Snow models provide continuous estimates of snow distribution in mountainous regions, which serve as headwaters for many US water basins. These models can be improved by integrating on-the-ground observations. The Community Snow Observations (CSO) project, a collaboration between snow scientists, recreationists and professionals, recently made a global snow depth dataset available. This study finds that CSO observations fill key gaps in our snow monitoring network, particularly in higher elevations, steeper slopes, and complex terrain.

This study evaluates CSO data as a stand-alone assimilation source and investigates the added value of assimilating CSO data in combination with station-based observations in high, average, and low precipitation years in Utah. Using both a simple and a more complex snow model, results show that CSO data can improve snow distribution estimates when used alone in the simpler model and provide additional benefit beyond station data when both are assimilated. In the more complex model, improvements from CSO data are more limited, likely due to the model’s enhanced calibration.

Future CSO participants are encouraged to submit observations near areas where they seek improved snow distribution estimates. This study demonstrates the potential of CSO data to improve snow distribution in regional and national snow modeling efforts.