Small-scale analysis to rank municipalities requiring slow-moving landslide risk mitigation measures: the case study of the Calabria region (southern Italy)

Giovanni Gullà,Settimio Ferlisi,Luigi Borrelli,Dario Peduto,Gianfranco Nicodemo

doi:10.1186/s40677-021-00202-1

Abstract

This paper proposes a three-phase method that combines multi-source (i.e. topographic, thematic, monitoring) input data in a GIS environment to rank—at small (1:250,000) scale—administrative units (e.g. municipalities) based on their exposure to slow-moving landslide risk within a selected area (e.g. a region) and, accordingly, detect those primarily requiring mitigation measures. The method is applied in the Calabria region (southern Italy) where several municipalities are widely affected by slow-moving landslides that systematically cause damage to buildings and infrastructure networks resulting in significant economic losses. The results obtained are validated based on the information gathered from previous studies carried out at large (municipal) scale. The work undertaken represents a first, fundamental step of a wider circular approach that can profitably facilitate the decision makers in addressing the issue of the slow-moving landslide risk mitigation in a sustainable way.

Highlights

Slow-moving landslides (SMLs) are slope instabilities with existing slip zones where the materials are predominately fine-grained with a visco-plastic behaviour (Bertini et al 1984; Borrelli and Gullà 2017; Di Maio et al 2013; Ferlisi 2004; Fernández-Merodo et al 2014; Grana and Tommasi 2014; Gullà 2014; Leroueil 2001; Picarelli et al 2004)
Salerno, Italy Full list of author information is available at the end of the article detrimental effects on the abovementioned exposed elements that are expected to be higher as displaced masses experience sudden accelerations due to rainfall or earthquakes (Donnini et al 2017; Gullà 2014; Mavrouli et al 2019; Negulescu et al 2014; Uzielli et al 2015). To address this issue, which is of particular concern for central and local authorities in charge of slow-moving landslides (SMLs) risk management, top-down multi-scale methodological approaches (Cascini 2015) may help in: prioritizing the municipalities—within a region— whose SML-affected urban areas require risk mitigation measures; planning well-defined categories of risk mitigation measures in the urban area of a municipality selected among the most exposed ones to SML risk based on the outcomes of small-scale analysis; scheduling the implementation, with a proper allocation of economic resources, of the most suitable structural/nonstructural interventions among the categories planned at
Starting from the available materials—geological map, digital terrain model (DTM), built-up urban area map, landslide inventory map, DInSAR data (Fig. 1)—each Terrain Computational Units (TCUs) (Fig. 2a) is associated with the pertaining information including: (i) the Lithological Units (LUs), namely groups of lithotypes with a mechanical behaviour that can be assumed as homogeneous at the scale of analysis; (ii) the Slope angle (S), as retrieved from the DTM; (iii) the presence/absence of an Urban Area (UA), as resulting from the built-up area map; (iv) the presence/absence of a SML, based on the landslide inventory map; (v) the DInSAR-derived velocity, computed by averaging the velocity values pertaining to the coherent pixels—if any—within the TCU perimeter (Cascini et al 2013)

Summary

Introduction

Slow-moving landslides (SMLs) are slope instabilities with existing (buried) slip zones where the materials are predominately fine-grained with a visco-plastic behaviour (Bertini et al 1984; Borrelli and Gullà 2017; Di Maio et al 2013; Ferlisi 2004; Fernández-Merodo et al 2014; Grana and Tommasi 2014; Gullà 2014; Leroueil 2001; Picarelli et al 2004) Owing to their particular kinematic features, associated with a permanent or episodic activity, these landslides mainly cause direct damages to exposed buildings and/or infrastructure networks the severity of which progressively increases over time (Antronico et al 2015; Ferlisi et al 2021; Peduto et al 2017). DInSAR data proved to be of value and both complementary and supplementary to the conventional geotechnical monitoring (Gullà et al 2017; Morelli et al 2020; Peduto et al 2021b, c; Refice et al 2019) providing useful information on both the identification of new SMLs boundaries (Herrera et al 2013; Peduto et al 2016; Wasowski 2006) and the updating of their state of activity (Cascini et al 2013; Cigna et al 2013) as well as in the analysis of past landslide evidences and in creating and updating, at small and medium scales, inventory maps in specific periods (Raspini et al 2019; Solari et al 2019)

Methods

Results

Conclusion