scholarly journals Derivation of Snow Water Equivalent in Boreal Forests Using Microwave Radiometry

ARCTIC ◽  
1991 ◽  
Vol 44 (5) ◽  
Author(s):  
J.L. Foster ◽  
A.T.C. Chang ◽  
D.K. Hall ◽  
A. Rango
Keyword(s):  
Boreal Forests ◽  
Snow Water Equivalent ◽  
Microwave Radiometry ◽  
Snow Water

Snow water equivalent estimation by microwave radiometry

1982 ◽  
Vol 5 (3) ◽  
pp. 259-267 ◽  
Author(s):  
A.T.C. Chang ◽  
J.L. Foster ◽  
D.K. Hall ◽  
A. Rango ◽  
B.K. Hartline
Keyword(s):  
Snow Water Equivalent ◽  
Microwave Radiometry ◽  
Snow Water

A Detailed and in Situ Assessment of the Snowpack Physical Properties in a Discontinuous Humid Boreal Forest

2021 ◽  
Author(s):  
Benjamin Bouchard ◽  
Daniel F. Nadeau ◽  
Florent Domine
Keyword(s):  
Physical Properties ◽  
Snow Cover ◽  
Boreal Forests ◽  
Snow Water Equivalent ◽  
Weather Forecasting ◽  
Large Fraction ◽  
Heat Fluxes ◽  
Homogeneous Layer ◽  
Eastern Canada ◽  
Snow Water

<p>Boreal forests occupy a large fraction of the continental surfaces and receive a lot of solid precipitation in winter. Evergreen canopies are often represented as a single and homogeneous layer in hydrological and weather forecasting models. However, in reality, boreal canopies are composed of a rather complex mosaic of trees unevenly spaced apart, with gaps of various sizes. Therefore, mass and energy inputs to the snowpack show remarkable variability at small scales resulting not only in strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE), but also in the vertical temperature gradient in the snow column (<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="48" height="17">). Unlike SD and SWE, <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="48" height="17"> has been little documented in discontinuous needleleaf forests, despite its impact on snow cover metamorphism and on a range of physical properties of snow such as density (<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.d6e05221ecff56228040161/sdaolpUECMynit/12UGE&app=m&a=0&c=e83ed3b230a37b46d23b9b7d13655568&ct=x&pn=gnp.elif&d=1" alt="" width="17" height="16">), specific surface area (SSA) and effective thermal conductivity (k<sub>eff</sub>). This work investigates the snowpack underneath the canopy and inside small forest gaps using continuous measurements of SD and k<sub>eff</sub> and weekly snow pit surveys during winter 2018-19 in a juvenile balsam fir stand of eastern Canada (47°17’18’’N, 71°10’05’’W). This site receives an average of almost 1600 mm of precipitation annually, including 40 % falling as snow. Snow cover typically lasts over 6 months. Observations show that less snow accumulates in the subcanopy and therefore <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="46" height="16"> is more pronounced than inside the gaps. Moreover, <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.d6e05221ecff56228040161/sdaolpUECMynit/12UGE&app=m&a=0&c=e83ed3b230a37b46d23b9b7d13655568&ct=x&pn=gnp.elif&d=1" alt="" width="17" height="16"> and SSA are lower underneath the canopy where faceted crystals are observed. Large <img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.8b7ab390ecff53808040161/sdaolpUECMynit/12UGE&app=m&a=0&c=763df4650e7419e8d52dae70af81e2ad&ct=x&pn=gnp.elif&d=1" alt="" width="49" height="17"> in that environment results in a decreasing k<sub>eff</sub> over time. Overall, kinetic grain growth takes place in the subcanopy whereas settlement and isothermal conditions prevail inside the gaps. This research provides accurate observations of the snowpack in forested environments needed for a better representation of SWE, heat fluxes and ground thermal regime in hydrological and meteorological models.</p>

Estimation of snow water equivalent using microwave radiometry over Arctic first-year sea ice

2003 ◽  
Vol 17 (17) ◽  
pp. 3503-3517 ◽  
Author(s):  
D. G. Barber ◽  
J. Iacozza ◽  
A. E. Walker
Keyword(s):  
Sea Ice ◽  
Snow Water Equivalent ◽  
Microwave Radiometry ◽  
First Year ◽  
Snow Water

scholarly journals On the possibility of retrieving snow water equivalent from satellite microwave radiometry data

2021 ◽  
Vol 18 (5) ◽  
pp. 47-64
Author(s):  
V.V. Tikhonov ◽  
◽  
Yu.V. Sokolova ◽  
D.A. Boyarskii ◽  
N.Yu. Komarova ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Microwave Radiometry ◽  
Snow Water ◽  
Satellite Microwave

scholarly journals Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

2021 ◽  
Vol 15 (2) ◽  
pp. 771-791
Author(s):  
Rhae Sung Kim ◽  
Sujay Kumar ◽  
Carrie Vuyovich ◽  
Paul Houser ◽  
Jessica Lundquist ◽  
...  
Keyword(s):  
North America ◽  
Land Surface ◽  
Boreal Forests ◽  
Snow Water Equivalent ◽  
Spatiotemporal Variability ◽  
Considerable Uncertainty ◽  
Mountainous Regions ◽  
Snow Storage ◽  
Snow Water

Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes.

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