The Globe and Mail - Loss of snow and impact on water supplies tied to climate change
In some Canadian cities this year, residents have noticed a decrease in snowfall compared to previous winters, reflecting a variable element of weather that's challenging to pin down amid broader climate change trends.
A recent study highlighted in this article published by The Globe and Mail has linked a decline in snowpack—the volume of snow on the landscape—to human-caused global warming, forecasting significant implications for ecosystems and watersheds reliant on melting snow each spring. This shift may fundamentally change how residents of northern countries like Canada experience winter.
The study, published in the journal Nature, forecasts significant implications for ecosystems and watersheds reliant on melting snow each spring. It suggests that even modest further warming could drastically reduce snowpack across various regions, affecting water supplies and winter experiences.
The findings highlight challenges in managing water resources, particularly in regions where decreased snowpack leads to more precipitation falling as rain rather than snow. This shift poses profound implications for water management systems designed under assumptions of substantial snowpack storage.
Initiatives like Canada1Water (C1W) play a critical role in addressing these challenges by providing research and data analysis. C1W contributes insights into adapting water management strategies amidst changing climate conditions. As climate change continues to affect snowfall patterns and impact water supplies, proactive measures are essential to mitigate future risks and ensure sustainable water management practices.
Click here to read the article at The Globe and Mail
The Canada1Water project includes a significant amount of research into snow depth modelling/projections in the coming decades. Want to learn more? Check out these earlier blog posts:
Clearing the path to project snow on a continental scale
RESEARCH HIGHLIGHT – Future snow changes over the Columbia Mountains, Canada, using a distributed snow model
CMOS BULLETIN - Bias Correcting Surface Snow Water Equivalent Estimates using Machine Learning