Abstract
Landslides of the slide-type movement represent common damaging phenomena in the Italian province of South Tyrol. Up to January 2019, the landslide inventory of the province lists 1928 accurately mapped landslides that required intervention by e.g. the local road service or the provincial geological survey. Thus, this landslide data set mainly includes events that caused damage. The aim of this contribution was to investigate and critically interpret statistical associations between the inventoried slide-type movements and a variety of spatial environmental variables. The assessment of conditional frequencies and the discriminatory power of single variables revealed conditions that are typically present at landslide mapping locations, e.g. topography, land cover, rock types, and proximity to infrastructure. A critical interpretation of the statistical results highlighted the need to consider the landslide data origin (i.e. background information) in order to avoid misleading statements and wrong inferences. The findings of the here presented work show that the availability of detailed landslide information does not always ensure that valid process-related conclusions can be drawn from subsequent statistical analyses (e.g. identification of important landslide controls). Despite considerable methodical advancements in the field of statistical data analysis and machine learning, we conclude that the principle ‘correlation does not necessarily imply (geomorphic) causation’ remains of particular relevance when exploiting available landslide information.
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References
Borgatti L, Soldati M (2010) Landslides as a geomorphological proxy for climate change: a record from the Dolomites (northern Italy). Geomorphology 120:56–64
Brenning A (2008) Statistical geocomputing combining R and SAGA: the example of landslide susceptibility analysis with generalized additive models. Hamburger Beiträge Phys Geogr Landschaftsökol 19:410
Brenning A, Schwinn M, Ruiz-Páez AP, Muenchow J (2015) Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province. Nat Hazards Earth Syst Sci 15:45–57
Conrad O, Bechtel B, Bock M et al (2015) System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci Model Dev 8:1991–2007
Corsini A, Pasuto A, Soldati M, Zannoni A (2005) Field monitoring of the Corvara landslide (Dolomites, Italy) and its relevance for hazard assessment. Geomorphology 66:149–165
Cruden DM, Varnes DJ (1996) Landslide types and processes landslides: investigation and mitigation. TRB special report, 247. National Academy Press, Washington, pp 36–75
Geokatalog (2019) Open geodatabase of the Autonomous Province of South Tyrol. Accessible via https://geokatalog.buergernetz.bz.it/geokatalog/. Accessed 20 Feb 2019
Hosmer DW, Lemeshow S (2000) Applied logistic regression. Wiley, New York
Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194
Jasiewicz J, Stepinski TF (2013) Geomorphons—a pattern recognition approach to classification and mapping of landforms. Geomorphology 182:147–156
Lennert M (2017) GRASS development team. Geographic resources analysis support system (GRASS) software, version 7
Piacentini D, Troiani F, Soldati M et al (2012) Statistical analysis for assessing shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy). Geomorphology 151–152:196–206
Reichenbach P, Rossi M, Malamud BD et al (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91
Schmaltz EM, Steger S, Glade T (2017) The influence of forest cover on landslide occurrence explored with spatio-temporal information. Geomorphology 290:250–264
Steger S, Glade T (2017) The challenge of “trivial areas” in statistical landslide susceptibility modelling. In: Proceedings of the 4th world landslide forum, 29 May–2 June, Ljubljana. Springer, Ljubljana
Steger S, Brenning A, Bell R et al (2016) Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps. Geomorphology 262:8–23
Steger S, Brenning A, Bell R, Glade T (2017) The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements. Landslides 14:1767–1781
Steger S, Schmaltz E, Glade T (2020) The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling. Geomorphology 354:107041
Stingl V, Mair V (2005) Einführung in die Geologie Südtirols: [aus Anlass des 32. Internationalen Geologischen Kongresses im Sommer 2004 in Florenz]. Autonome Provinz Bozen-Südtirol. Amt Geol Baustoffprüfung
Tasser E, Mader M, Tappeiner U (2003) Effects of land use in alpine grasslands on the probability of landslides. Basic Appl Ecol 4:271–280
Trigila A, Iadanza C, Spizzichino D (2010) Quality assessment of the Italian Landslide Inventory using GIS processing. Landslides 7:455–470
Wood SN (2006) Generalized additive models: an introduction with R. Chapman and Hall/CRC, Taylor and Francis Group, Boca Raton
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The authors thank the Autonomous Province of South Tyrol for providing spatial input data.
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Steger, S., Mair, V., Kofler, C., Pittore, M., Zebisch, M., Schneiderbauer, S. (2021). A Statistical Exploratory Analysis of Inventoried Slide-Type Movements for South Tyrol (Italy). In: Guzzetti, F., Mihalić Arbanas, S., Reichenbach, P., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60227-7_34
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DOI: https://doi.org/10.1007/978-3-030-60227-7_34
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