Seasonal Snow Depth Research Articles

Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach

We used the spatially distributed and physically based snow cover model SNOWGRID-CL to derive daily grids of natural snow conditions and snowmaking potential at a spatial resolution of 1 × 1 km for Austria for the period 1961–2020 validated against homogenized long-term snow observations. Meteorological driving data consists of recently created gridded observation-based datasets of air temperature, precipitation, and evapotranspiration at the same resolution that takes into account the high variability of these variables in complex terrain. Calculated changes reveal a decrease in the mean seasonal (November–April) snow depth (HS), snow cover duration (SCD), and potential snowmaking hours (SP) of 0.15 m, 42 days, and 85 h (26%), respectively, on average over Austria over the period 1961/62–2019/20. Results indicate a clear altitude dependence of the relative reductions (−75% to −5% (HS) and −55% to 0% (SCD)). Detected changes are induced by major shifts of HS in the 1970s and late 1980s. Due to heterogeneous snowmaking infrastructures, the results are not suitable for direct interpretation towards snow reliability of individual Austrian skiing resorts but highly relevant for all activities strongly dependent on natural snow as well as for projections of future snow conditions and climate impact research.

Snowdrift Landscape Patterns: An Arctic Investigation

AbstractBetween 2012 and 2018 we mapped near‐peak seasonal snow depths across two swaths covering 126 km2 in Northern Alaska using aerial structure‐from‐motion photogrammetry and lidar surveys. The surveys were validated by over a hundred thousand ground‐based depth measurements. Using a quantitative method for identifying drift areas, we conducted a snowdrift census that showed on average 18% of the study area is covered by snowdrifts each winter, with 40% of the snow‐water‐equivalent contained in the drifts. Within the census we identified six types of drifts, some of which fill each winter, others which do not. The seasonal drift evolution was distinctly different in the two swaths, a result largely explained by topographic differences. Using four metrics from the field of image quality analysis, we tested the year‐to‐year fidelity of these drift patterns, finding overall high year‐to‐year similarity, but with higher similarity values for filling drifts, and higher similarity values in one swath versus the other, again a function of the topography. These high drift fidelity values are best explained by climatically convergent winter‐cumulative windblown snow fluxes interacting with drift traps to produce the same drifts year after year. However, due to the existence of filling versus nonfilling drifts, and a predicted increasing frequency of rain‐on‐snow events in the Arctic, future snowdrift patterns and drift evolution pathways in the Arctic could diverge from those of today, with direct hydrologic impacts.

Spatiotemporal Variation of Snow Depth in the Northern Hemisphere from 1992 to 2016

A comprehensive and hemispheric-scale snow cover and snow depth analysis is a prerequisite for all related processes and interactions investigation on regional and global surface energy and water balance, weather and climate, hydrological processes, and water resources. However, such studies were limited by the lack of data products and/or valid snow retrieval algorithms. The overall objective of this study is to investigate the variation characteristics of snow depth across the Northern Hemisphere from 1992 to 2016. We developed long-term Northern Hemisphere daily snow depth (NHSnow) datasets from passive microwave remote sensing data using the support vector regression (SVR) snow depth retrieval algorithm. NHSnow is evaluated, along with GlobSnow and ERA-Interim/Land, for its accuracy across the Northern Hemisphere against meteorological station snow depth measurements. The results show that NHSnow performs comparably well with a relatively high accuracy for snow depth with a bias of −0.6 cm, mean absolute error of 16 cm, and root mean square error of 20 cm when benchmarked against the station snow depth measurements. The analysis results show that annual average snow depth decreased by 0.06 cm per year from 1992 to 2016. In the three seasons (autumn, winter, and spring), the areas with a significant decreasing trend of seasonal maximum snow depth are larger than those with a significant increasing trend. Additionally, snow cover days decreased at the rate of 0.99 day per year during 1992–2016. This study presents that the variation trends of snow cover days are, in part, not consistent with the variation trends of the annual average snow depth, of which approximately 20% of the snow cover areas show the completely opposite variation trends for these two indexes over the study period. This study provides a new perspective in snow depth variation analysis, and shows that rapid changes in snow depth have been occurring since the beginning of the 21st century, accompanied by dramatic climate warming.

Air/snow, snow/ice and ice/water interfaces detection from high-resolution vertical temperature profiles measured by ice mass-balance buoys on an Arctic lake

AbstractSnow and ice were monitored by thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) in Lake Orajärvi in northern Finland. An existing automatic SIMBA-algorithm was further developed to derive air/snow, snow/ice and ice/water interfaces based on the SIMBA environment temperature (ET) profiles. The identified interfaces agreed with in situ observations made in 2011/12 winter season. The method was capable to identify upward-moving snow/ice interface that was also visible from SIMBA heating temperature (HT) profiles, which responds to differences in the thermal diffusivities of air, snow, ice and water. The SIMBA data obtained in winters 2017/18 and 2018/19 were used to investigate snow and ice mass balance. An upward-moving snow/ice interface was detected as a result of meteoric ice (snow ice and superimposed ice) formation. Snow contributed to granular lake ice formation up to 40–55% of the total ice thickness on the seasonal mean. Heavy snowfalls and low air temperature in early winter are favourable for granular ice formation. The seasonal mean snow depth on nearby land was 2.7–2.9 times of that on the lake. The estimation of freeboard from snow and ice mass-balance measurement is sensitive to the snow density. Accurate ice freeboard calculation is still a challenge.

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

Winter CO2 emission and its production rate in cold temperate soils of northern Japan: 222Rn as a proxy for the validation of CO2 diffusivity

In the context of a changing seasonal snow-covered period and extent in northern high latitude regions, winter CO2 emissions are vulnerable to drastic climate change, prompting rigorous evaluation of regional carbon budgets. The significance of winter CO2 contributions to the atmosphere in these regions has likewise been recognized. In this study, concentrations of CO2 and 222Rn were observed in the snowpack and underlying cold temperate soils of northern Japan during two winter seasons. Winter CO2 emissions, estimated alternately by snow density, 222Rn tracer (under steady and non-steady states), and chamber, were 56 ± 2, 42 ± 6, 44 ± 15, and 34 ± 3 gCO2-C/m2/season, respectively, suggesting that contribution from average winter CO2 emission corresponds to 25% (range: 19–33%) of annual emission. Winter CO2 emission is highly regulated by soil temperature at 5 cm, depending on snow depth. Winter CO2 emission transport is not suppressed through the snowpack to the atmosphere; rather it is stimulated when seasonal snow depth is over the 40-cm threshold in the soils. The higher soil CO2 production of 0.11 ± 0.02 gCO2-C/m3/h estimated by 222Rn transfer velocity reflects vigorous microbial activity in soils underlying winter snowpack. Therefore, 222Rn is an important proxy for the estimation of winter CO2 emission and diffusivity in snowpack, and of CO2 production rate and transfer velocity in the seasonal, snow-covered.

Effects of snow-depth change on spring runoff in cryosphere areas of China

ABSTRACTBased on snow-depth and measured runoff data in the 19 river basins of the cryosphere areas of China, changes in cold season snow depth (CSSD) and spring runoff, and the relationship between CSSD and spring runoff were analysed. Decreasing trends in CSSD were detected mainly in the plains (plateaus), while increasing trends were found mainly in the mountainous regions. Different combinations of precipitation and temperature may be responsible for these results. The response of spring runoff to CSSD change varied greatly under climate warming. The runoff in April and May was commonly affected by CSSD, while the runoff in March was less affected by CSSD, but more by the increasing temperature causing more snowmelt in March. Due to relatively greater snow accumulation in spring, the runoff during spring was less affected by CSSD in the southern Tienshan Mountains.

Evaluation of homogenization methods for seasonal snow depth data in the Austrian Alps, 1930-2010.

Despite the importance of snow in alpine regions, little attention has been given to the homogenization of snow depth time series. Snow depth time series are generally characterized by high spatial heterogeneity and low correlation among the time series, and the homogenization thereof is therefore challenging. In this work, we present a comparison between two homogenization methods for mean seasonal snow depth time series available for Austria: the standard normal homogeneity test (SNHT) and HOMOP. The results of the two methods are generally in good agreement for high elevation sites. For low elevation sites, HOMOP often identifies suspicious breakpoints (that cannot be confirmed by metadata and only occur in relation to seasons with particularly low mean snow depth), while the SNHT classifies the time series as homogeneous. We therefore suggest applying both methods to verify the reliability of the detected breakpoints. The number of computed anomalies is more sensitive to inhomogeneities than trend analysis performed with the Mann–Kendall test. Nevertheless, the homogenized dataset shows an increased number of stations with negative snow depth trends and characterized by consecutive negative anomalies starting from the late 1980s and early 1990s, which was in agreement with the observations available for several stations in the Alps. In summary, homogenization of snow depth data is possible, relevant and should be carried out prior to performing climatological analysis.

Changing snow depth in the Great Lakes basin (USA): Implications and trends

Abstract In the Great Lakes basin of North America, snow plays a critical role in the regional hydroclimate, where snow ablation events can serve both as a resource and a hazard. The frequency and magnitude of an ablation event is governed by the availability of meteorological conditions to ablate snow, and the physical presence of snow to be ablated. While the meteorological conditions leading to ablation have been well documented, examining changes in atmospheric conditions alone have been unable to completely explain observed ablation trends. As such, this study applies a gridded snow depth dataset to evaluate snow depth variability, while speaking to the implications of such variability on ablation frequency and water resources. Snow cover is present in the basin from November – April, with the more northerly regions (Lake Superior and Lake Huron basins) exhibiting a deeper and more seasonally-persistent snowpack. Seasonal basin-wide snow depth decreased by approximately 25% from 1960 to 2009, with some of the most significant decreases occurring north of Lake Superior. Surface air temperatures are negatively associated snow depth, and warming temperatures are likely contributing to snow depth declines. These regional decreases in snow depth spatially corroborate previously observed decreases in the frequency of ablation events in the basin, and highlight the importance of examining both snow cover and meteorological conditions when seeking to explain snow ablation variability. The results from this study can be applied to inform water resource management decisions in the region.

Variability in snow depth time series in the Adige catchment

Study regionThe Upper and Middle Adige catchment, Trentino-South Tyrol, Italy. Study focusWe provide evidence of changes in mean seasonal snow depth and snow cover duration in the region occurred in the period from 1980 to 2009. New hydrological insights for the regionStations located above and below 1650 m a.s.l. show different dynamics, with the latter experiencing in the last decades a larger reduction of average snow depth and snow cover duration, than the former. Wavelet analyses show that snow dynamics change with elevation and correlate differently with climatic indices at multiple temporal scales. We also observe that starting from the late 1980s snow cover duration and mean seasonal snow depth are below the average in the study area. We also identify an elevation dependent correlation with the temperature. Moreover, correlation with the Mediterranean Oscillation Index and with the North Atlantic Oscillation Index is identified.

Possibility of Estimating Seasonal Snow Depth Based Solely on Passive Microwave Remote Sensing on the Greenland Ice Sheet in Spring

Sea level rise related to the melting and thinning of the Greenland Ice Sheet (GrIS), a subject of growing concern in recent years, will eventually affect the global climate. Although the melting of snow on the GrIS is actively monitored by passive microwave remote sensing, very few studies have estimated the seasonal GrIS snow depth using this technique. In this study, to estimate seasonal snowpack on GrIS, we investigated the microwave property and optimum physical parameters. We used our microwave radiative transfer model to create a lookup table and a simple satellite retrieval algorithm to estimate seasonal snow depth on GrIS in spring, based on the microwave satellite brightness temperature from AMSR-E and AMSR2. Our research suggests there is potential for estimating snow depth based solely on GrIS passive microwave remote sensing data. We validated these estimates against in situ snow depths at several sites and compared them with the snow spatial distributions over the entire GrIS of several major products (ERA-interim, MAR ver. 5.3.1 and GLDAS-CLM) that evaluate snow depth.

Performance of the Standard Normal Homogeneity Test for the homogenization of mean seasonal snow depth time series

ABSTRACTTime series of snow depth bear relevant information for environmental studies in Alpine areas. In addition, due to its sensitivity to air temperature, snow depth dynamic is a robust indicator of climate change effects in mountainous areas. Unfortunately, collecting accurate snow depth data is a difficult task and often time series are obtained by merging data from different sources. Especially in the past, observations were not standardized, such that changes in the operator and in the equipment were sources of inhomogeneities in the time series. To overcome this problem and make the most efficient use of the available information, homogenization techniques may be used. However, a standardized approach for homogenizing snow depth data is currently lacking, despite its importance in climatic and hydrological studies. We evaluate the performance of the Standard Normal Homogeneity Test (SNHT) to homogenize mean seasonal snow depth data collected in the Province of Trento, Northeastern Italy. The proposed algorithm showed good performance in both detecting breakpoints and identifying homogeneous time series. Breakpoints have been detected in about 20% of the analysed time series. A homogeneity analysis on mean seasonal snow depth datasets is hence recommended before performing climatological studies.

Asynchronous recruitment dynamics of snowshoe hares and white spruce in a boreal forest

Herbivores have the capacity to modify plant community composition and ecosystem structure and function via browsing. For example, moose and snowshoe hare facilitate succession in Alaska’s boreal forest by preferentially browsing early successional species over late successional conifers. Snowshoe hares also eat conifers, including white spruce, and this browsing may affect the pattern of spruce establishment over time. We measured over 800 spruce at 18 locations along the Tanana River floodplain in interior Alaska, USA and demonstrated that the proportion of spruce browsed annually positively correlates with annual hare abundance. Nearly all seedlings sampled had been browsed. Further, we modeled the pattern of spruce establishment over the last 40 years and found that hare abundance, growing season temperature, early season snow depth, and flooding explain the majority of this pattern. This model demonstrated that less spruce established during periods of high hare abundance than during periods of low hare abundance. The extensive browsing of white spruce that occurs during periods of high hare abundance may further compound the negative effects of climate warming on spruce recruitment in these floodplain forests.

Trends and Extremes in Northern Hemisphere Snow Characteristics

Recent studies of snow climatology show a mix of trends but a preponderance of evidence suggest an overall tendency toward decreases in several metrics of snow extremes. The analysis performed herein on maximum seasonal snow depth points to a robust negative trend in this variable for the period of winter 1960/1961–winter 2014/2015. This conclusion is applicable to North America. Maximum snow depth is also mostly decreasing for those European stations analyzed. Research studies show generally negative trends in snow cover extent and snow water equivalent across both North America and Eurasia. These results are mostly, but not fully, consistent with simple hypotheses for the effects of global warming on snow characteristics.

Weather conditions associated with autumn migration by mule deer in Wyoming.

Maintaining ecological integrity necessitates a proactive approach of identifying and acquiring lands to conserve unfragmented landscapes, as well as evaluating existing mitigation strategies to increase connectivity in fragmented landscapes. The increased use of highway underpasses and overpasses to restore connectivity for wildlife species offers clear conservation benefits, yet also presents a unique opportunity to understand how weather conditions may impact movement of wildlife species. We used remote camera observations (19,480) from an existing wildlife highway underpass in Wyoming and daily meteorological observations to quantify weather conditions associated with autumn migration of mule deer in 2009 and 2010. We identified minimal daily temperature and snow depth as proximate cues associated with mule deer migration to winter range. These weather cues were consistent across does and bucks, but differed slightly by year. Additionally, extreme early season snow depth or cold temperature events appear to be associated with onset of migration. This information will assist wildlife managers and transportation officials as they plan future projects to maintain and enhance migration routes for mule deer.

Characterizing Microclimate and Plant Community Variation in Wetlands

Groundwater-fed calcareous wetlands (fens) support diverse plant assemblages including rare species and boreal disjuncts, yet factors structuring related microclimatic and plant community variation have not been thoroughly examined. We investigated factors influencing microclimate variation in temperate fens relative to uplands and examined associated plant compositional gradients within fens. Large differences in average soil temperatures were observed between monitoring locations during a 161 day period in 2010 (range: 14.5 to 20.0 °C). Using a regression approach, we developed accurate daily resolution soil temperature models (min and max) from a 29-sensor network by integrating atmospheric and hydrologic terms. Models accurately predicted validation observations from an independent 17-sensor network (R2 values 0.92 minimums; 0.95 maximums) with low error compared to null models. Environmental variation in seasonal average soil temperature, snow depth, specific conductance, canopy closure, and non-vascular plant cover were correlated with spatial patterns in vascular plant community composition. Results of plant community analyses provided insufficient support to suggest boreal and non-boreal species occupied distinct microsites. Given the significant soil temperature variation observed, we suggest further investigation of climatic factors structuring wetland plant communities are warranted to inform the placement of conservation reserves that may be less vulnerable to climate change.

A new fractional snow‐covered area parameterization for the Community Land Model and its effect on the surface energy balance

One function of the Community Land Model (CLM4) is the determination of surface albedo in the Community Earth System Model (CESM1). Because the typical spatial scales of CESM1 simulations are large compared to the scales of variability of surface properties such as snow cover and vegetation, unresolved surface heterogeneity is parameterized. Fractional snow‐covered area, or snow‐covered fraction (SCF), within a CLM4 grid cell is parameterized as a function of grid cell mean snow depth and snow density. This parameterization is based on an analysis of monthly averaged SCF and snow depth that showed a seasonal shift in the snow depth–SCF relationship. In this paper, we show that this shift is an artifact of the monthly sampling and that the current parameterization does not reflect the relationship observed between snow depth and SCF at the daily time scale. We demonstrate that the snow depth analysis used in the original study exhibits a bias toward early melt when compared to satellite‐observed SCF. This bias results in a tendency to overestimate SCF as a function of snow depth. Using a more consistent, higher spatial and temporal resolution snow depth analysis reveals a clear hysteresis between snow accumulation and melt seasons. Here, a new SCF parameterization based on snow water equivalent is developed to capture the observed seasonal snow depth–SCF evolution. The effects of the new SCF parameterization on the surface energy budget are described. In CLM4, surface energy fluxes are calculated assuming a uniform snow cover. To more realistically simulate environments having patchy snow cover, we modify the model by computing the surface fluxes separately for snow‐free and snow‐covered fractions of a grid cell. In this configuration, the form of the parameterized snow depth–SCF relationship is shown to greatly affect the surface energy budget. The direct exposure of the snow‐free surfaces to the atmosphere leads to greater heat loss from the ground during autumn and greater heat gain during spring. The net effect is to reduce annual mean soil temperatures by up to 3°C in snow‐affected regions.

Trends in Average Snow Depth Across the Western United States

A network of cooperative observation stations was used to examine trends in average snow depth across the western United States. This station network includes a greater number of low- to moderate-elevation stations, where the snow is particularly sensitive to temperature changes, than the snow course and SNOTEL datasets used in other studies. Results indicate a widespread decrease in snow depth across the region; 70% of stations with statistically significant trends (p ≤ 0.5 or p ≤ 0.10) exhibit negative trends. Elevation is a key factor in explaining the spatial pattern of snow depth trends across the region. The fraction of stations with significant negative trends is greatest (80%) at the lower-elevation stations (<1000 m) and is somewhat less (62%) for stations between 2000 and 3000 m. Pacific climate indices, including the Pacific Decadal Oscillation (PDO) and North Pacific Index (NPI), are shown to have important but spatially varying influences upon average seasonal snow depths.

The influence of seasonal climatic parameters on the permafrost thermal regime, West Siberia, Russia

AbstractStatistical correlations between seasonal air temperatures and snow depths and active layer depths and permafrost temperatures were analysed for tundra (Marre‐Salle) and northern taiga (Nadym) sites in Western Siberia. Interannual variations in active layer depth in the tundra zone correlated with the average air temperature of the current summer, and in peatland and humid tundra, also with summer temperatures of the preceding 1–2 years. In the northern taiga zone, the active layer depth related to current summer air temperature and to a lesser extent, to spring and/or winter air temperatures. Variations in summer permafrost temperatures at 5–10 m depth were correlated with spring air temperatures in the current and preceding 1–2 years. The weather regime during the preceding 1–2 years, therefore, reinforced or weakened ground temperature variations in a given year. Overall, the most important factors influencing the permafrost regime were spring and summer air temperatures, and in one case snow depth. However, statistical links between meteorological and permafrost parameters varied between the tundra and northern taiga zones and among landscape types within each zone, emphasising the importance of analyses at short temporal scales and for individual terrain units. Copyright © 2009 John Wiley &amp; Sons, Ltd.

Hysteretic dynamics of seasonal snow depth distribution in the Swiss Alps

The dynamics of the spatial snow distribution is a key feature when investigating a seasonal snow cover on the catchment scale. This study explores the spatial variability of snow depth during the course of snow accumulation and depletion, based on daily data from 77 weather stations in the Swiss Alps during 6 consecutive seasons. We derive a statistical description of the snow cover dynamics by analyzing temporal trajectories in plots of the standard deviation σ() over mean , where xi is a set of snow depth measurements on one particular day. The analysis reveals that the evolution of σ() closely follows a power law σ() = 0.84±0.01 during the accumulation period for all 6 winters. However, during snowmelt, the trajectory of σ() displays a clearly different path as with classical hysteresis phenomena. Surprisingly, a simplistic model of uniform melting describes this path reasonably well. We believe that the conceptual framework outlined in this study is helpful in describing important characteristics of the dynamics of seasonal snow depth spatial variability. Future studies could apply the methodology introduced here to incorporate stochastic means in modeling snow distribution, even for a broader range of length scales and different alpine terrain.