The influence of ground- and lidar-derived forest structure metrics on snow accumulation and ablation in disturbed forests

Abstract

The current mountain pine beetle infestation in British Columbia’s lodgepole pine forests has raised concerns about potential impacts on water resources. Changes in forest structure resulting from defoliation, windthrow, and salvage harvesting may increase snow accumulation and ablation (i.e., spring runoff and flooding risk) below the forest canopy because of reduced snow interception and higher levels of radiation reaching the surface. Quantifying these effects requires a better understanding of the link between forest structure and snow processes. Light detection and ranging (lidar) is an innovative technology capable of estimating forest structure metrics in a detailed, three-dimensional approach not easily obtained from manual measurements. While a number of previous studies have shown that increased snow accumulation and ablation occur as forest cover decreases, the potential improvement of these relationships based on lidar metrics has not been quantified. We investigated the correlation between lidar-derived and ground-based traditional canopy metrics with snow accumulation and ablation indicators, demonstrating that a lidar-derived forest cover parameter was the strongest predictor of peak snow accumulation (r2 = 0.70, p < 0.001) and maximum snow ablation rate (r2 = 0.59, p < 0.01). Improving our ability to quantify changes in forest structure in extensive areas will assist in developing more robust models of watershed processes.