Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements

Abstract

It is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large-scale hydrological and land surface models. High-resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability.In this study, snow depth was measured by airborne Lidar at 0.5-m resolution over two mountainous areas in south-western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1-km × 1-km. Also, nine representative grids of 1-km × 1-km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1-km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non-Gaussian distribution, largely due to the no-snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth. KEYWORDS fractal dimension, no-snow areas effect, probability density function, snow, subgrid variability, variogram

Technical note: Snow Water Equivalence Estimation (SWEE) Algorithm from Snow Depth Time Series Using a Snow Density Model

“…The site was instrumented with a snow lysimeter, ranging snow depth sensor, and snow surface temperature, but there is no snow pillow at the NN Creek research site. More detailed information regarding this site is available in Pleasants et al (2017), Thayer et al (2018), andHe et al (2018). Figure 3 shows the computed SWE change ∆ during the snowmelt season of 2018, which includes the lysimetric snowmelt water flux at the bottom of snowpack, denoted in the blue-shaded area.…”

Modeling Subgrid Variability of Snow Depth Using the Fokker‐Planck Equation Approach

“…For example, snow redistribution in an open area requires additional parametrization for the downwind snowdrift effect and the snow particle dispersion because the snow distribution downwind of an object is typically influenced by snow drift and eddy effects (e.g., Ohara, 2017). Mixed forest may result in larger snow diffusion than seen in this small No-name watershed covered by relatively uniform forest (e.g., He et al, 2019). If the diffusion coefficient in the FPE can be reasonably estimated, complicated snowdrift process modeling may be avoided.…”

“…The spatial variability of snow depth can result in uncertainty in hydrological and atmospheric processes due to the presence of snow influencing the radiative and turbulent heat fluxes on the land surface (Essery, ; Liston, ; Luce et al, ; Menzel & Lang, ). Understanding snow spatial distribution is essential for quantifying it as a water resource and its effects on atmospheric circulation (He et al, ; Luce et al, ; Wetlaufer et al, ). In hydrological and land surface processes modeling, the total spatial variability can be treated as the sum of the small‐scale variability within a computational cell and the large‐scale variability between computational cells (Isaaks & Mohan Srivastava, ).…”

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

“…The seasonal snowpack exhibits marked variability both spatially and temporally across the landscape (Lopez‐Moreno et al, ), which exerts a strong influence on the timing and magnitude of snowmelt delivery to a watershed (Anderton, White, & Alvera, ; Liston, ; Luce, Tarboton, & Cooley, ) and its streamflow response (DeBeer & Pomeroy, ; Lundquist & Dettinger, ; Lundquist, Dettinger, & Cayan, ). Therefore, the representation of the sub‐grid or sub‐watershed snow variability in broad‐scale hydrologic models is particularly important for accurately simulating variations in energy fluxes, snowmelt dynamics and runoff response (Clark et al, ; He, Ohara, & Miller, ; Liston, , ). Snow depletion curves (SDCs) that relate the snow‐covered area (SCA) to the mean snow water equivalent (SWE) for a given hydrologic response unit (HRU) are often used to represent the sub‐grid variability of snowmelt processes within hydrologic models (Anderson, ; Driscoll, Hay, & Bock, ; Liston, ; Luce & Tarboton, ; Magand, Ducharne, Le Moine, & Gascoin, ; Markstrom et al, ; Martinec & Rango, ; Yang, Dickinson, Robock, & Vinnikov, ).…”