Scales of snow depth variability in high elevation rangeland sagebrush

Tedesche, Molly E.; Fassnacht, S. R.; Meiman, Paul J.

doi:10.1007/s11707-017-0662-z

Cited by 12 publications

(9 citation statements)

References 47 publications

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“…The results of this study can be applied to areas of similar climate and land cover, which included flat, bare soil, and bare soil with small furrows (<1 m); and therefore, the results of this study may not scale appropriately to different land cover types. Further studies of a shallow snowpack in sagebrush steppe [49], farmland, or non-densely forested environments may be able to replicate our study results and scale from 1000 m 2 to a larger area. The z 0 values observed here had a notable change between flat soil and small furrows, so the changes in z 0 values in different environments with even minimal vegetation will have much larger effects on the z 0 values.…”

Section: Discussionsupporting

confidence: 54%

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

et al. 2018

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When applied to a snow-covered surface, aerodynamic roughness length, z0, is typically considered as a static parameter within energy balance equations. However, field observations show that z0 changes spatially and temporally, and thus z0 incorporated as a dynamic parameter may greatly improve models. To evaluate methods for characterizing snow surface roughness, we compared concurrent estimates of z0 based on (1) terrestrial light detection and ranging derived surface geometry of the snowpack surface (geometric, z0G) and (2) vertical wind profile measurements (anemometric, z0A). The value of z0G was computed from Lettau’s equation and underestimated z0A but compared well when scaled by a factor of 2.34. The Counihan method for computing z0G was found to be unsuitable for estimating z0 on a snow surface. During snowpack accumulation in early winter, z0 varied as a function of the snow-covered area (SCA). Our results show that as the SCA increases, z0 decreases, indicating there is a topographic influence on this relation.

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Section: Discussionsupporting

confidence: 54%

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

et al. 2018

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“…This scale break was clearly attributed to wind-driven snow accumulation features. The interaction of wind with vegetation leads to similar spatial variability, with even smaller scale breaks of a few meters (Trujillo et al, 2012;Deems et al, 2013;Tedesche et al, 2017;Webb et al, 2017). even demonstrated that fractal parameters of snow depths were able to distinguish between wind-protected and wind-exposed areas and to describe the structure of snow depth change during more and less wind-influenced snowfall periods.…”

Section: Spatial Variability Of Snow Accumulationmentioning

confidence: 89%

The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes

2018

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The temporal evolution of seasonal snow cover and its spatial variability in environments such as mountains, prairies or polar regions is strongly influenced by the interactions between the atmospheric boundary layer and the snow cover. Wind-driven coupling processes affect both mass and energy fluxes at the snow surface with consequences on snow hydrology, avalanche hazard, and ecosystem development. This paper proposes a review on these processes and combines the more recent findings obtained from observations and modeling. The spatial variability of snow accumulation across multiple scales can be associated to wind-driven processes ranging from orographic precipitation at large scale to preferential-deposition of snowfall and wind-induced transport of snow on the ground at smaller scales. An overview of models of varying complexity developed to simulate these processes is proposed in this paper. Snow ablation is also affected by wind-driven processes. Over continuous snow covers, turbulent fluxes of latent and sensible heat, as well as blowing snow sublimation, modify the mass, and energy balance of the snowpack and their representation in numerical models is associated with many uncertainties. As soon as the snow cover becomes patchy in spring local heat advection induces the development of stable internal boundary layers changing heat exchange toward the snow. Overall, wind-driven processes play a key role in all the different stages of the evolution of seasonal snow. Improvements in process understanding particularly at the mountain-ridge and the slope scale, and processes representations in models at scales up to the mountain range scale, will be the basis for improved short-term forecast and climate projections in snow-covered regions.

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“…On very small scales snow accumulation patterns are assigned to wind redistribution of snow (e.g., Mott et al, 2011;Vionnet et al, 2017). Vegetation effects were found to be dominant at small scales and terrain effects dominate on scales of up to 1 km (Deems et al, 2006;Trujillo et al, 2012;Tedesche et al, 2017). Different dominant scales are reported for different slope expositions relative to the wind direction .…”

Section: Introductionmentioning

confidence: 98%

Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

et al. 2018

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Abstract. Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydropower, avalanche forecasting and freshwater resources. However, it is still challenging to quantitatively forecast precipitation, especially over complex terrain where the interaction between local wind and precipitation fields strongly affects snow distribution at the mountain ridge scale. Therefore, it is essential to retrieve high-resolution information about precipitation processes over complex terrain. Here, we present very-high-resolution Weather Research and Forecasting model (WRF) simulations (COSMO–WRF), which are initialized by 2.2 km resolution Consortium for Small-scale Modeling (COSMO) analysis. To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated COSMO–WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation enhancement in the model. The high precipitation amounts also lead to a higher spatial variability in the model compared to radar estimates. Overall, an autocorrelation and scale analysis of radar and COSMO–WRF precipitation patterns at a horizontal grid spacing of 450 m show that COSMO–WRF captures the spatial variability normalized by the domain-wide variability in precipitation patterns down to the scale of a few kilometers. However, simulated precipitation patterns systematically show a lower variability on the smallest scales of a few hundred meters compared to radar estimates. A comparison of spatial variability for different model resolutions gives evidence for an improved representation of local precipitation processes at a horizontal resolution of 50 m compared to 450 m. Additionally, differences of precipitation between 2830 m above sea level and the ground indicate that near-surface processes are active in the model.

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Scales of snow depth variability in high elevation rangeland sagebrush

Cited by 12 publications

References 47 publications

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

Geometric Versus Anemometric Surface Roughness for a Shallow Accumulating Snowpack

The Seasonal Snow Cover Dynamics: Review on Wind-Driven Coupling Processes

Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

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