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Snowdrift Landscape Patterns: An Arctic Investigation
  • Charles Parr,
  • Matthew Sturm,
  • Christopher F Larsen
Charles Parr
University of Alaska Fairbanks

Corresponding Author:[email protected]

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Matthew Sturm
University of Alaska Fairbanks
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Christopher F Larsen
University of Alaska Fairbanks
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Abstract

Between 2012 and 2018 we mapped near-peak seasonal snow depths across two swaths covering 126 km in Northern Alaska using aerial structure-from-motion photogrammetry and lidar surveys. The surveys were validated by over a hundred thousand ground-based depth measurements. Using a quantitative method for identifying drift areas, we conducted a snowdrift census that showed on average 18% of the study area is covered by snowdrifts each winter, with 40% of the snow-water-equivalent contained in the drifts. Within the census we identified six types of drifts, some of which fill each winter, others which do not. The seasonal drift evolution was distinctly different in the two swaths, a result largely explained by physiographic differences. Using four metrics from the field of image quality analysis, we tested the year-to-year fidelity of these drift patterns, finding overall high year-to-year similarity (>70%), but with higher similarity values for filling drifts, and higher similarity in one swath vs. the other, again a function of the physiography. These high drift fidelity values are best explained by climatically convergent cumulative wind-blown snow fluxes interacting with drift traps to produce the same drifts year after year despite considerable differences in winter weather. However, due to the existence of filling vs. non-filling drifts, and a predicted increasing frequency of rain-on-snow events in the Arctic, future snowdrift patterns and drift evolution in the Arctic are likely to diverge from those of today.
Dec 2020Published in Water Resources Research volume 56 issue 12. 10.1029/2020WR027823