Using Process Based Snow Modeling and Lidar to Predict the Effects of Forest Thinning on the Northern Sierra Nevada Snowpack

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 km 2 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 <10 and <20 m are removed. In general, thinning results in an overall increase in peak snow water equivalent and snowmelt. Areas around sheltered tree clusters have the largest increases of snowmelt due to decreases of canopy sublimation, while more open and exposed areas show a small decrease due to increases in snowpack sublimation. At the 30-m forest stand scale, existing forest structure controls the efficacy of thinning, where forest stands with mean leaf area index (LAI) >3 m 2 /m 2 and 5-15-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.

Estimating the Effects of Forest Structure Changes From Wildfire on Snow Water Resources Under Varying Meteorological Conditions

“…Despite this, approximately 30% of the domain showed decreases in postfire peak SWE and earlier melt‐out date for each water year, which resulted in higher overall variability. This is in general agreement with field observations, where increases and decreases in snow water have been measured at a nearby research site (Harpold, Biederman, et al, 2014), as well as in other environments (Goeking & Tarboton, 2020; Krogh et al, 2020; Maxwell et al, 2019). However, this differs from results of most other modeling studies that have shown only increases in SWE after a forest disturbance (Goeking & Tarboton, 2020).…”

“…This distribution was partially based on the pattern of canopy disturbance, and any differences in the disturbance pattern would change the SWE distribution and skew (larger areas of SWE loss vs. gain). However, the disturbance pattern and initial structural makeup of the canopy will dictate whether an area will experience a generalized increase or decrease in peak SWE and melt‐out date (Harpold et al, 2020; Krogh et al, 2020).…”

Increasing the efficacy of forest thinning for snow using high‐resolution modeling: A proof of concept in the Lake Tahoe Basin, California, USA

“…For example, it is difficult to extrapolate our results from mid‐elevations of the Lake Tahoe Basin to higher elevations, which receive more snowfall, have more cold content, and have later snowmelt. For those reasons, recent modeling uses a larger domain with substantial topographic variability to show similar forest height and density controls on melt volume sensitivity to thinning (Krogh, Broxton, Manley, Harpold, ). Better observations in different modelling domains could improve representation of processes that remain particularly challenging to simulate, such as the turbulent energy exchanges (i.e., sublimation) in forest canopy gaps (Conway, Pomeroy, Helgason, & Kinar, ), drainage of liquid water (rain and melt) within the snowpack (Leroux & Pomeroy, ; Pflug et al, ), blowing snow sublimation within the canopy (Sexstone et al, ), albedo changes due to forest litter (Winkler, Boon, Zimonick, & Baleshta, ), and longwave radiation within heterogeneous canopy (Musselman, Pomeroy, Centre, Musselman, & Pomeroy, ; Pomeroy et al, ).…”

“…Such effort will have to sort out issues with variable lidar collection techniques (i.e., scan angle and first return densities, Figure S7), as well as significant computational challenges due to the large amount of data that would have to be processed. However, simulating a larger domain, with larger topographic and vegetation gradients, could help develop a science‐based decision support tool for the Lake Tahoe Basin (Krogh et al, ). There is also potential to link these estimates of snowpack change with hydrological models to simulate the effects on streamflow and groundwater recharge, as this is of large interest in many areas.…”

Assessing the effects of forest biomass reductions on forest health and streamflow

“…Besides canopy evaporation, non-canopy evaporation processes (e.g., soil evaporation, litter evaporation, sublimation) may also be altered by biomass reductions. Changes in evaporation after biomass reduction may act as either a source of additional water that can be partitioned to transpiration of the remaining vegetation and streamflow (Krogh et al, 2020), or as a sink where evaporation processes increase (Biederman et al, 2014). This latter pathway is often not considered to be a hydrologic benefit, but nonetheless must be understood in order to properly quantify potential streamflow and forest health benefits.…”