Snowpack dynamics play a key role in controlling hydrological and ecological processes at various scales, but snow monitoring remains problematic. Data assimilation techniques are emerging as promising tools to improve uncertain snowpack simulations by fusing state-of-the-art numerical models with information rich, but noisy observations. However, the occlusion of the ground below the forest canopy limits the retrieval of snowpack information from remote sensing tools. Thus, remote sensing observations in these environments are spatially incomplete, impeding the implementation of fully distributed data assimilation techniques. Here we propose different experiments to propagate the information obtained in forest clearings, where it is possible to retrieve observations, towards the sub-canopy, where the point of view of remote sensors is occluded. The experiments were conducted in forests within Sagehen Creek watershed (California, USA), by updating simulations conducted with the Flexible Snow Model (FSM2) with airborne lidar snow data using the Multiple Snow data Assimilation system (MuSA). The successful experiments improved the reference simulations significantly both in terms of validation metrics (correlation coefficient from R=0.1 to R ~0.8 in average) and spatial patterns. Both data assimilation configurations, using geographical distances or a space of topographical dimensions, managed to improve the reference run. However, those creating a space of synthetic coordinates by combining the spatiotemporal data assimilation with a principal components analysis did not show any improvement, even degrading some validation metrics. Future data assimilation initiatives would benefit from building specific localization functions that are able to model the spatial snowpack relationships at different resolutions.

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.