Zonation des conditions d'enneigement en toundra forestière, Baie d'Hudson, Nouveau-Québec

1975 ◽  
Vol 53 (10) ◽  
pp. 1021-1030 ◽  
Author(s):  
Serge Payette ◽  
Jacques Ouzilleau ◽  
Louise Filion
Keyword(s):  
Picea Glauca ◽  
Snow Depth ◽  
Black Spruce ◽  
Ground Cover ◽  
Climatic Conditions ◽  
Snow Density ◽  
Forest Tundra ◽  
Snow Properties ◽  
Latitudinal Pattern ◽  
Snow Conditions

Data on snow depth and snow density of various forest–tundra coniferous stands are presented in this paper. A latitudinal pattern in snow conditions is observed in the forest–tundra environment, as predicted from the facts that are obtained when this phytogeographical region is subdivided, firstly, into a forested subzone in the southern part and a shrub subzone (or krummholz) in the northern part and, secondly, into a maritime ecoclimatic area near Hudson Bay and a continental ecoclimatic area inland. The most snowy coniferous stands are located in the shrub subzone; snow density rises gradually from the taiga to the tundra. The highest values in snow properties are found in the maritime ecoclimatic area. These data suggest the following observations: (1) maximum snow depth measured in the northern part of the forest–tundra is explained by an increase of barren ground cover and by the presence of more open coniferous stands, which favor snow drifting and snow trapping; (2) the gradual increase in snow density is related to more rigorous climatic conditions; wind exposure is rather important since these sites are getting more open; and (3) the differences in snow conditions between the ecoclimatic areas show that the maritime environment is more windy; the presence of scattered and erected white spruce (Picea glauca (Moench) Voss) in various krummholz formations in that area favors more efficient snow traps than those of krummholz formations located in the continental area. The latter is dominated by prostrate and erect black spruce (Picea mariana (Mill.) BSP.) always densely agglomerated. The latitudinal pattern in snow conditions reflects the climatic conditions of the forest–tundra, and this determines the specific ecological distribution of coniferous stands.

Critical snow density threshold for Dall’s sheep (Ovis dalli dalli)

2018 ◽  
Vol 96 (10) ◽  
pp. 1170-1177 ◽  
Author(s):  
Kelly J. Sivy ◽  
Anne W. Nolin ◽  
Christopher L. Cosgrove ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
Animal Movement ◽  
Population Declines ◽  
Snow Density ◽  
Energetic Costs ◽  
Snow Properties ◽  
Ovis Dalli ◽  
Snow Conditions ◽  
Dall’S Sheep ◽  
Density Threshold

Snow cover can significantly impact animal movement and energetics, yet few studies have investigated the link between physical properties of snow and energetic costs. Quantification of thresholds in snow properties that influence animal movement are needed to help address this knowledge gap. Recent population declines of Dall’s sheep (Ovis dalli dalli Nelson, 1884) could be due in part to changing snow conditions. We examined the effect of snow density, snow depth, and snow hardness on sinking depths of Dall’s sheep tracks encountered in Wrangell–St. Elias National Park and Preserve, Alaska. Snow depth was a poor predictor of sinking depths of sheep tracks (R2 = 0.02, p = 0.38), as was mean weighted hardness (R2 = 0.09, p = 0.07). Across competing models, top layer snow density (0–10 cm) and sheep age class were the best predictors of track sink depths (R2 = 0.58). Track sink depth decreased with increasing snow density, and the snowpack supported the mass of a sheep above a density threshold of 329 ± 18 kg/m3 (mean ± SE). This threshold could aid interpretation of winter movement and energetic costs by animals, thus improving our ability to predict consequences of changing snowpack conditions on wildlife.

Les coupes forestières dans les pessières à lichens: effets sur la croissance et la régénération des conifères (Whapmagoostui, Québec subarctique)

1988 ◽  
Vol 66 (6) ◽  
pp. 1013-1020 ◽  
Author(s):  
Ann Delwaide ◽  
Louise Filion
Keyword(s):  
Picea Glauca ◽  
Radial Growth ◽  
Growth Form ◽  
Black Spruce ◽  
Ground Cover ◽  
Climatic Conditions ◽  
Hudson Bay ◽  
Clear Cutting ◽  
Physical Conditions ◽  
Tree Harvesting

In the Whapmagoostui area (east of Hudson Bay), tree harvesting by Crée Indians in lichen woodlands affects the form and the growth of surviving trees and also the forest population dynamics. A study of the growth form of white spruce (Picea glauca (Moench) Voss.) and black spruce (Picea mariana (Mill.) B.S.P.) that have been pruned shows the efficiency of a total traumatic reiteration process. After several years, the annual radial growth was equivalent to that recorded before pruning. In clear-cutting areas (more than 75% of trees removed), the increase in the radial growth of spared trees was 400 to 700%. The main factors that govern the success of regeneration in cutting areas are the rather small extension of the openings (<0.005 km2), the low intensity of tree harvesting (<75% of trees over 90% of the surface), the physical conditions of the lichenous ground cover and the abundance of the lignified debris after clearing vegetation, and the climatic conditions in the subsequent years.

Effect of altered snow conditions on decomposition in three subalpine plant communities

2014 ◽  
Vol 9 (8) ◽  
pp. 811-822 ◽  
Author(s):  
Miroslav Zeidler ◽  
Martin Duchoslav ◽  
Marek Banaš
Keyword(s):  
Soil Temperature ◽  
Vegetation Type ◽  
Vegetation Types ◽  
Snow Density ◽  
Cellulose Decomposition ◽  
Snow Properties ◽  
Melting Period ◽  
Spatio Temporal ◽  
Snow Conditions

AbstractSnow cover and its spatio-temporal changes play a crucial role in the ecological functioning of mountains. Some human activities affecting snow properties may cause shifts in the biotic components of ecosystems, including decomposition. However, these activities remain poorly understood in subalpine environments. We explored the effect of human-modified snow conditions on cellulose decomposition in three vegetation types. Snow density, soil temperature, and the decomposition of cellulose were studied in Athyrium, Calamagrostis, and Vaccinium vegetation types, comparing stands intersected by groomed ski slope and natural (outside the ski slope) stands. Increased snow density caused the deterioration of snow insulation and decreased the soil temperature inside the ski slope only slightly in comparison with that outside the ski slope in all vegetation types studied. The decomposition was apparently lower in Athyrium vegetation relative to the other vegetation types and strongly (Athyrium vegetation) to weakly lower (other vegetation types) in groomed than in ungroomed stands. Wintertime, including the melting period, was decisive for overall decomposition. Our results suggest that differences in decomposition are influenced by ski slope operations and vegetation type. Alterations in snow conditions appeared to be subtle and long-term but with important consequences for conservation management.

scholarly journals GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

2021 ◽  
Author(s):  
Achille Capelli ◽  
Franziska Koch ◽  
Patrick Henkel ◽  
Markus Lamm ◽  
Florian Appel ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Low Cost ◽  
Elevation Gradient ◽  
Liquid Water Content ◽  
Climatic Conditions ◽  
Swiss Alps ◽  
Snow Density ◽  
Validation Data ◽  
Snow Water ◽  
Snow Conditions

Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.

scholarly journals Spatial and Temporal Variation of Bulk Snow Properties in North Boreal and Tundra Environments Based on Extensive Field Measurements

2016 ◽  
Author(s):  
H.-R. Hannula ◽  
J. Lemmetyinen ◽  
A. Kontu ◽  
C. Derksen ◽  
J. Pulliainen
Keyword(s):  
Land Cover ◽  
Snow Depth ◽  
Field Measurements ◽  
Northern Taiga ◽  
Spatial And Temporal Variation ◽  
Snow Properties ◽  
Spatial Frequencies ◽  
Forested Areas ◽  
Snow Conditions ◽  
Airborne Sar

Abstract. In this paper, an extensive dataset of snow in situ measurements, collected in support of airborne SAR-acquisitions in Sodankylä and Saariselkä test sites in northern Finland, is used to analyse the heterogeneity of bulk snow properties (snow depth, density and water equivalent) over different land cover types in northern taiga and tundra areas. In addition, the applicability of different spatial frequencies of snow sampling to estimate the true snow conditions is investigated. Overall, the highest variability in bulk snow properties was found over sparsely vegetated land cover groups, but the scale of variation was smaller in forested areas, as these areas exhibited a low correlation length in snow depth. This implies that more frequent measurements should be executed in forested (~ every < 5 m) than in open areas (~ every 7.5–12.5 m) to catch the true variability in snow depth. The results also indicated that the current spatial resolutions of space borne microwave radiometers and radars used for the remote retrieval of bulk snow properties are all well above the limit to fully describe the spatial variation of e.g. snow depth even in open areas. This conclusion supports the demand of research investigating high-resolution parameter retrieval in remote sensing of snow, e.g. using advanced SAR techniques.

Evoluzione della copertura vegetale in aree alpestri del Canton Ticino nel periodo 1971–2001 | Evolution of Vegetation Cover in the Alpine Regions of Canton Tessin: 1971–2001

2004 ◽  
Vol 155 (7) ◽  
pp. 284-289 ◽  
Author(s):  
Pietro Stanga ◽  
Niklaus Zbinden
Keyword(s):  
Initial Conditions ◽  
Ground Cover ◽  
Climatic Conditions ◽  
Human Pressure ◽  
Aerial Photos ◽  
Alpine Regions ◽  
Subsequent Decrease ◽  
Site Fertility ◽  
Strong Expansion ◽  
Evolution Of Vegetation

The retrospective study based on aerial photos (1971–2001) of the Canton Tessin made it possible to measure and analyze the evolution of the vegetation of eleven Alpine zones. The analysis shows a strong expansion of the arborescent vegetation and, at the same time, a decrease in other forms of ground cover (bush, shrub, meadow and unproductive spaces). Analysis of the data gives rise to the conjecture that the strong evolutionary dynamism evidenced by the areas under investigation is a result of the vast clearings carried out in past centuries to create pastures. Following the subsequent decrease in human pressure, nature today is attempting to rebalance the level of the biomass. These processes manifest themselves in different ways and with various intensity, depending on the interaction of numerous factors (e.g. climatic conditions, site fertility, initial conditions, evolution of anthropological pressure, etc.).

scholarly journals Seasonal influence of snow conditions on Dall’s sheep productivity in Wrangell-St Elias National Park and Preserve

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244787
Author(s):  
Christopher L. Cosgrove ◽  
Jeff Wells ◽  
Anne W. Nolin ◽  
Judy Putera ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Snow Accumulation ◽  
Population Declines ◽  
Climate Conditions ◽  
Remote Cameras ◽  
Snow Conditions ◽  
Dall’S Sheep

Dall’s sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall’s sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall’s sheep recruitment dynamics.

scholarly journals Theoretical framework for estimating snow distribution through point measurements

2015 ◽  
Vol 9 (1) ◽  
pp. 1-44
Author(s):  
E. Trujillo ◽  
M. Lehning
Keyword(s):  
Snow Depth ◽  
Survey Design ◽  
Spatial Location ◽  
Theoretical Framework ◽  
Statistical Properties ◽  
Spatial Covariance ◽  
Sample Mean ◽  
Snow Properties ◽  
Survey Designs

Abstract. In recent years, marked improvements in our knowledge of the statistical properties of the spatial distribution of snow properties have been achieved thanks to improvements in measuring technologies (e.g. LIDAR, TLS, and GPR). Despite of this, objective and quantitative frameworks for the evaluation of errors and extrapolations in snow measurements have been lacking. Here, we present a theoretical framework for quantitative evaluations of the uncertainty of point measurements of snow depth when used to represent the average depth over a profile section or an area. The error is defined as the expected value of the squared difference between the real mean of the profile/field and the sample mean from a limited number of measurements. The model is tested for one and two dimensional survey designs that range from a single measurement to an increasing number of regularly-spaced measurements. Using high-resolution (~1 m) LIDAR snow depths at two locations in Colorado, we show that the sample errors follow the theoretical behavior. Furthermore, we show how the determination of the spatial location of the measurements can be reduced to an optimization problem for the case of the predefined number of measurements, or to the designation of an acceptable uncertainty level to determine the total number of regularly-spaced measurements required to achieve such error. On this basis, a series of figures are presented that can be used to aid in the determination of the survey design under the conditions described, and under the assumption of prior knowledge of the spatial covariance/correlation properties. With this methodology, better objective survey designs can be accomplished, tailored to the specific applications for which the measurements are going to be used. The theoretical framework can be extended to other spatially distributed snow variables (e.g. SWE) whose statistical properties are comparable to those of snow depth.

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

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