In the present work, Raman, Fourier Transform Infrared (FTIR) and elemental Laser-Induced Breakdown Spectroscopy (LIBS) spectroscopic techniques were used for the assessment of the influence of plant root composition towards shallow landslide occurrence. For this purpose, analyses were directly carried out on root samples collected from chestnut forests of the Garfagnana basin (northern Apennines, Italy) in different areas devoid and affected by shallow landslides due to frequent heavy rain events. Results have highlighted a correlation between the biochemical constituents of wooden roots and the sampling areas. In particular, different content of lignin/cellulose, as well as minerals nutrients, have been detected in roots collected where shallow landslides occurred, with respect to more stable areas. The results achieved are in line with the scientific literature which has demonstrated the link between the chemical composition of roots with their mechanical properties and, in particular, tensile strength and cohesion. Finally, portable spectroscopic instrumentations were employed without the need for either any sample preparation for Raman and LIBS spectroscopy or minimal preparation for FTIR spectroscopy. This novel and fast approach has allowed achieving information on the content of the major constituents of the root cell, such as cellulose and lignin, as well as their mineral nutrients. This approach could be reasonably included among the vegetation protection actions towards instability, as well as for the evaluation of shallow landslide susceptibility, combining geological, vegetational and biochemical parameters with sustainability.

On 27–28 July 2019, in a catchment of the Mt. Amiata area (Italy), an extreme rainfall induced a debris flow, which caused a channeled erosive process just upstream of the Abbadia San Salvatore village, the obstruction of a culvert at the entrance to the urban area, and the subsequent flooding of the village. In this paper, we present the back analysis of this event. The complexity of this case study is due to several peculiar characteristics, but above all, to the clogging of the culvert, a phenomenon difficult to simulate numerically. The methodology used for the reconstruction of the event is based on a multidisciplinary approach. A geological field investigation was carried out to characterize the catchment and assess the availability of debris. Then, a cascade of numerical models was employed to reconstruct the debris flow: the FLO-2D software was used to model the runoff along the hydrographic network while the mobile-bed debris flow TRENT2D model, available through the WEEZARD system, was used to quantify both the erosion and deposition processes that occurred during the event. To simulate the culvert clogging, a novel modelling procedure was developed and applied. Despite the challenging framework, the results, in terms of debris volume, erosion rates, deposition area, and timing of the culvert obstruction, agree reasonably well with the observed data. It is worth noticing that these results were obtained mainly using parameters set a priori, namely calibrated on a physical basis. This proves that the proposed methodology is robust and effective, with good predictive capability. Therefore, it may be considered, according to the European Union (EU) Flood Directive, an “appropriate practice and the best available technology that does not imply excessive costs” to support predictive hazard mapping of situations as the one here considered.

<p>Mountain environments are naturally exposed to debris flows, a mass movement which represents one of the major geomorphological hazard sources for urbanized alluvial fan. In the last decades, climate change has contributed to extreme precipitations increase, making debris flows both larger and more frequent than in the past. The assessment and management of the risk associated with these events, according to UE Flood Directive, is feasible and desirable by using appropriate practices and the best available technology that do not imply excessive costs.<br>In line with the above-mentioned European Directive, we present a multidisciplinary approach for the numerical modelling of the debris flow event that occurred on July 27-28, 2019 in Abbadia San Salvatore, a  village located in a catchment of the Mt. Amiata area (Southern Tuscany, Italy). Debris flow was triggered by an extreme rainfall of 110 mm/1 h causing a channelled erosive process, and the subsequent obstruction of a culvert at the entrance to the village, flooding and damaging it.<br>Mt. Amiata is an extinct Pleistocene volcano mainly consisting of trachidacitic lavas characterized by a pervasive saprolite weathering, resulting in a large amount of residual loose debris resting on the hillslopes and along the hydrographic network. Specific geological and engineering-geological field investigations were carried out to assess the availability of debris material and its hydrological behaviour, providing more constraints for numerical modelling. <br>The Green-Ampt model, implemented in the FLO-2D software, was used for the evaluation of discharge values in the hydrographic network. Subsequently, the debris flow modelling was conducted applying the WEEZARD system, composed of a previously developed advanced two-phase debris-flow model (TRENT2D), re-coded as a web service. The mass movement was simulated to quantify erosive and depositional processes that occurred during the event. In addition, a specific approach was implemented to model the effect of the culvert that was clogged during the event.<br>Despite the challenging modelling aspects, the results in terms of debris volume, erosion rates, flooded area and timing of the culvert obstruction, are in agreement with observed data. <br>The WEEZARD system has therefore proved to be an effective tool, in line with the indications of the European Directive. Moreover, the reconstruction was obtained using most of the a priori parameters setting. This shows that the used modelling approach has a good predictive capacity and can therefore be reasonably used to support further predictive hazard mapping analyses. Finally, another important element to be highlighted is that an accurate input model based on the integration of detailed geological-geomorphological investigations is necessary to obtain reliable modelling results.</p>

<p>Landslides are considered one of the major hazards causing economic and human losses worldwide. Slope instability processes are affecting buildings and infrastructures in the towns of the eastern slope of the Mt. Amiata volcanic complex (Tuscany, Italy). These processes are relevant as they expose the inhabitants to risk, moreover their analysis provide hints about the mechanisms and roles of land sliding in the progressive disruption of extinct volcanic edifices.</p><p>In this study we present the first results of some monitoring and multi-temporal systems which are integrated to investigate the spatial-temporal ground displacement field in the eastern slope of the Mt. Amiata volcanic complex. In detail, we combine InSAR, GNSS, robotic total stations (TS) and levelling techniques to obtain a framework in terms of planimetric and vertical displacements. We apply the Multi-Temporal InSAR approach from 2014 to 2021 using the ESA Copernicus Sentinel-1 data. To perform the interferometry analysis, we implement the single master Stanford Method for Persistent Scatterers (StaMPS) approach for both ascending and descending geometries, and by combining both Line of Sight (LOS) results, we reveal the vertical and E-W components of the displacement. In addition, we perform multi-temporal survey-style GNSS measurements for some tens stations from 2019 to present day. About one hundred reflectors are continuously monitored by TS. Additionally, multi-temporal geometric levelling is performed to assess the vertical movements of selected relevant benchmarks. Finally, results from different monitoring systems are combined to model the ground displacements.</p><p>The InSAR results reveal mean velocity vectors with standard deviation less than 1 mm/y. The GNSS results have higher signal to noise ratio in the horizontal components with residuals lower than 10 mm. Accuracies of the geometrical levelling and TS results are ca. 1 mm and ca. 5 mm respectively. By combining the results, the magnitude of displacement field is ranging up to ca. 30 cm/y. The different systems provide results each other reasonably coherent in terms of magnitude and direction of the displacement vector. Integration of systems allows us to get solutions where one or more systems fail to provide data (i.e., when few or no PS are obtained by InSAR). Finally, we compare the results with seasonal data like rainfall. Velocities tend to reduce during summer low precipitation periods, while they increase during winter. Long term quantitative monitoring activities will allow us to better understand the spatial-temporal evolution of the landslide processes in the perspective of developing an early warning system.</p>

<p>Apart high elevation and arid regions, bedrock is generally covered by unconsolidated materials that result from recent or actual bedrock weathering and fracturing, consequent transport along the hillslope mainly by un-channelized flux or gravity-dominated processes and deposition. These slope deposits (SD) are largely affected by shallow landslides triggered during intense rainfall events, hence mapping SD spatial distribution and properties is a challenging task to perform accurate regionalized analysis of landslide hazard.</p><p>Nevertheless, geological and geomorphological maps typically represent the spatial distribution of SD following a 0/1 approach, instead of attempting to describe, in a more realistic perspective, the spatial variation of SD depth, even though this latter is a fundamental input parameter for landslide hazard estimation by physically based models.</p><p>In this work we present two different approaches to assess SD depth at regional scale, coupling field SD depth measurements, statistical analysis and then topographic-based regionalization: unsupervised clustering and multilinear regression analysis. Geo-environmental variables such as geology, land use and morphometric parameters, have been considered. The unsupervised clustering analysis has been based on some morphometric variables (eg. flow accumulation, slope and hillslope curvatures), derived from a digital elevation model with cell size of 10 m. These variables allowed us to extract, for homogeneous regions obtained by considering bedrock lithology, a set of morphometric units where the distribution of SD depth was assessed. The same variables were processed in the multilinear regression analysis in order to obtain equations estimating the spatial distribution of SD depth. The results of this work were then compared each other, as well as to the outputs obtained by implementing other methods of SD depth estimation known in the literature. Finally, the proposed methods were applied to evaluate SD depth in a test area, where field survey measurements were used as a check to assess the prediction capability. The results are analyzed and discussed in order to identify best solutions to evaluate and represent SD depth at regional scale.</p>