Universal Snow Avalanche Modeling Index Based on Safi–Flow-R Approach in Poorly-Gauged Regions

Datos Bibliográficos

ID22033656
AutoresUroš Durlević (0000-0003-3497-5239, University of Belgrade), Aleksandar Valjarević (0000-0003-2997-2164, University of Belgrade, autor de correspondencia), Ivan Novković (0000-0002-1706-0451, University of Belgrade), Filip Vujović (0000-0001-6131-5354, University of Montenegro), Nemanja Josifov (0009-0007-9148-2664, University of Belgrade), Jelka Krušić (0000-0003-3646-5873, University of Belgrade), Blaž Komac (0000-0003-4205-5790, Slovenian Academy of Sciences and Arts), Tatjana Djekic (0009-0007-3398-755X, University of Nis), Sudhir Kumar Singh (0000-0001-8465-0649, University of Allahabad), Goran Jović (University of East Sarajevo), Milan Radojković (University of Belgrade), Marko Ivanović (0000-0001-5281-4776, Department of Geography, Faculty of Sciences, University of Priština in Kosovska Mitrovica, Lole Ribara 29, 38220 Kosovska Mitrovica, Serbia)
Año2024
Volumen13
Número9
Páginas315
Fecha de publicación2024-09-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaISPRS International Journal of Geo-Information (JOURNAL)
Identificadores de la revistaISSN: 2220-9964 • E-ISSN: 2220-9964
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/ijgi13090315
OpenAlexW4402129692
IdiomaEN
Referencias citadas51

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

Meteorology · Physics · Snow · Climate change and permafrost · Computer Science · Cryospheric studies and observations · Landslides and related hazards · Geology