Snow Water Equivalent trends in North America through the lens of passive microwave remote sensing and deep learning models

Abstract. Over the past decades, snow cover trends in North America have been analyzed, providing vital information to the Global Climate Observatory System and other stakeholders about the looming signals of climate-driven snow declines. Detecting daily changes in snow parameters (e.g., depth of snow, extent of snow cover, and snow water equivalent) is, however, fraught with challenges, including internal variability unrelated to climate signals. We used GlobSnow’s passive microwave remote sensing data and a convolutional Siamese U-Net to track how snow water equivalent (SWE) changes daily over North America’s mid- and high-latitude regions. Daily changes in SWE were detected with F1-scores of 94.8% and 100.0% in locations where it was not trained, and 99.3% at the location where it was primarily trained; this suggests the model’s generalization potential to different climatologies and geographic locations. Using the model, we computed a similarity vector to compare SWE trends. We found that although lake-effect snowfall may be prevalent in the Great Lakes Basin during the winter months, the region consistently records the highest frequency of daily changes in SWE. Alaska, Yukon, and the Northwest Territories tended to have minimal daily changes in SWE, suggesting that latitudinal gradients may dominate changes in the snow regime and cryosphere’s processes in the warming climate scenarios.