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In this work, we assessed gully erosion susceptibility in two adjacent cultivated catchments of Sicily (Italy) by employing multivariate adaptive regression splines and a set of geo-environmental variables. To explore the influence of hydrological connectivity on gully occurrence, we measured the changes of performance occurred when adding one by one nine predictors reflecting terrain connectivity to a base model that included contributing area and slope gradient. Receiver operating characteristic (ROC) curves and the area under the ROC curve were used to evaluate model performance. Gully predictive models were trained in both the catchments and submitted to internal (in the calibration catchment) and external (in the adjacent one) validation, using samples extracted both from all cells of the catchments and only from cells located along flow concentration axes. Model evaluation on the entire catchments shows outstanding predictive performance of models that either include or do not include the predictors selected to reflect potential hydrological connectivity. Conversely, area under the ROC curve values measured on flow concentration axes reveals that almost all the additional predictors improve the performance of the base model, but the most enhanced increase of accuracy occurs when upstream drainage density of each landscape position is included as predictor of gully occurrence.

Many river systems in Europe suffer from human pressure. For this reason the European Water Framework Directive (WFD; 2000/60/EC) was created for river basin and floods management. In 2010 the Italian Environmental Minister issued the WFD and a protocol for the stream hydromorphological evaluation, analysis, and monitoring was established (IDRAIM). The Morphology Quality Index (MQI) is part of this procedure and defines the deviation of present geomorphic reach situation from reference conditions. It is composed by Indicators of Geomorphological Functionality (IGF), Indicators of Artificiality (IA) and Indicators of Channel Adjustments (ICA). The present work aims at illustrating the calculation of IGF in 34 river catchments located in the Eastern part of Sicily (Italy). The procedure was characterized by three main steps: i) subdivision of the river network into relatively homogeneous reaches depending on landscape units, valley setting, channel slope; ii) evaluation of confinement classes (the percentage of banks not directly in contact with the alluvial plain but with hillslopes or ancient terraces) and confinement index (the ratio between the alluvial plain width and the channel width); iv) identification of river morphological typologies (sinuosity, braiding and anastomosing indices). In order to calculate the IGF, GIS analysis, photo interpretation, historical data collection and geomorphological fieldwork were integrated. Results showed that the IDRAIM procedure for the calculation of the IGF can be successfully applied for the Sicilian Rivers. However, being the Sicilian rivers mainly characterized by a torrential regime, some adjustments of the IGF procedure could be suggested.

Debris flows are shallow landslides triggered by extreme meteorological events and propagating into the drainage lines of a slope as a fluid. A debris flow susceptibility map depicts the spatial probability for future phenomena to be triggered in a given area. Stochastic approaches are widely used in landslide studies for the assessment of the susceptibility. In fact, they allow to obtain a predictive function which relates a response variable (presence/absence of landslides) and a set of physical-environmental variables which are expected to control the slope failure phenomena. Future landslide occurrences are typically predicted by studying a past landslide inventory, under the basic assumption that “new landslides will occur under the same conditions which drove the past ones”. The present research is aimed at testing the basic assumption, in case of extreme event triggered landslide scenarios. The study case is the debris flow event occurred in the Messina area in 2009. In particular, by applying logistic regression, a model was calibrated by exploiting an inventory dated at 2007 and validated with respect the 2009 inventory (forward chrono-validation). Moreover, a model was calibrated with the 2009 and validated in predicting the 2007 landslides (backward chrono-validation). Under the basic assumption, the two modelling procedures should achieve the same results. Cross-validation procedures have been applied to investigate precision, reliability and robustness of the models, both in terms of predictive performance and inner structure of the model. The results of the research attest for high performance and good agreement between the two chrono-validated models. However, some differences arose, indicating possible limits in the basic assumption.

The steep Mediterranean catchments underwent drastic socio economic changes in the last decades. Moreover, these basins are also subject to climate variability and potential future climate change effects. Due to mechanization in agriculture a lot of surfaces changed from terraced vineyards and olive growth to abandoned land. The terraced areas are less productive and more cost and labour intensive and hence cannot compete with a highly mechanized agriculture. Furthermore, tourism based on the natural and cultural heritage of the region is more and more important for the income of the inhabitants. The abandonment has effects on the entire landscape system in terms of hydrological dynamics, sediment dynamics as well as soil characteristics and the terraces itself. These changes are leading to a succession in vegetation, destabilization of slope systems, and changes on the runoff and sediment discharges leading to disasters such as the Vernazza event on the 25.10.2011 or the Messina event on the 01.10.2009. In this study, we present a comparison between the two mentioned areas located respectively in Liguria in the Cinque Terre National park (World heritage site-UNESCO) and in the Messinean area, Sicily (Italy) and a test of transferability aimed to assess debris flow susceptibility. In the framework of this research extensive fieldwork, remote sensing and terrain analysis have been performed in order to parameterize both stochastic and physically based models. Moreover, few attempts of reproducing extreme events have been made. The results show that these Mediterranean catchments are highly sensitive to changes in landuse and climate. Hence, the study contributes to the understanding of landscape dynamics under global change conditions.

The South Western area of the Madonie Mountains is affected by large landslides; the major one developed starting from Portella Colla down to the Imera Settentrionale river, for a maximum length of about 6.2 km in a NE-SW direction. The study of the landslide, with the aim of reconstructing the geological, geomorphological and evolutionary aspects, has been carried out using integrated methodologies, including some geophysical investigations (in particular geoelectrical prospecting). The landslide is complex and characterized by superficial and deep gravitational deformations. The movement began in the Upper Pliocene and it is still active. The origin and evolution of the landslide are linked to the geological structural setting of the area, as well as to the neotectonic activity and Quaternary climatic variations. The tectonic compressive phases of the Middle Miocene have determined the overlapping of mesozoic dolomitic-calcareous bodies on prevalently clayey rocks of Tertiary age. In the Plio-Pleistocene, the original structural building was fragmentaried during tectonic phases producing calcareous blocks. The geological setting and the instability caused by the increased relief energy, consequent to the last tectonic phase, constitute the main preparative causes for the beginning of the movement. In the Pleistocene, periods cooler than the actuals favoured a significant activity of the body of the landslide, due to the soil moisture. Furthermore, due to cryoclastic processes, an intensive degradation of the fractured limestones outcropping along the slopes has produced a large talus zone which partially covers the body of landslide. The main recent activity of the landslide is linked to exceptional meteorological events, as they occurred in 1931, causing the movement of the front of the landslide. This moved forward about 30 m, reaching the Imera Settentrionale river and diverting the bed of the Rio Secco river. At the same time some flow-rate variations and shifts of water springs on the body of the landslide have been observed. Another large movement occurred in 1959 in an area called C.da S. Venera, along the right side of the landslide. The present activity consists of modest movements, which generally occur with annual frequency; however, the permanent instability of the landslide confers to the whole area dangerous conditions, so that special constraints in the use of territory - as suggested by possible evolutionary scenarios - should be imposed.

The research proposes a simple but geomorphologically adequate method to produce a combined landslide susceptibility map. In fact, in a logic of real use, offering type-specific landslide susceptibility maps to land use planners and administration could be not a successful solution. On the other hand, the simple grouping of more types of landslides could be misleading for model calibration considering that the relationships between slope failures and geo-environmental predictors should be conveyed by the abundance of each type of landslide resulting not specific and diagnostic for each typology. In this test, after having produced independent models for flow, slide and complex landslide by exploiting MARS (Multivariate Adaptive Regression Splines) and a set of type-specific geo-environmental variables, a combined landslide susceptibility map was obtained by combining the scores of the three source maps. The combined map was finally validated with a new unknown archive, showing very good performances.

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.

The coastal domain of central western Sicily is characterized by the presence of rocky coasts, which mainly consist of pocket beaches situated between bedrock headlands that constitute ecological niches of great touristic and economic value. In this peculiar morphodynamic system, the sedimentary contributions are mainly derived from the rockfall that affects the back of nearby cliffs or the sediment supply of small streams that flow into it. In this study, we investigated the geomorphological processes and related landforms that contribute to the evolution of a pocket beach area located in a coastal sector of NW Sicily Island. The cliffs in this are affected by several rockfalls, and deposits from these rockfalls also add to the rate of sedimentary contribution. The analysis was conducted through the application of traditional approaches and contemporary methods that have previously been used to forecast the collection of input data in the field, often under difficult conditions due to the accessibility of the sites, and which have been supported by UAV surveys. Through the analysis of the digital models of terrain and orthophotos, geometrical and multitemporal analyses of landforms were carried out. A dedicated software was utilized for the detection of rockfall runout zones and block trajectories and for defining the automatic extraction of rock mass discontinuities. The data were compared with those derived from traditional geomechanical surveys. The availability of the existing and acquired remote sensing data proved essential for this study for both defining the reference geological model and for performing the site-specific analysis of rockfall.

A map derived by rockfall analysis at Mount Pellegrino is presented herein. The study area is affected by several phenomena of rockfall which caused numerous damage and a strong social and economic impact. Official reports and maps that give a general assessment of rockfall hazard are available in this respect, however, it would be advisable to provide a more specific cartographic support useful for land management and planning. The drafting of new maps showing the rockfall runout areas is an additional tool that may be used in conjunction with the existing maps as a means of risk mitigation and reduction. On the basis of geological, geomorphological, and geomechanical analysis and exploiting the information relating to a landslides inventory obtained by using both analytical and empirical methods, two different rockfall propagation areas were reconstructed. The final thematic map permit to appreciate the differences and similarities between the obtained runout areas.

This research is focused on the evaluation of the reliability of regional landslide susceptibility models obtained by exploiting inhomogeneous (for quality, resolution and/or triggering related type and intensity) collected inventories for calibration. At a large-scale glance, merging more inventories can result in well-performing models hiding potential strong predictive deficiencies. An example of the limits that such kinds of models can display is given by a landslide susceptibility study, which was carried out for a large sector of the coastal area of El Salvador, where an apparently well-performing regional model (AUC = 0.87) was obtained by regressing a dataset through multivariate adaptive regression splines (MARS), including five landslide inventories from volcanic areas (Ilopango and Coatepeque caldera; San Salvador, San Miguel, and San Vicente Volcanoes). A multiscale validation strategy was applied to verify its actual predictive skill on a local base, bringing to light the loss in the predictive power of the regional model, with a lowering of AUC (20% on average) and strong effects in terms of sensitivity and specificity.

Optimally sizing grid cells is a relevant research issue in landslide susceptibility evaluation. In fact, the size of the adopted mapping units influences several aspects spanning from statistical (the number of positive/negative cases and prevalence and resolution/precision trade-off) and purely geomorphological (the representativeness of the mapping units and the diagnostic areas) to cartographic (the suitability of the obtained prediction images for the final users) topics. In this paper, the results of landslide susceptibility modelling in a 343 km2 catchment for three different types of landslides (rotational/translational slides, slope flows and local flows) using different pixel-size mapping units (5, 8, 10, 16 and 32 m) are compared and discussed. The obtained results show that the higher-resolution model (5 m) did not produce the best performance for any of the landslide typologies. The model with 8 m sized pixels displayed the optimal threshold size for slides and slope flows. In contrast, for local flows, an increasing trend of model prediction accuracy was reached with 32 m pixels, which was a higher value than that presented using 8 m pixels. The variable importance analysis demonstrated that the better performance of the 8 m cells was due to their effectiveness in capturing morphological conditions which favour slope instability (profile curvature and middle and high ridges).

This study, using different direct and indirect methodologies, evaluated the sedimentation rate in an artificial reservoir in central Italy. This reservoir is regionally representative and was built in the 1960s for hydroelectric purposes; it has experienced a strong decrease in trap efficiency and a loss of over 70% of the stored water volume. Direct measurements of the lake bottom bathymetry, carried out in 2006 and 2015, and 3D reconstructions performed in a GIS environment, made it possible to calculate the volume of filling material and to verify an increasing trend in the sedimentation rate since 2006. The sample reservoir denudation rate was compared with that obtained using the Revised Universal Soil Loss Equation method to calibrate the fundamental and critical factors of the method itself, and verify the contribution of a hydrological “direct” (through new channels or gullies) or “diffuse” (overland flow) connectivity. Furthermore, the comparison with the results obtained from past studies on ten other artificial regional reservoirs, performed with morphometric analysis, demonstrated a good relationship between soil erosion rate, stream frequency, and contributing area size. The study highlighted how a correct estimate of soil erosion and/or solid transport rates within a hydrographic basin is fundamental for the assessment of the trap efficiency of a reservoir, in a period in which the availability of water resources is becoming more and more vital.

In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model.

In this study, the ability of five topographic indices to predict the gully trajectories observed in two adjacent watersheds located in Sicily (Italy) was evaluated. Two of these indices, named MSPI and MTWI, as far as we know, have never been employed to this aim. They were obtained by multiplying the stream power index (SPI) and the topographic wetness index (TWI), respectively, by the convergence index (CI). The predictive ability of the topographic indices was measured by using both cut-off independent (AUC: area under the receiver operating characteristic curve) and dependent statistics (Cohen's kappa index κ, sensitivity, specificity). These statistics were calculated also for 100 MARS (multivariate adaptive regression splines) and 100 LR (logistic regression) model runs, which used as predictors the topographic variables (i.e. contributing area, slope steepness, plan curvature and convergence index) combined into the five indices. Performance statistics of both topographic indices and statistical models were calculated using 100 random samples of 2 m grid cells, which were extracted only from flow concentration lines. This was done in order to focus the validation process on where gully erosion is more likely to occur. MSPI achieved the best predictive skill (AUC > 0.93; κ > 0.71) among the topographic indices and exhibited similar and better accuracy than local (i.e. trained and validated in the same watershed) and transferred (i.e. trained in one watershed and tested in the other one) LR models, respectively. On the other hand, MSPI performed similarly to transferred MARS runs (AUC > 0.92; κ > 0.71) but slightly worse than local MARS runs (AUC > 0.95; κ > 0.77). Based on the results of this experiment, it can be inferred that (i) including CI helps in detecting hollow areas where gullies are more likely to occur and (ii) MPSI can be a valid alternative to a data driven approach for mapping gully erosion susceptibility in areas where a gully inventory is not available, which is necessary to calibrate statistical models.

Debris flows can be described as rapid gravity-induced mass movements controlled by topography that are usually triggered as a consequence of storm rainfalls. One of the problems when dealing with debris flow recognition is that the eroded surface is usually very shallow and it can be masked by vegetation or fast weathering as early as one-two years after a landslide has occurred. For this reason, even areas that are highly susceptible to debris flow might suffer of a lack of reliable landslide inventories. However, these inventories are necessary for susceptibility assessment. Model transferability, which is based on calibrating a susceptibility model in a training area in order to predict the distribution of debris flows in a target area, might provide an efficient solution to dealing with this limit. However, when applying a transferability procedure, a key point is the optimal selection of the predictors to be included for calibrating the model in the source area. In this paper, the issue of optimal factor selection is analysed by comparing the predictive performances obtained following three different factor selection criteria. The study includes: i) a test of the similarity between the source and the target areas; ii) the calibration of the susceptibility model in the (training) source area, using different criteria for the selection of the predictors; iii) the validation of the models, both at the source (self-validation, through random partition) and at the target (transferring, through spatial partition) areas. The debris flow susceptibility is evaluated here using binary logistic regression through a R-scripted based procedure. Two separate study areas were selected in the Messina province (southern Italy) in its Ionian (Itala catchment) and Tyrrhenian sides (Saponara catchment), each hit by a severe debris flow event (in 2009 and 2011, respectively). The investigation attested that the best fitting model in the calibration areas resulted poorly performing in predicting the landslides of the test target area. At the same time, the susceptibility models calibrated with an optimal set of covariates in the source area allowed us to produce a robust and accurate prediction image for the debris flows activated in the Saponara catchment in 2011, exploiting only the data known after the Itala-2009 event.

The main topic of this research was to evaluate the effect in the performance of stochastic landslide susceptibility models, produced by differences between the triggering events of the calibration and validation datasets. In the Caldera Ilopango area (El Salvador), MARS (multivariate adaptive regression splines)-based susceptibility modeling was applied using a set of physical–environmental predictors and two remotely recognized landslide inventories: one dated at 2003 (1503 landslides), which was the result of a normal rainfall season, and one which was produced by the combined effect of the Ida hurricane and the 96E tropical depression in 2009 (2237 landslides). Both the event inventories included shallow debris—flow or slide landslides, which involved the weathered mantle of the pyroclastic rocks that largely outcrop in the study area. To this aim, different model building and validation strategies were applied (self-validation, forward and backward chrono-validations), and their performances evaluated both through cutoff-dependent and -independent metrics. All of the tested models produced largely acceptable AUC (area under curve) values, albeit a loss in the predictive performance from self-validation to chrono-validation was observed. Besides, in terms of positive/negative predictions, some critical differences arose: using the 2009 extreme landslide inventory for calibration resulted in higher sensitivity but lower specificity; conversely, using the 2003 normal trigger landslide calibration inventory led to higher specificity but lower sensitivity, with relevant increase in type-II errors. These results suggest the need for investigating the extent of such effects, taking multitrigger intensities inventories as a standard procedure for susceptibility assessment in areas where extreme events potentially occur.

Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill–interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km 2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local communities. In order to model soil erosion by water, the spatial distribution of landforms due to rill–interrill and gully erosion was mapped and 12 environmental variables were selected as predictors. Four calibration and four validation subsets were obtained by randomly extracting sets of negative cases, both for rill–interrill erosion and gully erosion models. The results of validation, based on receiving operating characteristic (ROC) curves, showed excellent to outstanding accuracies of the models, and thus a high prediction skill. Moreover, SGT allowed us to explore the relationships between erosion landforms and predictors. A different suite of predictor variables was found to be important for the two models. Elevation, aspect, landform classification and land-use are the main controlling factors for rill–interrill erosion, whilst the stream power index, plan curvature and the topographic wetness index were the most important independent variables for gullies. Finally, an ROC plot analysis made it possible to define a threshold value to classify cells according to the presence/absence of the two erosion processes. Hence, by heuristically combining the resulting rill–interrill erosion and gully erosion susceptibility maps, an integrated water erosion susceptibility map was created. The adopted method offers the advantages of an objective and repeatable procedure, whose result is useful for local administrators to identify the areas that are most susceptible to water erosion and best allocate resources for soil conservation strategies.

Knowledge about the hydrogeological behaviour of heterogeneous low-permeability media is an important tool when designing anthropogenic works (e.g., landfills) that could potentially have negative impacts on the environment and on people&rsquo, s health. The knowledge about the biogeochemical processes in these media could prevent &ldquo, false positives&rdquo, when studying groundwater quality and possible contamination caused by anthropogenic activities. In this research, we firstly refined knowledge about the groundwater flow field at a representative site where the groundwater flows within an evaporite-bearing low-permeability succession. Hydraulic measurements and tritium analyses demonstrated the coexistence of relatively brief to very prolonged groundwater pathways. The groundwater is recharged by local precipitation, as demonstrated by stable isotopes investigations. However, relatively deep groundwater is clearly linked to very high tritium content rainwater precipitated during the 1950s and 1960s. The deuterium content of some groundwater samples showed unusual values, explained by the interactions between the groundwater and certain gases (H2S and CH4), the presences of which are linked to sulfate-reducing bacteria and methanogenic archaea detected within the saturated medium through biomolecular investigations in the shallow organic reach clayey deposits. In a wider, methodological context, the present study demonstrates that interdisciplinary approaches provide better knowledge about the behaviour of heterogeneous low-permeability media and the meaning of each data type.

The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios. The study area is the small catchment of the Itala torrent (10 km2), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chrono-validation, based on the time partition of the landslide inventory. It was therefore possible to analyse and compare the performances both of the 2007 calibrated model in predicting the 2009 debris flows (forward chrono-validation), and vice versa of the 2009 calibrated model in predicting the 2007 debris flows (backward chrono-validation). Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chrono-validated models. These are interpreted as effects of the non-linearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.

The Capo San Vito peninsula is located along the north-westernmost sector of the Sicilian coastline. It is characterized by a complex geomorphological setting, where a large variety of coastal, gravity-induced and karst landforms allow the visitor to easily detect the interactions between Quaternary tectonics and climate changes as well as morphodynamic processes responsible for shaping the landscape. Thanks to natural reserves, the peninsula preserves a typical Mediterranean natural environment, marked by spectacular and suggestive landforms.

A model building strategy is tested to assess the susceptibility for extreme climatic events driven shallow landslides. In fact, extreme climatic inputs such as storms typically are very local phenomena in the Mediterranean areas, so that with the exception of recently stricken areas, the landslide inventories which are required to train any stochastic model are actually unavailable. A solution is here proposed, consisting in training a susceptibility model in a source catchment, which was implemented by applying the binary logistic regression technique, and exporting its predicting function (selected predictors regressed coefficients) in a target catchment to predict its landslide distribution. To test the method, we exploit the disaster that occurred in the Messina area (southern Italy) on 1 October 2009 where, following a 250-mm/8-h storm, approximately two thousand debris flow/debris avalanches landslides in an area of 21 km2 triggered, killing 37 people and injuring more than 100, and causing 0.5 M € worth of structural damage. The debris flows and debris avalanches phenomena involved the thin weathered mantle of the Varisican low to high-grade metamorphic rocks that outcrop in the eastern slopes of the Peloritani Mounts. Two 10-km2-wide stream catchments, which are located inside the storm core area, were exploited: susceptibility models trained in the Briga catchment were tested when exported to predict the landslides distribution in the Giampilieri catchment. The prediction performance (based on goodness of fit, prediction skill, accuracy and precision assessment) of the exported model was then compared with that of a model prepared in the Giampilieri catchment exploiting its landslide inventory. The results demonstrate that the landslide scenario observed in the Giampilieri catchment can be predicted with the same high performance without knowing its landslide distribution: we obtained, in fact, a very poor decrease in predictive performance when comparing the exported model to the native random partition-based model.

The aim of this study is to analyze the susceptibility conditions to gully erosion phenomena in the Magazzolo River basin and to test a method that allows for driving the factors selection. The study area is one of the largest (225 km2) watershed of southern Sicily and it is mostly characterized by gentle slopes carved into clayey and evaporitic sediments, except for the northern sector where carbonatic rocks give rise to steep slopes. In order to obtain a quantitative evaluation of gully erosion susceptibility, statistical relationships between the spatial distributions of gullies affecting the area and a set of twelve environmental variables were analyzed. Stereoscopic analysis of aerial photographs dated 2000, and field surveys carried out in 2006, allowed us to map about a thousand landforms produced by linear water erosion processes, classifiable as ephemeral and permanent gullies. The linear density of the gullies, computed on each of the factors classes, was assumed as the function expressing the susceptibility level of the latter. A 40-m digital elevation model (DEM) prepared from 1:10,000-scale topographic maps was used to compute the values of nine topographic attributes (primary: slope, aspect, plan curvature, profile curvature, general curvature, tangential curvature; secondary: stream power index; topographic wetness index; LS-USLE factor); from available thematic maps and field checks three other physical attributes (lithology, soil texture, land use) were derived. For each of these variables, a 40-m grid layer was generated, reclassifying the topographic variables according to their standard deviation values. In order to evaluate the controlling role of the selected predictive variables, one-variable susceptibility models, based on the spatial relationships between each single factor and gullies, were produced and submitted to a validation procedure. The latter was carried out by evaluating the predictive performance of models trained on one half of the landform archive and tested on the other. Large differences of accuracy were verified by computing geometric indexes of the validation curves (prediction and success rate curves; ROC curves) drawn for each one-variable model; in particular, soil texture, general curvature and aspect demonstrated a weak or a null influence on the spatial distribution of gullies within the studied area, while, on the contrary, tangential curvature, stream power index and plan curvature showed high predictive skills. Hence, predictive models were produced on a multi-variable basis, by variously combining the one-variable models. The validation of the multi-variables models, which generally indicated quite satisfactory results, were used as a sensitivity analysis tool to evaluate differences in the prediction results produced by changing the set of combined physical attributes. The sensitivity analysis pointed out that by increasing the number of combined environmental variables, an improvement of the susceptibility assessment is produced; this is true with the exception of adding to the multi-variables models a variable, as slope aspect, not correlated to the target variable. The addition of this attribute produces effects on the validation curves that are not distinguishable from noise and, as a consequence, the slope aspect was excluded from the final multi-variables model used to draw the gully erosion susceptibility map of the Magazzolo River basin. In conclusion, the research showed that the validation of one-variable models can be used as a tool for selecting factors to be combined to prepare the best performing multi-variables gully erosion susceptibility model.

A statistical approach was employed to model the spatial distribution of rainfall-triggered landslides in two areas in Sicily (Italy) that occurred during the winter of 2004–2005. The investigated areas are located within the Belice River basin and extend for 38.5 and 10.3 km 2 , respectively. A landslide inventory was established for both areas using two Google Earth images taken on October 25th 2004 and on March 18th 2005, to map slope failures activated or reactivated during this interval. Geographic Information Systems (GIS) were used to prepare 5 m grids of the dependent variables (absence/presence of landslide) and independent variables (lithology and 13 DEM-derivatives). Multivariate Adaptive Regression Splines (MARS) were applied to model landslide susceptibility whereas receiver operating characteristic (ROC) curves and the area under the ROC curve ( AUC ) were used to evaluate model performance. To evaluate the robustness of the whole procedure, we prepared 10 different samples of positive (landslide presence) and negative (landslide absence) cases for each area. Absences were selected through two different methods: (i) extraction from randomly distributed circles with a diameter corresponding to the mean width of the landslide source areas; and (ii) selection as randomly distributed individual grid cells. A comparison was also made between the predictive performances of models including and not including the lithology parameter. The models trained and tested on the same area demonstrated excellent to outstanding fit ( AUC > 0.8). On the other hand, predictive skill decreases when measured outside the calibration area, although most of the landslides occur where susceptibility is high and the overall model performance is acceptable ( AUC > 0.7). The results also showed that the accuracy of the landslide susceptibility models is higher when lithology is included in the statistical analysis. Models whose absences were selected using random circles showed a significantly better performance when learning and validation samples were extracted from the same area; whereas, conversely, no significant difference was observed when testing the models outside the training area.

Calanchi (singular calanco) represent a typical example of badlands in the Italian peninsula, which rapidly evolve on clayey terrains such as the widespread Pliocene-Pleistocene marine clays. The present study aimed at investigating the role of the slope morphometry on the typology and distribution of morphogenetic processes in a calanchi area located in southern Italy. The research included detailed geomorphological surveying, morphometric and statistical analysis. The study area was first subdivided into individual hydrographic units (HUs), for which field survey allowed to identify the dominant denudation processes, their intensity and the distribution of the associated landforms (pipes, rills, gullies, landslides). The morphometry of each HU was characterized by calculating the Morphometric Slope Index (MSI) on a reconstructed digital elevation model (DEM). By statistically comparing the morphogenetic and morphometric data, the influence of the slope morphometry on the type, distribution and evolution of the calanchi erosion process has been highlighted. In particular, through a Cluster Analysis the groups of HUs having similar dominant processes were identified and, by applying the Analysis of the Variance, the effect of the MSI on the identified clusters was analysed. Two clusters were identified which revealed the relative importance of morphogenetic processes and the strict connection between surface and subsurface landforms. These two clusters were discriminated by the MSI value (high MSI favoured mass movements, whilst low MSI favoured gully erosion and piping), highlighting the importance of slope morphometry in driving the morphogenetic hillslope processes.

L’autrice affronta il delicato tema della violenza sulle donne strettamente connesso a quello della soggettività giuridica quale concetto storicamente segnato dall’idea del soggetto astratto di diritto in grado di riassumere le molteplicità delle individualità umane, neutralizzandole al suo interno. Richiamati i principi e valori che ispirano i testi di rango primario sia interni che sovranazionali a partire dalla nostra Costituzione, e dopo una breve disamina degli interventi realizzati dal legislatore in ambito di diritto di famiglia e delle persone, l’attenzione si concentra sulle diverse forme in cui la violenza sulle donne si manifesta nei diversi contesti sociali e culturali. La sollecitazione è di articolare efficaci misure di attuazione dei diritti delle donne nella ricerca di soluzioni tempestive ed efficaci in aggiunta o in alternative agli strumenti di tutela e alle sanzioni attualmente esistenti.

Gli studi sulla famiglia in ambito psico-sociale hanno, negli ultimi due decenni, ripetutamente ribadito la necessità di affrontarne l’analisi e l’osservazione a partire dalla complessità, multiprocessualità e multiformità delle nuove configurazioni familiari, che rendono imprescindibile utilizzare la «pluralità come principio metodologico». La sfida che le attuali forme familiari pongono a clinici e ricercatori riguarda innanzitutto la molteplicità di elementi trasformativi che ne hanno fortemente segnato l’identità e che si declinano nelle dinamiche interne e nella relazione con l’ambiente esterno, anch’esso in veloce mutamento.

This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north-eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence-only-based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of failure mechanisms for flow or avalanche type propagation. The set of predictors used in this experiment comprised primary and secondary topographic attributes, derived by processing a high resolution digital elevation model, CORINE land cover data and a set of vegetation and mineral indices obtained by processing multispectral ASTER images. All the selected data sources are dated before the disaster. A spatially random partition technique was adopted for validation, generating 50 replicates for each of the two considered movement typologies in order to assess accuracy, precision and reliability of the models. The debris slide and debris flow susceptibility models produced high performances with the first type being the best fit. The evaluation of the probability estimates around the mean value for each mapped pixel shows an inverted relation, with the most robust models corresponding to the debris flows. With respect to the role of each predictor within the modelling phase, debris flows appeared to be primarily controlled by topographic attributes whilst the debris slides were better explained by remotely sensed derived indices, particularly by the occurrence of previous wildfires across the slope. The overall excellent performances of the two models suggest promising perspectives for the application of presence-only methods and remote sensing derived predictors. Copyright © 2016 John Wiley & Sons, Ltd.

Il saggio, partendo da una riflessione sul relativismo culturale, mette in guardia sui limiti del multiculturalismo in una prospettiva di diritti umani che non siano egemonici e invita a ripensare il processo di costruzione degli esseri umani andando oltre le categorie di genere

The massacre of women, always present in the opera from its origin, is especially spread in the nineteenth century and has its popular specimen in La traviata by Giuseppe Verdi; it doesn’t disappear in the musical theatre of the twentieth century which indeed started with the death on stage of a female figure, mysterious and unconventional, the Mélisande by Maetherlimk and Debussy, killed by the common destiny for all women. Purpose of this paper is to show how the music in the theatre plays a different role which depends on different compositional modes in similar dramatic events and so it is able to give different sense to similar episodes of violence against women. Transgressive linguistic choices, detachment from conventional forms and structures, new relations between notes, new vocal and instrumental timbres release the music from its moving and involving qualities and give to it a vivid role in denouncing the aberrations produced by social conventions and in revealing to the spectator his collusion.

L’emersione di un fenomeno che solo apparentemente è in crescita, e che collide (si scontra) con una più generale emancipazione femminile, induce l'autrice a riflettere sulla inadeguatezza del considerare la “gender violence” come una variabile deviante dell’agire, e su come essa sia di fatto da ritenersi una vera e propria pratica “normalizzata” e istituente talune relazioni di coppia. Indicata da Ignazia Bartholini come "violenza prossimale" all'interno del saggio, per sottolinearne il carattere intimo e fiduciario che la contraddistingue, essa non viene descritta solo nei suoi effetti procedurali, ma analizzata nei suoi aspetti culturalmente radicati di "shame and honour" nell'area mediterranea, e posta a confronto con la crisi epocale dell'identità maschile. The emergence of a phenomenon only seemingly on the increase, which is in collision with a more general female emancipation, has prompted the author to reflect on the inadequacy of considering “gender violence” as a mere deviant variable of action itself, and how it can actually be deemed to be a truly “normalized” practice serving as a basis for relationships between couples. In this paper Ignazia Bartholini describes it as “proximal violence”, thus underlining the intimate character and air of trust that distinguishes it. It is not merely described in terms of procedural effect, but is analysed with regard to its cultural aspects, rooted in shame and honour in the Mediterranean area and is compared with the epochal crisis in masculine identity.

This work deals with an integrated geomorphological and chemical–physical study of “calanchi” landforms in two sites (Ottosalme and Catalfimo) of NW Sicily (southern Italy), developed on dominant silty-clay deposits. The calanchi fronts are characterized by different morphological features and dominant geomorphic processes. Sharp knife-edged ridges and concentrated water runoff dominate at Ottosalme, and smoother landforms affected by mass movements (mud flows and translational slides) prevail at Catalfimo. We focused on some geochemical and physical parameters such as pH , total dissolved salts, sodium adsorption ratio ( SAR ), porosity, plastic and liquid limits as possible causes of the above differences, with special emphasis on their role in discriminating the behavior of peculiar microforms, such as a weathered outer crust and the corresponding inner massive portion (unweathered substrate). Our results show that the main chemical–physical features of calanchi morphologies and microforms often display no clear correlations with respect to those reported in the existing literature, pointing to a higher complexity of patterns and behaviors. In particular, all samples consist of dispersive material ( SAR values > 10), but a more plastic and liquid behavior at Catalfimo can explain a higher occurrence of landslides and smoother morphology than at Ottosalme. Higher SAR values at Ottosalme well explain the dominance of concentrated water erosion. The chemical behavior of the studied microforms is better described by pH being alkaline with higher values in the inner substrate than the outer crust, and appears to minimize the effects of all other parameters and their responses to other external controlling factors. Lower SAR values in the crust suggest that clay dispersivity is not the prominent factor controlling its surface detachment and mobility downslope, which is more likely promoted by its higher porosity and consequent water absorption than the inner portion, with changes in the saturation state, shear strength and water pressure.

The Italian badlands, or “calanchi”, are common landforms in Mediterranean areas including central and southern Italy. Calanchi landforms may be compared to small hydrographic basins. These landforms are characterised by dense, hierarchical and rapidly evolving drainage systems carved into steep clayey slopes and by a sharply alternating pattern of furrows and narrow, generally sharp crests. This work presents a study of morphometric characteristics and a statistical analysis for two sites in northern Sicily (Italy), on outcrops of silty-clay deposits affected by active erosion processes, which give this area a typical calanchi landscape. In particular, factors closely linked to the characteristics of the hydrographic network and slope morphometry were considered and analysed. The initial geometry of the slopes was reconstructed and statistically compared with that of the current calanchi slopes including the drainage network. A new morphometric index (Morphometric Slope Index, MSI) was defined to represent the initial slope geometry as a whole. This index was found to be effective in defining the structure of hydrographic networks, summarising the characteristics and type of slope evolution, and quantifying the rate of soil erosion. The rate was determined based on both linear (gully erosion) and areal (landslides, sheet and rill erosion) morphogenetic processes, and our analysis based on MSI indicates the dominance of areal erosion. MSI could also be used for basins larger than calanchi to represent the characteristics of geomorphic processes.