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Implications of increasing snow ephemerality for Great Basin hydrology and vegetation
AuthorPetersky, Rose Sarah
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Ephemeral snow is defined in a common persistence threshold as snowpack that persists for less than 60 consecutive days. Current observation and modeling techniques for ephemeral snowpack are lacking or underutilized. Remote sensing is a promising technique for mapping ephemeral snow but only gridded data products that have daily temporal resolution can be used due to the short timespan of ephemeral events. We used the Great Basin U.S.A. as a case study because the lack of cloud cover, the climate and the corresponding vegetation gradient from arid to montane make it an optimal location for studying snow seasonality. We also developed two classification techniques for differentiating ephemeral snow from seasonal snow: The maximum consecutive snow duration and the snow seasonality metric (SSM). In Chapter 1, we used moderate resolution spectroradiometer (MODIS) and snow data assimilation system (SNODAS) data to answer the following questions: 1) How would shifts from seasonal to ephemeral snowpacks affect the availability of melt water? 2) How does topography affect snow seasonality and 3) What mechanisms cause ephemeral snowpacks and how does that vary with climate? We noted the differences in snowpack and soil moisture dynamics between ephemeral and seasonal snow cover at snow telemetry (SNOTEL) and soil climate analysis network (SCAN) stations. Then, we compared maximum consecutive snow duration against elevation and aspect. Lastly, we created a process to categorize ephemeral snowpack based on the dominant mechanism limiting snow cover. In Chapter 2, we used MODIS data and a Random Forest (RF) model to answer the following questions: 1) What topographic and climatic variables are the most influential when predicting snow ephemerality?, 2) Will increases in the average winter temperature lead to a shift from seasonal to ephemeral snowpack?, and 3) What vegetation types are most at-risk to extreme changes in ephemerality relative to their historic conditions? We incorporated topographic and climate variables into our Random Forest model to evaluate which variables were the most influential. We then adjusted the average winter temperature by 2°C and 4°C, and noted how much of the Great Basin shifted from seasonal to ephemeral snow. We also brought in Landfire vegetation classification data to determine what vegetation types are most at-risk from a seasonal-ephemeral shift. In Chapter 4 we summarize the advancements offered by this thesis, but also note the many limitations in our current observations and modeling of ephemeral snow that require future research.