A Citizen Science Campaign to Validate Snow Remote Sensing Products
Snow in alpine regions is a life-sustaining part of the water cycle. Both glaciers and snowpack form natural reservoirs that gradually release water during the drier summer months, providing drinking water and ecosystem services (the varied benefits that organisms gain from the natural environment and from properly-functioning ecosystems). Changes to the snowpack can change the chemical and physical properties of streams impacting natural habitats.
Community Snow Observations (CSO) is a group of scientists who seek a better understanding of snow in mountainous regions. The project scientists train citizen scientists, including backcountry professionals and recreationists, to help gather snow observations. The volunteers collect snow-depth data using snow probes and will take photographs of snow crystals using their smartphones. The participants then enter their data into MountainHub, a smartphone application.
The snowpack data gathered by citizen scientists is being used to better interpret satellite and airborne snow measurements collected by NASA and other agencies. CSO is also using these datasets to improve water runoff models. Predicting and understanding variability in water runoff will help policy makers manage snow avalanche hazards, water resources, ecology, tourism, and the impacts of a changing climate in Alaska and the Pacific Northwest.
Update October 2019
CSO has improved the capacity for visualizing spatial and temporal trends in snow depth data collected by citizen scientists. Users can now choose a polygon selection tool, outline a series of observations, and get a chart showing the temporal evolution of snow depths within that window.
CSO also developed new algorithms and tools for making use of the snow depth observations for hydrological modeling purposes. They collected a wide range of existing snow depth and density data from across the United States to test existing algorithms and develop new ones for carrying out conversion to snow water equivalent data. CSO was able to develop a new method that predicts snow water equivalent from snow depth.
One of the most widely used approaches currently categorizes snow depth according to land cover type. That method was compared to the new method that predicts snow water equivalent from snow depth, day of water year, and climatological variables. The new approach was found to be more accurate than previous methods in nearly all test cases.
Visit the Community Snow Observations site for more information.
Hill, D. F., Burakowski, E. A., Crumley, R. L., Keon, J., Hu, J. M., Arendt, A. A., Wikstrom-Jones, K., and Wolken, G. J. (2019). Converting Snow Depth to Snow Water Equivalent Using Climatological Variables. The Cryosphere. doi: 10.5194/tc-2018-286
Crumley, R.C., Hill, D.F., Arendt, A., Wolken, G., Wikstrom-Jones, K. (2019). Improving Snow Modeling by Using Smartphones and Crowdsourced Snow Data Collection. University of New Hampshire, Colloquium Series in Earth Sciences, Jan. 24th, 2019.
Crumley, R.C., Hill, D.F., Arendt, A., Wolken, G., Wikstrom-Jones, K. (2018). Improving Snow Modeling by Assimilating Observational Data Collected by Citizen Scientists. American Geophysical Union, Dec. 10-14th, 2018.
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Hill, D. (2019). COMMUNITY SNOW OBSERVATIONS Using Citizen Scientist Data to Build Better Snow Model. The Avalanche Review, vol 37, 16-17.
Hill, D., Wolken, G. J., Jones, K. W., Crumley, R., & Arendt, A. (2018). Crowdsourcing snow depth data with citizen scientists. Eos, 99. doi: 10.1029/2018EO108991
Wikstrom-Jones, K., Wolken, G.J., Hill, D., Crumley, R., Arendt, A., Joughin, J. & Setiawan, L. (2018). Community Snow Observations (CSO): A citizen science campaign to validate snow remote sensing products and hydrological models. Proceedings of the International Snow Science Workshop Innsbruck 2018, 420-242.
Wikstrom-Jones, K., Wolken, G.J., Hill, D., Crumley, R., Arendt, A., Joughin, J. & Setiawan, L. (2018). Community Snow Observations (CSO): A citizen science campaign to validate remote sensing products. Bergundsteigen, 105, 6 p.
Last Updated: Jan 16, 2020 at 4:40 PM EST