scholarly journals Influence of Mountain Spruce Forest Dieback on Snow Accumulation and Melt

2019 ◽  
Vol 67 (1) ◽  
pp. 59-69 ◽  
Author(s):  
Martin Bartík ◽  
Ladislav Holko ◽  
Martin Jančo ◽  
Jaroslav Škvarenina ◽  
Michal Danko ◽  
...  
Keyword(s):  
Coefficient Of Variation ◽  
Snow Depth ◽  
Large Scale ◽  
Snow Accumulation ◽  
Spruce Forest ◽  
Snow Density ◽  
Forest Dieback ◽  
Dead Trees ◽  
Opposite Order ◽  
Disturbed Forest

Abstract Large-scale forest dieback was reported in recent decades in many parts of the world. In Slovakia, the most endangered species is Norway spruce (Picea Abies). Spruce dieback affects also indigenous mountain forests. We analysed changes in snow cover characteristics in the disturbed spruce forest representing the tree line zone (1420 m a.s.l.) in the Western Tatra Mountains, Slovakia, in five winter seasons 2013-2017. Snow depth, density and water equivalent (SWE) were measured biweekly (10-12 times per winter) at four sites representing the living forest (Living), disturbed forest with dead trees (Dead), forest opening (Open) and large open area outside the forest (Meadow). The data confirmed statistically significant differences in snow depth between the living and disturbed forest. These differences increased since the third winter after forest dieback. The differences in snow density between the disturbed and living forest were in most cases not significant. Variability of snow density expressed by coefficient of variation was approximately half that of the snow depth. Forest dieback resulted in a significant increase (about 25%) of the water amount stored in the snow while the snowmelt characteristics (snowmelt beginning and time of snow disappearance) did not change much. Average SWE calculated for all measurements conducted during five winters increased in the sequence Living < Dead < Meadow < Open. SWE variability expressed by the coefficient of variation increased in the opposite order.

scholarly journals Exploration of the Snow Ablation Process in the Semiarid Region in China by Combining Site-Based Measurements and the Utah Energy Balance Model—A Case Study of the Manas River Basin

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1058 ◽  
Author(s):  
Yan Liu ◽  
Pu Zhang ◽  
Lei Nie ◽  
Jianhui Xu ◽  
Xinyu Lu ◽  
...  
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
River Basin ◽  
Time Scales ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Semiarid Region ◽  
Snow Density ◽  
Ablation Process ◽  
Manas River Basin

Understanding the snow accumulation and melting process is of great significance for the assessment and regulation of water resources and the prevention of meltwater flooding, especially for the semiarid region in the Manas River Basin. However, the lack of long snow measurement time series in this semiarid region prevents a full understanding of the detailed local-scale snow ablation process. Additionally, the modeling of snow accumulation and melting is challenging due to parameter uncertainty. In this study, the snow ablation process in the Manas River Basin was quantitatively explored with long time-series of 3-h measurements of snow depth, snow density and snow water equivalent (SWE) at the Wulanwusu (WLWS), Hanqiazi (HQZ), and Baiyanggou (BYG) sites. This study explored the ability of the Utah energy balance (UEB) snow accumulation and melt model to simulate SWE, energy flux and water loss in the study area. Furthermore, the uncertainty in the ground surface aerodynamic roughness index zos in the UEB model was also analyzed. The results showed that: (1) noticeable variations in snow depth, SWE and snow density occurred on seasonal and interannual time scales, and variations in melting time and melting ratios occurred on short time scales; (2) a rapid decrease in snow depth did not influence the variations in SWE, and snow melting occurred during all time periods, even winter, which is a typical characteristic of snow accumulation in arid environments; (3) the UEB model accurately simulated the snow ablation processes, including SWE, snow surface temperature, and energy flux, at WLWS, HQZ, and BYG sites; (4) the lowest contribution of net radiation to melting occurred in the piedmont clinoplain, followed by the mountain desert grassland belt and mountain forest belt, whereas the contributions of net turbulence exhibited the opposite pattern; (5) the optimal zos in the UEB model was experimentally determined to be 0.01 m, and the UEB model-simulated SWE based on this value was the most consistent with the measured SWE; and (6) the results may provide theoretical and data foundations for research on the snow accumulation process at the watershed scale.

scholarly journals Reconstruction of historical surface mass balance, 1984–2017 from GreenTrACS multi-offset ground-penetrating radar

2020 ◽  
pp. 1-10
Author(s):  
Tate G. Meehan ◽  
H. P. Marshall ◽  
John H. Bradford ◽  
Robert L. Hawley ◽  
Thomas B. Overly ◽  
...  
Keyword(s):  
Mass Balance ◽  
Ground Penetrating Radar ◽  
Greenland Ice Sheet ◽  
Snow Accumulation ◽  
Surface Mass Balance ◽  
Snow Density ◽  
Surface Mass ◽  
Age Model ◽  
Ground Penetrating ◽  
Good Agreement

Abstract We present continuous estimates of snow and firn density, layer depth and accumulation from a multi-channel, multi-offset, ground-penetrating radar traverse. Our method uses the electromagnetic velocity, estimated from waveform travel-times measured at common-midpoints between sources and receivers. Previously, common-midpoint radar experiments on ice sheets have been limited to point observations. We completed radar velocity analysis in the upper ~2 m to estimate the surface and average snow density of the Greenland Ice Sheet. We parameterized the Herron and Langway (1980) firn density and age model using the radar-derived snow density, radar-derived surface mass balance (2015–2017) and reanalysis-derived temperature data. We applied structure-oriented filtering to the radar image along constant age horizons and increased the depth at which horizons could be reliably interpreted. We reconstructed the historical instantaneous surface mass balance, which we averaged into annual and multidecadal products along a 78 km traverse for the period 1984–2017. We found good agreement between our physically constrained parameterization and a firn core collected from the dry snow accumulation zone, and gained insights into the spatial correlation of surface snow density.

Kill rate by wolves on moose in the Yukon

2000 ◽  
Vol 78 (1) ◽  
pp. 49-59 ◽  
Author(s):  
R D Hayes ◽  
A M Baer ◽  
U Wotschikowsky ◽  
A S Harestad
Keyword(s):  
Canis Lupus ◽  
Snow Depth ◽  
Prey Density ◽  
Large Scale ◽  
Rangifer Tarandus ◽  
Prey Selection ◽  
Ecological Factors ◽  
Predation Rate ◽  
Wolf Predation ◽  
Kill Rate

We studied the kill rate by wolves (Canis lupus) after a large-scale wolf removal when populations of wolves, moose (Alces alces), and woodland caribou (Rangifer tarandus caribou) were all increasing. We followed a total of 21 wolf packs for 4 winters, measuring prey selection, kill rates, and ecological factors that could influence killing behavior. Wolf predation was found to be mainly additive on both moose and caribou populations. Kill rates by individual wolves were inversely related to pack size and unrelated to prey density or snow depth. Scavenging by ravens decreased the amount of prey biomass available for wolves to consume, especially for wolves in smaller packs. The kill rate by wolves on moose calves was not related to the number of calves available each winter. Wolves did not show a strong switching response away from moose as the ratio of caribou to moose increased in winter. The predation rate by wolves on moose was best modeled by the number and size of packs wolves were organized into each winter.

scholarly journals Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA183-WA193 ◽  
Author(s):  
W. Steven Holbrook ◽  
Scott N. Miller ◽  
Matthew A. Provart
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Accumulation ◽  
Accurate Estimation ◽  
Snow Density ◽  
Snow Stratigraphy ◽  
Flood Estimation ◽  
Snow Water ◽  
Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

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 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 An assessment of the population status of the threatened medicinal plant Illicium griffithii Hook.f. & Thomson in West Kameng District of Arunachal Pradesh, India

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tashi Dorjee Bapu ◽  
Gibji Nimasow
Keyword(s):  
Medicinal Plant ◽  
Large Scale ◽  
Arunachal Pradesh ◽  
Mature Trees ◽  
Regeneration Rate ◽  
Dead Trees ◽  
Threatened Medicinal Plant ◽  
The Family ◽  
Proper Training ◽  
Large Scale Cultivation

Illicium griffithii Hook.f. & Thomson, a medicinal plant of the family Schisandraceae, is an Endangered species listed by the IUCN.  A decline in population of this plant due to climate change as well as increasing human influences on the natural resources has been a matter of great concern among the researchers.  In order to estimate the existing population of this plant, a field-based study employing linear transect method was conducted in four phases, May–June 2017, May–June 2018, April–May 2019, October–November 2019 covering an area of 700km² (approx.) in West Kameng District of Arunachal Pradesh that lies within the Himalayan biodiversity hotspot.  The study recorded 3,044 live individuals of I. griffithii including 1,372 seedlings, 1,358 saplings, and only 314 mature trees.  Additionally, 126 dead trees were also recorded.  The study confirmed that the plant has a good regeneration rate but with a poor survival rate of saplings.  Besides, large-scale collection of its fruits for trade and anthropogenic disturbances in the study area appears to be the major threat to its existing population.  Therefore, proper training of the local people on large-scale cultivation of this plant together with awareness towards judicious harvesting of fruits from the wild may be the significant approach to conservation. 

scholarly journals Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

2021 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan I. López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals A snow-transport model for complex terrain

1998 ◽  
Vol 44 (148) ◽  
pp. 498-516 ◽  
Author(s):  
Glen E. Liston ◽  
Matthew Sturm
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Snow Water Equivalent ◽  
Transport Model ◽  
Vegetation Type ◽  
Cold Season ◽  
Snow Accumulation ◽  
Surface Shear Stress ◽  
Surface Shear ◽  
Snow Transport

AbstractAs part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

scholarly journals Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway

2017 ◽  
Author(s):  
Hanneke Luijting ◽  
Dagrun Vikhamar-Schuler ◽  
Trygve Aspelien ◽  
Mariken Homleid
Keyword(s):  
Land Surface ◽  
Snow Depth ◽  
Water Resource Management ◽  
Snow Accumulation ◽  
Surface Model ◽  
Snow Melt ◽  
Grid Spacing ◽  
Snow Covered Area ◽  
Covered Area ◽  
Southern Norway

Abstract. In Norway, thirty percent of the annual precipitation falls as snow. Knowledge of the snow reservoir is therefore important for energy production and water resource management. The land surface model SURFEX with the detailed snowpack scheme Crocus (SURFEX/Crocus) has been run with a grid spacing of approximately 1 km over an area in southern Norway for two years (01 September 2014–31 August 2016), using two different forcing data sets: 1) hourly meteorological forecasts from the operational weather forecast model AROME MetCoOp (2.5 km grid spacing), and 2) gridded hourly observations of temperature and precipitation (1 km grid spacing) in combination with the meteorological forecasts from AROME MetCoOp. We present an evaluation of the modeled snow depth and snow cover, as compared to point observations of snow depth and to MODIS satellite images of the snow-covered area. The evaluation focuses on snow accumulation and snow melt. The results are promising. Both experiments are capable of simulating the snow pack over the two winter seasons, but there is an overestimation of snow depth when using only meteorological forecasts from AROME MetCoOp, although the snow-covered area throughout the melt season is better represented by this experiment. The errors, when using AROME MetCoOp as forcing, accumulate over the snow season, showing that assimilation of snow depth observations into SURFEX/Crocus might be necessary when using only meteorological forecasts as forcing. When using gridded observations, the simulation of snow depth is significantly improved, which shows that using a combination of gridded observations and meteorological forecasts to force a snowpack model is very useful and can give better results than only using meteorological forecasts. There is however an underestimation of snow ablation in both experiments. This is mainly due to the absence of wind-induced erosion of snow in the SURFEX/Crocus model, underestimated snow melt and biases in the forcing data.

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