Snowcover Accumulation, Relocation and Management

Cline, Don; Pomeroy, J. W.; Gray, Donald M.D.

doi:10.2307/1551979

Cited by 38 publications

(12 citation statements)

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“…This effect was seen most during large storm events with high wind speeds and a shallow preexisting snowpack. Our results align with expectations from other studies (Pahaut, 1976;Pomeroy & Gray, 1995) and may have yielded larger DMs had wind drifting also been considered. Wind effects usually yielded higher magnitude DMs than radiation effects and similar to larger magnitude DMs than mass effects.…”

Section: Discussionsupporting

confidence: 92%

“…'Delivery effects' on density, are caused when snowfall is intercepted by the canopy and then later added to the underlying snowpack as meltwater drip or unloaded snow that may have a higher density than fresh snowfall (Bründl et al, 1999;Lundberg et al, 1997;Storck et al, 2002). 'Wind effects' occur when wind speed is reduced by the forest structure resulting in lower snow density relative to exposed open areas where wind packing can densify falling snow and the snow surface (Pahaut, 1976;Pomeroy & Gray, 1995). 'Radiation effects', caused by altered shortwave and longwave fluxes under a forest canopy, may increase or decrease snow density through changes in snow temperature, snowpack temperature gradients, and melt-refreeze cycles (Essery et al, 2008;Lawler & Link, 2011;Sicart et al, 2004).…”

mentioning

confidence: 99%

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Isolating forest process effects on modelled snowpack density and snow water equivalent

Bonner

Raleigh

Small

2022

Hydrological Processes

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Understanding how the presence of a forest canopy influences the underlying snowpack is critical to making accurate model predictions of bulk snow density and snow water equivalent (SWE). To investigate the relative importance of forest processes on snow density and SWE, we applied the SUMMA model at three sites representing diverse snow climates in Colorado (USA), Oregon (USA), and Alberta (Canada) for 5 years. First, control simulations were run for open and forest sites. Comparisons to observations showed the uncalibrated model with NLDAS-2 forcing performed reasonably. Then, experiments were completed to isolate how forest processes affected modelled snowpack density and SWE, including: (1) mass reduction due to interception loss, (2) changes in the phase and amount of water delivered from the canopy to the underlying snow, (3) varying new snow density from reduced wind speed, and (4) modification of incoming longwave and shortwave radiation. Delivery effects (2) increased forest snowpack density relative to open areas, often more than 30%.Mass effects (1) and wind effects (3) decreased forest snowpack density, but generally by less than 6%. The radiation experiment (4) yielded negligible to positive effects (i.e., 0%-10%) on snowpack density. Delivery effects on density were greatest at the warmest times in the season and at the warmest site (Oregon): higher temperatures increased interception and melted intercepted snow, which then dripped to the underlying snowpack. In contrast, mass effects and radiation effects were shown to have the greatest impact on forest-to-open SWE differences, yielding differences greater than 30%. The study highlights the importance of delivery effects in models and the need for new types of observations to characterize how canopies influence the flux of water to the snow surface.

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Section: Discussionsupporting

confidence: 92%

mentioning

confidence: 99%

Isolating forest process effects on modelled snowpack density and snow water equivalent

Bonner

Raleigh

Small

2022

Hydrological Processes

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“…Trend or sensitivity studies of hydrological flows and fluxes alone are inadequate for defining resilience. Black (1997) defined hydrological functions as one of collection, storage and Snowpack storage is dependent on winter climate and redistribution of snowfall (Gordon, Brooks, et al, 2022;Pomeroy & Gray, 1995).…”

Section: Hydrological Functionmentioning

confidence: 99%

“…Storage distribution in relation to where water is collected has impacts on the release function (McNamara et al, 2011). Snowpack storage is dependent on winter climate and redistribution of snowfall (Gordon, Brooks, et al, 2022; Pomeroy & Gray, 1995). Groundwater storage fluctuation, capacity and residence times are dependent on surficial geology, frozen soils, and climate and surface water interactions (Gleeson & Manning, 2008; Hayashi, 2020; Jasechko et al, 2016).…”

Section: A Framework For Physical Hydrological Resilience Studiesmentioning

confidence: 99%

JAMES BUTTLE REVIEW: A resilience framework for physical hydrology

Newton

Spence

2023

Hydrological Processes

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“…In snow dominated areas, it is important to understand the quantity and distribution of snow for purposes of streamflow forecasting [1]. In mountainous terrain, one of the most apparent characteristics of the snowpack is its spatial heterogeneity [2][3][4][5][6][7][8]. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation [9].…”

Section: Introductionmentioning

confidence: 99%

Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

et al. 2021

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A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

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Snowcover Accumulation, Relocation and Management

Cited by 38 publications

References 0 publications

Isolating forest process effects on modelled snowpack density and snow water equivalent

Isolating forest process effects on modelled snowpack density and snow water equivalent

JAMES BUTTLE REVIEW: A resilience framework for physical hydrology

Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

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