contributing to increasing the saturation and the weight of the soil covers. To verify the effective influence of antecedent rainfall as a preparatory factor in the earthquake triggering of soil covers, stability conditions for both static and dynamic scenarios were validated by reconstructing different saturation conditions related to a rainfall event that occurred before the earthquake. Soil cover surveying was performed within a 150 km2 area to output its spatial distribution in terms of their compositional features and thickness, whose variability was constrained through empirical models. Based on laboratory test results, 1D infiltration numerical models were performed through the Hydrus-1D free domain software to estimate the saturation degree of the soil cover and the water infiltration depth, taking as a reference the intensity of the rainfall event. Soil cover sequential charts of water content were obtained at different depths and times up to those recorded at the time of earthquake occurrence by the performed numerical modelling. Safety factors (SFs) of the slope covers were quantified assuming an unsaturated condition in the slope stability equation. The outputs reveal that pore pressure spatial distribution in the unsaturated medium infers on the earthquake-induced scenario of shallow landsliding, demonstrating its role as a preparatory factor for earthquake-induced shallow landslides.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.