Session 9

Observation and modelling of snow processes: New advances in an era of big data, UAVs, and high-performance computing

Conveners: Chris Marsh1, Phillip Harder1, Vincent Vionnet2

1 University of Saskatchewan, Emails:,

2 Environmental Numerical Research Prediction, Environment and Climate Change Canada, Dorval, QC, Email:

Session Description

Seasonal snowpacks store substantial volumes of water and their melt provides fresh water supplies to downstream users and ecosystems. Globally, they are estimated to provide essential flows for about one-sixth of the world’s population. Ongoing anthropogenic climate and land use change are dramatically impacting the snowpacks driving these critical flows. There is therefore significant incentive to provide better estimates of these snowpacks and their physical processes through improved observations, analysis, and modelling.

Innovations in modelling, analysis, and observations have expanded predictive and observation capacity in unprecedented ways. Tremendous advances in all types of remote sensing platforms have expanded observation capabilities. For example, the rapid democratisation of remote sensing technology via UAVs have allowed individual researchers the capabilities to observe particular snow processes at unprecedented spatial and temporal resolutions. Broad access to high performance computing resources through academic institutions and commercial vendors have enabled increased resolution, larger spatial and temporal coverage of numerical models, and improved representation of physical processes. The creation of massive datasets constitutes a challenge for the snow community and requires new developments to generate substantial scientific advances in the coming years.

In this session we invite contributions from the broader snow science community who are interested in observations, analysis, and/or models to share their experiences, insights, and new advances in utilizing these next-generation tools. Canada boasts impressive, but at times disconnected, snow science capabilities and we envision this session to be a forum to highlight and discuss areas of recent progress and collective gaps.

Primary Affiliation: Hydrology