Staff Research Highlight - Future snow changes over the Columbia Mountains, Canada, using a distributed snow model

Mortezapour, M., Menounos, B., Jackson, P. L., & Erler, A. R. (2022). Future Snow Changes over the Columbia Mountains, Canada, using a Distributed Snow Model. In Climatic Change (Vol. 172, Issues 1–2). Springer Science and Business Media LLC. https://doi.org/10.1007/s10584-022-03360-9

This paper, co-authored by Andre Erler and researchers from the University of Northern British Columbia, investigates climate change impacts on snow depth using a distributed snow model called SnowModel. Snowmelt is an essential water source for communities, and seasonal snow accumulation in many regions is decreasing with each passing year. Water managers, communities, and policymakers can benefit from improved snow modeling forecasts to inform their decision making and understand vulnerabilities to their water supply systems. This study was conducted in the Columbia River basin in British Columbia and focused on projected changes in seasonal snow cover using SnowModel and different long-term climate forcing data inputs (i.e., dynamically downscaled regional climate forcing data using the Weather Research and Forecasting (WRF) model, and statistically downscaled forcing data provided by the Pacific Climate Impacts Consortium (PCIC)). As this study was based in BC, the complex, mountainous topography was an important factor to consider. The data produced by SnowModel was cross-checked with past studies to ensure accuracy and was consistent with earlier research.

The results of the study indicate that elevation and season are the most impactful variables driving snowpack loss, along with temperature and precipitation. Mid-elevation locations (1000–2000 m above sea level) will see the largest loss of snowpack. Locations below 2000 m above sea level may see a 60% reduction in snow depth and snow water equivalent. The authors also found that dynamically downscaled forcing data (WRF) result in larger forecasted loss in snowpack compared to the statistically downscaled forcing data (PCIC).

Regardless of the model and forcing data used, it’s clear that some areas of BC are warming at twice the global average, resulting in decreased snow depth and snow water equivalents. Decreasing snow depth in the winter means that the snow will melt faster in the spring and autumn. While statistically downscaled data and dynamically downscaled data yield different projected changes in snow depth, they both have their advantages. For example, the dynamically downscaled input data was able to encompass a wider range of interactions across different geographic conditions, while statistically downscaled data had a lower computational demand.

Snowmelt and accumulation are important drivers of hydrology across many regions of Canada.