Waterhackweek 2019: Snowmelt

With Airborne Snow Observatory (ASO) Lidar Data

Slack channel: #snowmelt

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Collaborators:

• Lisa Katz (Project Lead)
• Steven Pestana (spestana@uw.edu) (Data Science Lead)

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The Problem:

• What are the patterns (spatial, temporal) of snowmelt in the Tuolumne River Basin?
• Can we deliniate a snowmelt elevation band using in situ data from Dana Meadows, and an assumed air temperature lapse rate?

Specific Questions/Goals:

• Learn how to read, plot, and manipulate ASO raster data, and time series meteorological data in python

Broader Impacts and Applicaitons:

• The winter snowpack of the Tuolumne River Basin (TRB) is a major water supply for human use in California

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Data:

Lidar from the NASA Airborne Snow Observatory provides snapshots in time of snow depth across a watershed

• Snow depth (geotiff raster at 30m resolution, ASO lidar-derived) 2014, 2015, 2016
• DEM (geotiff raster at 30m resolution, ASO lidar-derived)
• Data readme
• Google drive folder
• Data access demo notebook

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Existing Methods/Tools and Prior Work:

• https://github.com/NCristea/NCristeaGEE
• https://github.com/geohackweek/ghw2018_snowmelt
• Creating a hydroshare resource, and in pysumma
• environment file from Geohackweek tutorial materials

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Python Packages Used:

• numpy
• pandas
• rasterio

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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.