If you have any problems related to the accessibility of any content (or if you want to request that a specific publication be accessible), please contact us at email@example.com.
Lidar-Derived Forest Metrics Are Critical for Predicting Snow Accumulation and Ablation in the Central Sierra Nevada, USA
AuthorPiske, Cara Ryanne
AdvisorHarpold, Adrian A.
AltmetricsView Usage Statistics
Snowmelt is a critical source of water resources for ecosystems and communities surrounding the Sierra Nevada. Forest canopy controls critical mass and energy balance dynamics that can alter snowpack accumulation and ablation. In addition to changing climate dynamics that could shift the precipitation regimes of this region, an increase in ecosystem disturbance (e.g., drought, wildfires) creates dynamic forest structures that have the ability to drastically alter the snowpack. Forest management aims at creating resilient ecosystems but is often less explicitly focused on retaining the snowpack as a crucial water reservoir. It is important to constrain how fine-scale forest structures impact snowpack accumulation and persistence to predict future dynamics and inform management. However, while broad-scale forest structure metrics have been studied extensively in relation to snowpack, less is known about how fine-scale forest structure impacts snowpack. Light detection and ranging (lidar) data provide the opportunity to understand these complex dynamics using high resolution, spatially distributed points that capture detailed forest structure and snow depth. We use lidar collected over the course of multiple accumulation seasons both pre- and post-disturbance in Sagehen Creek Basin in the central Sierra Nevada to investigate how snowpack accumulation is impacted by fine-scale forest structure metrics, like leaf area index (LAI’) and the ratio of gap width to average tree height (openness) in a 30-meter grid cell. In addition, we use a series of measurements taken during the ablation season to understand how forest structures impact snow persistence. Through developing a refined space-for-structure processing protocol, we show a delicate balance between the fraction of forest cover (fVEG) and openness in an area that promotes snowpack accumulation and reduces ablation. In general, areas with lower fVEG (0.3) and smaller gaps (diameter/height ~0.1) increase accumulation. However, decreasing gap sizes and increasing fVEG can also lead to more ablation, supporting climate-driven paradigms that predict more ablation under the canopy in regions like the Sierra Nevada. Pre- and post-disturbance analyses show inconsistent patterns because of confounding accumulation and ablation dynamics at the date of collection. Our processing protocol and space-for-structure analysis provide a unique opportunity to understand lidar-derived forest-snow dynamics in a way that is transferrable to areas with varying vegetation and climate regimes.