Rainfall thresholds define the conditions leading to the triggering of shallow landslides over wide areas. They can be empirical, which exploit past rainfall data and landslide inventories, or physicallybased, which integrate slope physical–hydrological modeling and stability analyses. In this work, a comparison between these two types of thresholds was performed, using data acquired in Oltrepò Pavese (Northern Italian Apennines), to evaluate their reliability. Empirical thresholds were reconstructed based on rainfalls and landslides triggering events collected from 2000 to 2018. The same rainfall events were implemented in a physicallybased model of a representative testsite, considering different antecedent pore-water pressures, chosen according to the analysis of hydrological monitoring data. Thresholds validation was performed, using an external dataset (August 1992–August 1997). Soil hydrological conditions have a primary role on predisposing or preventing slope failures. In Oltrepò Pavese area, cold and wet months are the most susceptible periods, due to the permanence of saturated or close-to-saturation soil conditions. The lower the pore-water pressure is at the beginning of an event, the higher the amount of rain required to trigger shallow failures is. physicallybased thresholds provide a better reliability in discriminating the events which could or could not trigger slope failures than empirical thresholds. The latter provide a significant number of false positives, due to neglecting the antecedent soil hydrological conditions. These results represent a fundamental basis for the choice of the best thresholds to be implemented in a reliable earlywarning system.

Assessing hazard of rainfall-induced shallow landslides represents a challenge for the risk management of urbanized areas for which the setting up of early warning systems, based on the reconstruction of reliable rainfall thresholds and rainfall monitoring, is a solution more practicable than the delocalization of settlements and infrastructures. Consequently, the reduction in uncertainties affecting the estimation of rainfall thresholds conditions, leading to the triggering of slope instabilities, is a fundament task to be tackled. In such a view, coupled soil hydrological monitoring and physics-based modeling approaches are presented for estimating rainfall thresholds in two different geomorphological environments prone to shallow landsliding. Based on the comparison of results achieved for silty–clayey soils characterizing Oltrepò Pavese area (northern Italy) and ash-fall pyroclastic soils mantling slopes of Sarno Mountains ridge (southern Italy), this research advances the understanding of the slope hydrological response in triggering shallow landslides. Among the principal results is the comprehension that, mainly depending on geological and geomorphological settings, geotechnical and hydrological properties of soil coverings have a fundamental control on the timing and intensity of hydrological processes leading to landslide initiation. Moreover, results obtained show how the characteristics of the soil coverings control the slope hydrological response at different time scales, making the antecedent soil hydrological conditions a not negligible factor for estimating landslide rainfall thresholds. The approaches proposed can be conceived as an adaptable tool to assess hazard to initiation of shallow rainfall-induced landslides and to implement early-warning systems from site-specific to distributed (catchment or larger) scales.

Soil-atmosphere interaction has implications in different scientific research contexts and is increasingly investigated through field measurements. This paper reports a detailed description of interaction between shallow soil and atmosphere at two test sites in Oltrepò Pavese area (Northern Italy). The two test sites are in the same climatic area but are characterised by different geological features. In fact, the first objective is to compare the behaviour of two different soils, namely a clayey-sandy silt (CL) and a silty clay (CH), under similar meteorological events. Soil-atmosphere interaction is studied on the basis of long-term (about 87 and 42 months for the two test sites, respectively) monitoring data of both volumetric water content and soil water potential, recorded at different depths along two vertical soil profiles in the first two metres from ground level. Field measurements, together with meteorological data such as precipitation and air temperature, allow for clear identification of the seasonal fluctuations of unsaturated soil hydraulic properties. To infer detailed information, the recorded data were processed and relationships between soil water potential and water content were investigated. Different time spans, from several months to a few days, even including single rainy events, are considered to show the hydraulic soil behaviour. The hysteretic cycles of water content with respect to soil water potential and non-equilibrium flow are highlighted. In particular, the measured soil water potential is in the range of 0–800 kPa and of 0–1500 kPa for the CL and CH soil, respectively. At both sites, the observed hysteretic cycles are more frequent in the hot season (summer) than in the cold season (winter) and tend to reduce with depth. The experimental results are compared with the soil water characteristic curves (SWCCs) to assess whether and to what extent the SWCCs are reliable in modelling the hydraulic behaviour of partially saturated soils, under atmospheric forcing, at least in the considered climatic contexts.

The aim of this work is to develop an innovative methodology to analyse the time series (TS) of interferometric satellite data. TS are important tools for the ground displacement monitoring, mostly in areas in which in situ instruments are scarce. The proposed methodology allows to classify the trend of TS in three classes (uncorrelated, linear, non-linear) and to obtain the parameters of non-linear time series to characterise the magnitude and timing of changes of ground instabilities. These parameters are the beginning and end of the non-linear deformation break(s), the length of the event(s) in days, and the quantification of the cumulative displacement in mm. The methodology was tested on two Sentinel-1 datasets (2014–2020) covering the Alpine and Apennine sectors of the Piemonte region, an area prone to slow-moving slope instabilities. The results were validated at the basin scale (Pellice-Chisone and Piota basin) and at a local scale (Brenvetto, Champlas du Col and Casaleggio Boiro landslides) comparing with in situ monitoring system measurements, possible triggering factors (rainfall, snow) and already-collected events of the territory. The good correlation of the results has proven that the methodology can be a useful tool to local and regional authorities for risk planning and management of the area, also in terms of near real-time monitoring of the territory both at local and regional scale.

Shallow landslides triggered by heavy rainfalls are slope instabilities, developed in the most superficial eluvial layers, involving the first 2 m from the ground level. A crucial predisposing factor in shallow landslides occurrence is the soil water content, generally measured trough sensors installed in the first soil layers. However, despite being a very precise approach, this monitoring technique provides for a site-specific dataset. An integrated method to extend the hydrological characterization from site-specific to slope scale is presented, combining geotechnical analyses, field data monitoring, and geophysical investigations, in two experimental test sites located on Italian Apennines. Ten Electrical Resistivity Tomographies (ERT) of the first soil horizons were performed through different array geometries (2D-3D-Time-Lapse), calibrated and interpreted basing on stratigraphic logs, trenches, and monitored soil water content field data. The test sites colluvial covers composition was analyzed and compared to resistivity values to build conceptual hydrogeological models of the deep-water circulation. In addition, two time-lapse (4D) ERT surveys were performed in both test sites simulating very intense precipitations, to determine the resistivity variations at different soil drainage conditions, thus estimating the average bulk permeability. Bulk permeability can be also a useful input parameter for slope stability models, widely employed in engineering practices. This integrated method proved to be very useful for the hydrogeological characterization of the subsoil at slope scale, where it is susceptible to slope instability, improving the knowledge of water circulation, as well as the bulk permeability heterogeneities, which are shallow landslides triggering parameters.

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS.