Quantifying the effects of vegetation structure on snow accumulation and ablation in mixed‐conifer forests

Abstract

Snowmelt from forested, mountainous environments in the western United States is a critical regional water resource for streamflow and ecological productivity. These landscapes are undergoing rapid changes from the combined effects of forest fires, insect infestation and climate change. Numerous observational studies demonstrate that trees control snowpack accumulation and ablation over scales of tens of metres. Representing forest heterogeneity in models is important for understanding how changes in climate and vegetation cover affect the snowpack; yet, many snow models simplify a forest into two categories: canopy covered and non-canopy covered. We combine existing parameterizations of mass and energy fluxes within a new three-dimensional framework informed by Airborne Laser Swath Mapping (ALSM)-derived canopy maps and evaluated with ALSM-derived snow depth maps to explicitly simulate snow cover in relation to heterogeneous canopy. Model results capture much of the observed snow variability depicted in the 1-m ALSM-derived snow depth maps. Observations and modelled results identify open areas <15 m from tree canopies as having more snow and more snow variability than areas >15 m from tree canopies, and modelled results predict that open areas <15 m from tree canopies have 30-40% more net snow water input than areas that are underneath tree canopies and 10-25% more net snow water input than areas that are >15 m from tree canopies. Furthermore, 1-m simulations give higher estimates for net snow water input than coarser resolution simulations, mainly in areas with fewer trees. These results suggest the importance of explicitly representing canopy edges in snow models.

Evaluating the functionality and streamflow impacts of explicitly modelling forest–snow interactions and canopy gaps in a distributed hydrologic model

“…Snowmelt provides a critical source of runoff in the mountainous areas of mid‐ to high‐latitudes (Barnett et al, ; Yan et al, ) and sustains summer low flow vital for healthy aquatic habitat. There has been growing recognition over the last century that snow accumulation and ablation in mountain forest environments depend critically on forest structure (Broxton et al, ; Connaughton, ; Moore & McCaughey, ; Pomeroy, Parviainen, Hedstrom, & Gray, ; Varhola, Coops, Weiler, & Moore, ). Despite climate and topographic impacts of varying degrees, many studies found that forested areas, in contrast to open areas, commonly accumulate less snow and thus produce less water available for runoff, due mainly to canopy interception and evapotranspiration of up to 60% of accumulated snow (Cristea, Lundquist, Loheide, Lowry, & Moore, ; Hedstrom & Pomeroy, ; Marks, Kimball, Tingey, & Link, ; McCabe & Clark, ; McCabe, Hay, & Clark, ; Pomeroy et al, ; Regonda, Rajagopalan, Clark, & Pitlick, ; Stednick, ; Stewart, Cayan, & Dettinger, ; Troendle & King, ).…”

“…In the Cedar River Watershed located on the western slope of the Cascade Range (characterized by a maritime climate) in the Pacific Northwest, the mean snow duration in a circular gap cut in the forest with a diameter of 20 m (equal to approximately one tree height) was observed to be 1–2 weeks longer than in the adjacent control forest covered by untreated second‐growth forest dominated by western hemlock and Douglas‐fir (Dickerson‐Lange et al, ). These unique benefits of forest gaps have led to recent modelling developments that address the distinct radiation scheme in a forest gap from entirely open or forested areas (Lawler & Link, ; Musselman, Molotch, Margulis, Lehning, & Gustafsson, ), the energy budget at the forest gap floor (Seyednasrollah & Kumar, ), net canopy interception (Moeser, Morsdorf, & Jonas, ; Moeser, Stähli, & Jonas, ), and snow distributions (Broxton et al, ) in the presence of forest gaps.…”

Laser vision: lidar as a transformative tool to advance critical zone science

“…Certain hydrological modeling fields are well poised to utilize high-resolution topography, such as movement of water in urban environments (Fewtrell et al, 2008), in-channel flow modeling (Mandlburger et al, 2009;Legleiter et al, 2011), and hyporheic exchange and ecohydraulics in small streams (e.g., Wheaton et al, 2010b). Finally, high-resolution, three-dimensional lidar measurements of canopy and vegetation structure (Vierling et al, 2008) have direct implications for modeling the surface energy balance (Musselman et al, 2013;Broxton et al, 2014) and evapotranspiration processes (Mitchell et al, 2011) at scales critical to increasing fidelity in physically based models.…”

“…Lidar has also proven to be instrumental in the verification of model states. For example, lidar data sets have been used to verify physically based models, including landscape evolution models (Pelletier et al, 2014;Pelletier and Perron, 2012;Rengers and Tucker, 2014), aeolian models Pelletier, 2013), physiological models , snowpack energy balance models (Essery et al, 2008, Broxton et al, 2014, and an ecosystem dynamics model (Antonarakis et al, 2014). Simpler, empirical models have also been developed using lidar-derived estimates of soil erosion (Pelletier and Orem, 2014) and snow accumulation and ablation (Varhola et al, 2014).…”

A Terrestrial Landscape Ecology Approach to the Critical Zone

“…For instance, from a catchment scale to the scale of an individual tree, each element has its own level of variability. A direct relationship exists between snowpack variability and tree-canopy variability (Broxton et al, 2014). Landscape ecologists call this phenomenon a nested hierarchy, in which each level contains the level below.…”