Heuristic Method for Landslide Susceptibility ...

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Gabriele Leoni, Danilo Campolo, Luca Falconi, Carmelo Gioè,. Silvia Lumaca .... Valutazione della pericolosità da frana nel territorio del Comune di. Messina.
Heuristic Method for Landslide Susceptibility Assessment in the Messina Municipality

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Gabriele Leoni, Danilo Campolo, Luca Falconi, Carmelo Gioè, Silvia Lumaca, Claudio Puglisi, and Antonino Torre

Abstract

October 1st 2009 a heavy rainfall caused more than one thousand debris flows in two small basins south of Messina town (North–East Sicily, Italy). After the disaster Messina Municipality entrusted the risk assessment of its whole territory to a geomorphology and GIS team, led by ENEA, that applied and improved a heuristic method aimed at the identification of the areas prone to landslide triggering, based on the recognition of physical and dynamical characteristics of phenomena. This landslide susceptibility method is a GIS based process that consists in four steps: Field Survey, Site Analysis, Macro-Area Analysis and Susceptibility Analysis. Through the Field Survey of natural and anthropic conditions of past and recent phenomena a landslide inventory is generated, and each thematic map is stored in a GIS database. In the Site Analysis a univariate statistical analysis of the inventory leads to classify each causative factor as a discriminating parameter (condition necessary for slope instability) or as a predisposing factor (condition that works together in worsening slope stability). In the Macro-Area Analysis the GIS overlay of all thematic maps is performed to recognize, in surrounding areas, features similar to those of past events. Finally a susceptibility function runs the weighted sum of Predisposing Factors in zones where all Discriminating Parameters are present, deriving the Susceptibility Map. The here discussed heuristic GIS method allows the integration of multidisciplinary knowledge, both quantitative and qualitative, thus exploiting field experience. Keywords

Field survey

  GIS

Landslide susceptibility

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G. Leoni (&)  D. Campolo  S. Lumaca  A. Torre Via Ploner 19, 00123 Rome, Italy e-mail: [email protected] L. Falconi  C. Puglisi ENEA—Italian Agency for New Technologies, Energy and Sustainable Economic Development, Via Anguillarese, 301, 00123 Rome, Italy C. Gioè Messina Municipality, Piazza Unione Europea, 98122 Messina, Italy



Hazard assessment

Introduction

Number and consequences of shallow landslide disasters raised during last years. It is clearly due to the increase of extreme rainfall events caused by climate changes. To face this challenge it is necessary to deal with geomorphologic risk mitigation following a methodology that is based on the understanding of physical and dynamical characteristics of phenomena. Next step is to properly model causative factors, and finally to lead to an effective hazard and risk assessment, both existing and potential. A heuristic method aimed at the identification of the areas prone to landslide triggering was developed in more than 10 years by our geomorphology and GIS modeler team, led by ENEA researchers; this method

G. Lollino et al. (eds.), Engineering Geology for Society and Territory – Volume 2, DOI: 10.1007/978-3-319-09057-3_82, © Springer International Publishing Switzerland 2015

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82.2.1 Field Survey The first step is the detailed geological and multidisciplinary survey of the study area, collecting as much data as possible about the natural and anthropic conditions of past and recent landslides. Even if affordable quantitative measures are not always available, also qualitative data or simple observations, depicted as GIS thematic layers, can contribute to develop the geological model of the phenomena. A dedicated landslide survey form guides the data collection, assuring the completion of the landslide inventory and the proper data acquisition and data processing.

82.2.2 Site Analysis Fig. 82.1 Debris flows of the 2009 event, near Giampilieri village

has been also tested in many sites and at different scales (Delmonaco et al. 2003; Casagli et al. 2004; Delmonaco et al. 2005; Leoni et al. 2009; Puglisi et al. 2013). Here we discuss the case of the disaster occurred on October 1st 2009 in Giampilieri and surrounding areas (North–East Sicily): heavy rainfall (more than 200 mm in 7 h) triggered more than 1,000 debris flows, in an area of about 20 km2. The flows hit 16 villages causing 37 casualties, 140 injured, 1,642 evacuated and the interruption of national road and railway (see Fig. 82.1). After this event the Messina Municipality asked our team to carry on the landslide hazard assessment of its whole territory, to obtain a decision support system for sustainable urban planning and to raise people awareness about geomorphologic risk.

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Methodology

The landslide susceptibility method that we developed consists, basically, in the GIS overlay of indexed maps representing each known factor that affects slope instability; it is based on the knowledge of past events and local conditions, that need to be studied by an appropriate field survey. Then the univariate statistical analysis of field data leads to identify particular conditions and their mutual relationships. Once recognized the causes of past landslide triggering it is possible to extend the analysis to surrounding areas, to point out zones affected by the same hazardous conditions. The methodology involves 4 steps: Field Survey, Site Analysis, Macro-Area Analysis, Susceptibility Analysis.

The recurrence of a certain characteristic in past landslides is evaluated by univariate statistical analysis of the landslide inventory, thus leading to distinguish between “Discriminating Parameters” (conditions necessary for slope instability) and “Predisposing Factors” (all conditions that work together in worsening slope stability): the first, commonly lithology and slope angle, indicate potential susceptible zones, while the last contribute to quantify the susceptibility degree. This first analysis points out the significance of Discriminating Parameters, indexed in a binary way: 0 for the absence and 1 for the presence. Then the same analysis drives the indexing of Predisposing Factors as integer values ranging from 0 to 9, to obtain an indexed map from each thematic layer (See Fig. 82.2). This evaluation starts from frequency analysis, then the result is compared with the inventory, in order to calibrate the model with the field experience, heuristically correcting typical underestimations or overestimations due to the usual incompleteness of statistical sample. Each landslide typology is separately analyzed, in order to properly evaluate their peculiar characteristics.

82.2.3 Macro-Area Analysis GIS overlay of all thematic maps depicts parcels of land characterized by a singular combination of discriminating parameters and of predisposing factors: the “Homogeneous Territorial Units” (HTU). Since each HTU will respond the same way to the same trigger it is possible to extend the knowledge of past events to surrounding areas with similar features.

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Heuristic Method for Landslide Susceptibility Assessment in the Messina Municipality

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Fig. 82.2 Detail of the predisposing factor Geology (left), the index table (center) and the derived Indexed Map (right)

82.2.4 Susceptibility Analysis For each HTU where all Discriminating Parameters are present, thus enabling slope instability, a susceptibility function runs the weighted sum of Predisposing Factors, deriving the susceptibility map. After the methodology is applied to each landslide typology it is possible to summarize the susceptibility degree for typology class, e.g. for velocity class susceptibility they can be summarized in rapid or slow, and for depth of rupture surface susceptibility they can be summarized in shallow and deep.

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Results

Geomorphologic survey has been carried out by field surveys on events from 2009 until 2012, digital stereoscopic air photo interpretation on 2007 flight and GIS photointerpretation of 2009 and 2010 orthophoto. In a 220 km2 wide area more than 3,000 landslides of 4 typologies has been recorded: debris flows (74 %), rock falls (17 %), rotational slides (7 %) and translational slides (2 %). Univariate statistical analysis showed low reliability for slow slope movements (rotational and translational slides) depending on the small statistical sample, respectively about 200 and 50 events. For rapid slope movements statistical analysis pointed out the great significance of lithology and slope angle: these two discriminating parameters show a great differentiation

between class values with and without associated events. In the study area discriminating parameters associated with debris flows are all lithologies, except clays and chalks, for slope angle varying from 17 to73°; while for rock falls the associated discriminating parameters are all pre-quaternary deposits for slope angle greater than 26°. Given the general framework of discriminating parameters (Site Analysis), which outlines potential susceptible and non-susceptible areas, some landslide factors are more recurrent than others: these conditions represent the predisposing factors. In most cases debris flows occur in Miocene Flysch and in metamorphic Units (except Marbles), with slope angle ranging from 36 and 59°, weakly convex shaped slopes, terraced bare areas or with shallow vegetation and areas close to natural or artificial slope breaks. For rock falls predisposing factors are represented by Miocene coarse grained Flysch, Plio Pleistocene Calcarenites and metamorphic Units (except Phyllites), with slope angles greater than 40°, strong convex shaped slopes, and bare areas close to artificial slope breaks. The susceptibility function summarizes predisposing factors indices producing the susceptibility map, i.e. the spatial distribution of susceptibility index for the whole study area (See Fig. 82.3). High and very high debris flows susceptible areas are located in the central and southern part of Messina Municipality, already hit by 2007, 2009 and 2010 events, and in the northern zone, close to neighboring Municipalities hit by 2011 event. It is remarkable that rapid slope movements mostly occur as neoformation phenomena. Thus it is necessary to carry on more detailed studies on high

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Fig. 82.3 Detail of the susceptibility map for debris flows

and very high susceptibility areas, and to setup proper monitoring systems for triggers.

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Conclusions

Though quantitative methods (both statistical and deterministic) are more sophisticated and could rise to more reliable results, this heuristic method has different values: • it integrates multidisciplinary knowledge, both quantitative and qualitative; • it focuses the importance of the field survey to prevent statistic bias; • it’s easy to use and it’s easy to show, it allows the use also by operators lacking specific statistical or modeling expertise and it can be easily explained to stakeholders; • it gives a framework of large areas, pointing out at small scale emergencies that need prior response or further detailed survey. Research funded by the Messina Municipality

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