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In-place automatic inclinometers are typical devices used to monitor displacements of extremely slow to slow-moving landslides. The significance of these measurements requires methodologies able to distinguish real measures from anomalous ones, to quantify significant moments of acceleration in deformation trends and to determine the main factors that influence the kinematic behavior measured by an automatic inclinometer. This work aimed at developing a novel method, which allows to cover all the steps of analysis of data acquired by automatic inclinometers. The methodology is composed by five steps: (I) evaluation of the reliability of the instruments; (II) identification and elimination of anomalous measures from displacement time-series; (III) recognition of significant moments of acceleration in the rate of displacement, through thresholds based on the mean rate of displacement and on the cumulated amount of the deformation; (IV) clustering of the events of significant acceleration, to characterize different typologies of events according to different landslides kinematic behaviors; (V) identification of the main meteorological and groundwater parameters influencing the deformation pattern measured by an automatic inclinometer. The methodology was developed and tested using displacement time-series of 89 automatic inclinometers, belonging to the regional monitoring network of Piemonte region (northern Italy), managed by Arpa Piemonte. Two representative inclinometric time-series were selected to validate all the steps of the methodology for different types of monitored slow-moving landslides. The developed method is reliable in the estimation of anomalous measures and in the identification of significant accelerations, helping in the comprehension of the response of displacement trends during activity phases. Moreover, it is able to identify the factors which influence more the deformation pattern measured in correspondence of an automatic inclinometer.

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.

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.

The aim of this research is testing a new statistical model able to describe the interaction between soil and atmosphere. The model is based on a robust parametric LTS (Least Trimmed Squares) method and harmonic functions. It has been developed taking into account field measurements of quantities involved in both infiltration and evapo-transpiration phenomena, such as volumetric water content, soil-water potential, air temperature, rainfall amounts and solar radiation. The proposed statistical model allows assessing the volumetric water content at different sites using only time series of daily rainfall amount as input data. The model was applied in different test sites, whose data were assumed by the International Soil Moisture Network (ISMN). In fact, ISMN allows getting free time series of soil and meteorological data from monitoring stations all over the world. This note shows how the proposed model is accurate with respect to field data in estimating the volumetric water content in different soils, climates and depths. Future implications of this research will regard water content predictions, especially in areas where field data are scarce. Since the proposed LTS algorithm is very efficient and the computational workload is rather low, the possibility of coupling it with a slope stability analysis over large areas will be investigated, in order to get a distributed real-time model for shallow landslides susceptibility.

Rainfall-induced shallow landslides, which mobilize the first few meters of soil cover (usually

SAR images can be used to measure changes in the surface of the Earth over time using Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) techniques. TS-InSAR enables the detection and measurement of very small changes in surface deformation, often on the order of millimetres or less. This makes it a powerful tool for monitoring a wide range of natural and man-made phenomena, such as tectonic activity, subsidence, ground water extraction, and the behaviour of engineered structures like buildings and bridges. While TS-InSAR provides deformation measurements, further analysis must be taken to understand the underlying cause of the deformation. In this study, a novel framework has been developed to extract the vast amount of information embedded within the large number of ground deformation Measurement Points (MPs) derived from the Small BAseline Subset (SBAS; Berardino et al., 2002) TS-InSAR technique. The proposed automatic data-mining approach begins with clusterization the TS-InSAR MPs by applying a nonlinear dimensionality-reduction technique, Uniform Manifold Approximation and Projection (UMAP; McInnes et al., 2018), prior to performing clustering with Hierarchical Density based Spatial Clustering of Applications with Noise (HDBSCAN; Campello et al. 2013) in order to group together MPs exhibiting similar deformation behaviour on a large scale. Next, every extracted cluster time series is further investigated by applying a piecewise linear function as a method to detect and quantify accelerations and decelerations of deforming areas.A test of the method has been conducted over the Bandung Basin (Indonesia) using Sentinel-1 data from October 2015 to December 2020. Application of the method provides an objective way to identify changes in displacement rates over time and provides a wealth of information on the dynamics of surface displacement over a large area. The displacement rates, their spatial variation, and the timing and location of accelerations and decelerations can be used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to causal and triggering factors.ReferencesBerardino, P., Fornaro, G., Lanari, R., Sansosti E. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40 (11) (2002), pp. 2375-2383.Campello, R.J., Moulavi, D., Sander, J. Density-based Clustering Based on Hierarchical Density Estimates. In Advances in Knowledge Discovery and Data Mining, Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining; Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu,G., Eds.; Springer: Berlin, Germany, 2013; pp. 160–172 McInnes, L. and Healy, J. UMAP: uniform manifold approximation and projection for dimensionreduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

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 physically-based, which integrate slope physical–hydrological modeling and stability analyses.A comparison between these two types of thresholds was performed in this work, using data acquired in hillslopes characterized by clayey soils of 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 physically-based model of a representative test-site susceptible to shallow landslides, considerino different antecedent pore-water pressures, chosen according to the analysis of field hydrological monitoring data.Soil hydrological conditions have a primary role on predisposing or preventing slope failures. In clayey soils of 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. Physically-based 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 early warning system.

Nowadays, changing climate is continuously posing new challenges to land management and conservation, also in built areas and agricultural fields. Polymers such as polyacrylamide (PAM) offer a suitable tool to stem soil degradation, as well as having a wide range of applications. We performed a detailed investigation about this polymer applications with particular attention to the field of slope instabilities through a systematic literature review to assess the extent and the specifics of the research done on Polyacrylamide as an improver of soil features, browsing between more than 800 articles published from 1990 to 2022. Research on polyacrylamide application increased since the 2000s, its main use being the prevention of irrigation-connected erosion, even if in more recent years its ability as a soil stabilizer became more and more newsworthy. We then proceeded to reconstruct soil samples in laboratory to observe the effects of application of anionic polyacrylamide (PAM) on their physical, volumetric, mechanical, and hydrological properties. First, two sets of samples were reconstructed using kaolin and sandy loam soil, respectively, with three different dry densities (varying between 1.2 and 1.6 g/cm3), three different initial water contents (varying between 10% and 40%) and five different polymer application rates (0%, 0.003%, 0.03%, 0.3%, and 1%by weight). The polymer, a granular anionic polyacrylamide, provided by Micronizzazione Innovativa Srl, has been manually applied and mixed with the samples, constituted by pvc cylinders with a diameter of 9.5 cm and 15 cm high. Preliminary results showed that the increase of PAM percentage in samples generally coincided with increase of liquid limit and plasticity index, causing at the same time a more gradual and regular release of samples water compared to untreated samples. These preliminary results can stress on the possible application of PAM to improve other soil features which could impact on slope instabilities occurrence, in a frame of sustainable solutions for reduction of landslides susceptibility, hazard and risk. Keywords PolyacrylamideLandslidesSoil conservationLand degradation

Shallow landslides induced by heavy rainfall are a worldwide widespread phenomena and their related hazard is expected to increase due to more intense rainfall as a consequence of climate change (EEA Report No 15/2017). Since 2017, a decision-making tool based on Multi-Criteria Analysis (MCA) has been proposed as an objective approach to obtain landslide susceptibility maps and plan proper remedial works along linear infrastructure corridors (Tamburini et al., 2017). The study of low-cost sensors for Landslides Early Warning Systems (LEWS) as a risk mitigation tool to these phenomena along highways, railways and pipelines is here presented. Soil hydrological conditions before a rainfall event for the estimation of trigger moments (Bordoni et al., 2019) are the starting point of a LEWS. Different sensors for the measure of these parameters, particularly soil volumetric water content, exist with different pros and cons. The aim of the research is to compare seven low-cost sensors selected by IMAGEO Srl company together with HORTUS Srl. The sensors have been engineered with a datalogging system and an automatic in-cloud transmission of the data and in June 2022 have been located on field at 2 different depths (-0.6 m and -1.2 m) at the test-site of Montuè in the Northern Apennines (Italy) where an Hydrometeorological Monitoring Station (Andromeda Project) is operating since 2012 with high-cost TDR probes present at the same depths. In November 2022 a volumetric water content profiler with nine measurement depths up to 1 m deep has been added to the new monitoring system.The comparison between the hydrological data acquired by different sensors allows to evaluate the quality and reliability of the low-cost system before its final installation along the infrastructures lines. Monitored data together with rainfall parameters provided by both in situ rain gauges and ERA5-LAND satellite-derived data are used as input for the reconstruction of soil moisture values physically based thresholds. The near real-time access to the monitored data allows to send warning alert when the established thresholds are exceeded, resulting in a LEWS able to identify periods of imminent landslide danger and to assess security along the lines.

Land subsidence induced by groundwater withdrawal affects many regions around the world and is considered one of the most extensive phenomena caused by human activity nowadays. The Gediz River Basin is located in the western part of Türkiye, and develops along a regional extension of a horst-graben system. This basin has an agricultural and industrial importance in the region, increasing the water demand and positioning the Gediz Basin as one of the most stressed basins in the country. The main aims of this study are to evaluate the role of tectonics and groundwater withdrawal on land subsidence and on the evolution of faults in the Gediz River Basin. Additionally, other conditioning factors such as soft soil thickness layers are also studied. For this purpose, we processed 123 SAR images in descending orbit and 98 in ascending orbit acquired from Sentinel-1 between 2016 and 2021 by the parallel solution of the Small BAseline Subset (SBAS) algorithm (P-SBAS), allocated in the Geohazard Exploitation Platform (GEP). Secondly, we applied an Independent Component Analysis (ICA) to the InSAR time series in order to separate spatiotemporal patterns of long-term deformation and seasonal variations. P-SBAS results reveal that the maximum subsidence rates measured along the line of sight (-6.40 cm/year) are mainly concentrated in agricultural and urban areas. The results also suggest that there is a direct relationship between InSAR deformation and soft soil thickness, indicating that land subsidence is induced by the compaction of aquitard layers due to the groundwater withdrawal and piezometric head depletion. The analysis of the time series through the ICA shows two types of spatiotemporal deformation trends, one of them corresponds to long term and quasi-linear deformation due to the compaction of the aquitard, and the other represents the long-term deformations with seasonal rebounds produced by the seasonal loading and unloading cycles due to water level fluctuations. AcknowledgementsThis research was funded by the PRIMA programme supported by the European Union under grant agreement No 1924, project RESERVOIR and by ESA-MOST China DRAGON-5 project (ref. 59339)

Land subsidence is a natural or anthropogenic process triggering the settlement of the Earths surface. When this phenomenon is induced by groundwater withdrawal, compaction of unconsolidated sediments causes land displacement. Differential Interferometric Synthetic Aperture Radar (DInSAR) is widely used nowadays to monitor subsidence over extensive areas. However, validation of DInSAR measurements with in-situ techniques is lacking in many case studies, reducing the reliability of further analyses. The aim of this study is to propose a systematic methodology to validate DInSAR measurements with in-situ techniques to obtain reliable subsidence measurements. The paper provides a literature review of the most common approaches to validate DInSAR measurements and a description of the proposed systematic methodology, which is supported by a MATLAB open-source code. The methodology allows the analysis of both DInSAR-based velocity and displacement time series. We propose a set of statistics to assess the accuracy of the DInSAR estimates. For this purpose, RMSE parameters have been normalised with the range and the average of the in-situ deformation values. Moreover, combining these normalised parameters with the Pearson correlation coefficient (R2) a classification scheme is recommended for accepting/rejecting the DInSAR data for further analyses. This methodology has been applied in three study areas characterised by very well-documented subsidence processes: The Alto Guadalentn valley and Murcia city in Spain, and San Luis Potos in Mexico. María I. Navarro-Hernández is funded by the PRIMA programme supported by the European Union under grant agreement No 1924, project RESERVOIR. It also has been supported by the Spanish Ministry of Science and Innovation, the State Agency of Research (AEI) and the European funds for Regional Development (EFRD) under project PID2020-117303GB-C22. The research was carried out in the framework of ESA-MOST China DRAGON-5 project (ref. 59339).

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.

According to the Centre for Research on Epidemiology of Disasters, every year landslides are to be blamed worldwide for at least 17% of all fatalities from natural disasters. Rainfall-induced shallow landslides are responsible for a significant number of those: they mobilize the first few meters (usually The runout of a landslide, defined as the difference between the total area of a landslide and its source area, from which the sediment is first mobilized, is what determines how far a landslide travels and how big the affected area is, and yet the runout is often neglected when it comes to analysing the overall hazard caused by potential landslides.The land use practices have been proven as one of the factors which impact the susceptibility of an area to the formation of shallow landslides, it is however less clear if the land use also plays a role in influencing the size of the area of runout.The aim of the present work is to investigate the correlation between the runout area and the land use in which the shallow landslide develops.To do so, two inventories of landslides, which occurred in neighbouring regions in Northern Italy (Lombardy and Piedmont), comparable for lithology, land use, geomorphology and climate, were analysed.The result of the analysis was that there were statistical differences in the distribution of the runout among different land use classes, meaning that an influence of the land use on the runout was highly probable. Such results could improve the comprehension on shallow landslides mobility and runout and could lead to the development of possible models of assessment of the runout at different scales.

High-resolution characterisation of land deformation and its spatio-temporal response to external triggering mechanisms is an important step towards improving geological hazard forecasting and management. The work presented here is part of the ResEau-Tchad project (www.reseau-tchad.org), with a focus on the city of N’Djamena. The extraction of groundwater to sustain this rapidly growing capital city has increased the pressure on water supply and urban sanitation infrastructures which are failing to meet the current water demand. In this study we exploit Synthetic-Aperture Radar (SAR) data acquired by the Sentinel-1 satellite to investigate the temporal variability and spatial extent of land deformation to assist in the development of a sustainable water management program in N’Djamena city. The objectives of the work are: 1) to analyse the recent evolution of land deformation using two multi-temporal differential interferometry techniques, SBAS and PS-InSAR; and, 2) to investigate the land deformation mechanism in order to identify the factors triggering surface movements. The PS-InSAR and SBAS techniques are implemented on SAR images obtained in both ascending and descending orbits from April 2015 to May 2021 to generate high resolution deformation measurements representing the total displacement observed at the surface. While the pattern of displacement indicated by the two datasets is similar, the average velocity values obtained with PS-InSAR tend to be noisier than the ones derived using the SBAS technique, particularly when the SBAS time-series shows non-linear deformation trends.Characterisation of the subsidence areas by means of statistical analyses are implemented to reveal the surface deformation patterns which are related to different geo-mechanical processes. The integration of the spatio-temporal distribution of PS and SBAS InSAR results with geological, hydrological, and hydrogeological data, along with subsurface lithological modelling shows a relationship between vertical displacements, clay sediments, and surface water accumulation. These areas are located mostly in the surroundings of the urban area. The city centre is observed to be mostly stable, which might be the result of the removal of the surface water through the city drainage system. Investigation of the relationship between vertical displacements and seasonal groundwater fluctuations or effects due to the groundwater withdrawal is limited due to the temporally sparse piezometric dataset; however, the recent deformation rates appear to be correlated with the groundwater level trend at some locations.

Vineyards cultivated in steep terrains are widespread all over the world, constituting the main economic activity and landscape element in many territories. However, these vineyards can be affected by several problems, mainly due to water stress in dry periods and shallow slope instabilities during very intense thunderstorms or prolonged rainy periods. Both these problems provoke significant damages to the grapevines and the implants, together with a general loss of fertility and biodiversity in the soil horizons. Sustainable managements of the inter-rows, which limit or exclude tillage operations in soils, can represent a useful solutions to reduce the phenomena of water stress and of the triggering of shallow slope failures. Since they could modify the soil hydrological behaviors, a continuous-in-time soil hydrological monitoring is required to highlight differences on soil moisture along different dry and wet periods between vineyards managed in different ways. The aim of this work is, then, to present the results of a field hydrological monitoring carried on vineyards managed in different ways to highlight differences on soil water content trends in different seasons. The results of this monitoring can infer to different responses in terms of possible water stress phenomena and susceptibility towards shallow slope failures. Some test-sites were selected in northern Italian Appenines, in two important wine districts very prone to water stress and slope instabilities. The field monitoring is carried out with a set of soil water content sensors, installed at different depths in the soil profile and in inter-rows characterized by different soil managements, both with traditional tillages and sustainable practices (permanent grass cover, alternation between grass cover). Soil water content trends collected at the same depth in different test-sites allow to highlight the impact of the different practices in soil hydrological behaviors and on the probability of low soil moisture (predisposing factor to water stress) or saturated conditions (predisposing factor to slope instaibilities). This work is realized in the frame of VIRECLI (funded by Regione Lombardia) and LIFE-DRIVE projects.

Rainfall-induced shallow landslides can provoke severe consequences to people, infrastructures, cultivations and environment. For these reasons, it is necessary assessing the spatial and temporal probability of occurrence of these phenomena in the most prone zones of a territory, for early warning system strategies and land planning. The most adopted method for the determination of triggering events are rainfall thresholds. Empirical thresholds consider only rainfall attributes, such as duration, intensity and cumulated amount, while physically-based thresholds take into consideration also soil attribute, representing the soil conditions at the beginning of an event, such as the soil saturation degree at the depth of the sliding surface. This work focused to develop hydro empirical and physically-based thresholds for the occurrence of shallow landslides, taking into account field rainfall observations and soil moisture data, retrieved by hydrometeorological monitoring stations datasets. Monitoring stations were placed in 2 test sites representative of the hilly area of northern Italian Apennines and provided hydrometeorological time series, collecting data every 10 minutes. Empirical thresholds showed a good capacity to detect True Positives (TP: 95%) but they resulted affected by a high percentage of False Positives (FP: 24%), while physically-based thresholds detected 100% of TP and only 7% of FP, confirming the importance of soil conditions at the beginning of the event. Physically-based thresholds are reconstructed through a data-driven technique, based on “random forest”, that allows to find the best pair of parameters chosen within rainfall cumulated amounts and mean soil moisture conditions between 1 and 7 days. The model is calibrated considering a time span of 11 years (2007-2018) and validated using data between 2019 and 2021. The methodological approach is testing in different catchments of Oltrepò Pavese hilly area (northern Italy), that is representative of Italian Apennines environment. This work was made in the frame of the ANDROMEDA project, funded by Fondazione Cariplo.

In areas with similar soil properties and geomorphological conditions, different land uses could promote or reduce the failure probability towards shallow landslides, especially due to differences in the reinforcement acted in the soil by plant roots. In this paper, root density, mechanical properties, and reinforcement provided by different land use types (sowed grasses used to produce animal feed, vineyards cultivated with different agronomical management practices, shrublands, woodlands of broadleaved species) of a representative area of northern Italian Apennines (Oltrepo Pavese hilly sector) were quantified and inserted into a slope stability model to estimate the effects of different land uses on shallow landslide failure probability. Root reinforcement is correlated proportionally to root density in soil and root mechanical properties. Furthermore, a greater root reinforcement in the soil guarantees a lower failure probability of a slope. Sowed grasses and shrublands in abandoned areas are the most land uses which are more prone to shallow landsliding, followed by vineyards with tilled inter-rows. Vineyards with permanent grass cover or alternation in the inter-rows and woodlands promote the stability of slopes in a wide range of steepness (> 21–25° for vineyards with permanent grass cover in the inter-rows, 28–33° for vineyards with alternation in the inter-rows and woodlands). According to these results, possible land use managements acting as mitigation measures for shallow landslides could be implemented.

Understanding the hydrological mechanism of rainfall-induced shallow landslides is critical to make reliable predictive assessments and to reduce risks of economic loss and fatalities. An integrated approach was identified by combining the field-based monitored hydrological data with a hydro-mechanical analysis, which can be utilized to better understand seasonal hydrological features in shallow soils and the corresponding triggering mechanism of rainfall induced shallow landslides, with the aim of setting up an improved landslide early warning system. A hydrological monitoring system on a representative slope that was experiencing frequent local shallow failures in the Ren River catchment was constructed to provide available seasonal hydrological features with regard to time trends in volumetric water content and pore water pressure in shallow soils. A transition of vertical downward flow from unsaturated to near-saturated conditions has been fully detected since the significant saturation degree and associated pore pressure varies with vertical depths. Consequently, the triggering mechanism of shallow landslides should be associated with the combined effects of dissipation of matrix suction in the upper unsaturated zone and increased positive pore pressure due to the generation of a temporal perched water table in the deeper saturated zone. This process was confirmed through the field evidence and a back analysis on the long-term hydrological evolution of slope soil using a calibrated one-dimensional hydrological model. Antecedent hydrological conditions were also found to have an effect on rainfall intensity–duration thresholds in the landslide early warning system based on a hydro-mechanical analysis, with the aim of improving the accuracy of the model’s predictive ability. The results indicated that this research promoted the effectiveness of a landslide early warning system through a hydro-mechanical analysis based on capturing the real hydrological features of a landslide.

Soil water potential is a key factor to study water dynamics in soil and for estimating the occurrence of natural hazards, as landslides. This parameter can be measured in field or estimated through physically-based models, limited by the availability of effective input soil properties and preliminary calibrations. Data-driven models, based on machine learning techniques, could overcome these gaps. The aim of this paper is then to develop an innovative machine learning methodology to assess soil water potential trends and to implement them in models to predict shallow landslides. Monitoring data since 2012 from test-sites slopes in Oltrepò Pavese (northern Italy) were used to build the models. Within the tested techniques, Random Forest models allowed an outstanding reconstruction of measured soil water potential temporal trends. Each model is sensitive to meteorological and hydrological characteristics according to soil depths and features. Reliability of the proposed models was confirmed by correct estimation of days when shallow landslides were triggered in the study areas in December 2020, after implementing the modeled trends on a slope stability model, and by the correct choice of physically-based rainfall thresholds. These results confirm the potential application of the developed methodology to estimate hydrological scenarios that could be used for decision-making purposes.

Groundwater is a critical resource that provides fresh drinking water to at least 50% of the global population and accounts for 43% of all of the water used for irrigation (Siebert et al., 2010; UNESCO, 2012). A main consequence of groundwater depletion in overexploited aquifers is land subsidence, which ensues other impacts, such as increasing flooding risk (specially in coastal areas), damages to infrastructures and reduction of storage capacity in aquifer systems. Aquifer deformation and groundwater flow models are essential to design sustainable management strategies. In this context, A-DInSAR techniques provide valuable surface displacement data to understand the deformational behaviour of the aquifer and to characterise its properties.RESERVOIR project, which is part of the PRIMA programme supported by the European Union, aims to provide new products and services for a sustainable groundwater management model to be developed and tested in four water-stressed Mediterranean pilot sites. Each of them is representative of a different aquifer system flow scheme. They are located in Italy (coastal aquifer of Comacchio), Spain (Alto Guadalentín Basin), Turkey (Gediz River Basin) and Jordan (Azraq Wetland Reserve). The water usages of these aquifers are irrigation, drinking water and/or power generation. Each site is prone to different issues such as land subsidence, salt water intrusion, water pollution, over-exploitation and insufficient recharge.One of the primary objectives of the project is the use of advanced satellite-based Earth Observation (EO) techniques for the hydrogeological characterization and their integration into numerical groundwater flow and geomechanical models. This will lead to improve the knowledge about the current capacity to store water and the future response of aquifer systems to natural and human-induced stresses. Free Sentinel-1 SAR acquisitions available at the Copernicus Open Access Hub will be used to perform A-DInSAR processing in representative areas of each pilot site. Additionally, the InSAR processing tools of the Geohazards Exploitation Platform (GEP) funded by the European Space Agency, will be used for a first assessment of ground deformation. In this work we present the preliminary results obtained with Sentinel-1 images using the P-SBAS web tool on GEP (De Luca et al., 2015) at the four pilot sites. De Luca, C., Cuccu, R., Elefante, S., Zinno, I., Manunta, M., Casola, V., Rivolta, G., Lanari, R., and Casu, F., 2015, An on-demand web tool for the unsupervised retrieval of earth’s surface deformation from SAR data: The P-SBAS service within the ESA G-POD environment: Remote Sensing, v. 7, no. 11, p. 15630-15650.Siebert, S., Burke, J., Faures, J.-M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T., 2010, Groundwater use for irrigation—a global inventory: Hydrology and earth system sciences, v. 14, no. 10, p. 1863-1880.UNESCO, 2012, World’s Groundwater Resources Are Suffering from Poor Governance, UNESCO Publishing: Paris, France, UNESCO Publishing.

Land subsidence produced from the exhaustive groundwater exploitation is a phenomenon that has critical effects in many areas around the world, especially in water-scarce areas. Among the objectives of the RESources managEment by integrating eaRth observation deriVed monitoring and flOw modelIng Results (RESERVOIR) project (GA nº 1924), one of them is the identification of the stakeholder requirements and groundwater conceptual model setup for several pilot sites. The present work is related with the Alto Guadalentín aquifer study case (SE Spain). To achieve the mentioned objective, a workshop was organized and a large number of potential stakeholders from local authorities, environmental agency, water supply companies, NGOs, insurance companies and regulatory institutions were summoned. During the workshop most important RESERVOIR activities were presented. Once the workshop was finished, a questionnaire that proposes the evaluation of the main problems in the area related to the groundwater management was sent to all participants. Results disclose that 55% of stakeholders consider that the principal problem in the Guadalentín aquifer is the overexploitation, and 50% of stakeholders regard that the lack of managerial, planning, and technical instruments are the most relevant issues to be addressed and solved. In more detail, the survey reveals that the monitoring coverage is inadequate in terms of: (a) groundwater levels monitoring (50%); (b) water quality measurement frequency (40%); and (c) water quality measurement points spatial distribution (55%) Even that area is reporting the highest subsidence rates in Europe, this problem is not considered as the most important, probably because affects agricultural areas and minimal damages were reported. Finally, 90% of the stakeholders have previous knowledge about groundwater modelling, but only 45% within them have information about the specific applications for which the model is employed in the Alto Guadalentín aquifer. Main conclusion leads to confirm that the measurements taken in the past have proved to be insufficient to reverse the state of the overexploitation of the aquifer. From this, some management measures must be strengthened, and several technical tools should be introduced to improve the groundwater management. On the other hand, improving the use of the numerical model of the Alto Guadalentín aquifer to support decision-making, as well as, updating the model by coupling it with the subsidence phenomenon through the definition of subsidence risk indexes would lead to a sustainable and holistic groundwater management.

Rainfall-induced shallow landslides affect buildings, roads, facilities, cultivations, causing several damages and, sometimes, loss of human lives. It is necessary assessing the most prone zones in a territory where these phenomena could occur and the triggering conditions of these events, which generally correspond to intense and concentrated rainfalls. The most adopted methodologies for the determination of the spatial and temporal probability of occurrence are physically-based models, that quantify the hydrological and the mechanical responses of the slopes according to particular rainfall scenarios. Whereas, they are limited to be applied in a reliable way in little catchments, where geotechnical and hydrological characteristics of the materials are homogeneous. Data-driven models could constraints these, when the predisposing factors of shallow instabilities, allowing to estimate only the susceptibility of a territory, are combined with triggering factors of shallow landslides to allow these methods to estimate also the probability of occurrence and, then, the hazard. This work presents the implementation of a data-driven model able to assses the spatio-temporal probability of occurrence of shallow landslides in large areas by means of a data-driven techniques. The models are based on Multivariate Adaptive Regression Technique (MARS), that links geomorphological, hydrological, geological and land use predisposing factors to triggering factors of shallow failures. These triggering factors correspond to soil saturation degree and rainfall amounts, which are available thanks to satellite measures (ASCAT and GPM). The methodological approach is testing in different catchments of Oltrepò Pavese hilly area (northern Italy), that is representative of Italian Apeninnes environment. This work was made in the frame of the project ANDROMEDA, funded by Fondazione Cariplo.

In this study, we compare infinite slope and the three-dimensional stability analysis performed by SCOOPS 3D (software to analyze three-dimensional slope stability throughout a digital landscape). SCOOPS 3D is a model proposed by the U. S. Geological Survey (USGS), the potentialities of which have still not been investigated sufficiently. The comparison between infinite slope and 3D slope stability analysis is carried out using the same hydrological analysis, which is performed with TRIGRS (transient rainfall infiltration and grid-based regional slope-stability model)—another model proposed by USGS. The SCOOPS 3D model requires definition of a series of numerical parameters that can have a significant impact on its own performance, for a given set of physical properties. In the study, we calibrate these numerical parameters through a multi-objective optimization based on genetic algorithms to maximize the model predictability performance in terms of statistics of the receiver operating characteristics (ROC) confusion matrix. This comparison is carried out through an application on a real case study, a catchment in the Oltrepò Pavese (Italy), in which the areas of triggered landslides were accurately monitored during an extreme rainfall on 27–28 April 2009. Results show that the SCOOPS 3D model performs better than the 1D infinite slope stability analysis, as the ROC True Skill Statistic increases from 0.09 to 0.37. In comparison to other studies, we find the 1D model performs worse, likely for the availability of less detailed geological data. On the other side, for the 3D model we find even better results than the two other studies present to date in the scientific literature. This is to be attributed to the optimization process we proposed, which allows to have a greater gain of performance passing from the 1D to the 3D simulation, in comparison to the above-mentioned studies, where no optimization has been applied. Thus, our study contributes to improving the performances of landslide models, which still remain subject to many uncertainty factors.