Written by: Ryan Crumley.
After snow depth measurements are recorded by CSO participants, we’ve developed a way to integrate those observations into the process of snowpack modeling. Snow models use data from weather stations and landscape characteristics to build a snowpack during the winter and melt it away when the weather gets warmer in spring and summer. But our ability to accurately predict snow depths is dependent upon accurate measurements of snowpack conditions as they change throughout the year. Since most mountainous areas are difficult to access on a daily or weekly basis, scientists are hoping that CSO participants will fill the data gap. Citizen science based measurements will allow us to monitor snowpack conditions more effectively, and possibly improve our snow modeling capacity.
Better results from snow models will inform multiple interest groups. Avalanche prediction relies on snow models to produce accurate snow depths, elevations, and aspects for wind loading. Flood prediction related to rain-on-snow events depend on snow models to accurately predict the density and water content of a snowpack. Water resource managers use on snow models to predict the amount of water that will end up in our reservoirs and river systems when the snow melts in springtime.
The initial results from CSO project study area in Alaska are promising. We chose an area near Valdez, AK along the Richardson Hwy that is often visited by winter recreationalists for snowmobiling and backcountry skiing. Last year we received hundreds of snow depth measurements from CSO participants within the study area at Thompson Pass. Here is a map of the Thompson Pass along with markers of the locations of snow observations.
We ran the model hundreds of times to make sure we have it ‘tuned-in’ to local environmental conditions. Then, we selected the best model run to integrate the measurements from CSO participants. The takeaway: the model results using citizen scientist observations of snow depth greatly improved the accuracy of the snow model when compared to a highly-trusted dataset called Snotel, which includes precise measurements of actual snow depths. (https://www.wcc.nrcs.usda.gov/snow/). Going forward, we are developing methods to quickly and accurately integrate CSO participants’ snow depth observations into the modeling process and we hope to apply these methods to additional study sites in locations around the globe.
Image 1: A map of the Thompson Pass study area near Valdez, Alaska. The markers show the locations of each of the observations collected by CSO participants during the 2016-2017 snow season.
Image 2: The above image shows snow depth within the Thompson Pass study area on April 30th, 2017. This map is from the best run of 266 model runs, but does not have any citizen science observations integrated into the modeling process.
Image 3: Snow depth on April 30th, 2017 after citizen science observations have been integrated into the modeling process.