Calculating snow-avalanche return period from tree-ring data

Meseşan, Flaviu; Gavrilă, Ionela-Georgiana; Pop, Olimpiu

doi:10.1007/s11069-018-3457-y

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…Overall, this study region provides an ideal natural setting for studying avalanches due to its geography, inclusion of transportation and recreation corridors potentially impacted by avalanches, relative accessibility, and no artificial avalanche hazard mitigation. Northwest Montana's avalanche climate is classified as both a coastal transition and intermountain avalanche climate (Mock and Birkeland, 2000), but it can exhibit characteristics of both continental and coastal climates. The elevation of avalanche paths within the study sites range from approximately 1100 to 2700 m, and the starting zones of these paths are distributed among all aspects (Table 1).…”

Section: Study Sitementioning

confidence: 99%

A regional spatiotemporal analysis of large magnitude snow avalanches using tree rings

Peitzsch

Hendrikx

Stahle

et al. 2021

Nat. Hazards Earth Syst. Sci.

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Abstract. 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.

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Section: Study Sitementioning

confidence: 99%

A regional spatiotemporal analysis of large magnitude snow avalanches using tree rings

Peitzsch

Hendrikx

Stahle

et al. 2021

Nat. Hazards Earth Syst. Sci.

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“…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;Meses ¸an 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.…”

Section: Tree-ring Methodsmentioning

confidence: 99%

Patterns of snow avalanche activity during the last century in Chornohora Range (Eastern Carpathians, Ukraine): Tree-ring reconstruction coupled with synoptic conditions analysis

Decaulne,

Răchită,

Kholiavchuk

et al. 2023

CATENA

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“…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.…”

Section: Resiliencementioning

confidence: 99%

“…where 𝜇 is mathematical expectations, 𝜎 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.…”

Section: Constraint Conditionmentioning

confidence: 99%

Allocation method of coupled PV‐energy storage‐charging station in hybrid AC/DC distribution networks balanced with economics and resilience

Ma,

Zhang,

Cai

et al. 2023

IET Renewable Power Gen

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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.

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Calculating snow-avalanche return period from tree-ring data

Cited by 11 publications

References 29 publications

A regional spatiotemporal analysis of large magnitude snow avalanches using tree rings

A regional spatiotemporal analysis of large magnitude snow avalanches using tree rings

Patterns of snow avalanche activity during the last century in Chornohora Range (Eastern Carpathians, Ukraine): Tree-ring reconstruction coupled with synoptic conditions analysis

Allocation method of coupled PV‐energy storage‐charging station in hybrid AC/DC distribution networks balanced with economics and resilience

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