Various methods have been proposed in the literature to predict the rainfall conditions that are likely to trigger landslides in a given area. Most of these methods, however, only consider the rainfall events that resulted in landslides and provide deterministic thresholds with a single possible output (landslide or no‐landslide) for a given input (rainfall conditions). Such a deterministic view is not always suited to landslides. Slope stability, in fact, is not ruled by rainfall alone and failure conditions are commonly achieved with a combination of numerous relevant factors. When different outputs (landslide or no‐landslide) can be obtained for the same input a probabilistic approach is preferable. In this work we propose a new method for evaluating rainfall thresholds based on Bayesian probability. The method is simple, statistically rigorous, and returns a value of landslide probability (from 0 to 1) for each combination of the selected rainfall variables. The proposed approach was applied to the Emilia‐Romagna Region of Italy taking advantage of the historical landslide archive, which includes more than 4000 events for which the date of occurrence is known with daily accuracy. The results show that landsliding in the study area is strongly related to rainfall event parameters (duration, intensity, total rainfall) while antecedent rainfall seems to be less important. The distribution of landslide probability in the rainfall duration‐intensity shows an abrupt increase at certain duration‐intensity values which indicates a radical change of state of the system and suggests the existence of a real physical threshold.

We present a new method for the automatic classification of Persistent Scatters Interferometry (PSI) time series based on a conditional sequence of statistical tests. Time series are classified into distinctive predefined target trends, such as uncorrelated, linear, quadratic, bilinear and discontinuous, that describe different styles of ground deformation. Our automatic analysis overcomes limits related to the visual classification of PSI time series, which cannot be carried out systematically for large datasets. The method has been tested with reference to landslides using PSI datasets covering the northern Apennines of Italy. The clear distinction between the relative frequency of uncorrelated, linear and non-linear time series with respect to mean velocity distribution suggests that different target trends are related to different physical processes that are likely to control slope movements. The spatial distribution of classified time series is also consistent with respect the known distribution of flat areas, slopes and landslides in the tests area. Classified time series enhances the radar interpretation of slope movements at the site scale, pointing out significant advantages in comparison with the conventional analysis based solely on the mean velocity. The test application also warns against potentially misleading classification outputs in case of datasets affected by systematic errors. Although the method was developed and tested to investigate landslides, it should be also useful for the analysis of other ground deformation processes such as subsidence, swelling/shrinkage of soils, or uplifts due to deep injections in reservoirs.

Rainfall thresholds for the occurrence of debris flows are commonly defined by Intensity‐Duration curves (IDthresholds). Interestingly, many empiricalIDthresholds show up as straight lines in a log‐log plot and therefore can be expressed by power‐law functions of the formI = α ⋅ D^β, whereαis the scaling coefficient andβis the exponent of the power function. The different values ofαandβreflect the variability of geological and hydrological conditions in the different areas. In most cases, however, field conditions are so complex that a quantitative interpretation of the empirical rainfall threshold is impossible and the physical meaning ofαandβremains obscure. In this work, we provide a physical interpretation of the rainfall thresholds that characterize an active debris flow catchment in the Eastern Italian Alps (the Dimai Basin, Belluno Province). The catchment is affected by frequent debris flows generated by surface‐water runoff and has been monitored since 2010 to investigate the initiation process. Monitoring data allowed for the detection of two rainfall thresholds: a lower one that identifies the arrival of water in the initiation area (Catchment Outflow Threshold) and an upper one that identifies the initiation of debris flows by channel runoff (Debris Flow Threshold). We demonstrate that these two thresholds can be satisfactorily reproduced by a simple physically based model in which the excess rainfall is routed over the catchment using a kinematic‐wave scheme. This simple analysis provides a sound explanation of the observed thresholds and can be used to develop physically based thresholds for runoff‐generated debris flows.