A regional spatio-temporal analysis of large magnitude snow avalanches using tree rings

Abstract

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 historic avalanche patterns. In this study, we developed a tree-ring based avalanche chronology for large magnitude avalanche events using dendrochronological techniques for a sub-region of the northern United States 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 sub-regions, and the region). We analysed 673 total samples from 647 suitable trees collected from 12 avalanche paths, from which 2,134 growth disturbances were identified over years 1636 to 2017 Common Era (C.E.). 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 three years (mean = 5.21 years). The median large magnitude avalanche return interval (3–8 years) and the total number of avalanche years (12–18) vary throughout the four sub-regions, suggesting the important influence of local terrain and weather factors. We tested subsampling routines for regional representation, finding that sampling eight 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.

Climate drivers of large magnitude snow avalanche years in the U.S. northern Rocky Mountains

“…Using an I t index that provides an indication of the number of GDs per samples alive in any given year combined with a minimum number of GDs based on sample size helped to distinguish actual avalanche signal from noise. In addition to this double threshold, a weighted index W it , accounts for the quality classification threshold of each GD (see Peitzsch et al 29 for specific equations) and results in the identification of 30 regional large-magnitude avalanche years over 1867 to 2017 (Fig. 1 , Table 1 ).…”

“…Of those 673 samples, we collected 614 cross sections (91%) and 59 cores (9%). See Peitzsch et al 29 for processing methodology and tree-ring avalanche signal classification, and Peitzsch et al 30 for further details on cross-dating methods and accuracy calculation for this dataset.…”

“…For tree-ring data, we used the package slideRun , an extension of the burnR library for forest fire history data 49 . We employed a multi-step process to generate a regional avalanche chronology 29 . First, we implemented a double threshold approach 50 , 51 using the number of growth disturbances (GD) per year and an avalanche index I t 52 to distinguish between avalanche signals and noise (i.e., irregular growth, snow creep, non-avalanche disturbance).…”

“…Using the concept of the scale triplet 28 , we designed and sampled a regional network of 12 avalanche paths in the northern Rocky Mountains in northwest Montana, USA (Fig. 1 ) 29 , 30 . We strategically designed the sampling so that the avalanche occurrence data inherently reflects large magnitude avalanche activity on the local scale as well as the regional scale.…”