Natural Hazards

, Volume 65, Issue 3, pp 1313–1330 | Cite as

Data-driven mapping of avalanche release areas: a case study in South Tyrol, Italy

  • A. PistocchiEmail author
  • C. Notarnicola
Original Paper


Avalanche hazard and risk mapping is of utmost importance in mountain areas in Europe and elsewhere. Advanced methods have been developed to describe several aspects of avalanche hazard assessment, such as the dynamics of snow avalanches or the intensity of snowfall to assume as a reference meteorological forcing. However, relatively little research has been conducted on the identification of potential avalanche release areas. In this paper, we present a probabilistic assessment of potential avalanche release areas in the Italian Autonomous Province of Bolzano, eastern Alps, using the Weights of Evidence and Logistic Regression methods with commonly available GIS datasets. We show that a data-driven statistical model performs better than simple, although widely adopted, screening criteria that were proposed in the past, although the complexity of observed release areas is only partly captured by the model. In the best case, the model enables predicting about 70 % of avalanches in the 20 % of area classified at highest hazard. Based on our results, we suggest that probabilistic identification of potential release areas could provide a useful aid in the screening of sites for subsequent, more detailed hazard assessment.


Avalanche release areas Weights of evidence Logistic regression Alps 



The research was partly funded within the Interreg IVb Project CLISP ( Geographical data, and particularly the CLPV avalanche register data, were provided by the Avalanche service of the Civil Protection Department of the Province of Bolzano. S. Kass, M. Zebisch and S. Schneiderbauer of the EURAC Institute for Applied Remote Sensing are gratefully acknowledged for technical help and discussion. S. Lermer helped performing some preliminary calculations during his internship at EURAC under the supervision of A. Pistocchi; results obtained in that context inspired parts of the present paper and will be a subject of a dedicated, upcoming contribution. Avalanche data were provided by the Civil Protection Service of the Autonomous Province of Bolzano—Alto Adige (Italy).


  1. Agterberg FP (1989) LOGDIA–FORTRAN 77 program for logistic regression with diagnostics. Comput Geosci 15(4):599–614CrossRefGoogle Scholar
  2. Barbolini M, Gruber U, Keylock CJ, Naaim M, Savi F (2000) Application of statistical and hydraulic-continuum dense-snow avalanche models to five real european sites. Cold Reg Sci Technol 31(2):133–149CrossRefGoogle Scholar
  3. Barbolini M, Natale L, Tecilla G, Cordola M (2001) Linee guida metodologiche per la perimetrazione delle aree esposte al pericolo di valanghe. AINEVA: Last accessed 12 June 2010
  4. Barbolini M, Natale L, Savi F (2002) Effects of release conditions uncertainty on avalanche hazard mapping. Nat Hazards 25(3):225–244CrossRefGoogle Scholar
  5. Barbolini M, Pagliardi M, Ferro F, Corradeghini P (2011) Avalanche hazard mapping over large undocumented areas. Nat Hazards 56(2):451–464CrossRefGoogle Scholar
  6. Barpi F (2004) Fuzzy modelling of powder snow avalanches. Cold Reg Sci Technol 40(3):213–227CrossRefGoogle Scholar
  7. Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology/un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Bull Int As Sci Hydrol 24(1):43–69CrossRefGoogle Scholar
  8. Bocchiola D, Medagliani M, Rosso R (2006) Regional snow depth frequency curves for avalanche hazard mapping in central italian alps. Cold Reg Sci Technol 46(3):204–221CrossRefGoogle Scholar
  9. Bonham Carter G (1994) GIS for geoscientists. Pergamon, New YorkGoogle Scholar
  10. Bonham Carter G, Agterberg FP, Wright DF (1989) Weights of evidence modeling: a new approach to mineral potential mapping. In: Bonham Carter G, Agterberg FP (eds) Statistical applications in the earth sciences, paper 89–9. Geological Survey of Canada, Ottawa, pp 171–183Google Scholar
  11. Chung C-JF, Fabbri AG (1993) The representation of geoscience information for data integration. Nat Resourc Res 2(2):122–139CrossRefGoogle Scholar
  12. Chung CF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65(12):1389–1399Google Scholar
  13. Chung C-JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472CrossRefGoogle Scholar
  14. Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008) Predictive modelling of rainfall-induced landslide hazard in the lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102(3–4):496–510CrossRefGoogle Scholar
  15. Davis RE, Elder K, Howlett D, Bouzaglou E (1999) Relating storm and weather factors to dry slab avalanche activity at Alta, Utah, and Mammoth mountain, California, using classification and regression trees. Cold Reg Sci Technol 30(1–3):79–89CrossRefGoogle Scholar
  16. Delparte D, Jamieson B, Waters N (2008) Statistical runout modeling of snow avalanches using GIS in glacier national park. Canada. Cold Reg Sci Technol 54(3):183–192CrossRefGoogle Scholar
  17. Eckert N, Parent E, Bélanger L, Garcia S (2007a) Hierarchical bayesian modelling for spatial analysis of the number of avalanche occurrences at the scale of the township. Cold Reg Sci Technol 50(1–3):97–112CrossRefGoogle Scholar
  18. Eckert N, Parent E, Richard D (2007b) Revisiting statistical “topographical methods for avalanche predetermination: Bayesian modelling for runout distance predictive distribution. Cold Reg Sci Technol 49(1):88–107CrossRefGoogle Scholar
  19. EEA European Environmental Agency (2010) Mapping the impacts of natural hazards and technological accidents in Europe. Technical report No 13/2010, 149 ppGoogle Scholar
  20. Ghinoi A (2008) Una nuova metodologia per la valutazione della suscettibilità valanghiva: come migliorare la carta di localizzazione Probabile delle valanghe (C.L.P.V.) utilizzando informazioni contenute nel catasto valanghe/A new methodology to evaluate land susceptibility to snow avalanches: how to improve the Location Map of Probable Snow Avalanches (L.M. P.S.A.) from the Snow Avalanches Inventory Data. Mem. Descr. Carta Geol. d’It., LXXVIII, pp. 93-104. Accessed 5 Sep 2012
  21. Ghinoi A, Chung C-J (2005) STARTER: a statistical GIS-based model for the prediction of snow avalanche susceptibility using terrain features” application to Alta val Badia, Italian dolomites. Geomorphology 66(1–4):305–325CrossRefGoogle Scholar
  22. Gruber U, Bartelt P (2007) Snow avalanche hazard modelling of large areas using shallow water numerical methods and GIS. Environ Model Softw 22(10):1472–1481CrossRefGoogle Scholar
  23. Hebertson EG, Jenkins MJ (2003) Historic climate factors associated with major avalanche years on the Wasatch plateau, Utah. Cold Reg Sci Technol 37(3):315–332CrossRefGoogle Scholar
  24. Hendrikx J, Owens I, Carran W, Carran A (2005) Avalanche activity in an extreme maritime climate: the application of classification trees for forecasting. Cold Reg Sci Technol 43(1–2):104–116CrossRefGoogle Scholar
  25. Hervas J (ed) (2003) Recommendations to deal with snow avalanches in Europe. EC-JRC Publication EUR 20839 EN. Office for Official Publications of the European Union, LuxembourgGoogle Scholar
  26. Jomelli V, Delval C, Grancher D, Escande S, Brunstein D, Hetu B, Filion L, Pech P (2007) Probabilistic analysis of recent snow avalanche activity and weather in the French alps. Cold Reg Sci Technol 47(1–2):180–192CrossRefGoogle Scholar
  27. Keylock CJ (2005) An alternative form for the statistical distribution of extreme avalanche runout distances. Cold Reg Sci Technol 42(3):185–193CrossRefGoogle Scholar
  28. Maggioni M, Gruber U (2003) The influence of topographic parameters on avalanche release dimension and frequency. Cold Reg Sci Technol 37(3):407–419CrossRefGoogle Scholar
  29. McCammon I, Hägeli P (2007) An evaluation of rule-based decision tools for travel in avalanche terrain. Cold Reg Sci Technol 47(1–2):193–206CrossRefGoogle Scholar
  30. McClung D, Schaerer P, Schaerer PA (2006) The Avalanche handbook, 3rd edn. The Mountaineers Books, SeattleGoogle Scholar
  31. Mears AI (1992) Snow-Avalanche hazard analysis for land-use planning and engineering. Bulletin 49, Colorado Geological Survey, DenverGoogle Scholar
  32. Mölg N, Rastner P, Irsara L, Notarnicola C, Steurer C, Zebisch M (2010) Multi-temporal MODIS snow cover monitoring over the alpine regions for civil protection applications. In: 30th EARSel symposium, 31st May–3rd June, 2010, Paris, FranceGoogle Scholar
  33. Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using weights-of-evidence applied to a study area at the jurassic escarpment (SW-germany). Geomorphology 86(1–2):12–24CrossRefGoogle Scholar
  34. Oh H-J, Lee S (2010) Assessment of ground subsidence using GIS and the weights-of-evidence model. Eng Geol 115(1–2):36–48CrossRefGoogle Scholar
  35. Pistocchi A, Luzi L, Napolitano P (2002) The use of predictive modeling techniques for optimal exploitation of spatial databases: a case study in landslide hazard mapping with expert system-like methods. Environ Geol 41(7):765–775CrossRefGoogle Scholar
  36. Porwal A, González-Álvarez I, Markwitz V, McCuaig TC, Mamuse A (2010) Weights-of-evidence and logistic regression modeling of magmatic nickel sulfide prospectivity in the Yilgarn Craton, Western Australia. Ore Geol Rev 38(3):184–196CrossRefGoogle Scholar
  37. Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115(1–2):172–187CrossRefGoogle Scholar
  38. Sawatzky DL, Raines GL, Bonham-Carter GF, Looney CG (2009) Spatial Data Modeller (SDM): ArcMAP 9.3 geoprocessing tools for spatial data modelling using weights of evidence, logistic regression, fuzzy logic and neural networks. Accessed 10 March 2010
  39. Schmidt-Thomé P (ed) (2006) The spatial effects and management of natural and technological hazards in Europe. ESPON 1.3.1 Report:; Accessed 12 June 2010
  40. Schweizer J, Mitterer C, Stoffel L (2009) On forecasting large and infrequent snow avalanches. Cold Reg Sci Technol 59(2–3):234–241CrossRefGoogle Scholar
  41. Straub D, Grêt-Regamey A (2006) A bayesian probabilistic framework for avalanche modelling based on observations. Cold Reg Sci Technol 46(3):192–203CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.GECOsistema srlJenesien/BolzanoItaly
  2. 2.EURAC ResearchBolzanoItaly

Personalised recommendations