Introduction
Mountain snowpacks serve as natural water towers, dictating the timing
of peak soil moisture, controlling evapotranspiration dynamics, and
sustaining streamflow into the dry summer months . A warming climate
suggests a future with increased precipitation variability and a
decrease in snowpack accumulation and retention . Climate change has
also affected frequency and magnitude of disturbances from fire,
insects, and drought in forested areas that are seasonally snow covered.
Montane forest ecosystems across the western U.S. are particularly
vulnerable to these shifting snow dynamics because they have
historically relied on snowmelt to support water demand during the
growing season . The Sierra Nevada mountains, for example, have a
snow-dominated precipitation regime and supply over half of California’s
water needs . In the Sierra Nevada, efforts are underway to introduce
low-to-moderate severity fires and selective thinning to create forest
conditions that promote ecosystem function resembling historical,
pre-fire suppression conditions . A knowledge gap in predicting the
effects of forest disturbance on snow retention is limiting forest
managers’ ability to contend with current and future water supplies .
Precipitation patterns are typically the primary control on snowpack
accumulation, while energy input patterns normally dictate ablation;
however, terrain and vegetation characteristics serve as secondary
controls (. In most locations, orographic precipitation causes deeper
snowpacks at higher elevations with limited redistribution of snow by
wind . North-facing slopes in the northern hemisphere receive less
radiation, particularly during the winter months when sun angles are
lower. Therefore, higher elevation, steeper, and more northern-facing
slopes in the Sierra Nevada typically both accumulate and retain more
snow .
Predicting snow response to forest canopy (and its removal) remains
challenging, despite decades of research, because of complex inter- and
counter-acting processes controlling the surface energy and water budget
. Snowfall is intercepted in forest canopies, where it sublimates
rapidly, but predictions of snow interception are uncertain . In warmer
regions with minimal wind redistribution, like the Sierra Nevada, up to
60% of precipitation can be intercepted and sublimated from the forest
. Forest canopy also changes wind patterns, reducing snow redistribution
and changing turbulent heat fluxes , and alters radiation fluxes by both
shading the snowpack from shortwave radiation and emitting longwave
radiation (Lundquist et al., 2013; Safa et al., 2021). Shortwave
radiation typically dominates energy fluxes available for snow ablation
in the Sierra Nevada, but longwave radiation from forests is more
important when solar irradiance is low and air temperatures are warmer .
The interplay between long and shortwave radiation inputs in the winter
and spring - when solar irradiance is relatively low - causes snow to
ablate more quickly and disappear earlier under forest canopy in warmer
places like the Sierra Nevada . However, the impact of forest canopy
loss on snow ablation rates across complex topography is not well
understood because it cannot be captured well by point-scale or coarser
remote sensing and because we lack consistent methodology using newer
fine-scale remote sensing.
Forest effects on snow accumulation and ablation are well documented,
yet a cohesive framework for predicting forest disturbance effects on
snow is lacking . Early studies in the Sierra Nevada showed that forests
with open glades accumulated more snow than dense forest patches
(Church, 1933; Anderson, 1956, 1963). Varhola et al. (2010) showed
through a meta-analysis that reducing the fraction of area covered by
forests (fVEG) promoted accumulation, while ablation showed the opposite
trend. Their synthesis of 30 studies had large uncertainties that cannot
be attributed to local climate or terrain . The Varhola et al. (2010)
model was based on coarse-scale forest cover information; however,
recent evidence shows that fine-scale forest structure metrics, like
tree-scale forest gaps , forest clumps, edges of different vegetation
density, and tree height , are critical to simulating larger scale snow
distributions and amounts . The methodological framework used by Varhola
et al. (2010), which used an open reference site approach, remains a
current predictive paradigm but does not utilize information on
fine-scale forest structure. Studies commonly focus on binary canopy
classifications (i.e., open/ closed) or coarser scale (10-1000
m2) fractional canopy metrics to investigate
forest-snow interactions as opposed to finer scale
(1-30m2) metrics that classify vegetation structure
and density (e.g., leaf area index, or LAI) as well as gap
characteristics (e.g., ratio of vegetation heigh to gap size, or
openness) .
Fine-scale forest structure and snow information from airborne light
detection and ranging (lidar) data have revolutionized our understanding
of snow-forest interactions , but remain underutilized for predicting
forest management effects on snowpack. Lidar offers substantial
advantages over other remote sensing tools because emitted light pulses
can penetrate vegetation canopy and be used to create spatially
distributed models of the ground/snow surface, canopy height, and canopy
density at a decimeter-scale accuracy over extents greater than 100
km2 . An influx of lidar datasets from initiatives
like SnowEX and organizations like the Airborne Snow Observatories, Inc.
(ASO) and the National Center for Airborne Laser Mapping (NCALM) present
unique opportunities to take advantage of high-resolution spatially
distributed data with increasing temporal distribution. However, there
are few consistent processing methods to resolve snow depth and snow
water equivalent (SWE) from lidar in dense forests due to various
complications. For example, the number of returns is reduced due to
interactions with dense vegetation, low branches can be confounded with
the snow surface, and variable snow density adds spatial heterogeneity
(both vertically throughout the snowpack and horizontally over the
landscape). Moreover, large-scale processing of dense point clouds is
computationally intensive and requires expert knowledge of vertical and
lateral biases typical in these datasets . Developing a consistent and
open-source, lidar-based tool to estimate the effects of vegetation on
snow accumulation and ablation has been the focus of the scientific and
forest management community for the last several years . Existing
decision support tools show varied results when applied at different
scales and no current product effectively utilizes snow depth lidar
datasets that are becoming increasingly available .
Taking advantage of a unique set of lidar datasets from the densely
forested Sagehen Creek Basin in the central Sierra Nevada, California,
we investigate how forest structure metrics interact with terrain to
control snowpack accumulation and retention. Ongoing management in this
study area includes the introduction of low to moderate severity fires
and selective thinning to create more resilient forests . There is
promise for increasing hydrological outputs from restoring forests,
including an increase in snowmelt magnitude and streamflow . We develop
a novel method for processing multitemporal lidar datasets to examine
patterns over complex topography and heterogenous forest canopy. We pose
the following questions:
How does coarser-scale fractional canopy cover influence snow
accumulation and ablation as predicted by the current Varhola et al.
(2010) paradigm?
What role do terrain (elevation, aspect, and slope) and finer-scale
vegetation structure (openness) play on snow accumulation and
ablation?
Beyond addressing these questions, a primary goal of this study is to
create a replicable, open-source workflow to process point clouds into
novel snow and canopy metrics. Our work serves as an important
proof-of-concept for a new lidar-based method to develop local forest
information relevant to decision support needed in forest restoration
planning.