Forests, Snow, and Change: How Modeling History Is Shaping Our Predictions for the Future

Thursday, 18 December 2014: 9:15 AM
Jessica D Lundquist1, Ethan D Gutmann2 and Martyn P Clark2, (1)University of Washington, Seattle, WA, United States, (2)NCAR, Boulder, CO, United States
For decades, forest and water managers have been investigating how forest management (e.g., through strategic thinning or gap cutting) can increase snow retention and help water resources. Predictions of warming temperatures and decreasing snowpacks have renewed interest in taking such actions, but model results about the combined impact of forest change and temperature change on snow are conflicting. Forest interception processes vary with winter temperatures, due to changes in branch stiffness and in the relative cohesion (stickiness) of snowflakes. Models prescribe forest interception to be some fraction of falling snow (interception efficiency, Ie, see Figure), and the total amount of snow that a canopy can hold (Imax, see Figure). However, interception models developed in colder regions (Hedstrom and Pomeroy 1998) prescribe Imax decreasing with temperature, while those developed in warmer regions (Andreadis et al. 2009) prescribe Imax increasing with temperature (see Figure). Here, we use a modular model framework (SUMMA) to isolate model choices related to interception and test the impact of these choices on modeled snow sensitivity to prescribed climate change (delta-T) and forest change (delta-tree) at study sites in both the Pacific Northwest (warm) and the Boreal forest (cold). Independently calibrating model parameters allows snow modeled with either interception formulation to match observations well in both warm and cold study sites, but these different model configurations, while producing matching snow simulations in the calibration year, lead to different predictions of the snow response to change. We discuss when these differences matter most and how carefully-controlled modeling and coordinated multi-site field observations are required to ensure that we have confidence in our modeled prediction of forest snow response under changing conditions.