Increasing the efficacy of forest thinning for snow using high-resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA

Forest manipulation using forest thinning is one of the few means to manage water resources supplying downstream populations that are derived from snow-covered montane forests. The challenges of simulating processes at the tree scale have precluded generalizable recommendations for removing tree canopy to maximize snow water resources. Here, we apply the high-resolution Snow Physics and Lidar Mapping (SnowPALM) model to simulate snow mass and energy budgets at 1-m scale over a 1,200 by 1,200 m domain on the west shore of Lake Tahoe, Sierra Nevada, USA. The SnowPALM model verifies well against observations of snow depth and snow temperature in open and under canopy locations. This supported the application of SnowPALM under virtual thinning experiments, where all trees <5, <10, <15, and <20 m height were removed during a 3-year simulation. Increasing snowpack sublimation losses are smaller than reductions in canopy interception resulting in an overall increase in melt volume. Despite relatively modest melt volume increases of 4-8% across the entire domain, individual 30-m stands could have >30% melt volume increases. On average, a 0.5 decrease in lidar-derived leaf area resulted in an ~10.5% increase in melt volume, with shorter, denser 30-m forest stands having greater melt volume sensitivity. These dense forest stands, where forest thinning was most effective, were found in valley bottoms and north-facing slopes across the west shore region. This proof of concept supports large domain simulations using high-resolution models to inform landscape-scale restoration decisions.