Forests modify snow accumulation and ablation rates, and overall snow storage amounts and durations, with multiple processes acting simultaneously and often in different directions. To synthesize complex forest-snow relations and help guide near-term management decisions, we present a decision tree model based on a hypothesized hierarchy of processes and associated variables that predict forest effects on snow storage. In locations with high wind speeds, forests enhance snow storage magnitude and duration relative to open areas. Where wind speeds are low, and winter and spring air temperatures are colder, forests diminish snow storage magnitude but enhance duration. Where air temperatures are warmer, forests diminish both magnitude and duration. Forest structure and aspect are secondary influences that shift the net effect of forest on snow storage. We apply the model to map the influence of forests on snow storage under historic and warming climate conditions across the western United States, but this model is applicable in any region with forests and snow. The decision tree model provides practitioners a first-step evaluation to guide management decisions that consider where and how forests can be managed to optimize in-situ water storage alongside other objectives, such as reducing wildfire fuels. This framework also articulates geospatial hypotheses, in order of anticipated importance, to be tested in future investigations of forest-snow-climate relations.

The data and code included herein are described in Dickerson-Lange, et al. 2021, Ranking forest effects on snow storage: a decision tool for forest management, Water Resources Research. The repository contains all input data, model code, and results.