scholarly journals Modeling Snow–Canopy Processes on an Idealized Mountain

2011 ◽  
Vol 12 (4) ◽  
pp. 663-677 ◽  
Author(s):  
Ulrich Strasser ◽  
Michael Warscher ◽  
Glen E. Liston
Keyword(s):  
Snow Cover ◽  
Forest Canopy ◽  
Distributed Simulation ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Radiative Energy ◽  
Late Winter ◽  
Canopy Interception ◽  
Simulation Engine ◽  
Shadowing Effect

Abstract Snow interception in a coniferous forest canopy is an important hydrological feature, producing complex mass and energy exchanges with the surrounding atmosphere and the snowpack below. Subcanopy snowpack accumulation and ablation depends on the effects of canopy architecture on meteorological conditions and on interception storage by stems, branches, and needles. Mountain forests are primarily composed of evergreen conifer species that retain their needles throughout the year and hence intercept snow efficiently during winter. Canopy-intercepted snow can melt, fall to the ground, and/or sublimate into the air masses above and within the canopy. To improve the understanding of snow–canopy interception processes and the associated influences on the snowpack below, a series of model experiments using a detailed, physically based snow–canopy and snowpack evolution model [Alpine Multiscale Numerical Distributed Simulation Engine (AMUNDSEN)] driven with observed meteorological forcing was conducted. A cone-shaped idealized mountain covered with a geometrically regular pattern of coniferous forest stands and clearings was constructed. The model was applied for three winter seasons with different snowfall intensities and distributions. Results show the effects of snow–canopy processes and interactions on the pattern of ground snow cover, its duration, and the amount of meltwater release, in addition to showing under what conditions the protective effect of a forest canopy overbalances the reduced accumulation of snow on the ground. The simulations show considerable amounts of canopy-intercepted snowfall can sublimate, leading to reduced snow accumulation beneath the forest canopy. In addition, the canopy produces a shadowing effect beneath the trees that leads to reduced radiative energy reaching the ground, reduced below-canopy snowmelt rates, and increased snow-cover duration relative to nonforested areas. During snow-rich winters, the shadowing effect of the canopy dominates and snow lasts longer inside the forest than in the open, but during winters with little snow, snow sublimation losses dominate and snow lasts longer in the open areas than inside the forest. Because of the strong solar radiation influence on snowmelt rates, the details of these relationships vary for northern and southern radiation exposures and time of year. In early and high winter, the radiation protection effect of shadowing by the canopy is small. If little snow is available, an intermittent melt out of the snow cover inside the forest can occur. In late winter and spring, the shadowing effect becomes more efficient and snowmelt is delayed relative to nonforested areas.

scholarly journals Observations of a Coniferous Forest at 9.6 and 17.2 GHz: Implications for SWE Retrievals

2018 ◽  
Vol 11 (1) ◽  
pp. 6 ◽  
Author(s):  
Aaron Thompson ◽  
Richard Kelly
Keyword(s):  
Forest Canopy ◽  
Forest Cover ◽  
Snow Water Equivalent ◽  
Incidence Angle ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Ancillary Data ◽  
Volume Scattering ◽  
Component Decomposition ◽  
Forested Landscapes

UWScat, a ground-based Ku- and X-band scatterometer, was used to compare forested and non-forested landscapes in a terrestrial snow accumulation environment as part of the NASA SnowEx17 field campaign. Field observations from Trail Valley Creek, Northwest Territories; Tobermory, Ontario; and the Canadian Snow and Ice Experiment (CASIX) campaign in Churchill, Manitoba, were also included. Limited sensitivity to snow was observed at 9.6 GHz, while the forest canopy attenuated the signal from sub-canopy snow at 17.2 GHz. Forested landscapes were distinguishable using the volume scattering component of the Freeman–Durden three-component decomposition model by applying a threshold in which values ≥50% indicated forested landscape. It is suggested that the volume scattering component of the decomposition can be used in current snow water equivalent (SWE) retrieval algorithms in place of the forest cover fraction (FF), which is an optical surrogate for microwave scattering and relies on ancillary data. The performance of the volume scattering component of the decomposition was similar to that of FF when used in a retrieval scheme. The primary benefit of this method is that it provides a current, real-time estimate of the forest state, it automatically accounts for the incidence angle and canopy structure, and it provides coincident information on the forest canopy without the use of ancillary data or modeling, which is especially important in remote regions. Additionally, it enables the estimation of forest canopy transmissivity without ancillary data. This study also demonstrates the use of these frequencies in a forest canopy application, and the use of the Freeman–Durden three-component decomposition on scatterometer observations in a terrestrial snow accumulation environment.

A Distributed Snow-Evolution Modeling System (SnowModel)

2006 ◽  
Vol 7 (6) ◽  
pp. 1259-1276 ◽  
Author(s):  
Glen E. Liston ◽  
Kelly Elder
Keyword(s):  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Vegetation Type ◽  
Atmospheric Model ◽  
Snow Accumulation ◽  
Blowing Snow ◽  
Canopy Interception ◽  
Meteorological Forcing ◽  
Spatially Distributed ◽  
Evolution Modeling

Abstract SnowModel is a spatially distributed snow-evolution modeling system designed for application in landscapes, climates, and conditions where snow occurs. It is an aggregation of four submodels: MicroMet defines meteorological forcing conditions, EnBal calculates surface energy exchanges, SnowPack simulates snow depth and water-equivalent evolution, and SnowTran-3D accounts for snow redistribution by wind. Since each of these submodels was originally developed and tested for nonforested conditions, details describing modifications made to the submodels for forested areas are provided. SnowModel was created to run on grid increments of 1 to 200 m and temporal increments of 10 min to 1 day. It can also be applied using much larger grid increments, if the inherent loss in high-resolution (subgrid) information is acceptable. Simulated processes include snow accumulation; blowing-snow redistribution and sublimation; forest canopy interception, unloading, and sublimation; snow-density evolution; and snowpack melt. Conceptually, SnowModel includes the first-order physics required to simulate snow evolution within each of the global snow classes (i.e., ice, tundra, taiga, alpine/mountain, prairie, maritime, and ephemeral). The required model inputs are 1) temporally varying fields of precipitation, wind speed and direction, air temperature, and relative humidity obtained from meteorological stations and/or an atmospheric model located within or near the simulation domain; and 2) spatially distributed fields of topography and vegetation type. SnowModel’s ability to simulate seasonal snow evolution was compared against observations in both forested and nonforested landscapes. The model closely reproduced observed snow-water-equivalent distribution, time evolution, and interannual variability patterns.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923 ◽  
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

2016 ◽  
Vol 9 (2) ◽  
pp. 633-646 ◽  
Author(s):  
T. Marke ◽  
E. Mair ◽  
K. Förster ◽  
F. Hanzer ◽  
J. Garvelmann ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Forest Canopy ◽  
Model Performance ◽  
Snow Accumulation ◽  
Meteorological Conditions ◽  
Mountain Range ◽  
Point Energy ◽  
Snow Model ◽  
Mean Square Errors

Abstract. This article describes the extension of the ESCIMO.spread spreadsheet-based point energy balance snow model by (i) an advanced approach for precipitation phase detection, (ii) a method for cold content and liquid water storage consideration and (iii) a canopy sub-model that allows the quantification of canopy effects on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand. To provide the data for model application and evaluation, innovative low-cost snow monitoring systems (SnoMoS) have been utilized that allow the collection of important meteorological and snow information inside and outside the canopy. The model performance with respect to both, the modification of meteorological conditions as well as the subsequent calculation of the snow cover evolution, are evaluated using inside- and outside-canopy observations of meteorological variables and snow cover evolution as provided by a pair of SnoMoS for a site in the Black Forest mountain range (southwestern Germany). The validation results for the simulated snow water equivalent with Nash–Sutcliffe model efficiency values of 0.81 and 0.71 and root mean square errors of 8.26 and 18.07 mm indicate a good overall model performance inside and outside the forest canopy, respectively. The newly developed version of the model referred to as ESCIMO.spread (v2) is provided free of charge together with 1 year of sample data including the meteorological data and snow observations used in this study.

A Novel Perspective on Mapping Snow Cover Under Forest Canopy With Sentinel-2 Multispectral Optical Satellite Sensor Over Black Forest Germany

2020 ◽  
Author(s):  
Arnab Muhuri
Keyword(s):  
Snow Cover ◽  
Forest Canopy ◽  
Snow Accumulation ◽  
Optical Data ◽  
Federal State ◽  
Mountain Range ◽  
Black Forest ◽  
Forested Areas ◽  
Forested Landscapes ◽  
Sentinel 2

<p>Previous investigations have reported that the performance of the traditional snow cover mapping algorithms based on the Normalized Difference Snow Index (NDSI), derived from a multispectral optical airborne/spaceborne sensor, significantly degrades on transitioning from non-forested to forested landscapes. The thick canopy cover in forested landscapes obscures both the upwelling and the downwelling radiance and hence impairs the detection of the underlying snow cover on the forest floor via NDSI thresholding due to the shift in the apparent threshold. Although NDSI has been reported to be an ineffective index for extracting snow information from forested areas, this investigation presents contrary views. A novel perspective is introduced on exploiting the temporal NDSI-NDVI statistics for extracting snow information under the canopy, as has been also reported important in the past literature when considered together, to reconstruct the actual snow cover scenario over the mixed landscape, comprising both forested areas of varying densities and open vegetation-free patches. The Black Forest (Schwarzwald) is a large forested mountainous terrain at about 200-1500 m above sea level situated in the Federal State of Baden-Württemberg in the southwest corner of Germany. The region is bounded by the Rhine river valley to the west and south stretching in an oblong manner with a length of about 160 km and breadth of up to 50 km. The Black Forest consists of approximately 80% coniferous (spruce, fir, and pine) and 20% deciduous (beech, birch, and oak), with about 70% of the region under forest cover. Seasonal snowmelt water and natural springs originating in this region sources major European rivers like the Danube and the tributaries of the Rhein like the Murg and the Neckar. Therefore, it is essential to monitor snow accumulation under the canopy to accurately forecast and investigate the influence of the snowmelt runoff in such major catchments. One of the test sites is situated in the Murg catchment at Hundseck near the town of Baden-Baden at the north-western border of the Black Forest mountain range. This investigation employs Sentinel-2 multispectral optical data from the previous season in order to test the proposed approach. The proposed method is tested with the European Space Agency's open-access Sentinel-2 multispectral optical satellite data, over the Hundseck test site in the Black Forest. The snow extent map is validated with the Normalized Difference Forest Snow Index (NDFSI), which was proposed as an alternative for NDSI to map the canopy underlying snow in evergreen forests. The proposed algorithm is simple and computationally frugal. Temporal NDSI-NDVI statistics in conjunction with mathematical morphological operation has resulted in significant improvement in the detection of under canopy snow cover. It is noteworthy that the performance of the algorithm inherently shows a dependence on the forest LAI. An improvement of more than 50% is achieved in the under-canopy snow cover mapping. A priori knowledge regarding the LAI of forests will enable adaptive tuning of the algorithm locally for better performance under dense canopy conditions.</p>

scholarly journals ESCIMO.spread (v2): parameterization of a spreadsheet-based energy balance snow model for inside-canopy conditions

2015 ◽  
Vol 8 (9) ◽  
pp. 8155-8191
Author(s):  
T. Marke ◽  
E. Mair ◽  
K. Förster ◽  
F. Hanzer ◽  
J. Garvelmann ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Forest Canopy ◽  
Model Performance ◽  
Snow Accumulation ◽  
Meteorological Conditions ◽  
West Germany ◽  
Mountain Range ◽  
Point Energy ◽  
Snow Model

Abstract. This article describes the extension of the spreadsheet-based point energy balance snow model ESCIMO.spread by (i) an advanced approach for precipitation phase detection, (ii) a concept for cold and liquid water storage consideration and (iii) a canopy sub-model that allows to quantify the effect of a forest canopy on the meteorological conditions inside the forest as well as the simulation of snow accumulation and ablation inside a forest stand. To provide the data for model application and evaluation, innovative low-cost Snow Monitoring Systems (SnoMoS) have been utilized that allow the collection of important meteorological and snow information in and outside the canopy. The model performance with respect to both, the modification of meteorological conditions as well as the subsequent calculation of the snow cover evolution are evaluated using in- and outside-canopy observations of meteorological variables and snow cover evolution as provided by a pair of SnoMoS for a site in the Black Forest mountain range (south-west Germany). The validation results indicate a good overall model performance in and outside the forest canopy. The newly developed version of the model refered to as ESCIMO.spread (v2) is provided free of charge together with one year of sample data including the meteorological data and snow observations used in this study.

scholarly journals Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment

2016 ◽  
Author(s):  
Travis R. Roth ◽  
Anne W. Nolin
Keyword(s):  
Forest Canopy ◽  
Forest Cover ◽  
Snow Accumulation ◽  
Forest Site ◽  
Canopy Interception ◽  
Study Region ◽  
Wind Speeds ◽  
Open Site ◽  
Forest Sites ◽  
Energy Inputs

Abstract. Forest cover modifies snow accumulation and ablation rates via canopy interception and changes in sub-canopy energy balance processes. However, the ways in which snowpacks are affected by forest canopy processes vary depending on climatic, topographic and forest characteristics. Here we present results from a 4 year study of snow-forest interactions in the Oregon Cascades. We continuously monitored snow and meteorological variables at paired forested and open sites at three elevations representing the Low, Mid, and High seasonal snow zones in the study region. On a monthly to bi-weekly basis, we surveyed snow depth and snow water equivalent across 900 m transects connecting the forested and open pairs of sites. Our results show that the dense, relatively warm on forests at Low and Mid sites impede snow accumulation through increased canopy snow interception and increase energy inputs to the sub-canopy snowpack. Compared with the Forest sites, snowpacks are deeper and last longer in the Open site at the Low and Mid sites (4 – 26 days and 11 – 33 days, respectively). However, we see the opposite relationship at the relatively colder High sites with the Forest site maintaining snow longer into the spring by 15 – 29 days relative to the nearby Open site. Over a 4 year study, canopy interception efficiency (CIE) values in the Low- and Mid-Forest sites, were 79 % and 76 % of the total event snowfall, whereas CIE was 31 % at the lower density High-Forest site. At all elevations, longwave radiation in forested environments appears to be the primary energy component due to the maritime climate and forest presence, accounting for 82 %, 88 %, and 59 % of total energy inputs to the snowpack at the Low-, Mid-, and High-Forest sites, respectively. Higher wind speeds in the High-Open site significantly increase turbulent energy exchanges and snow sublimation. Lower wind speeds in the High-Forest site create preferential snowfall deposition. These results show the importance of understanding the effects of forest cover on sub-canopy snowpack evolution and highlight the need for improved forest cover model representation to accurately predict water resources in maritime forests.

Snow-ice accretion and snow-cover depletion on Antarctic first-year sea-ice floes

2001 ◽  
Vol 33 ◽  
pp. 51-60 ◽  
Author(s):  
Martin O. Jeffries ◽  
H. Roy Krouse ◽  
Barbara Hurst-Cushing ◽  
Ted Maksym
Keyword(s):  
Snow Cover ◽  
Ice Cover ◽  
Snow Accumulation ◽  
Ice Formation ◽  
Ice Thickness ◽  
Late Winter ◽  
First Year ◽  
Frazil Ice ◽  
Ice Floes ◽  
Autumn And Winter

AbstractBetween austral late winter 1993 and austral autumn 1998, during five cruises aboard the research vessel Nathaniel B. Palmer, almost 300 m of core was obtained from first-year ice floes in the Ross, Amundsen and Bellingshausen Seas. Analysis of the texture, stratigraphy and stable-isotopic composition of the ice was used to assess the magnitude of the role of flooding and snow-ice formation at the base of the snowpack in the thickening of the ice cover and the thinning of the snow cover. Snow ice occurred in all ice-thickness categories and made a significant contribution to the total ice mass (12−36%) in both autumn and winter. Although the amount of snow ice was often exceeded by the amount of frazil ice and congelation ice, the thickness of individual layers of each ice type indicated that snow ice often made a greater contribution to the thermodynamic thickening of the ice cover than the other ice types. The larger quantities of frazil ice and congelation ice were primarily the result of dynamic thickening. Flooding and snow-ice formation reduced the snow cover to 42−70% of the total snow accumulation depending on time and location. On the basis of this information, ship-based snow-depth estimates were adjusted to estimate the total snow accumulation on different ice-thickness categories.

Seasonal snow accumulation in the mid-latitude forested catchment

Biologia ◽  
2014 ◽  
Vol 69 (11) ◽  
Author(s):  
Václav Šípek ◽  
Miroslav Tesař
Keyword(s):  
Snow Cover ◽  
Forest Canopy ◽  
Forest Cover ◽  
Snow Water Equivalent ◽  
Elevation Gradient ◽  
Snow Accumulation ◽  
Forested Catchment ◽  
Seasonal Snow ◽  
Snow Water ◽  
Fagus Sylvatica L

AbstractThe study deals with the snow cover characteristics (snow depth — SD and snow water equivalent — SWE) concerning the mid-latitude forested catchment. Namely, the influence of the forest canopy (Picea abies (L.) Karst. and Fagus sylvatica L.) and altitude (ranging from 835 m a.s.l. to 1118 m a.s.l.) was investigated. Forest cover was proved to have a significant influence on the snow cover accumulation, reducing SWE by 50 % on average, compared to open sites. The elevation gradient concerning SWE ranged from 30 to 40 mm and from 5 to 20 mm per 100 m in open and forested sites, respectively. Its magnitude was found to be temporarily variable and positively related to the total seasonal snowfall amount. The SWE/SD variability among measurement sites (with different altitude) was higher in open sites compared to forested ones. The catchment SWE/SD variability increases significantly in the snowmelt period (March–April) both in open and forested locations. The differences among snow interception losses, concerning various elevations and the forest canopy, were not statistically significant.

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