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A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster

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Abstract

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.

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References

  • Agnesi V, Rasà R, Puglisi C, Gioè C, Privitera B, Cappadonia C, Conoscenti C, Pino P, and Rotigliano E (2009) La franosità diffusa dell’1 Ottobre 2009 nel territorio ionico-peloritano della Provincia di Messina: stato delle indagini e prime considerazioni sulle dinamiche geomorfiche attivate. Geologi di Sicilia 4:23–30

  • Akgün A, Türk N (2011) Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalik region, NW Turkey. Comput Geosci 37:1515–1524

    Article  Google Scholar 

  • Aronica GT, Brigandá G, Morey N (2012) Flash floods and debris flow in the city area of Messina, north-east part of Sicily, Italy in October 2009: the case of the Giampilieri catchment. Nat Hazards Earth Syst Sci 12(5):1295–1309. doi:10.5194/nhess-12-1295-2012

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy. Comput Geosci 24(4):373–385. doi:10.1016/S0098-3004(97)00117-9

    Article  Google Scholar 

  • Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115(1–2):23–31. doi:10.1016/j.geomorph.2009.09.025

    Article  Google Scholar 

  • Borgatti L, Soldati M (2010) Landslides as a geomorphological proxy for climate change: a record from the Dolomites (northern Italy). Geomorphology 120(1–2):56–64. doi:10.1016/j.geomorph.2009.09.015

    Article  Google Scholar 

  • Brenning A (2005) Spatial prediction models for landslide hazards: review, comparison and evaluation. Nat Hazards Earth Syst Sci 5:853–862. doi:10.5194/nhess-5-853-2005

    Article  Google Scholar 

  • Büttner G, Kosztra B, Maucha G, Pataki R (2012) Implementation and achievements of CLC2006. European Environment Agency, technical report—revised final draft, 65

  • Can T, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology 72(1–4):250–271. doi:10.1016/j.geomorph.2005.05.011

    Article  Google Scholar 

  • Carrara A, Pike RJ (2008) GIS technology and models for assessing landslide hazard and risk. Geomorphology 94(3–4):257–260. doi:10.1016/j.geomorph.2006.07.042

    Article  Google Scholar 

  • Carrara A, Catalano E, Sorriso Valvo M, Reali C, Merenda L, Rizzo V (1977) Landslide morphometry and typology in two zones, Calabria, Italy. Bull Int As Eng Geol 16:8–13

    Article  Google Scholar 

  • Carrara A, Guzzetti F, Cardinali M, Reichenbach P (1999) Use of GIS technology in the prediction and monitoring of landslide hazard. Nat Hazards 20(2–3):117–135

    Article  Google Scholar 

  • Carrara A, Crosta G, Frattini P (2003) Geomorphological and historical data in assessing landslide hazard. Earth Surf Proc Land 28(10):1125–1142. doi:10.1002/esp.545

    Article  Google Scholar 

  • Chung CJ, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472. doi:10.1023/B:NHAZ.0000007172.62651.2b

    Article  Google Scholar 

  • Conforti M, Robustelli G, Muto F, Critelli S (2012) Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria, south Italy). Nat Hazards 61(1):127–141. doi:10.1007/s11069-011-9781-0

    Article  Google Scholar 

  • Costanzo D, Cappadonia C, Conoscenti C, Rotigliano E (2012) Exporting a Google Earth™ aided earth-flow susceptibility model: a test in central Sicily. Nat Hazards 61(1):103–114. doi:10.1007/s11069-011-9870-0

    Article  Google Scholar 

  • Costanzo D, Chacón J, Conoscenti C, Irigaray C, Rotigliano E (2013) Forward logistic regression for earth-flow landslide susceptibility assessment in the Platani river basin (southern Sicily, Italy). Landslides 1–15. doi:10.1007/s10346-013-0415-3

  • Crozier MJ (2005) Multiple-occurrence regional landslide events in New Zealand: hazard management issues. Landslides 2(4):247–256. doi:10.1007/s10346-005-0019-7

    Article  Google Scholar 

  • Crozier MJ (2010) Deciphering the effect of climate change on landslide activity: a review. Geomorphology 124(3–4):260–267. doi:10.1016/j.geomorph.2010.04.009

    Article  Google Scholar 

  • De Guidi G, Scudero S (2013) Landslide susceptibility assessment in the Peloritani Mts. (Sicily, Italy) and clues for tectonic control of relief processes. Nat Hazards Earth Syst Sci 13:949–963. doi:10.5194/nhess-13-949-2013

    Article  Google Scholar 

  • Del Ventisette C, Garfagnoli F, Ciampalini A, Battistini A, Gigli G, Moretti S, Casagli N (2012) An integrated approach to the study of catastrophic debris-flows: geological hazard and human influence. Nat Hazards Earth Syst Sci 12:2907–2922. doi:10.5194/nhess-12-2907-2012

    Article  Google Scholar 

  • Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77–4:802–813. doi:10.1111/j.1365-2656.2008.01390

    Article  Google Scholar 

  • Fabbri AG, Chung CJ (2008) On blind tests and spatial prediction models. Nat Resour Res 17(2):107–118. doi:10.1007/s11053-008-9072-y

    Article  Google Scholar 

  • Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1–4):62–72. doi:10.1016/j.enggeo.2009.12.004

    Article  Google Scholar 

  • Giunta G, Somma R (1996) Nuove osservazioni sulla struttura dell‘Unità di Alì (Monti Peloritani, Sicilia). Boll Soc Geol Ital 115:489–500

    Google Scholar 

  • Glade T, Anderson MG, Crozier MJ (2002) Landslide hazard and risk. John Wiley ans Sons Ltd., Chichester

    Google Scholar 

  • Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129(3–4):376–386. doi:10.1016/j.geomorph.2011.03.001

    Article  Google Scholar 

  • Gullà G, Caloiero T, Coscarelli R, Petrucci O (2012) A proposal for a methodological approach to the characterisation of Widespread Landslide Events: an application to Southern Italy. Nat Hazards Earth Syst Sci 12:165–173. doi:10.5194/nhess-12-165-2012

    Article  Google Scholar 

  • Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216. doi:10.1016/S0169-555X(99)00078-1

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72(1–4):272–299. doi:10.1016/j.geomorph.2005.06.002

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Ardizzone F, Cardinali M, Galli M (2006) Estimating the quality of landslide susceptibility models. Geomorphology 81(1–2):166–184. doi:10.1016/j.geomorph.2006.04.007

    Article  Google Scholar 

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression, wiley series in probability and statistics. Wiley, London

    Book  Google Scholar 

  • Hungr O, Evans SG, Bovis MJ, Hutchinson JN (2001) A review of the classification of landslides of the flow type. Environ Eng Geosci 7(3):221–238

    Article  Google Scholar 

  • Hungr O, McDougall S, Bovis M (2005) Entrainment of material by debris flows. Debris-flow hazards and related phenomena. Springer, Berlin, pp 135–158

    Book  Google Scholar 

  • Irigaray C, Fernández T, El Hamdouni R, Chacón J (2007) Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain). Nat Hazards 41(1):61–79. doi:10.1007/s11069-006-9027-8

    Article  Google Scholar 

  • Iverson RM (1997) The physics of debris flows. Rev Geophys 35:245–296

    Article  Google Scholar 

  • Jakob M, Lambert S (2009) Climate change effects on landslides along the southwest coast of British Columbia. Geomorphology 107(3–4):275–284. doi:10.1016/j.geomorph.2008.12.009

    Article  Google Scholar 

  • Lentini F, Catalano S, Carbone S (2000) Note illustrative della Carta Geologica della Provincia di Messina, scala 1: 50.000. Provincia Regionale di Messina, Assessorato Servizio Territorio—Servizio Geologico

  • Lucà F, Conforti M, Robustelli G (2011) Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: northern Calabria, South Italy. Geomorphology 134:297–308. doi:10.1016/j.geomorph.2011.07.006

    Article  Google Scholar 

  • Messina A, Somma R, Careri G, Carbone G, Macaione E (2004) Peloritani continental crust composition (southern Italy): geological and petrochemical evidences. Boll Soc Geol Ital 123:405–444

    Google Scholar 

  • Mondini AC, Guzzetti F, Reichenbach P, Rossi M, Cardinali M, Ardizzone F (2011) Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images. Remote Sens Environ 115(7):1743–1757. doi:10.1016/j.rse.2011.03.006

    Article  Google Scholar 

  • Nandi A, Shakoor A (2008) Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed, Ohio, USA. Georisk 2(1):16–27. doi:10.1080/17499510701842221

    Google Scholar 

  • Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191. doi:10.1016/j.enggeo.2008.01.004

    Article  Google Scholar 

  • Ohlmacher GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91(2–4):117–134. doi:10.1016/j.enggeo.2007.01.005

    Article  Google Scholar 

  • Ohlmacher GC, Davis JC (2003) Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Eng Geol 69(3–4):331–343. doi:10.1016/S0013-7952(03)00069-3

    Article  Google Scholar 

  • Petschko H, Brenning A, Bell R, Goetz J, Glade T (2014) Assessing the quality of landslide susceptibility maps—case study Lower Austria. Nat Hazards Earth Syst Sci 14:95–118. doi:10.5194/nhess-14-95-2014

    Article  Google Scholar 

  • Rakotomalala R (2005) Tanagra: un logiciel gratuit pour l’enseignement et la recherche. In: Actes De EGC, pp 697–702

  • Regional Department of Civil Protection of Sicily (2009) Relazione descrittiva sull’emergenza colate di fango nel territorio della provincia di Messina, Italia, 1 ottobre 2009, Internal Report, 2009 (in Italian)

  • Rossi M, Guzzetti F, Reichenbach P, Mondini AC, Peruccacci S (2010) Optimal landslide susceptibility zonation based on multiple forecasts. Geomorphology 114(3):129–142. doi:10.1016/j.geomorph.2009.06.020

    Article  Google Scholar 

  • Rotigliano E, Agnesi V, Cappadonia C, Conoscenti C (2011) The role of the diagnostic areas in the assessment of landslide susceptibility models: a test in the sicilian chain. Nat Hazards 58(3):981–999. doi:10.1007/s11069-010-9708-1

    Article  Google Scholar 

  • Somma R (2006) The south-western side of the Calabrian Arc (Peloritani Mountains): geological, structural and AMS evidence for passive clockwise rotations. J Geodyn 41(4):422–439. doi:10.1016/j.jog.2005.11.00

    Article  Google Scholar 

  • Somma R, Messina A, Mazzoli S (2005) Syn-orogenic extension in the Peloritani Alpine Thrust Belt (NE Sicily, Italy): evidence from the Alì Unit. Compte Rendus Gèoscience Paris 337:861–871

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679. doi:10.1007/s00254-003-0917-8

    Article  Google Scholar 

  • Tartaglione N, Speranza A, Dalan F, Nanni T, Brunetti M, Maugeri M (2006) The mobility of Atlantic baric depressions leading to intense precipitation over Italy: a preliminary statistical analysis. Nat Hazards Earth Syst Sci 6(3):451–458

    Article  Google Scholar 

  • Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, Vandekerkhove L (2006) Prediction of landslide susceptibility using rare events logistic regression: a case study in the Flemish Ardennes (Belgium). Geomorphology 76:392–410. doi:10.1016/j.geomorph.2005.12.003

    Article  Google Scholar 

  • Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9(2):507–521. doi:10.5194/nhess-9-507-2009

    Article  Google Scholar 

  • Van Den Eeckhaut M, Marre A, Poesen J (2010) Comparison of two landslide susceptibility assessments in the Champagne-Ardenne region (France). Geomorphology 115(1–2):141–155. doi:10.1016/j.geomorph.2009.09.042

    Article  Google Scholar 

  • von Ruette J, Papritz A, Lehmann P, Rickli C, Or D (2011) Spatial statistical modeling of shallow landslides—validating predictions for different landslide inventories and rainfall events. Geomorphology 133(1–2):11–22. doi:10.1016/j.geomorph.2011.06.010

    Article  Google Scholar 

  • Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39. doi:10.1016/j.ecolmodel.2011.12.007

    Article  Google Scholar 

  • Wilson JP, Gallant JC (2000) Terrain analysis: principles and applications. Wiley, New York

    Google Scholar 

  • Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. Catena 85(3):274–287. doi:10.1016/j.catena.2011.01.014

    Article  Google Scholar 

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Acknowledgments

The findings and discussion of this research were carried out in the framework of the PhD research projects of Luigi Lombardo and Mariaelena Cama at the Department of Earth and Sea Sciences, University of Palermo. Luigi Lombardo PhD thesis is internationally co-tutored with the Department of Geography of the University of Tuebingen (Germany). This research was supported by the project SUFRA_SICILIA funded by the ARTA-Re Sicilia and the FFR 2012/2013 project funded by the University of Palermo.

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Authors and Affiliations

  1. Department of Earth and Sea Sciences, University of Palermo, Via Archirafi, 20, 90123, Palermo, Italy

    L. Lombardo, M. Cama & E. Rotigliano

  2. Department of Physical Geography and GIS, University of Tübingen, Tübingen, Germany

    L. Lombardo

  3. Department of Earth Sciences, University of Florence, Florence, Italy

    M. Maerker

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  1. L. Lombardo
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  2. M. Cama
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  4. E. Rotigliano
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Correspondence to L. Lombardo.

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Lombardo, L., Cama, M., Maerker, M. et al. A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster. Nat Hazards 74, 1951–1989 (2014). https://doi.org/10.1007/s11069-014-1285-2

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