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The return period is a key element used for snow-avalanche characterization. To calculate the return period, historical data regarding past snow-avalanche activity are required. In mountain areas where past snow avalanches are poorly documented, dendrogeomorphic approaches constitute a reliable method for the reconstruction of past snow avalanches...
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... This resulted in a binary value (1 = avalanche, 0 = no avalanche) for any pixel within the avalanche event extents for every year in the period of record. We then used these values to estimate spatially explicit return periods for Path 1163 and Shed 7 using methods developed by Meseșan, Gavrilă, and Pop (2018). For each pixel within each avalanche path, we calculated the return period (RP) using: ...
Snow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88-92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths.
... But once it happens, it will cause great damage to the distribution network. The frequency of suffering from extreme disasters in an area can be measured by the return period [20]. The return period refers to the average number of time intervals between repeated occurrences of an event in many trials. ...
... where is mathematical expectations, 2 is variance. In summary, the constraints of the PV-ES-CSs are as follows: (20) where P ES is charging and discharging power of ES. P ES_min and P ES_max are lower and upper limits of charging and discharging power for ES. ...
The hybrid AC/DC distribution network has become a research hotspot because of the wide access to multiple sources and loads. Meanwhile, extreme disasters in the planning period cause huge losses to the hybrid AC/DC distribution networks. A coupled PV‐energy storage‐charging station (PV‐ES‐CS) is an efficient use form of local DC energy sources that can provide significant power restoration during recovery periods. However, over investment will happen if too many PV‐ES‐CSs are installed. Therefore, it is important to determine the optimal numbers and locations of PV‐ES‐CS in hybrid AC/DC distribution networks balanced with economics and resilience. Firstly, the advantages of PV‐ES‐CS in normal operation and extreme disasters are analysed and the payment function is quantified accurately. Secondly, a bi‐level optimal allocation model of PV‐ES‐CS in hybrid AC/DC distribution networks is established. In this model, the payment function using Nash equilibrium to balance economics and resilience is addressed at the upper‐level, and the typical scenarios are simulated, and the optimal results are obtained using the genetic algorithm in lower level. Finally, a series of examples are analysed, which demonstrate the necessity of balancing economics and resilience, and advantages of DC lines in network restoration after disasters.
... In the Romanian Carpathians, dendrogeomorphologic analysis has been used to reconstruct the temporal patterns of snow avalanche activity in the Parâng (Meseșan et al., 2018;Pop et al., 2015) and Piatra Craiului Mountains (Pop et al., 2017). Furthermore, remote sensing and dendrochronological techniques have been employed to reconstruct the spatial extent of past avalanches in the Parâng Mountains (Meseșan et al., 2019). ...
... Finally, the temporal reconstruction and spatial extent of the avalanche winters have been obtained in the three avalanche paths investigated. The return intervall (or return period) considered as being the average interval of time within which the runout distance is reached by snow avalanches or exceeded at a given location (McClung and Schaerer 2006;Meseşan et al., 2018) was calculated by dividing the time interval (in years) of the tree-ring reconstructed snow-avalanche chronology by the number of snow avalanches events recorded during this period (Boucher et al., 2003). For each of three paths investigated, the average return intervalls at the scale of the entire snow-avalanche path were obtained. ...
This contribution presents a first comprehensive study of snow-avalanche activity in three paths of the Chornohora range, located in southwestern Ukraine, based on historical chronicles and dendrochronology. The results are combined with a statistical analysis of meteorological drivers conducive to snow-avalanche release. While the written chronicles last from 1966 to 2015, the dendrological approach offers results back to the end of the 19th century; however, if the information covers a longer time-lapse, it loses accuracy as only the winter scale is documented through the analysis of tree-ring growing patterns. Weather data highlight the synoptic scenarios over some of the avalanche events that have been recognized as major, as the three paths were concerned: 1947–48, 1976–77, 1993–94, 1998–99, 2001–02. Three weather variables are highlighted: the formation of a consistent snow cover as early as November; positive mean daily temperature in April commands late winter avalanches if the snow cover is maintained with recurrent snow fall. Temperature warming and precipitation increase are also noted on the climatological trends in Chornohora range; however, the winter temperature remains stable, and the snow-avalanche regime might not be affected in the area in the near future.
... In addition to snow, avalanches often contain other materials (rock debris, soil, plants) which are transported and accumulated in the lower areas. The aftermaths of avalanches include loss of human lives and impact on the human environment, settlements and transport infrastructure, biodiversity, landscape, etc. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. A large number of human casualties have been reported in Switzerland, Austria, Italy, Türkiye, Afghanistan, Pakistan, Tajikistan and Canada [14,[24][25][26][27][28][29]. ...
Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed, where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can will help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches, and methodology and criteria used in this study can be tested in other high mountainous regions.
... In addition to snow, avalanches often contain other materials (rock debris, soil, plants) which are transported and accumulated in the lower areas. The aftermaths of avalanches include loss of human lives and impact on the human environment, settlements and transport infrastructure, biodiversity, landscape, etc. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. A large number of human casualties have been reported in Switzerland, Austria, Italy, Türkiye, Afghanistan, Pakistan, Tajikistan and Canada [14,[24][25][26][27][28][29]. ...
Snow avalanches are one of the most devastating natural hazards in the highlands that often cause human casualties and economic losses. The complex process of modeling terrain susceptibility requires the application of modern methods and software. The prediction of avalanches in this study is based on the use of geographic information systems (GIS), remote sensing, and multicriteria analysis—analytic hierarchy process (AHP) on the territory of the Šar Mountains (Serbia). Five indicators (lithological, geomorphological, hydrological, vegetation, and climatic) were processed,
where 14 criteria were analyzed. The results showed that approximately 20% of the investigated area is highly susceptible to avalanches and that 24% of the area has a medium susceptibility. Based on the results, settlements where avalanche protection measures should be applied have been singled out. The obtained data can help local self-governments, emergency management services, and mountaineering services to mitigate human and material losses from the snow avalanches. This is the first research in the Republic of Serbia that deals with GIS-AHP spatial modeling of snow avalanches,
and methodology and criteria used in this study can be tested in other high mountainous regions.
... First, it should be mentioned that the dendrogeomorphological analysis used in this study is that of Pop et al. (2016) and Meseşan et al. (2018Meseşan et al. ( , 2019. Indeed, in the context of the anticipated future development of the ski resort, a preliminary analysis of snow avalanche risk was required based on existing data. ...
... Conversely, the occurrence of avalanches related to the development of long-term (season-wide) precursor factors and which combine winter and spring conditions, seem more difficult to model accurately. • These results, as well as the spatial delimitation of the runout distances for past avalanche events, clearly illustrate the danger that snow avalanches represent in the Parâng Mountains, as reported previously (Pop et al., 2016;Meseşan et al., 2018Meseşan et al., , 2019Todea et al., 2020), and more specifically in this study for the planned ski area. Therefore, further risk analysis (e.g. ...
This paper explores the snow-avalanche regime based on tree-ring reconstructions and their triggering weather conditions with classification tree algorithms.
The results show a significant increased frequency of avalanche events on Zăvoaie NE slope for the second half of the 20th century by comparison to the Scărița SW slope. The classification tree models highlight the weather conditions leading to avalanche release with three scenarios in each path. The first scenario underlines the wind's effect as a key weather variable on both slope aspects. The second scenario corresponds to a spring regime, while rain and warm temperatures are the main triggers. The third general weather condition favouring snow avalanche activity are persistent low temperatures and important snowfall throughout the winter season. However, this triggering condition was mainly found on the NE avalanche path, probably related to the pattern of snow accumulation, the prevailing winds, but above all the lower solar radiation which favours a slower and later melting of the snow cover. Finally, the return periods and runout distances calculated from tree ring analysis show a high risk for the location of the infrastructure planned for the ski area expansion, showing once again the usefulness of dendrogeomorphology in natural hazard assessment where historical data are lacking.
One of the purposes of dendrogeomorphic studies is to provide long and continuous reconstructions of mass movements and to detect climate-induced trends in process activity. The development of regional chronologies—in which information from different sites are aggregated—is often needed to identify process–climate relations and to overcome local-scale specificities, sparse data available for individual sites and to extract a signal that is common to a larger region and possibly driven by past climate fluctuations or large-scale environmental changes. Yet, such chronologies are scarce and consensus neither exists on how to compile local data at the regional scale nor on the methods to be used to extract a common signal. In the case of snow avalanches, existing regional tree-ring studies typically included less than ten paths, and they discriminated years of high/low avalanche activity based on a regional index representing the proportion of disturbed trees in any given year. However, such an index does not account for potential non-stationarities in local tree-ring reconstructions such as e.g., time-varying sample size, decreasing dendrogeomorphic potential of trees after the occurrence of an extremely large, devastating avalanche or socio-environmental changes. Here we combine a dendrogeomorphic approach to reconstruct snow avalanche events in 11 paths located in the Goms valley (Swiss Alps) with an innovative statistical modelling approach. For each path, we compute reconstructions using a 4-step procedure to disentangle potential effects of snow avalanches from disturbance pulses in trees caused by climatic or other exogenous factors. We then process the regional dataset (spanning the period 1766–2014) within a Bayesian hierarchical spatio-temporal framework specifically designed to homogenise time series of avalanche events by i) removing trends related to the decreasing number of living trees back in time and ii) inferring robust trends in mean annual/regional avalanche activity in time and space. This contribution has the merit to introduce a methodological approach allowing rigorous extraction of common, average avalanche signals from snow avalanche paths characterized by heterogeneous process activity. Despite its stringency, we show that 11 avalanche paths may not suffice to yield a signal that is independent from the selection of couloirs. As a result, the approach also does not highlight a clear climatic control of snow avalanche activity but rather points to a complex, yet combined impact of afforestation and management strategies on reconstructed avalanches.
The present study addresses, for the first time, the problem of spatio-temporal reconstruction of geomorphic processes using tree-rings in the Sâmbăta Valley (Romanian Carpathians). The dendrogeomorphic analysis was conducted in two different sites, one affected by snow avalanches and the other by rockfall. A total number of 130 Picea Abies were sampled in the two sites. The results yield 13 major snow avalanches between 1950 and 2020 and a return period of 3.3 years. The winters with the highest activity index were 1988, 1997 and 2012. The rockfall reconstruction highlights several years of intense activity: 1952, 1955, 2003 and 2012. Thus, the results of the present study provide evidence of active geomorphic processes in the studied area, indicating that tourists are highly exposed to geomorphic hazards, as both sites interfere with popular hiking trails. (Because Sâmbăta Valley is one of the most intensely frequented by tourists in the Făgăraș Mountains, it is a need for warning signs to be installed on the exposed trails.
Snow avalanches affect transportation corridors and settlements worldwide. In many mountainous regions, robust records of avalanche frequency and magnitude are sparse or non-existent. However, dendrochronological methods can be used to fill this gap and infer historical avalanche patterns. In this study, we developed a tree-ring-based avalanche chronology for large magnitude avalanche events (size ≥∼D3) using dendrochronological techniques for a portion of the US northern Rocky Mountains. We used a strategic sampling design to examine avalanche activity through time and across nested spatial scales (i.e., from individual paths, four distinct subregions, and the region). We analyzed 673 samples in total from 647 suitable trees collected from 12 avalanche paths from which 2134 growth disturbances were identified over the years 1636 to 2017 CE. Using existing indexing approaches, we developed a regional avalanche activity index to discriminate avalanche events from noise in the tree-ring record. Large magnitude avalanches, common across the region, occurred in 30 individual years and exhibited a median return interval of approximately 3 years (mean = 5.21 years). The median large magnitude avalanche return interval (3–8 years) and the total number of avalanche years (12–18) varies throughout the four subregions, suggesting the important influence of local terrain and weather factors. We tested subsampling routines for regional representation, finding that sampling 8 random paths out of a total of 12 avalanche paths in the region captures up to 83 % of the regional chronology, whereas four paths capture only 43 % to 73 %. The greatest value probability of detection
for any given path in our dataset is 40 %, suggesting that sampling a
single path would capture no more than 40 % of the regional avalanche
activity. Results emphasize the importance of sample size, scale, and spatial extent when attempting to derive a regional large magnitude
avalanche event chronology from tree-ring records.
