Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged Regions Article Swipe
Uroš Durlević
,
Aleksandar Valjarević
,
Ivan Novković
,
Filip Vujović
,
Nemanja Josifov
,
Jelka Krušić
,
Blaž Komac
,
Tatjana Djekić
,
Sudhir Kumar Singh
,
Goran Jović
,
Milan Radojković
,
Marko Ivanović
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi13090315
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi13090315
Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi13090315
- OA Status
- gold
- Cited By
- 13
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402129692
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4402129692Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijgi13090315Digital Object Identifier
- Title
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Universal Snow Avalanche Modeling Index Based on SAFI–Flow-R Approach in Poorly-Gauged RegionsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-01Full publication date if available
- Authors
-
Uroš Durlević, Aleksandar Valjarević, Ivan Novković, Filip Vujović, Nemanja Josifov, Jelka Krušić, Blaž Komac, Tatjana Djekić, Sudhir Kumar Singh, Goran Jović, Milan Radojković, Marko IvanovićList of authors in order
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https://doi.org/10.3390/ijgi13090315Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/ijgi13090315Direct OA link when available
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Snow, Index (typography), Meteorology, Physics, Geology, Computer science, World Wide WebTop concepts (fields/topics) attached by OpenAlex
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13Total citation count in OpenAlex
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2025: 12, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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