4.6 Article

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

Journal

NATURAL HAZARDS AND EARTH SYSTEM SCIENCES
Volume 21, Issue 2, Pages 533-557

Publisher

COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/nhess-21-533-2021

Keywords

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Funding

  1. US Geological Survey Land Resources Western Mountain Initiative project

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The study utilized dendrochronological methods to investigate the historical patterns of large magnitude avalanches in the northern Rocky Mountains region of the United States. Results showed variations in avalanche activity across different time periods and spatial scales, highlighting the importance of sample size, scale, and spatial extent in deriving a regional chronology of large magnitude avalanche events from tree-ring records.
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 >=similar to 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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