Melting mountain snow and ice are an important source of water in the summer months, when conditions tend to be more dry. Warming temperatures associated with climate change are projected to cause snow to melt earlier in the season, potentially leading to less water available in summer. This could lead to serious consequences for agricultural and municipal water supplies, fish and the health of aquatic and terrestrial ecosystems.

However, if forests can be managed to retain snow longer, some of these environmental and financial impacts may be mitigated. Research from our team demonstrates that in areas with relatively warm winters, strategically-cut forest gaps could offset climatic warming by increasing snow retention on the landscape and delaying runoff. In areas with colder winters, however, the opposite is true, as snow lasts longer under the forest canopy.

With this project, we built on previous studies of forest cover and snow melt to develop a decision tree model in collaboration with regional forest and water managers to help them identify areas and approaches that could maximize snow retention and thereby increase the amount of meltwater available in summer.

This two-year project was loosely structured in two phases: In the first, we gathered and synthesized observational data to apply, test and refine the findings of Lundquist et al., 2013. (which showed winter temperature is a key predictor of the net effect of forest on snow disappearance timing). In the second, we developed a flexible conceptual model to rank the relative influence of forest-snow processes on snow storage in a particular location. We actively solicited feedback on both approaches and the relevance of the proposed research products to management decisions via communication with stakeholders.

In the first year of the project, members of our research team traveled to meet with collaborators and visit field sites at Oregon State University and University of Idaho, and coordinated collection of data to fill spatial data gaps. Field collection efforts included collaborating with University of Idaho McCall Outdoor Science School (MOSS) to implement a citizen science program in their educational curriculum and to deploy remote instrumentation at one site just to the east of the Cascade crest in Washington. Since the target audiences for these efforts are land managers and land management stakeholders, we convened a series of meetings across the region to communicate preliminary results and solicit feedback in year one, and delivered a national webinar on the resulting binary decision tree model in year two.

We found that managers are applying a wide range of forest practices, including clear cut harvesting, thinning, and gap creation at scales ranging from one to thousands of hectares. Since these decisions are made based on regulatory constraints, financial viability, and ecosystem goals, we heard that our proposed research products must be easily integrated with existing tools. We have also heard from stakeholders that although higher certainty in predictions and higher resolution modeling are always of interest, broad estimates based on the best available understanding are useful, particularly in the context of supporting decisions made to achieve several goals.

In total, we connected with representatives of the U.S. Forest Service (Mt. Baker-Snoqualmie National Forest, Willamette National Forest), WA Department of Natural Resources, ID Department of Lands, Seattle Public Utilities, the Nooksack Tribe, the Yakama Nation, the Bureau of Reclamation, WA Department of Fish and Wildlife, the Upper Columbia Salmon Recovery Board, The Nature Conservancy, Washington Water Trust, Kittitas County Department of Natural Resources, and Chelan County Department of Natural Resources through six in-person meetings and numerous phone calls. We additionally presented a webinar hosted by the National Climate Change and Wildlife Science Center that was attended by over 65 call-in participants.

Assessing the Impacts of Global Warming on Snowpack in the Washington Cascades: Uses calculations of temperature sensitivity as a basis for inferring the impacts of global warming on snowpack.