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.

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.

scholarly journals Spatial and Temporal Variation of Net Snow Accumulation in a Small Alpine Watershed, Emerald Lake Basin, Sierra Nevada, California, U.S.A.

1989 ◽  
Vol 13 ◽  
pp. 56-63 ◽  
Author(s):  
K. Elder ◽  
J. Dozier ◽  
J. Michaelsen
Keyword(s):  
Sierra Nevada ◽  
Field Measurements ◽  
Snow Accumulation ◽  
Spatial And Temporal Variation ◽  
Lake Basin ◽  
Snow Properties ◽  
Digital Elevation ◽  
Snow Water ◽  
Elevation Model ◽  
Topographic Parameters

Distribution of snow-water equivalence (SWE) in the Emerald Lake watershed located in Sequoia National Park, California, U.S.A, was examined during the 1987 water year. Elevations at this site range from 2780 to 3416 m a.s.l., and the total watershed area is about 122 ha. A stratified sampling scheme was evaluated by identifying and mapping zones of similar snow properties, based on topographic parameters that account for variations in both accumulation and ablation of snow. Elevation, slope, and radiation values calculated from a digital elevation model were used to identify these zones. Field measurements of SWE were combined with characteristics of the sample locations and clustered to identify similar classes of SWE. The entire basin was then partitioned into zones for each set of survey data. The topographic parameters of the basin used in classification, namely slope and elevation, are constant in time and did not change between survey dates. The radiation data showed temporal variability providing a physically justified basis for changes in SWE distribution through time. Although results do not identify which of the classification attempts is superior to the others, net radiation is clearly of primary importance, and slope and elevation appear to be important to a lesser degree. The peak accumulation for the 1987 water year was 598 mm SWE, which is about half the 50 year mean.

scholarly journals Spatial and Temporal Variation of Net Snow Accumulation in a Small Alpine Watershed, Emerald Lake Basin, Sierra Nevada, California, U.S.A.

1989 ◽  
Vol 13 ◽  
pp. 56-63 ◽  
Author(s):  
K. Elder ◽  
J. Dozier ◽  
J. Michaelsen
Keyword(s):  
Sierra Nevada ◽  
Field Measurements ◽  
Snow Accumulation ◽  
Spatial And Temporal Variation ◽  
Lake Basin ◽  
Snow Properties ◽  
Digital Elevation ◽  
Snow Water ◽  
Elevation Model ◽  
Topographic Parameters

Distribution of snow-water equivalence (SWE) in the Emerald Lake watershed located in Sequoia National Park, California, U.S.A, was examined during the 1987 water year. Elevations at this site range from 2780 to 3416 m a.s.l., and the total watershed area is about 122 ha. A stratified sampling scheme was evaluated by identifying and mapping zones of similar snow properties, based on topographic parameters that account for variations in both accumulation and ablation of snow. Elevation, slope, and radiation values calculated from a digital elevation model were used to identify these zones. Field measurements of SWE were combined with characteristics of the sample locations and clustered to identify similar classes of SWE. The entire basin was then partitioned into zones for each set of survey data. The topographic parameters of the basin used in classification, namely slope and elevation, are constant in time and did not change between survey dates. The radiation data showed temporal variability providing a physically justified basis for changes in SWE distribution through time. Although results do not identify which of the classification attempts is superior to the others, net radiation is clearly of primary importance, and slope and elevation appear to be important to a lesser degree. The peak accumulation for the 1987 water year was 598 mm SWE, which is about half the 50 year mean.

scholarly journals Spatial and temporal variation of bulk snow properties in northern boreal and tundra environments based on extensive field measurements

2016 ◽  
Vol 5 (2) ◽  
pp. 347-363 ◽  
Author(s):  
Henna-Reetta Hannula ◽  
Juha Lemmetyinen ◽  
Anna Kontu ◽  
Chris Derksen ◽  
Jouni Pulliainen
Keyword(s):  
Land Cover ◽  
Sample Size ◽  
Snow Depth ◽  
Spatial And Temporal Variation ◽  
Data Set ◽  
Synthetic Aperture Radar Data ◽  
Land Cover Types ◽  
Sample Spacing ◽  
Measurement Protocol

Abstract. An extensive in situ data set of snow depth, snow water equivalent (SWE), and snow density collected in support of the European Space Agency (ESA) SnowSAR-2 airborne campaigns in northern Finland during the winter of 2011–2012 is presented (ESA Earth Observation Campaigns data 2000–2016). The suitability of the in situ measurement protocol to provide an accurate reference for the simultaneous airborne SAR (synthetic aperture radar) data products over different land cover types was analysed in the context of spatial scale, sample spacing, and uncertainty. The analysis was executed by applying autocorrelation analysis and root mean square difference (RMSD) error estimations. The results showed overall higher variability for all the three bulk snow parameters over tundra, open bogs and lakes (due to wind processes); however, snow depth tended to vary over shorter distances in forests (due to snow–vegetation interactions). Sample spacing/sample size had a statistically significant effect on the mean snow depth over all land cover types. Analysis executed for 50, 100, and 200 m transects revealed that in most cases less than five samples were adequate to describe the snow depth mean with RMSD < 5 %, but for land cover with high overall variability an indication of increased sample size of 1.5–3 times larger was gained depending on the scale and the desired maximum RMSD. Errors for most of the land cover types reached  ∼ 10 % if only three measurements were considered. The collected measurements, which are available via the ESA website upon registration, compose an exceptionally large manually collected snow data set in Scandinavian taiga and tundra environments. This information represents a valuable contribution to the snow research community and can be applied to various snow studies.

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.

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.

KuKa altimeter mode data gathered during MOSAiC: scattering from snow covered sea ice and snow depth determination using dual-frequency and polarimetric approaches

2021 ◽  
Author(s):  
Rosemary Willatt ◽  
Julienne Stroeve ◽  
Vishnu Nandan ◽  
Rasmus Tonboe ◽  
Stefan Hendricks ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Field Measurements ◽  
Dual Band ◽  
Altimetry Data ◽  
Dual Frequency ◽  
Sea Ice Thickness ◽  
Ka Band ◽  
Snow And Ice

&lt;p&gt;Retrieving the thickness of sea ice, and its snow cover, on long time- and length-scales is critical for studying climate. Satellite altimetry has provided estimations of sea ice thickness spanning nearly three decades, and more recently altimetry techniques have provided estimations of snow depth, using dual-band satellite altimetry data. These approaches are based on&amp;#160;assumptions about the main scattering surfaces of the radiation. The dominant scattering surface is often assumed to be the snow/ice interface at Ku-band frequencies and the air/snow interface at Ka-band and laser frequencies. It has previously&amp;#160;been shown that these assumptions do not always hold, but field data to investigate the dominant scattering surfaces and investigate how these relate to the physical snow and ice characteristics were spatially and temporally limited. The MOSAiC expedition provided a unique opportunity to gather data using a newly-developed Ku- and Ka-band radar 'KuKa' deployed over snow-covered sea ice, along with coincident field measurements of snow and ice properties. We present transect data gathered with the instrument looking at nadir to demonstrate how the scattering characteristics vary spatially and temporally in the Ku- and Ka-bands, and discuss implications for interpretation of dual-frequency satellite radar altimetry data. We compare KuKa data with field measurements to demonstrate snow depth retrieval using Ku- and Ka-band data.&lt;/p&gt;

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