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.

Forest management can enhance forest resiliency against natural disturbances such as fire, drought, or disease. Mechanical thinning, followed by a prescribed burn, is a useful technique to achieve a desired forest structure, usually maximizing large tree basal area or decreasing fuel loads, meant to protect against wildfire or reduce water stress in the western US. Changing forest structure can alter ecosystem function by reducing competition and exposing soil, modifying microclimates and creating suitable conditions for shrubs and grasses to encroach. Typically, forest treatments are expected to make the remaining trees more productive through competitive release, and an open canopy helps the understory to thrive. This enhanced plant water use often contradicts the expected result of increased streamflow following thinning. In mountainous terrain, water yield is further complicated by hillslope-scale processes of subsurface lateral flow and groundwater recharge. This research seeks to understand how management-derived forest structure influences hillslope-scale forest regrowth and water yield. We apply a spatially-distributed ecohydrologic model (RHESSys) to an experimental hillslope in the Sierra Nevada, CA. We incorporate multi-temporal Lidar observations and U.S. Forest Service Forest Inventory & Analysis (FIA) survey data to estimate post-thinning regrowth in treated plots in the watershed, which is used to verify RHESSys accuracy of vegetation regrowth. Then, we run long-term virtual thinning experiments to understand how the combination of thinning and prescribed burns in upslope and riparian sites separately and concurrently influences regrowth and water fluxes in these sites. We expect that an intermediate forest density will yield the most co-benefits in terms of carbon sequestration and water yield. However, these patterns will likely be modified along a hillslope, such that riparian forest stands will be less sensitive to the competitive release that thinning provides, whereas dense upslope forests will be highly sensitive to treatment since they are more water-limited. Water yield is likely to be confounded by multiple factors, including topography, whether a burn follows thinning to remove understory fluxes, and interactions between upslope thinning and processes of lateral flow and groundwater recharge when increased riparian water use compensates for additional upslope subsidies.

The entire western US is in the midst of a megadrought. Combined with high temperatures, increasingly severe droughts are causing widespread forest mortality. In the Sierra Nevada, CA in particular, the Mediterranean climate exposes montane forests to water stress due to the summer drought. Normally, the slow melting of the winter snowpack helps to alleviate summer water stress, especially in riparian ecosystems that benefit from subsurface lateral inputs along a hillslope. However, the loss of the snowpack due to snow drought could potentially eliminate these buffering effects. This research aims to address the role of subsurface lateral redistribution in mediating vegetation responses to drought along a hillslope. We apply a spatially-distributed ecohydrologic model (RHESSys) to an experimental hillslope in a snow-dominated watershed in the Sierra Nevada, CA. We incorporate observed sap flow data from the experimental hillslope to estimate the relative differences in onset of water stress for upslope and riparian sites, which is used to constrain RHESSys drainage parameter uncertainty. Then, we run hypothetical multi-year drought experiments to investigate how climate variability translates to water stress on a hillslope. Our results challenge the common assumption that riparian forests are buffered against drought stress by subsurface lateral inputs. For all drought types, both upslope and riparian sites experience severe losses of net primary productivity (NPP), and on average upslope sites are more adversely affected (upslope loss of NPP = 50% vs. riparian = 35%). But even in a wet year, as temperatures rise and the snowpack disappears (i.e., warm snow drought), vegetation approaches a threshold response that destabilizes the riparian buffering effect. Our results show that for 12% of all scenarios, riparian NPP decreases more than upslope NPP, as a consequence of earlier snowmelt. Interactions between climate variability and ecophysiological uncertainty produce scenarios that exhibit the riparian threshold response. By recognizing the conditions that determine riparian sensitivity to drought, management actions can be proactive in preserving this important hydrological refugia.