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In recent decades, the increasing number and severity of wildfires have become a major concern in many regions of the world. Besides having direct consequences for forests, crops, and settlements, wildfires can have significant effects on the catchment's hydrology, altering their response to rainfall and promoting enhanced runoff and erosion. Increased runoff in burnt areas can result in catastrophic damage and loss of life from destructive debris flows and floods. Although observed in many regions of the world, post-wildfire debris flows/floods seem to prevail in the western USA and southern Europe. In Italy, post-wildfire debris flows/floods have been observed in a number of sectors of the Apennine range, and a total of 113 events occurred between 2001 and 2021 in the Campania region. A recent event occurred on August 24, 2020, in the Fosso delle Pescine catchment, on the southwestern slope of Mt. Omo in the L'Aquila municipality, hit by a severe wildfire at the beginning of the month. The consequences of the event on people and properties indicate the need for an evaluation of debris flow/flood hazard in both pre- and post-wildfire conditions. The analysis of these events may often be complex, requiring time and many input data. In this context, a simplified method of analysis which accounts for event initiation and propagation through a shallow water equation-based approach would represent a new and innovative solution for fast and preliminary debris flow/flood analyses. In this research, a reduced complexity analysis based on probability analysis of rainfall and 2D hydrodynamic modeling was carried out by estimating (i) wildfire extent and severity, (ii) sediment characteristics and availability, (iii) potential runoff induced by rainfall, and (iv) inundation by debris flow/flood. A debris flow/flood hazard model was developed on the basis of multiple return periods (10, 50, 100, and 300 years) for debris flow inundation scenarios derived by non-Newtonian 2D hydrodynamic modeling of debris flow hydrograph and qualitatively validated on the basis of the extent of alluvial fan deposits. Potential drawbacks of the model are related to the possible overestimation of surface runoff, related to the consideration of the whole catchment contributing area, and the limited volumetric sediment budget available at the headwater, which might not sustain a high return period event generation. A debris flow/flood event might originate from rainfall concentrated at the headwater of the catchment. [ABSTRACT FROM AUTHOR]

Terraced landscapes represent one of the most widespread human-induced/man-made transformations of hilly-mountainous environments. Slope terracing produces peculiar morphologies along with unusual soil textures and stratigraphic features, which in turn strongly influence slope hydrology. The investigation of the hydrological features of terraced soils is of fundamental importance for understanding the hydrological dynamics occurring in these anthropogenic landscapes, especially during rainfall events. To this purpose, the availability of extensive field monitoring data series and of information on subsoil properties and structure is essential. In this study, multi-sensor hydrological data were acquired over a period longer than 2 years in the experimental site of Monterosso al Mare, in the Cinque Terre National Park (Liguria region, Italy), one of the most famous examples of terraced landscape worldwide. Monitoring data were coupled with accurate engineering-geological investigations to achieve the hydro-mechanical characterization of backfill soils and to investigate their hydrological response at both the seasonal and the single rainstorm scale. The results indicated that the coarse-grained, and anthropically remolded texture of the soils favors the rapid infiltration of rainwater, producing sharp changes in both soil volumetric water content and pore water pressure. Furthermore, the pattern of hydrological parameters showed seasonal trends outlined by alternating phases of slow drying and fast wetting. The study outcomes provide useful insights on the short and long-term evolution of hydrological factors operating in agricultural terraces. These findings represent a useful basis for a better understanding of the time-dependent processes that guide water circulation in terraced systems, which have a key role in controlling the occurrence of erosion and landslide processes. [ABSTRACT FROM AUTHOR]

A key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm. [ABSTRACT FROM AUTHOR]

Landslides represent a severe geohazard in many countries. The availability of inventories depicting the spatial and temporal distribution of landslides is crucial for assessing landslide susceptibility and risk for territorial planning or investigating landscape evolution. Nevertheless, these inventories are usually affected by limitations due to their nonpublic availability and inhomogeneities in characterization and mapping. Such problems are fully recognizable by the analysis of the multiple landslide inventories of the Campania region, which is one of the Italian regions with the highest exposure to landslide hazard and risk. On this basis, a revised Landslide Inventory of the Campania region (LaICa), resulting from the processing of multiple existing landslide inventories, has been reconstructed. It aims to (i) provide a new geodatabase that is able to overcome issues derived from the coexistence of multiple inventories and (ii) provide a methodological paradigm able to support the reorganization of existing official inventories. The implication of LaICa, with its 83,284 records, will possibly improve the assessment of landslide susceptibility and then reassess the related risk. Measurement(s) landslide Technology Type(s) Data Set [ABSTRACT FROM AUTHOR]

In this paper, we aim to define a procedure of flood hazard assessment applicable to large river basins in which flood events can be induced/sustained by the full basin area or by fractions of the total area as functions of the extent of the triggering precipitation event. The proposed procedure is based on a combined approach accounting for (1) the reconstruction of intensity–duration–frequency curves expressing the magnitude in terms of intensity for multiple return periods; (2) the application of the soil conservation service method for runoff estimation from a selected rainfall scenario considering some characteristics of the basin (i.e. soil type, land use/treatment, surface condition, and antecedent moisture conditions); (3) 2D hydrodynamic modelling conducted by the HEC-RAS model using runoff hydrographs as hydrological input data; (4) the reconstruction of flood hazard maps by overlaying multiple inundation maps depicting flood extent for different return periods. To account for the variability in the extent of the triggering precipitation event and the resulting input hydrograph, multiple contributing areas are considered. The procedure is tested at the archaeological site of Sybaris in southern Italy, which is periodically involved in flood events of variable magnitude. The obtained results highlight that the variable extent of the floodable area is strongly conditioned by the extent of the contributing area and return period, as expected. The archaeological site is always involved in the simulated flooding process, except for the smallest contributing area for which only a 300-year event involves this part of the site. Our findings may be useful for developing and supporting flood risk management plans in the area. The developed procedure might be easily exported and tested in other fluvial contexts in which evaluations of multiple flood hazard scenarios, due to the basin geometry and extent, are needed. [ABSTRACT FROM AUTHOR]

The assessment of timing and potential locations of rainfallinduced shallow landslides through mathematical models represents a challenge for the assessment of landslide hazard, especially in cases with limited or not available data. In fact, modeling slope hydrological response and stability requires accurate estimates of unsaturated/saturated hydraulic and geotechnical properties of materials involved in landsliding, as well as climate and topography. Such aspect is relevant for the prediction of location and timing of landslide events, which is greatly needed to reduce their catastrophic effects in terms of economic losses and casualties. To such a scope, we present the comparison of results of two physics-based models applied to the assessment of susceptibility to shallow rainfall-induced landslides in Valtellina region (northern Italy). The analyses were carried out considering effects of availability, resolution and type of data concerning spatial distribution, thickness and properties of soils coverings. For such a scope, the Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) and the Climatic Rainfall Hydrogeological Modeling Experiment (CHRyME) models were considered. The study emphasizes issues in performing distributed numerical slope stability modeling depending on the availability of spatially distributed soil properties which hamper the quality of physic-based models. Further analyses aimed at the probabilistic assessment of landslide spatial distribution, related to a specific value of rainfall threshold, can be considered as potentially applicable to multi-scale landslide hazard mapping and extendable to other similar mountainous frameworks. [ABSTRACT FROM AUTHOR]

This work presents a sinkhole susceptibility and risk assessment mapping in Guidonia-Bagni di Tivoli plain (Italy), a travertine sinkhole-prone area where sudden occurrences of sinkholes have happened in past and recent times. We collected a point-like sinkhole inventory and we considered a series of different sinkholecontrolling and precursory factors over the study area, related to its geo-litho-hydrological setting and to its terrain deformational scenario, i.e. ground motion rates derived from InSAR COSMOSkyMed imagery. A sinkhole susceptibility map was produced through a machine learning model, namely Maximum Entropy algorithm (MaxEnt). Results highlight that the most determining factors for sinkhole formation are the lithology, the travertine thickness, groundwater and the land use. The sinkhole susceptibility map was then combined with data on vulnerability and elements-at-risk economic exposure in order to provide a sinkhole risk map of the area. The outcomes show that areas at higher risk covers about 2% of the total study area and primarily relies on the zoning of the main urban fabric. In particular, it is worth to highlight that 5% of the whole road-network pavement and 27% of all the residential buildings fall into High and Very High risk classes. Overall, results of this work demonstrate capabilities of machine learning models to assess sinkhole susceptibility for predicting potential sinkhole areas, and provide a sinkhole risk map, along with information on urban environment, as a useful tool for urban planning and geohazard risk management. [ABSTRACT FROM AUTHOR]

The building stock around the world is exposed to different types of natural actions such as earthquakes or landslides. In particular, Italy is one of the countries worldwide most affected by landslides. Mitigation of landslide risk is a topic of great interest for the evaluation and management of its consequences. Periodical monitoring of the landslide-induced damage on structures require high costs due to the large number of exposed elements. With respect to the reinforced concrete structures, slow-moving landslides can affect primary structural elements, but more frequently damage occurs on the most vulnerable elements of the structure such as infills. The aim of this work is to demonstrate the potential utility of satellite data derived from a remote sensing technique, known as differential synthetic aperture radar interferometry, to support the structural health monitoring of reinforced concrete buildings affected by landslides. This article shows the structural health monitoring process for a reinforced concrete infilled building within a landslide-affected area, using the differential synthetic aperture radar interferometry data as input for the structural analysis in order to investigate the evolution of damage over the years. Three-dimensional structure, including the explicit infills consideration, has been modeled based on the information available from a visual survey, obtaining the missing parameters from a simulated design process and from the literature. In the field of the civil protection programs for the landslide risk reduction, this methodology can be quickly repeated for large sets of reinforced concrete buildings. Evidence of the visual survey showed a significant damage pattern in some infills. A good agreement has been found between analytical previsions and existing damage. Moreover, a global infills damage assessment of the case study building is proposed. Finally, assuming a constant increase in displacements in future years, a prediction of the future expected damage is shown. [ABSTRACT FROM AUTHOR]

Thickness and stratigraphic settings of soils covering slopes potentially control susceptibility to initiation of rainfall-induced shallow landslides due to their local effect on slope hydrological response. Notwithstanding the relevance of the assessment of hazard to shallow landsliding at a distributed scale by approaches based on a coupled modelling of slope hydrological response and slope stability, the spatial variability of soil thickness and stratigraphic settings are factors poorly considered in the literature. Under these premises, this paper advances the well-known case study of rainfall-induced shallow landslides involving ash-fall pyroclastic soils covering the peri-Vesuvian mountains (Campania, southern Italy). In such a unique geomorphological setting, the soil covering is formed by alternating loose ash-fall pyroclastic deposits and paleosols, with high contrasts in hydraulic conductivity and total thickness decreasing as the slope angle increases, thus leading to the establishment of lateral flow and an increase of pore water pressure in localised sectors of the slope where soil horizon thickness is less. In particular, we investigate the effects, on hillslope hydrological regime and slope stability, of irregular bedrock topography, spatial variability of soil thickness and vertical hydraulic heterogeneity of soil horizons, by using a coupled three-dimensional hydrological and a probabilistic infinite slope stability model. The modelling is applied on a sample mountain catchment, located on Sarno Mountains (Campania, southern Italy), and calibrated using physics-based rainfall thresholds derived from the literature. The results obtained under five simulated constant rainfall intensities (2.5, 5, 10, 20 and 40 mm h−1) show an increase of soil pressure head and major failure probability corresponding to stratigraphic and morphological discontinuities, where a soil thickness reduction occurs. The outcomes obtained from modelling match the hypothesis of the formation of lateral throughflow due to the effect of intense rainfall, which leads to the increase of soil water pressure head and water content, up to values of near-saturation, in narrow zones of the slope, such as those of downslope reduction of total soil thickness and pinching out of soil horizons. The approach proposed can be conceived as a further advance in the comprehension of slope hydrological processes at a detailed scale and their effects on slope stability under given rainfall and antecedent soil hydrological conditions, therefore in predicting the most susceptible areas to initiation of rainfall-induced shallow landslides and the related I-D rainfall thresholds. Results obtained demonstrate the occurrence of a slope hydrological response depending on the spatial variability of soil thickness and leading to focus slope instability in specific slope sectors. The approach proposed is conceived to be potentially exportable to other slope environments for which a spatial modelling of soil thickness would be possible. [ABSTRACT FROM AUTHOR]

Flow-like landslides triggered by rainfall are a major concern in the peri-Vesuvian region of southern Italy. On December 19th, 2019, a landslide occurred along the Crocelle slope (Sarno Mountains), which had previously experienced a similar event in February 1986. This study was conducted to assess the characteristics of this event in terms of triggering and propagation conditions and, with reference to the 1986 landslide, to evaluate the evolution of residual landslide hazards at recurring landslide sites. Data collection included field surveys, visual analysis of a Unmanned Aerial Vehicle (UAV)-based photogrammetric model, geotechnical laboratory testing, rainfall and slope stability analysis, and propagation modeling. Results suggest that the landslide i) originated below a trail, ii) involved an initial volume of 450 m3, which did not significantly grow due to the limited entrainment of slope sediments, iii) was initiated after 13 h of rainfall (return period ∼4 years), and iv) propagated along a channel with an estimated velocity of up to 10 m/s. Comparison of the 2019 event with the 1986 event revealed a significant difference in terms of landslide magnitude (i.e., volume), potentially due to the occurrence of multiple events, which may have led to the temporal depletion of available materials along a slope repeatedly affected by landslides, and possibly to the different behavior of primary layered deposits (involved in the 1986 event) compared to reworked deposits (involved in the 2019 event). This suggests a reduction in the residual landslide hazard associated with a decrease in the expected magnitude and frequency of prospective events. • The 2019 Palma Campania landslide was initiated following 13 h of precipitation; • The landslide encompassed reworked pyroclastic deposits underneath a trail; • The landslide propagated within a channel with a velocity of up to 10 m/s; • Slope failure is facilitated by prior hydrological conditions; • Slope-scale landslide hazard potentially decreases due to multiple landslides. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new procedure for Groundwater Flooding Hazard (GFH) assessment has been developed. • A newly developed MATLAB™ code for hazard mapping has been provided. • The procedure is based on a probability model depicting long-term water table fluctuation. • A sixty-two-years piezometric time series and basement depths data were used. • The procedure has been tested and validated in a semi-urban aquifer. Many areas of the world are experiencing groundwater flooding (GF) events with serious impacts on the environment and human health. In Europe, GF has received specific attention with the Directive 2007/60/EC, which requires Member States to map GF hazard and risk and propose mitigation measures. In Italy, despite the commitment assigned by the Legislative Decree 49/2010 to River Basin District Authorities, no models have been developed for groundwater flooding hazard (GFH) evaluation also for the lack of standard methods based on commonly available data. In this paper, a new method has been developed through a specific procedure that accounts for i) probability of GF events estimation based on available time series of groundwater levels and ii) probability mapping through a newly developed MATLAB™ code, high-resolution topographic data and groundwater table model. The methodology has been tested and validated in the Metropolitan City of Naples (Italy), affected by GF since 2010, for which i) sixty-two-year long time series of groundwater levels, ii) high-resolution Digital Elevation and Digital Terrain Models and iii) basement/building geometry, are available. The results show that about 59% of the buildings present in the study area have potentially floodable underground structures. In the 2010–2021 period, only 2% of these buildings were flooded and most of them were built in the period 1980–2000, when the piezometric levels were deeper due to over-pumping of the groundwater. The obtained GFH map shows that only 2% of the built area is involved by 1- to 2- year events, while 6% is affected by 1- to 10- year events. A good spatial correspondence between the return period and locations of GF events occurred during 2010–2021 period was observed, thus indicating the significant performance of the model. The obtained GFH map output can support decisions of authorities and policy-makers to reduce the GF risk. [ABSTRACT FROM AUTHOR]

Deep-seated landslides are important agents of relief shaping and can have substantial impact on human settlements. Many examples are present in the Campania region of southern Italy, where the association of complex lithostratigraphic and structural geologic conditions might have effect on landslide initiation and evolution. On this basis and considering the effect that the deep-seated landslide have had in the last years on the urban settlement, the Buonalbergo landslide was analyzed in terms of decadal kinematics and geological control and insights in prospective evolution were provided. The analysis of data derived by geological and landslide field mapping, settlement damage analysis, Persistent Differential Interferometry SAR data analysis, and Finite Elements stability analysis revealed i) the approximately constant rate movement of the landslide in the last three decades with a consistent enlargement of its area, ii) the presence of a large overturned anticline, characterized by a complex lithostratigraphic arrangement and multiple discontinuity sets, iii) the presence of faults responsible for morphological depression development, iv) a rotational mechanism characterizing the upper sector of the landslide and the ongoing translational sliding of blocks located at its toe and v) the effect of potential pore pressure scenarios on prospective kinematics (i.e. single to multiple stages creep) with the observed constant rate movement potentially related to a pore pressure at the landslide base ranging between 300 and 400 kPa. Result interpretation suggests i) the role of the structural and lithostratigraphic setting of the slope as predisposing factor for landslide development, ii) the potential of fold-related fabric in controlling landslide geometry and fragmentation, iii) the possible occurrence of multiple landslide events affecting the slope affected by the deep-seated landslide and iv) the potential of hydrologic conditions in modulating landslide velocity and evolution toward slope failure. The relevance of results is related to the representativeness of the Buonalbergo landslide as common process in the Apennine context in terms of potential long-term morphologic evolution of the slope and the existence of many landslides that develop in similar geological conditions driving sediment production and inducing persistent topographic changes in mountainsides. [ABSTRACT FROM AUTHOR]

Natural hazards such as landslides, soil erosion and floods are increasingly affecting many regions of the world in relation to ongoing climate change and the abandonment of rural areas. In the last decades, the need to mitigate such phenomena and preserve natural resources, including landscapes, has motivated a number of local and national authorities to promote the use of soil bioengineering techniques. Consequently, these techniques have been increasingly used with variable success rates. This study analysed the efficiencies of different bioengineering structures to mitigate geo-hydrological risks by examining the interventions installed between 2007 and 2013 in the Avellino Province southern Italy. To this end, a specific project consistency analysis and functionality classification were carried out and the causes of inefficiencies were identified. The analysis showed that after a few years, only a small number of the installed bioengineering structures were fully functional and efficient. Overall, the installed structures were generally affected by maintenance issues and in many cases they suffered from design and execution issues, inducing a generally lower degree of efficiency, with most of them classified as 'not very efficient'. The conclusions of this work are that i) the long-term efficiency of a bioengineering structure is related to its correct design, installation, and maintenance; ii) the design of a bioengineering structure should account for the geological, geotechnical and hydrological characteristics of the area of interest, and iii) maintenance must not affect the short-term efficiency of a structure, but may affect the long-term efficiency. • The efficiency of bioengineering structure was analysed. • Bioengineering structure functionality was estimated three years after the installation. • A matrix-based approach accounting for causes of inefficiency was used. • Only 7% of the interventions were fully working and integrated with the landscape. • Most of the bioengineering structures were affected by design and installation issues. [ABSTRACT FROM AUTHOR]