资源环境科技发展态势分析平台

Air temperature (T-air) plays an important role in determining how a canopy intercepts snow and apart from event size is the single most important micrometeorological variable found to adequately influence interception rates and magnitude. We present results from a 6-year study on snow-forest interactions. This data set reveals the central role T-air plays in how a forest intercepts snow and the need to effectively incorporate this within snow process models. Warm temperature events show a higher canopy interception efficiency (CIE) for all sites across the study period, while colder T-air events demonstrated a lower corresponding CIE. Additionally, there is a structural component of the forest itself that plays a role in the ability of canopy to intercept snow. Recognizing the physical vertical structure of a forest as nontrivial, we present a simple canopy interception model that includes a novel three-dimensional forest metric that captures canopy complexity, G(z), combined with event size and T-air to adequately predict event-based interception. Low complexity forests decrease interception capacity from the outset, whereas a highly complex or diverse forest increases interception potential and leads to a nonlinear increase in temperature-based canopy interception due to more surface area able to intercept falling snow. Essentially, forest structure sets the boundary condition of the potential to intercept, while event size and T-air determines the rate or amount of interception. We develop a simple canopy snow interception model from these data and compare modeled output against three commonly used interception models in both a maritime and continental snow climate. Partitioning the relative importance of snow-forest interactions on canopy interception will help provide the information to accurately model snow-forest interactions and also aid in our water resources predictive capabilities now and into the future.