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The Lueyang and Xunyang counties in the Shaanxi province (China) are highly susceptible to rainfall-induced landslides. Rainfall thresholds are the most used tool to predict the occurrence of rainfall-induced landslides over large areas. However, the definition of robust thresholds may be difficult for unbalanced datasets, for which the number of non-landslide observations is much higher than the number of landslide observations. This study aims at defining adequate rainfall thresholds for the two study areas using landslide datasets that are strongly unbalanced in terms of occurrences vs. non-occurrences. Two types of rainfall thresholds are determined using a frequentist method at several non-exceedance and exceedance probabilities, separately considering rainfall events responsible for landslides (positive thresholds) and rainfall events not responsible for landslides (negative thresholds). The comparison between the two sets of thresholds shows that the method based on non-triggering events allows defining rainfall thresholds characterized by lower uncertainties and a better performance than the ones defined considering the triggering events, in both the study areas. In particular, the best-performing thresholds are the negative threshold defined at 15% exceedance probability for the Lueyang county and the negative threshold defined at 20% exceedance probability for the Xunyang county. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a procedure for assessing—at the municipal scale—the level of risk (or attention required) of stretches of roads exposed to the activity of existing or potential slow-moving landslides. This procedure can be applied to support studies of road networks over large areas aimed at the prioritization of risk-mitigation measures and the identification of road sections requiring further investigation. Slow-moving landslides are widespread natural hazards that can affect social and economic activities, causing damage to structures and infrastructures. This paper aims at proposing a procedure to analyze road damage induced by slow-moving landslides based on the joint use of landslide susceptibility maps, a road-damage database developed using Google Street View images and ground-displacement measurements derived from the interferometric processing of satellite SAR images. The procedure is applied to the municipalities of Vaglio Basilicata and Trivigno in the Basilicata region (southern Italy) following a matrix-based approach. First, a susceptibility analysis is carried out at the municipal scale, using data from landslide inventories and thematic information available over the entire municipalities. Then, the susceptibility index, the class of movement and the level of damage are calculated for the territorial units corresponding to the road corridors under investigation. Finally, the road networks are divided into stretches, each one characterized by a specific level of risk (or attention required) following the aggregation of the information provided by the performed analyses. The results highlight the importance of integrating all of these different approaches and data for obtaining quantitative information on the spatial and temporal behavior of slow-moving landslides affecting road networks. [ABSTRACT FROM AUTHOR]

Weather-induced shallow landslides in coarse-grained soils are first failure phenomena often occurring as multiple slides in natural slopes over wide areas. Diffuse monitoring over the area of interest is required if one wants to predict the possible location of future landslides or the soil conditions leading to triggering for early warning purposes. This study aims at highlighting the role played by ground-based monitoring devices when they are used, singularly or combined together, as tools to assess the conditions of slopes in granular soils leading to shallow first-failure slides. Monitoring is discussed in relation to expectations coming from analytical and numerical models within an "observational modelling" framework, i.e. methods and procedures that use inverse analysis techniques to update the numerical model of a boundary value problem using monitoring data. The influence of the location of the monitoring instruments is assessed in different stratigraphic conditions and considering the saturation degree of the soil layers. The time-dependent soil parameters considered are the following: positive pore water pressure, suction, soil water content, stress, surface and subsurface displacements. All these parameters can be monitored employing static permanently deployed point sensors and, potentially, using low-cost instruments providing high resolution data in time and space. The focus of the contribution is on the meaning of the measurements as potential detectors of soil conditions leading to failure within a slope. To this aim, a series of parametric analyses employing analytical and numerical methods, in 1D and 2D conditions, will be presented. The analyses investigate the soil response to applied boundary conditions. When only hydraulic modelling is performed, the prediction of the groundwater regime in a slope in relation to time-dependent atmospheric conditions is used as input in limit equilibrium slope stability analyses to derive the variation with time of the factors of safety along potential slip surfaces. When hydromechanical coupling is explicitly considered, the numerical simulations are aimed at reproducing the stress-strain evolution associated with the transient seepage processes induced by changing hydraulic boundary conditions. In both cases, the results of the parametric analyses are used to evaluate the modelled response of a slope at the onset of failure, in different locations and in relation to many monitoring parameters (e.g., pore water pressures, soil water contents, stresses, displacements). The presented results should be seen as a contribution towards the aim of defining and adopting an economically sustainable and technically reliable monitoring strategy for early warning purposes, by predicting the conditions leading to the triggering of shallow slides in coarse-grained soils over wide areas. [ABSTRACT FROM AUTHOR]

Landslide early warning systems (LEWS) represent a significant option for addressing weather-induced landslides worldwide. The structure of LEWS can be schematized as an interrelation of three main modules: landslide model, warning model, and warning system. Warning models developed for weather-induced landslides are mainly based on correlation laws, for which thresholds are defined considering one or more combinations of measured rainfall that have led (or not led) to slope movements. However, the distinction between triggering and non-triggering rainfall conditions is not trivial, thus conventional methods adopted to this aim are often highly subjective. Moreover, data on landslide occurrences and rainfall measurements are often either not available or accessible only to a restricted number of scientists. In this study information from an open-access non-conventional landslide inventory and rainfall data from satellite monitoring have been employed within a conceptual framework for the development of a probabilistic warning model. The main steps of the proposed approach are: collection of input data, correlation between landslides and rainfall combinations, and probabilistic analysis. Data on landslide occurrences are derived from the FraneItalia catalog (https://data.mendeley.com/datasets/zygb8jygrw/1), a landslide inventory based on information retrieved from online Italian news. The database currently spans from 2010 to 2017 and contains 8931 landslides, most of them triggered by rainfall. Rainfall measurements are derived from the satellite-based NASA Global Precipitation Measurement (GPM) database, which contains gridded precipitation and precipitation-error estimates, with a half-hour temporal resolution and a 0.10-degree spatial resolution, covering most of the earth starting from March 2014. Successively, an algorithm approach based on a reduced set of parameters has been applied to reconstruct the rainfall events, to identify the rainfall conditions that resulted in landslides and to measure the duration and the cumulated rainfall for the events. Finally, a Bayesian methodology has been developed to determine landslide conditional probabilities associated to each rainfall combination.The proposed procedure has been tested by analysing the reported landslides from March 2014 to December 2017 within the eight warning zones defined for hydrogeological risk management in the Campania region (Italy). Although this study is only in a preliminary phase, the results achieved herein demonstrates the potential of the proposed probabilistic method to define objective and reproducible rainfall thresholds for landslide early warning purposes. [ABSTRACT FROM AUTHOR]