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Discussion papers
https://doi.org/10.5194/nhess-2018-358
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2018-358
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Dec 2018

Research article | 06 Dec 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Mapping snow avalanches hazard in poorly monitored areas. The case of Rigopiano avalanche, Apennines of Italy

Daniele Bocchiola, Mattia Galizzi, Giovanni Martino Bombelli, and Andrea Soncini Daniele Bocchiola et al.
  • Department ICA, Politecnico di Milano, L. da Vinci, 32, Milano, Italy

Abstract. Hazard mapping is carried out in Italy according to the AINEVA guidelines, which require (i) data driven avalanche dynamic modelling to assess end mark and pressure, and (ii) assessment of maximum yearly three-day snow depth increase h72 for 30 to 300 years return period. When no historical avalanche data are present, model tuning and data based assessment of avalanche return periods are hardly feasible. Also when (very) short series of h72 are available, station based quantile estimation for such high return periods is very uncertain, and regionally based approaches can be used. We apply an index value approach for the case study avalanche of Rigopiano, where a 105m3 snow mass hit the Rigopiano Hotel killing 29 persons on January 18th 2017. This area is poorly monitored avalanche wise, and displays short series (max 14 years) of snow depth measurements, no historical avalanche maps are available on the avalanche track, and no hazard maps have been developed hitherto. First, we tune the recently developed Poly-Aval dynamic avalanche model (1D/q2D) against the 18th January event data (release zone, release depth, end mark) from different sources. We then use snow data from 7 snow stations in Abruzzo (75 equivalent years of data) to tune a regionally valid distribution of h72. We then calculate the 30-years, 100-years, and 300-years runout zone and flow pressures, including confidence limits. We demonstrate that (i) properly tuned 1D/quasi2D models can be used for avalanche modeling even within poorly monitored area as here, and (ii) the use of regional analysis allows hazard mapping for large return periods, reducing greatly the uncertainty against canonical, single site analysis. Our approach is usable in poorly monitored regions like Abruzzo here, and we suggest that (i) avalanche hazard mapping needs to be pursued with regional approaches for h72, and (ii) confidence limits need to be provided for the proposed zoning.

Daniele Bocchiola et al.
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Daniele Bocchiola et al.
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Short summary
We investigate hazard for the poorly monitored area of Rigopiano (PE), where an avalanche killed 29 persons on January 2017. Using Poly-Aval model (1D/q2D), and a regional approach to calculate snow depth at release h72 we map hazard zones as per AINEVA guidelines, with confidence limits. We demonstrate that regionally based hazard mapping at poorly measured sites as here allows mapping even for large return periods, considerably reducing the uncertainty against canonical single site analysis.
We investigate hazard for the poorly monitored area of Rigopiano (PE), where an avalanche killed...
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