Lidar-Derived Forest Metrics Predict Snow Accumulation and Ablation in the Central Sierra Nevada, USA

Snowmelt is a critical water resource in the Sierra Nevada impacting populations in California and Nevada. In this region, forest managers use treatments like selective thinning to encourage resilient ecosystems but rarely prioritize snowpack retention due to a lack of simple recommendations and the importance of other management objectives like wildfire mitigation and wildlife habitat. We use light detection and ranging (lidar) data collected over multiple snow accumulation seasons in the Sagehen Creek Basin, central Sierra Nevada in California, USA, to investigate how snowpack accumulation and ablation are affected by forest structure metrics at coarse, stand-scales (e.g., fraction of vegetation, or fVEG) and fine, tree-scales (e.g., a modified leaf area index, and the ratio of gap-width to average tree height). Using a newly developed lidar point cloud filtering method and an “open-area reference” approach, we show that for each 10% decrease in fVEG there is a ~30% increase in snow accumulation and a ~15% decrease in ablation rate at the Sagehen field site. To understand variability around these relationships, we use a random forest analysis to demonstrate that areas with fVEG greater than ~30% have the greatest potential increased accumulation response after forest removal. This spatial information allows us to assess the utility of completed and planned forest restoration strategies in targeting areas with the highest potential snowpack response. Our new lidar processing methods and reference-based approach are easily transferrable to other areas where they could improve decision support and increase water availability from landscape-scale forest restoration projects.