26 Apr 2022
26 Apr 2022
Status: this preprint is open for discussion.

Interactions between thresholds and spatial discretizations of snow: insights from wolverine habitat assessments in the Colorado Rocky Mountains

Justin M. Pflug1,a,b, Yiwen Fang2, Steven A. Margulis2, and Ben Livneh1,3 Justin M. Pflug et al.
  • 1Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado, Boulder, CO, 80309, USA
  • 2Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, 90095, USA
  • 3Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, CO, 80309, USA
  • anow at: Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
  • bnow at: ESSIC, University of Maryland, College Park, College Park, MD, 20742, USA

Abstract. Thresholds can be used to interpret environmental data in a way that is easily communicated and useful for decision making purposes. However, thresholds are often developed for specific data products and time periods, changing findings when the same threshold is applied to datasets or periods with different characteristics. Here, we test the impact of different spatial discretizations of snow on annual estimates of wolverine habitat in the Colorado Rocky Mountains, defined using a snow water equivalent (SWE) threshold (0.20 m) and threshold date (15 May) from previous habitat assessments. Annual wolverine habitable area (WHA) was thresholded from a 36-year (1985–2020) snow reanalysis at three different spatial discretizations: 1) 480 m grid cells, 2) 90 m grid cells, and 3) 480 m grid cells with implicit representations of subgrid snow spatial heterogeneity. Relative to the 480 m grid cells, 90 m grid cells resolved shallower snow deposits on slopes between 3050 and 3350 m elevation, decreasing WHA by 10 %, on average. In years with warmer and/or drier winters, grid cells with subgrid representations of snow heterogeneity increased the prevalence of 15 May snow deposits that exceeded the 0.20 m SWE threshold, even within grid cells where mean SWE was less than the threshold. These simulations increased WHA by upwards of 30 % in low snow years, as compared to simulations without subgrid snow heterogeneity. Despite WHA sensitivity to different snow spatial discretizations, WHA was controlled more by annual variations in winter precipitation and temperature. However, small changes to the SWE threshold (± 0.07 m) and threshold date (± 2 weeks) also affected WHA by as much as 82 %. Across these threshold ranges, WHA was approximately 18 % more sensitive to the SWE threshold than the threshold date. However, the sensitivity to the threshold date was larger in years with late spring snowfall, when WHA depended greatly on whether the date SWE was thresholded was before, during, or after spring snow accumulation. Our results demonstrate that snow thresholds are useful but may not always provide a complete picture of the annual variability in snow-adapted wildlife habitat. Studies thresholding spatiotemporal datasets could be improved by including 1) information about the fidelity of thresholds across multiple spatial discretizations, and 2) uncertainties related to ranges of realistic thresholds.

Justin M. Pflug et al.

Status: open (until 21 Jun 2022)

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Justin M. Pflug et al.

Justin M. Pflug et al.


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Short summary
Wolverine habitat inferred using a snow threshold differed for three different spatial representations of snow. These differences were annually repeatable and based on the volume of snow and the elevation of the snow line. While habitat was most influenced by winter meteorological conditions, our results show that studies applying thresholds to environmental datasets should report uncertainties stemming from different spatial resolutions and uncertainties introduced by the thresholds themselves.