Abstract
Reductions in snow accumulation and melt in headwater basins are increasing the water stress on forest ecosystems across the western US. Forest thinning has the potential to reduce water stress by decreasing sublimation losses from canopy interception; however, it can also increase snowpack exposure to sun and wind. We used the high-resolution (1 m) energy and mass balance Snow Physics and Lidar Mapping (SnowPALM) model to investigate the effect of two virtual forest thinning scenarios on the snowpack of two adjacent watersheds (54 km2 total) in the Lake Tahoe Basin, California, where forest thinning is being planned. SnowPALM realistically represents small-scale snow-forest interactions to simulate the impact of virtual thinning experiments in which trees 3 m2/m2 and 5 to 15-m tall show the largest increases in snow accumulation (up to 450 mm) and melt volume (up to 650 mm). Despite the role of tree- and stand-scale thinning on snowmelt, macroscale effects were limited to slightly larger increases in melt volumes at mid to low elevation slopes (<2,300 masl) and south facing areas per unit of LAI removed. A decision support tool using machine learning (random forest) was developed to synthesize SnowPALM results, and was applied to neighboring watersheds. These results will inform ongoing forest management practices in California, and improve our understanding of the effects of snow-forest interactions at scales relevant to water management.
Highlights
Upland snowmelt is a vital water resource for downstream populations and local ecological systems
We found that these recommendations are generally consistent with co-management for multiple resource goals, such as fire suppression, wildlife habitat conservation and water conservation, which is a key to maximizing resources invested into expanding forest restoration efforts
Given this societal need, continued work at the interface of basic process research and large-scale forest restoration applications are an avenue that could yield important advances
Summary
Upland snowmelt is a vital water resource for downstream populations and local ecological systems. Snow is important in regions with Mediterranean climates, such as the Sierra Nevada in the western US, where snowmelt provides the majority of the water supply. Increasing temperatures in such regions are shortening the snow accumulation season, increasing. Targeted forest thinning, has the potential to increase snow accumulation and melt through a reduction in snowfall that is intercepted and sublimated from forest canopy (Varhola et al, 2010; Tague et al, 2019). Large domain simulations that capture key fine-scale processes are needed to predict hydrologic responses to forest restoration efforts, which span substantial gradients of climate, topography, and vegetation removal
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