Avalanches are among the most significant natural hazards in the mountainous regions of the Central Alborz, particularly in areas with tourism activities and sensitive infrastructure, where they pose a serious threat to human life and economic systems. The objective of this study is to identify and analyze the factors influencing avalanche occurrence in the upstream area of the Karaj Dam watershed using multivariate statistical methods. Avalanche occurrence data for the period 2023–2024 were collected based on field observations and local evidence, including 150 avalanche points and 142 non-avalanche points used as control data. In addition, 20 topographic, morphometric, geological, land use, and climatic variables were extracted using a 10 m resolution Digital Elevation Model (DEM), station-based climatic records, and thematic maps. Principal Component Analysis (PCA) was applied to reduce data dimensionality and extract dominant underlying patterns. Hierarchical clustering using Ward’s method was performed to group variables with similar characteristics, and Discriminant Function Analysis (DFA) was used to differentiate avalanche-prone and non-avalanche areas. The PCA results led to the extraction of six independent components that collectively explained 81.51% of the total variance. Based on the clustering outcomes, the variables influencing avalanche occurrence were categorized into three homogeneous clusters. The DFA results showed that Relative Slope Position, Valley Depth, Topographic Wetness Index, and Distance to River had the highest discriminative power between avalanche and non-avalanche areas. The overall classification accuracy of the model was 85.9%, while the cross-validation accuracy reached 84.5%, demonstrating the high effectiveness of multivariate statistical approaches in predicting avalanche occurrence.
Extended Abstract
Introduction
Avalanches are one of the most significant natural hazards in the mountainous regions of the Central Alborz, particularly in areas with tourism activities and sensitive infrastructure. Avalanches pose a serious threat to human lives and economic activities, especially in areas with tourism and critical infrastructure. Every year, avalanches cause considerable damage to residential areas, transportation routes, and vital infrastructures. Therefore, identifying areas prone to avalanches and implementing appropriate measures to control and mitigate them plays a crucial role in managing and reducing the impacts of this natural phenomenon. Several factors influence the formation and occurrence of avalanches, with the most important being snow cover characteristics, meteorological factors such as the type and amount of precipitation, temperature, and morphological features including slope, aspect, elevation, slope curvature, land use, and geological conditions. In recent years, global climate changes, which are primarily associated with rising temperatures, have significantly altered weather patterns in terms of spatial and temporal distribution. This warming affects the formation, stability, and transformation of snow cover, which in turn influences avalanche occurrence. The increase in temperature enhances the freeze-thaw cycles, contributing to the formation of weak layers in snow masses. Therefore, the objective of this research is to identify and analyze the factors affecting avalanche occurrence in the upstream area of the Karaj Dam watershed, including the sub-watersheds of Velayat Rud, Kasil Nesa, and Varange Rud, using multivariate statistical methods.
Material and Methods
In this study, the factors influencing avalanche occurrence were analyzed, focusing on morphological, topographical, climatic, and environmental characteristics. For this purpose, 20 factors affecting avalanche occurrence were considered, including morphometric, topographical, geological, geomorphological, land use, and climatic variables. Avalanche occurrence data were collected based on field observations, historical evidence, and surveys conducted between 2023 and 2024. A total of 150 avalanche-prone points from 50 potential areas were identified, and 142 non-avalanche points were selected as control data. To reduce data dimensions and identify the most significant factors, Principal Component Analysis (PCA) was used. For clustering areas with similar features, hierarchical clustering with the Ward method was employed. Then, to differentiate and compare avalanche-prone and non-prone areas, Discriminant Function Analysis (DFA) was applied. Statistical tests such as KMO, Bartlett’s test, Wilks' Lambda, and cross-validation were used to evaluate the models. The statistical and processing analyses in this study were performed using ArcGIS, SAGA GIS, SPSS, and R software.
Results and Discussion
The results of the Principal Component Analysis (PCA) showed that six principal components were extracted, which explained 81.51% of the total data variance. The first component indicated the relative position of the slope and the distance from rivers, the second component showed elevation, precipitation, and temperature, and the third component represented plan curvature and slope length index. Other components highlighted slope, terrain ruggedness index, aspect, and profile curvature, showing the role of topography and solar radiation in avalanche occurrence. Hierarchical clustering analysis identified three homogeneous clusters: The first cluster included topographic ruggedness index (TRI), slope, aspect, valley depth (VD), topographic wetness index (TWI), analytical hillshading (AH), elevation, and precipitation, representing areas with unstable slopes and high snow accumulation. The second cluster included plan curvature (PL), convergence index (CI), topographic position index (TPI), profile curvature (PRL), distance from rivers (RD), relative position of slopes (RSP), vector ruggedness index (VRM), slope length (LS), and distance from faults (DTF), which indicated areas influenced by morphometric and geomorphological features. The third cluster included only temperature, which separately represented the climatic effects. This clustering analysis indicates that avalanche occurrence is influenced by a combination of morphometric, environmental, and climatic factors, and it allows for accurate identification of avalanche-prone areas. Discriminant Function Analysis (DFA) showed significant differences between avalanche and non-avalanche groups (Wilks' Lambda = 0.432, p = 0.001). The relative position of the slope (RSP), valley depth (VD), and topographic wetness index (TWI) were identified as the most important factors distinguishing avalanche-prone areas. The DFA model correctly classified 85% of the points, and its accuracy was 84% in cross-validation. The results suggest that the selected topographic and environmental indices well represent the spatial pattern of avalanche occurrence. Moreover, 88.7% of avalanche-prone areas were found to have pasture land use. In terms of geology, the majority of the area lies in the Karaj formation (56.1%), Quaternary deposits (9.2%), and the Shemshak formation (5.7%). Avalanche occurrence is the result of a complex interaction of topographic, environmental, and climatic factors, and the models can be useful for avalanche risk prediction and management. The results obtained can be applied in the preventive management of hazards, land use planning, and reducing human and financial losses, especially in high-traffic tourist areas such as the Dizin Ski Resort.
Conclusion
The results of this study showed that avalanche occurrence in the upstream area of the Karaj Dam watershed is the result of a complex and multidimensional interaction of topographic, geomorphological, and climatic factors, and no single variable can explain the spatial pattern of this phenomenon. Principal Component Analysis (PCA) revealed the dominant structures between environmental variables by reducing data dimensions, showing that the elevation-precipitation-temperature gradient, the relative position of slopes to the drainage network, surface geometry of the slope, and moisture conditions are among the most significant components affecting snow mass instability. The extraction of six independent components, which explain more than 80% of the total data variance, indicates the high potential of this method for simplifying and interpreting complex environmental data. Hierarchical clustering results showed that the factors affecting avalanche occurrence could be categorized into three main groups: factors related to terrain roughness and slope, geomorphological factors guiding avalanche movement, and the climatic factor of temperature. Discriminant Function Analysis (DFA) confirmed the key role of some variables in distinguishing between avalanche-prone and non-prone areas. The relative position of the slope, valley depth, topographic wetness index, and distance from rivers were identified as the most important distinguishing factors. In contrast, variables such as vector ruggedness index, slope aspect, and distance from faults showed limited influence on distinguishing avalanche-prone areas. Therefore, this study demonstrates that the combined use of multivariate statistical methods can be an effective tool for identifying key factors, reducing uncertainty, and improving avalanche risk zoning in mountainous areas.
Acknowledgements
The authors would like to thank financial supporting of Ferdowsi University of Mashhad (Poject No. 64099) in the present study.