Topographic Influences on Snowmelt in a Warmer World

With Airborne Snow Observatory (ASO) Lidar Data

Geohackweek 2018

Slack channel: #snowmelt

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Collaborators:

• Nicoleta Cristea (cristn@u.washington.edu) (Data Science Lead)
• Cassie Lumbrazo (lumbraca@uw.edu) (Project Lead)
• Steven Pestana (spestana@uw.edu)
• Caitlin Littlefield (clittlef@uw.edu)
• Emma Collins (collinsel2@g.cofc.edu)
• Ian McNabb (mcnabbi@uw.edu)
• Deeksha Rastogi (drastogi@vols.utk.edu)
• Vivian Griffey (vgriffey@uw.edu)
• Nick Gottlieb (ngottlieb@gmail.com)

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The Problem:

• Lidar from the NASA Airborne Snow Observatory can provide snapshots in time of snow depth across a watershed
• Previous geohackweek projects have developed tools for spatial analyses of these data
• We want to expand these tools to investigate temporal changes in snow depth as a function of topgraphic and climatic variables.

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Data:

https://drive.google.com/drive/folders/1uhxMHkf9YgU2qVDntqTGSbzZlJY0v94X

• Snow depth (30m, ASO lidar-derived) 2014 - 2016
• DEM (30m, ASO lidar-derived)
• PRISM temperature data

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Specific Questions:

• How does the change in snow depth (melt and accumulation) within one melt season behave as a function of topography (slope, aspect, elevation) in the Tuolumne River watershed?
• How does snow depth vary between two melt seasons as a function of change in minimum, maximum, and mean temperature?
• How do these behaviors compare between relatively “normal” snowpack years (2014, 2016) and a year with much lower snowpack (2015 - representative of future conditions due to climate change)?

Ultimate goal:

• Can we conclude that there is “slower snowmelt in a warmer world” (as posited in Musselman et al. 2017)?

Broader Impacts:

• The Tuolumne River Basin (TRB) is a major water supply for human use in California
• Winter snowpack in the TRB is a natural form of water storage, that may change due to climate change
• 2015 was an anamolous year in precipitation and temperature
• It is expected that with climate change, more future years will resemble the 2015 water year
• We can test the hypothesis suggested by previous work - that snowmelt will be slower in warmer temperatures

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Application Examples and Future Investigations:

• Incorporating streamflow, the results could improve water resources modeling and prediction
• Effects of changing snow depth patterns on species bahavior and ranges
• Snow depth change in topographically complex depressions (current models may not capture this well)

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Existing Methods/Tools:

• https://github.com/NCristea/NCristeaGEE

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Proposed Methods/Tools:

• Raster/array math
• Linear regressions

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Background Reading:

• NASA JPL - Airborne Snow Observatory
• Musselman, Keith N., et al. "Slower snowmelt in a warmer world." Nature Climate Change 7.3 (2017): 214. doi: 10.1038/nclimate3225 https://www.nature.com/articles/nclimate3225.pdf
• Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S., Gehrke, F., ... & Mattmann, C. (2016). The Airborne Snow Observatory: Fusion of scanning lidar, imaging spectrometer, and physically-based modeling for mapping snow water equivalent and snow albedo. Remote Sensing of Environment, 184, 139-152.