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.

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 A Study on the Prediction of Daily Snowmelt Depth using Multiple Linear Regression

2020 ◽  
Vol 20 (6) ◽  
pp. 311-321
Author(s):  
YeoungRok Oh ◽  
Gyumin Lee ◽  
Kyung Soo Jun ◽  
Wooyeon Sunwoo ◽  
SeungWoo Baek ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Snow Depth ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Independent Variables ◽  
Fresh Snow ◽  
Humidity Range

In this study, daily snowmelt was predicted using observed meteorological data and multiple regression analysis. Five observation stations (located in Daegwallyeong, Gwangju, Seosan, Mokpo, and Jeonju) were selected to analyze fresh snow depth from 2000 to 2010. The dependent variable used in the multiple regression analysis was daily snowmelt depth, and the independent variables were fresh snow depth, diurnal temperature range, temperature interception, diurnal humidity range, humidity intercept, and solar radiation. Seventy percent of the total observed data was used to develop a multiple regression model and the regression model was verified using the 30% of remaining data. The adjusted R-squared and Root Mean Square Deviation (RMSE) were used to examine the developed regression model. As a result, the adjusted R-squared was higher than 0.769 (except Daegwallyeong); thus the developed model represented well the daily snowmelt depth. Even Jeonju had an adjusted R-squared of 0.869. Also, the RMSE in all of the five stations was lower than 2.5 cm. The lowest value in Seosan was 1.7 cm. From the two types of verification, the developed multiple regression model was judged to be suitable to predict the daily snowmelt depth. However, multicollinearity should be explained, as rapid increases in temperature and sustained high temperature could not be reflected in the model. Therefore, if the limitations were resolved in further research, the model could be used to predict the amount of daily snowmelt depth more reliably.

scholarly journals Recent fluctuations of meteorological and snow conditions in Japanese mountains

2011 ◽  
Vol 52 (58) ◽  
pp. 209-215 ◽  
Author(s):  
Satoru Yamaguchi ◽  
Osamu Abe ◽  
Sento Nakai ◽  
Atsushi Sato
Keyword(s):  
Air Temperature ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Earth Science ◽  
Meteorological Data ◽  
Winter Precipitation ◽  
Mountainous Areas ◽  
Snow Water ◽  
Notable Increase ◽  
Snow Conditions

AbstarctMeteorological data from mountainous areas of Japan have been collected by the National Research Institute for Earth Science and Disaster Prevention (NIED) for almost 20 years. The collected long-period data indicate that neither a notable increase in mean winter temperature nor a reduction in snow depth has occurred in these areas. The maximum snow depth, SDmax, and maximum snow water equivalent, SWEmax, show similar fluctuation trends, although with large year-to-year variations in value and a larger fluctuation range for SWEmax than for SDmax. This result suggests that monitoring of only SDmax in mountainous areas is not sufficient for understanding the quantitative fluctuation of water resources originating from snow. The SDmax fluctuation trends in mountainous areas sometimes differ from those in flatland areas because mountain SDmax depends more on winter precipitation than on mean winter air temperature, whereas the opposite is true for flatlands. In addition, the dependence ratio of SDmax on fluctuations in winter precipitation changes with altitude because the distributions of precipitation with air temperature change with altitude.

scholarly journals Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California

2018 ◽  
Vol 10 (4) ◽  
pp. 2115-2122
Author(s):  
Roger C. Bales ◽  
Erin M. Stacy ◽  
Xiande Meng ◽  
Martha H. Conklin ◽  
Peter B. Kirchner ◽  
...  
Keyword(s):  
Water Balance ◽  
Sierra Nevada ◽  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Soil Water Storage ◽  
Spatial And Temporal Patterns ◽  
Online Data ◽  
Data Repositories ◽  
Sequoia National Park

Abstract. Accurate water-balance measurements in the seasonal, snow-dominated Sierra Nevada are important for forest and downstream water management. However, few sites in the southern Sierra offer detailed records of the spatial and temporal patterns of snowpack and soil-water storage and the fluxes affecting them, i.e., precipitation as rain and snow, snowmelt, evapotranspiration, and runoff. To explore these stores and fluxes we instrumented the Wolverton basin (2180–2750 m) in Sequoia National Park with distributed, continuous sensors. This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. Clustered sensors record lateral differences with regards to aspect and canopy cover at approximately 2250 and 2625 m in elevation, where two meteorological stations are installed. Meteorological stations record air temperature, relative humidity, radiation, precipitation, wind speed and direction, and snow depth. Data are available at hourly intervals by water year (1 October–30 September) in non-proprietary formats from online data repositories (https://doi.org/10.6071/M3S94T).

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2017 ◽  
Author(s):  
Esteban Alonso-González ◽  
J.¬Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database.

scholarly journals Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

2017 ◽  
Vol 21 (2) ◽  
pp. 805-820 ◽  
Author(s):  
Rafael Pimentel ◽  
Javier Herrero ◽  
María José Polo
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Scale Effects ◽  
Control Area ◽  
Snow Accumulation ◽  
Sigmoid Function ◽  
Snow Cover Fraction ◽  
Snow Model ◽  
Snow Conditions ◽  
High Level

Abstract. Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation–depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30  ×  30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m2 m−2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

scholarly journals Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

2018 ◽  
Vol 10 (1) ◽  
pp. 303-315 ◽  
Author(s):  
Esteban Alonso-González ◽  
J. Ignacio López-Moreno ◽  
Simon Gascoin ◽  
Matilde García-Valdecasas Ojeda ◽  
Alba Sanmiguel-Vallelado ◽  
...  
Keyword(s):  
Iberian Peninsula ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Rate Coefficients ◽  
Balance Model ◽  
Winter Tourism ◽  
Snow Water

Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km  ×  10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km  ×  10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

scholarly journals Spatially distributed water-balance and meteorological data from the Wolverton catchment, Sequoia National Park, California

2018 ◽  
Author(s):  
Roger C. Bales ◽  
Erin M. Stacy ◽  
Xiande Meng ◽  
Martha H. Conklin ◽  
Peter B. Kirchner ◽  
...  
Keyword(s):  
Water Balance ◽  
Sierra Nevada ◽  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Soil Water Storage ◽  
Spatial And Temporal Patterns ◽  
Online Data ◽  
Data Repositories ◽  
Sequoia National Park

Abstract. Accurate water-balance measurements in the seasonal, snow-dominated Sierra Nevada are important for forest and downstream water management. However, few sites in the southern Sierra offer detailed records of the spatial and temporal patterns of snowpack and soil-water storage, and the fluxes affecting them, i.e. precipitation as rain and snow, snowmelt, evapotranspiration, and runoff. To explore these stores and fluxes we instrumented the Wolverton basin (2180–2750 m) in Sequoia National Park with distributed, continuous sensors. This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies the hydrologic inputs and storage in a mostly undeveloped catchment. Clustered sensors record lateral differences with regards to aspect and canopy cover at approximately 2250 and 2625 m in elevation, where two meteorological stations are installed. Meteorological stations record air temperature, relative humidity, radiation, precipitation, wind speed and direction, and snow depth. Data are available at hourly intervals by water year (1 October–30 September) in non-proprietary formats from online data repositories ( https://doi.org/10.6071/M3S94T).

scholarly journals Top carnivore decline has cascading effects on scavengers and carrion persistence

2018 ◽  
Vol 285 (1892) ◽  
pp. 20181582 ◽  
Author(s):  
Calum X. Cunningham ◽  
Christopher N. Johnson ◽  
Leon A. Barmuta ◽  
Tracey Hollings ◽  
Eric J. Woehler ◽  
...  
Keyword(s):  
Severe Disease ◽  
Tasmanian Devil ◽  
Population Declines ◽  
Mesopredator Release ◽  
Landscape Of Fear ◽  
Feral Cat ◽  
Cascading Effects ◽  
Remote Cameras

Top carnivores have suffered widespread global declines, with well-documented effects on mesopredators and herbivores. We know less about how carnivores affect ecosystems through scavenging. Tasmania's top carnivore, the Tasmanian devil (Sarcophilus harrisii) , has suffered severe disease-induced population declines, providing a natural experiment on the role of scavenging in structuring communities. Using remote cameras and experimentally placed carcasses, we show that mesopredators consume more carrion in areas where devils have declined. Carcass consumption by the two native mesopredators was best predicted by competition for carrion, whereas consumption by the invasive mesopredator, the feral cat ( Felis catus ), was better predicted by the landscape-level abundance of devils, suggesting a relaxed landscape of fear where devils are suppressed. Reduced discovery of carcasses by devils was balanced by the increased discovery by mesopredators. Nonetheless, carcasses persisted approximately 2.6-fold longer where devils have declined, highlighting their importance for rapid carrion removal. The major beneficiary of increased carrion availability was the forest raven ( Corvus tasmanicus ). Population trends of ravens increased 2.2-fold from 1998 to 2017, the period of devil decline, but this increase occurred Tasmania-wide, making the cause unclear. This case study provides a little-studied potential mechanism for mesopredator release, with broad relevance to the vast areas of the world that have suffered carnivore declines.

Fire regimes and interval-sensitive vegetation in semiarid Gregory National Park, northern Australia

2010 ◽  
Vol 58 (4) ◽  
pp. 300 ◽  
Author(s):  
Jeremy Russell-Smith ◽  
Cameron P. Yates ◽  
Chris Brock ◽  
Vanessa C. Westcott
Keyword(s):  
National Park ◽  
Fire Regime ◽  
Woody Species ◽  
Fire Regimes ◽  
Stem Density ◽  
Northern Australia ◽  
Juvenile Period ◽  
Climate Conditions ◽  
Fine Fuels ◽  
Ecologically Sustainable

Few data are available concerning contemporary fire regimes and the responses of fire interval-sensitive vegetation types in semiarid woodland savanna landscapes of northern Australia. For a 10 300 km2 semiarid portion of Gregory National Park, in the present paper we describe (1) components of the contemporary fire regime for 1998–2008, on the basis of assessments derived from Landsat and MODIS imagery, (2) for the same period, the population dynamics, and characteristic fine-fuel loads associated with Acacia shirleyi Maiden (lancewood), an obligate seeder tree species occurring in dense monodominant stands, and (3) the fire responses of woody species, and fine-fuel dynamics, sampled in 41 plots comprising shrubby open-woodland over spinifex hummock grassland. While rain-year (July–June) rainfall was consistently reliable over the study period, annual fire extent fluctuated markedly, with an average of 29% being fire affected, mostly in the latter part of the year under relatively harsh fire-climate conditions. Collectively, such conditions facilitated short fire-return intervals, with 30% of the study area experiencing a repeat fire within 1 year, and 80% experiencing a repeat fire within 3 years. Fine fuels associated with the interior of lancewood thickets were characteristically small (<1 t ha–1). Fine fuels dominated by spinifex (Triodia spp.) were found to accumulate at rates equivalent to those observed under higher-rainfall conditions. Stand boundaries of A. shirleyi faired poorly under prevailing fire regimes over the study period; in 16 plots, juvenile density declined 62%, and adult stem density and basal area declined by 53% and 40%, respectively. Although the maturation (primary juvenile) period of A. shirleyi is incompletely known, assembled growth rate and phenology data indicated that it is typically >10 years. Of 133 woody species sampled, all trees (n = 26), with the exception of A. shirleyi, were resprouters, and 58% of all shrub species (n = 105) were obligate seeders, with observed primary juvenile periods <5 years. Assembled data generally supported observations made from other northern Australian studies concerning the responses of fire-sensitive woody taxa in rugged, sandstone-derived landscapes, and illustrated the enormous challenges facing ecologically sustainable fire management in such settings. Contemporary fire regimes of Gregory National Park are not ecologically sustainable.

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