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.

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

10.5194/hess-2016-426 ◽

2016 ◽

Author(s):

Rafael Pimentel ◽

Javier Herrero ◽

María José Polo

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Scale Effects ◽

Control Area ◽

Sigmoid Function ◽

Snow Cover Fraction ◽

Snow Distribution ◽

Snow Model ◽

Snow Conditions ◽

High Level

Abstract. Subgrid variability introduces non-negligible scale effects on the GIS-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 depletion curves (DCs). In this study, terrestrial photography (TP) of a cell-sized area (30 x 30 m) was used to define local snow DCs at a Mediterranean site. Snow cover fraction (SCF) and snow depth (h) values obtained with this technique constituted the two datasets used to define DCs. 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 DCs were able to capture certain physical features of the snow, which were used in a decision-tree and included in the point snow model formulated by Herrero et al. (2009). The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this DC-snow model was found to have a high level of accuracy with global RMSE values of 84.2 mm for the average snow depth and 0.18 m2 m-2 for the snow cover fraction in the control area. The use of DCs 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.

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A systematic review of ecological responses to variation in seasonal snow cover

10.32942/osf.io/4ce37 ◽

2020 ◽

Author(s):

Rachel Slatyer ◽

Pieter Andrew Arnold

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Terrestrial Ecosystems ◽

Ecosystem Change ◽

Future Research ◽

Snow Layer ◽

Spring Phenology ◽

Ecological Responses ◽

Seasonal Snow ◽

Snow Conditions

Seasonal snow is among the most important factors governing the ecology of many terrestrial ecosystems, but rising global temperatures are changing snow regimes and driving widespread declines in the depth and duration of snow cover. Loss of the insulating snow layer will fundamentally change the environment. Understanding how individuals, populations, and communities respond to different snow conditions is thus essential for predicting and managing future ecosystem change. We synthesized 365 studies that have examined ecological responses to variation in winter snow conditions. This research encompasses a broad range of methods (experimental manipulations, natural snow gradients, and long-term monitoring approaches), locations (35 countries), study organisms (plants, mammals, arthropods, birds, fish, lichen, and fungi), and response measures. Earlier snowmelt was consistently associated with advanced spring phenology in plants, mammals, and arthropods. Reduced snow depth also often increased mortality and/or physical injury in plants, although there were few clear effects on animals. Neither snow depth nor snowmelt timing had clear or consistent directional effects on body size of animals or biomass of plants. With 96% of studies from the northern hemisphere, the generality of these trends across ecosystems and localities is also unclear. We identified substantial research gaps for several taxonomic groups and response types, with notably scarce research on winter-time responses. We have developed an agenda for future research to prioritize understanding of the mechanisms underlying responses to changing snow conditions and the consequences of those responses for seasonally snow-covered ecosystems.

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Decreasing snow cover alters functional composition and diversity of Arctic tundra

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2001254117 ◽

2020 ◽

Vol 117 (35) ◽

pp. 21480-21487

Author(s):

Pekka Niittynen ◽

Risto K. Heikkinen ◽

Miska Luoto

Keyword(s):

Snow Cover ◽

Functional Trait ◽

The Arctic ◽

Acquisition Strategies ◽

Tundra Vegetation ◽

Key Factor ◽

Local Functional ◽

Vegetation Models ◽

Snow Cover Duration ◽

Snow Conditions

The Arctic is one of the least human-impacted parts of the world, but, in turn, tundra biome is facing the most rapid climate change on Earth. These perturbations may cause major reshuffling of Arctic species compositions and functional trait profiles and diversity, thereby affecting ecosystem processes of the whole tundra region. Earlier research has detected important drivers of the change in plant functional traits under warming climate, but studies on one key factor, snow cover, are almost totally lacking. Here we integrate plot-scale vegetation data with detailed climate and snow information using machine learning methods to model the responsiveness of tundra communities to different scenarios of warming and snow cover duration. Our results show that decreasing snow cover, together with warming temperatures, can substantially modify biotic communities and their trait compositions, with future plant communities projected to be occupied by taller plants with larger leaves and faster resource acquisition strategies. As another finding, we show that, while the local functional diversity may increase, simultaneous biotic homogenization across tundra communities is likely to occur. The manifestation of climate warming on tundra vegetation is highly dependent on the evolution of snow conditions. Given this, realistic assessments of future ecosystem functioning require acknowledging the role of snow in tundra vegetation models.

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The Development of Ski Areas in Romania. What Environmental, Political, and Economic Logic?

Sustainability ◽

10.3390/su13010274 ◽

2020 ◽

Vol 13 (1) ◽

pp. 274

Author(s):

Sorina Cernaianu ◽

Claude Sobry

Keyword(s):

Statistical Analysis ◽

Snow Cover ◽

Environmental Sustainability ◽

Snow Depth ◽

Winter Season ◽

The Status ◽

Ski Areas ◽

Potential Impact ◽

Low Efficiency ◽

Snow Conditions

In the last years, Romania has made major efforts to develop the skiing areas and some important projects have been implemented in the Carpathian Mountains. This research highlights the low efficiency of ski slopes and ski areas concerning the functionality during the winter season, even though a number of investments have been made. Some examples of bad practices regarding the development of skiing infrastructure in link with the potential impact on the environment are presented. The status of ski slopes, slope conditions, and snow depth were collected daily, during the 2016–2017 and 2017–2018 winter seasons, from a Romanian website specialized in snow cover information. A statistical analysis based on the collected data has been done. The 225 ski slopes studied have been opened, on average, less than 62 days and more than 20% of them have not even been opened. Only 17.8% of the slopes complied with the “100-day rule” during the first season and 21.3% of them during the second one, which does not ensure profitability. In conclusion, too many ski slopes have been created without considering the actual snow conditions. The investors wasted capital that is unprofitable and needlessly, affecting the environmental sustainability.

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Snow cover duration trends observed at sites and predicted by multiple models

10.5194/tc-2020-182 ◽

2020 ◽

Author(s):

Richard Essery ◽

Hyungjun Kim ◽

Libo Wang ◽

Paul Bartlett ◽

Aaron Boone ◽

...

Keyword(s):

Solar Radiation ◽

Snow Cover ◽

Strongly Correlated ◽

Seasonal Snow ◽

Physically Based ◽

Physically Based Models ◽

The Common ◽

Snow Cover Duration ◽

Two Parameters ◽

Seasonal Snow Cover

Abstract. Thirty-year simulations of seasonal snow cover in 22 physically based models driven with bias-corrected meteorological reanalyses are examined at four sites with long records of snow observations. Annual snow cover durations differ widely between models but interannual variations are strongly correlated because of the common driving data. No significant trends are observed in starting dates for seasonal snow cover, but there are significant trends towards snow cover ending earlier at two of the sites in observations and most of the models. A simplified model with just two parameters controlling solar radiation and sensible heat contributions to snowmelt spans the ranges of snow cover durations and trends. This model predicts that sites where snow persists beyond annual peaks in solar radiation and air temperature will experience rapid decreases in snow cover duration with warming as snow begins to melt earlier and at times of year with more energy available for melting.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-21-805-2017 ◽

2017 ◽

Vol 21 (2) ◽

pp. 805-820 ◽

Cited By ~ 13

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.

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Variations of snow cover in the source regions of the Yangtze and Yellow Rivers in China between 1960 and 1999

Journal of Glaciology ◽

10.3189/002214307783258350 ◽

2007 ◽

Vol 53 (182) ◽

pp. 420-426 ◽

Cited By ~ 9

Author(s):

Yang Jianping ◽

Ding Yongjian ◽

Liu Shiyin ◽

Liu Jun Feng

Keyword(s):

Snow Cover ◽

Air Temperature ◽

Snow Depth ◽

Monthly Precipitation ◽

Measured Temperature ◽

Annual Maximum ◽

Study Region ◽

Source Regions ◽

Precipitation Decrease ◽

Snow Cover Duration

AbstractVariations in annual maximum and accumulated snow depths, snow-cover duration, precipitation and air temperature have been analyzed using daily snow depth, monthly air temperature and monthly precipitation data from 1960 to 1999 from six meteorological stations in the source regions of the Yangtze and Yellow Rivers in China. Annual maximum snow depth, snow-cover duration and precipitation increased by ~0.23, ~0.06 and ~0.05% a–1, respectively, during the study period, while annual accumulated snow depth increased by ~2.4% a–1. Annual mean air temperature increased by ~0.6°C over the study period. An unusually heavy snow cover in 1985 coincided with historically low air temperatures. Data from Tuotuohe and Qingshuihe meteorological stations are used to examine inter-station variability. The annual maximum and accumulated snow depths increased by ~0.35 and ~10.6% a–1 at Tuotuohe, and by ~0.42 and ~2.3%a–1 at Qingshuihe. However, from the late 1980s until 1999 the climate in the study region has become warmer and drier. The precipitation decrease in the 1990s (and not the rapid rise in measured temperature) is thought to be the primary cause of the decrease in snow depth in those years.

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Scenarios of Future Snow Conditions in Styria (Austrian Alps)

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0035.1 ◽

2015 ◽

Vol 16 (1) ◽

pp. 261-277 ◽

Cited By ~ 27

Author(s):

Thomas Marke ◽

Ulrich Strasser ◽

Florian Hanzer ◽

Johann Stötter ◽

Renate Anna Irma Wilcke ◽

...

Keyword(s):

Climate Change ◽

Snow Cover ◽

Impact Analysis ◽

Season Length ◽

Average Decrease ◽

Climate Change Effects ◽

Scenario Period ◽

Natural Snow ◽

Snow Conditions ◽

Projected Changes

Abstract A hydrometeorological model chain is applied to investigate climate change effects on natural and artificial snow conditions in the Schladming region in Styria (Austria). Four dynamically refined realizations of the IPCC A1B scenario covering the warm/cold and wet/dry bandwidth of projected changes in temperature and precipitation in the winter half-year are statistically downscaled and bias corrected prior to their application as input for a physically based, distributed energy-balance snow model. However, owing to the poor skills in the reproduction of past climate and snow conditions in the considered region, one realization had to be removed from the selection to avoid biases in the results of the climate change impact analysis. The model’s capabilities in the simulation of natural and artificial snow conditions are evaluated and changes in snow conditions are addressed by comparing the number of snow cover days, the length of the ski season, and the amounts of technically produced snow as simulated for the past and the future. The results for natural snow conditions indicate decreases in the number of snow cover days and the ski season length of up to >25 and >35 days, respectively. The highest decrease in the calculated ski season length has been found for elevations between 1600 and 2700 m MSL, with an average decrease rate of ~2.6 days decade−1. For the exemplary ski site considered, the ski season length simulated for natural snow conditions decreases from >50 days at present to ~40 days in the 2050s. Technical snow production allows the season to be prolonged by ~80 days and hence allows ski season lengths of ~120 days until the end of the scenario period in 2050.

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Recent fluctuation of snow cover on mountainous areas in Japan

Annals of Glaciology ◽

10.3189/172756401781818987 ◽

2001 ◽

Vol 32 ◽

pp. 97-101 ◽

Cited By ~ 7

Author(s):

Masujiro Shimizu ◽

Osamu Abe

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Global Solar Radiation ◽

High Elevation ◽

Meteorological Conditions ◽

Mountainous Areas ◽

The North ◽

Observation Network ◽

Snow Conditions ◽

The Relationship

AbstractTo monitor the snow-cover distribution in relation to meteorological conditions on high mountainous areas in Japan, NIED constructed a snow-observation network which it has operated for approximately 10 years. The network consists of seven pairs of stations, each comprising a mountainous site and a low-lying flatland site, from Hokkaido district in the north to San-in in the southwest. Data obtained include snow depth, snow weight, air temperature and global solar radiation. This study presents recent fluctuation of snow cover on mountainous areas for several recent winters in Japan. Most winters were warmer than average, but winter 1995/96 was normal, and maximum snow depths were recorded in high-elevation areas. Seventy-seven avalanche accidents occurred in winter 1995/96. The relationship between meteorological and snow conditions in mountainous areas and flatland areas was analyzed.

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Assessing snow cover changes in the Kola Peninsula, Arctic Russia, using a synthesis of MODIS snow products and station observations

10.5194/tc-2019-9 ◽

2019 ◽

Cited By ~ 1

Author(s):

Rebecca M. Vignols ◽

Gareth J. Marshall ◽

W. Gareth Rees ◽

Yulia Zaika ◽

Tony Phillips ◽

...

Keyword(s):

Snow Cover ◽

Kola Peninsula ◽

Land Surface ◽

Snow Depth ◽

Remote Sensing Data ◽

Snow Season ◽

Meteorological Observations ◽

Socio Economic Impact ◽

Snow Cover Duration ◽

Arctic Russia

Abstract. The very high albedo of snow means that changes in its coverage have a significant impact on the Earth's global energy budget. Thus, Northern Hemisphere snow cover, which comprises approximately 98 % of the global total area of seasonal snow, is responsible for the largest annual and inter-annual contrasts in land surface albedo. Here, we examine recent changes in snow cover (2000–2016) in the western mountain regions (hereinafter WMR) of the Kola Peninsula in Arctic Russia, an area that has undergone significant climate change in recent decades. Future changes in snow cover have the potential to have a major socio-economic impact in this region, which is primarily dependent on mining and tourism for its economy. We used a combination of remote sensing data, the first time it has been used to assess snow cover in this region, and meteorological observations in our analysis. The snow cover products were processed to maximise the number of cloud-free days. First and last days of snow cover were derived for each year from snow depth observations at meteorological stations. MODIS-derived snow cover dates were compared to these station-derived dates to look for systematic biases in the satellite data. We find that for 85.8 % of pixels investigated the deviation between the MODIS-derived and station-derived snow cover start and end dates is less than 20 days. These locally calibrated MODIS data were then used in combination with data from meteorological stations to determine the trends and variability in the duration of the snow season in the WMR in the past half century. Snow cover was found to be highly variable both spatially and at inter-annual timescales. Overall, the duration of the snow season decreased at higher altitudes and increased in valleys and plains. High spatial variability in trends in the snow cover season and snow depth across the region can be partially explained by the effect of orography and wind scouring. Between 2000 and 2016, opposing trends in the duration of the snow cover season occur at different stations within the WMR, though more consistent trends appear over a 25-year common interval wherein the snow cover duration has decreased statistically significantly at four of five stations. Finally, MODIS is shown to provide a highly reliable snow dataset for assessing regional snow cover changes, being able to identify correctly the only statistically significant trend observed at meteorological station.

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