Le frane superficiali del 25/10/2011 nel bacino del Torrente Pogliaschina (Liguria Orientale): studio dei fattori d'innesco e della suscettibilità di frana

Il 25 Ottobre 2011 un violento e persistente sistema temporalesco ha investito le zone della Liguria Orientale e della Toscana nord-occidentale, concentrandosi soprattutto sulla Val di Vara e sulle Cinque Terre (La Spezia). La perturbazione ha avuto una durata complessiva di 24 ore, ma l’evento parossistico si è soprattutto verificato tra le 10:00 e le 16:00, in cui sono stati registrati valori di pioggia superiori ai 400 mm. Lo scopo della presente Tesi di Laurea ha riguardato il censimento e lo studio delle frane superficiali innescate dall'evento nel bacino del Torrente Pogliaschina (affluente destro del Fiume Vara). Per perseguire tale obiettivo è stata realizzata una banca dati dei dissesti dell’evento, includendovi le principali caratteristiche geologiche, geomorfologiche e di uso del suolo delle aree di innesco dei movimenti franosi. Attraverso le interrogazioni della banca dati, è stato possibile effettuare un’analisi statistica dell’influenza dei principali fattori predisponenti (geologia del bedrock, uso del suolo, inclinazione dei versanti, giacitura della superficie di stratificazione e tipo di copertura) sull'occorrenza dei fenomeni franosi. Inoltre è stata effettuata un’analisi di alcuni parametri morfometrici del bacino: esposizione pendii, pendenza versanti, curvatura planare. In aggiunta, è stata ottenuta una carta di suscettibilità di frana del bacino attraverso il modello deterministico SHALSTAB (Shallow Landslides Stability Model). Le frane censite dall'evento sono risultate 658, la maggior parte delle quali è stata classificata come soil slip-debris flow, scorrimento della copertura eluvio-colluviale evoluto in colata. Esse si sono per lo più innescate nelle coperture delle formazioni arenacee, rappresentate dal Macigno e dalle Arenarie di Monte Gottero, con una maggior distribuzione in quest’ultima, probabilmente a causa dei maggiori afflussi meteorici nell'area di specifico affioramento e dei relativi peggiori parametri geotecnici. Per quanto riguarda l’uso del suolo, è emerso che il maggior numero di frane si è verificato nei boschi di castagno, seguiti dai boschi di conifere. Sebbene le zone agricole ricoprano una piccola porzione del territorio, in esse si è collocato un numero non trascurabile di dissesti. Inoltre, concavità morfologiche, esposizione verso S dei versanti e pendenza compresa tra 31°-35° sono risultati i fattori geomorfologici più determinanti nell'innesco delle frane superficiali. Infine, la sovrapposizione della carta di suscettibilità di frana del bacino, ottenuta con SHALSTAB, con la carta di inventario delle frane ha evidenziato una discreta correlazione tra aree instabili generate dal modello ed aree in cui si sono realmente verificati i dissesti. Questo lavoro può rappresentare un punto di partenza per la definizione di mappe di suscettibilità di frana del bacino. In futuro dovranno essere effettuate ulteriori prove geotecniche di laboratorio ed in situ, per migliorare la variabilità e la qualità dei parametri geotecnici e idrologici di ingresso nel modello SHALSTAB

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Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity

Abstract. Landslides cause severe damage to the road network of the hit zone, in terms of both direct (partial or complete destruction of a road or blockages) and indirect (traffic restriction or the cut-off of a certain area) costs. Thus, the identification of the parts of the road network that are more susceptible to landslides is fundamental to reduce the risk to the population potentially exposed and the financial expense caused by the damage. For these reasons, this paper aimed to develop and test a data-driven model for the identification of road sectors that are susceptible to being hit by shallow landslides triggered in slopes upstream from the infrastructure. This model was based on the Generalized Additive Method, where the function relating predictors and response variable is an empirically fitted smooth function that allows fitting the data in the more likely functional form, considering also non-linear relations. This work also analyzed the importance, on the estimation of the susceptibility, of considering or not the sediment connectivity, which influences the path and the travel distance of the materials mobilized by a slope failure until hitting a potential barrier such as a road. The study was carried out in a catchment of northeastern Oltrepò Pavese (northern Italy), where several shallow landslides affected roads in the last 8 years. The most significant explanatory variables were selected by a random partition of the available dataset in two parts (training and test subsets), 100 times according to a bootstrap procedure. These variables (selected 80 times by the bootstrap procedure) were used to build the final susceptibility model, the accuracy of which was estimated through a 100-fold repetition of the holdout method for regression, based on the training and test sets created through the 100 bootstrap model selection. The presented methodology allows the identification, in a robust and reliable way, of the most susceptible road sectors that could be hit by sediments delivered by landslides. The best predictive capability was obtained using a model in which the index of connectivity was also calculated according to a linear relationship, was considered. Most susceptible road traits resulted to be located below steep slopes with a limited height (lower than 50 m), where sediment connectivity is high. Different land use scenarios were considered in order to estimate possible changes in road susceptibility. Land use classes of the study area were characterized by similar connectivity features. As a consequence, variations on the susceptibility of the road network according to different scenarios of distribution of land cover were limited. The results of this research demonstrate the ability of the developed methodology in the assessment of susceptible roads. This could give the managers of infrastructure information about the criticality of the different road traits, thereby allowing attention and economic budgets to be shifted towards the most critical assets, where structural and non-structural mitigation measures could be implemented

shallow landslides susceptibility assessment in different environments

The spatial distribution of shallow landslides is strongly influenced by different climatic conditions and environmental settings. This makes difficult the implementation of an exhaustive monitoring technique for correctly assessing the landslide susceptibility in different environmental contexts. In this work, a unique methodological strategy, based on the statistical implementation of the generalized additive model (GAM), was performed. This method was used to investigate the shallow landslide predisposition of four sites with different geological, geomorphological and land-use characteristics: the Rio Frate and the Versa catchments (Southern Lombardy) and the Vernazza and the Pogliaschina catchments (Eastern Liguria). A good predictive overall accuracy was evaluated computing by the area under the ROC curve (AUROC), with values ranging from 0.76 to 0.82 and estimating the mean accuracy of the model (0.70–0.75). The method showed a high flexibility, which led to a good identification of the most significant predisposing factors for shallow landslide occurrence in the different investigated areas. In particular, detailed susceptibility maps were obtained, allowing to identify the shallow landslide prone areas. This methodology combined with the use of the rainfall thresholds for triggering shallow landslides may provide an innovative tool useful for the improvement of spatial planning and early warning systems

Use of statistical and physically based models in assessing landslide hazard: test cases in the Northern Apennines

Rainfall-induced shallow landslides are one of the most serious natural hazards in hilly and mountain regions worldwide because of fast movement, difficulty of spatial-temporal prediction, lack of knowledge about triggering mechanisms and long travel distance. In the last decades, extreme meteorological events frequently occurred in the northern Apennines, provoking hundreds of shallow landslides that led to loss of lives, damages to infrastructures and buildings. In some cases, the inadequate settlement development planning, the intense urbanisation and the lack of in-depth knowledge about geological-morphological features of many territories can make such areas more vulnerable to shallow landslide triggering. Since the last 30 years, the scientific community is continuously being developed methods and techniques improvements to predict shallow landslides. However, a comprehensive evaluation of the applicability, reliability and predictive capability of different methods and validation techniques for landslide susceptibility is fairly undeveloped in the specialized scientific literature. Indeed, existing works only deal with some different methods and post-processing operations techniques for landslide susceptibility assessment that are often focused on single study area. A poor attention was often paid in the overall evaluation of the effectiveness of the tools under examination, testing their effectiveness and predictive capability in different areas, in comparison with different methods and validations techniques and also changing some input criteria (e.g. different landslide inventories, different landslide types). The present Thesis aims to fill this gap not only by focusing on ongoing challenges in methods improvements, but mainly providing a detailed and comprehensive review of some methodological approaches and validation techniques for landslide susceptibility modelling, through their application in different areas. Thus, different study cases concerning some aspects of landslide susceptibility assessment were presented inside the Thesis, whose main objectives consists in: i) the evaluation of role played by some morphological, geological and land use predisposing factors in shallow landslide source areas distribution; ii) the development of a data-driven methodology based on GAM (Generalized Additive Model) easily applicable in various environmental contexts; iii) the implementation a of a bivariate methodology (LR; Likelihood Ratio) from a proprietary software into a free software developing a detailed and reliable procedure; iv) the comparison of the statistical procedures developed and reviewed with a physically based method (SHALSTAB; Shallow Landslide Stability Model), as well as the comparison between different validation techniques (PRCs, Prediction Rate Curves; ROCs, Receiver Operating curves). All these analyses carried out referred to some basins of the northern Apennines recently involved by rainfall-induced shallow landslides. The research was not planned with the only goal to broaden knowledge about landslide susceptibility of northern-west Italy settings, but also to provide a detailed evaluation of some aspects connected to landslide susceptibility assess from the methodological standpoint. Study cases were mainly exploited with the aim to promote a review of methods and techniques for pure research purpose, where the computer processing have been the main tools to reach this goal. The main results arising from the study cases were that geology, curvature and land use are ones of most significant predisposing variables in landslide occurrence. In particular, the health condition of woodlands and the degree of maintenance of agricultural areas have a great influence on the landslide distribution. The statistical procedure based on the GAM method was characterized by a good predictive capability and reliability in the investigated areas. Overall, statistical methods are more effective to predict future shallow landslides, as they showed a higher predictive capability and a greater reliability of outcomes than the SHALSTAB method. However, slight better results were found for the LR method in the Pogliaschina T. basin, thus suggesting that this more simple method can reach high performance as well as the more complex GAM method. Statistical models built with fewer number of predisposing factors are adequate to modelling landslide susceptibly, since the adding of more variables was not capable to raise significantly the model performance. The PRC and ROC curves validation techniques provide very similar results, indicating that both can be equally used in model validation operations. This Thesis can represent a step forward in the evaluation of some aspects related to landslide susceptibility analyses, both for furnishing significant outcomes that can be used by decision-makers for a more appropriate planning strategies of the investigated areas and for providing valid and reliable statistical procedures that can be applied everywhere to predict shallow landslide-prone areas

Assessing shallow landslide susceptibility by using the SHALSTAB model in Eastern Liguria (Italy)

On 25 October 2011 a heavy rainstorm hit Eastern Liguria (Vara Valley and Cinque Terre) and North-western Tuscany (Magra Valley), causing floods and hundreds of shallow landslides. This study aims at assessing the shallow landslide susceptibility using the physically based model SHALSTAB (Shallow Landslide Stability Model) in the Pogliaschina Torrent basin (Vara Valley). The susceptibility map elaborated with SHALSTAB was compared with the landslide inventory map, which confirmed the good performance of this model for the study area. The implementation of the SHALSTAB model provided a preliminary shallow landslide susceptibility map of the Pogliaschina T. basin and quite promising results on the shallow landslide spatial prediction

Geological and landslide map of the Pogliaschina T. basin (northern Apennines, Italy)

On 25 October 2011, the eastern Liguria (Vara Valley and Cinque Terre area) and northwestern Tuscany (Magra Valley) were affected by an extreme rainstorm (almost 600 mm/24 h) that caused floods, thousands of shallow landslides, 13 casualties and damage to villages and infrastructure. This study aims at analysing the main features of the 25 October 2011 shallow landslides occurred in the Pogliaschina Torrent basin (25 km2 wide, Vara Valley), in order to investigate the influence of specific predisposing factors (land use, geological and structural setting, plan and profile curvature, slope angle and aspect) on landslide occurrence. For this purpose, both a landslide inventory map and a geology map (scale 1:10,000) were prepared. In addition, a database including the main geological, geomorphological, structural and land use features of the landslide source areas was implemented. The relationship between landslide source areas and the main predisposing factors was evaluated through spatial and statistical analysis