Thesis (M.S.)--University of Wyoming, 2007. / Title from PDF title page (viewed on Mar. 5, 2009). Includes bibliographical references.

Mountain snowline is important as it is an easily identifiable measure of the phase state of water in the landscape. However, frequent observation of the snowline in Scotland is difficult as reduced visibility is common, obscuring ground based and remotely sensed methods. Changes in seasonal snowline elevation can indicate long-term climate trends. Snow cover influences local flora and fauna, and knowledge of snowline can inform management of water and associated risks. Complete Scottish Snow Survey of Great Britain (SSGB) records were transcribed and form the primary snow cover dataset used for this work. Voluntary observers collected the SSGB between 1945 and 2007. Other snow cover data used includes remotely sensed (Moderate-resolution Imaging Spectroradiometer: MODIS) and Met Office station observations (as point observations and interpolated to form UK Climate Projections 2009, UKCP09). I present a link between the North Atlantic Oscillation (NAO) index and days of snow cover in Scotland between winters from 1875 to 2013. Broad (5 km resolution) scale datasets (e.g. UKCP09) are used to extract nationwide patterns, supporting these findings using SSGB hillslope scale data. The strongest correlations between the NAO index and snow cover are found in eastern and southern Scotland; these results are supported by both SSGB and UKCP09 data. Correlations between NAO index and snow cover are negative with the strongest relationships found for elevations below 750 m. A degree-day snow model was developed using daily precipitation and temperature data to derive snow cover and melt. This model was run between 1960 and 2011 using point data from five Met Office stations and data on a 5 km grid (UKCP09 temperature and CEH GEAR precipitation) across Scotland. Due to CEH GEAR data underestimating precipitation at higher elevations, absolute values of melt are uncertain. However, relative correlations are apparent, e.g. the proportion of precipitation as melt and number of days with snow cover each year are generally decreasing through time, except around Ben Nevis. Notably, this increase correlates with positive NAO, and it is thought Ben Nevis remains cold enough to accumulate lying snow in the face of a warming climate. Snowmelt rates were found to annually exceed the maximum snowmelt rate used for fluvial impoundment structure design, but this was only at the highest elevations in areas like the Cairngorms.

A research on analysing the feasibility and the benefits obtained from the utilisation of the heat surplus from temperature independent snowmaking systems is proposed. The interest of this research relies on the fact that it is a way of making it viable both economically and environmentally, to use these systems, which are the solution for snow scarcity due to global warming.This will be done by studying the performance of three different alternatives for the installation of a TIS, considering it with and without heat recovery in order to show the importance of the latter. The study will be performed for the ski resort of Astún, in the Spanish Pyrenees, considering the desire to guarantee a ski slope of 3 km from November till end of April. The three cases studied are: • Case I: Temperature Independent Snowmaking system without heat recovery • Case II: Temperature Independent Snowmaking system with direct heat recovery • Case III: Temperature Independent Snowmaking system with direct heat recovery and snow storage The feasibility of each of the cases will be studied based on costs and energy savings and consumptions, while ecological impact, maintenance costs or the interest rates will not be included in order to simplify the results. The heat recovery will be performed thanks to a CO2 heat pump that will deliver over 6 GWh through water at 70 ºC to the residential and commercial buildings of the ski resort. The heat recovery has only been studied in detail for direct recovery, but the possibility of indirect heat recovery would be interesting in other situations, therefore, it has also been briefly described.As a way of introducing and justifying the project, a literature review has been performed, on the impact of climate change and the need for snowmaking, and also on the different snowmaking technologies and their limitations, leading finally to the need for temperature independent snowmaking. Moreover, calculations and simulations including heat transfer, fluid dynamics and theory of refrigeration technology are conducted. Finally, putting that together with estimated investment costs and prices gathered either from available public sources or personal communication with suppliers, the final comparison of the cases is performed.Based on the obtained results, the most suitable solution for the ski resort of Astún, considering the current heat demand of its buildings, is the case II, installation of a TIS with direct heat recovery. This case has a total investment cost of 1.957.464 €, and due to the savings generated from the reused heat, it comes with yearly savings of -249.872 €, which implies a payback time lower than 8 years, making it the most viable alternative.

Increased temperatures, changed snow conditions, increasing demand for skiing and other winter sports lead to increased demand and production of artificial snow. Besides the positive aspects of artificial snow, it is important to understand potentially negative effects of artificial snow on the environment and vegetation. This study investigated the differences between artificial snow and natural snow in two adjacent ski slopes. This was done by measuring and comparing the snow depth, duration of the snow cover, snow density, and ground vegetation between the slopes. Snow depth and density showed differences between artificial snow and natural snow. The artificial snow cover stayed about 11 days longer than the natural snow. A few variations in the vegetation were also found; earlier flowering and more species and colors in the natural snow slope compared to in the slope using artificial snow. Therefore, it is clear that there is a difference between artificial snow and natural snow, and that this results in effects on the environment, but it is difficult to assess to what extent and how large the negative impact is. For more general conclusions, I think it is important to study the vegetation under artificial snow during a longer period and in several areas.

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Rapid warming in the Arctic is highly impacting the cryosphere in the region, causing melting of the sea ice, retreat of glaciers and reduction in the snow cover. If suffering further temperature increase, the albedo of the region would reduce due to higher absorption of the solar radiation in snow-free areas. The variations in seasonal snow cover in Arctic regions can impact a lot of things including the ecosystem, biodiversity, hydrological cycle, and many other physical processes. Therefore, it is beneficial to have the knowledge of processes determining the snow distribution and to understand the trends and patterns of the seasonal snowcover.In this project, seasonal snow cover trends and patterns have been studied for a 30-year period from 1991 to 2020 using a newly developed reanalysis dataset called Copernicus Arctic Regional Reanalysis (CARRA). A validation of the CARRA data set has been done for the snow depth using point observation data from the Norwegian weather stations and a visual snow cover comparison using Sentinel-2 remote sensing data. Thereafter, interannual variability in day of snow disappearance, day of snow onset, duration of snow-free period, and maximum snow depth have been analysed and these trends are then discussed in detail.The results show that for the most non-glaciated regions in Svalbard, the snow onset is happening later in the winter season while the day of snow disappearance is arriving earlier in the spring. Consequently, the duration of snow-free period has increased in almost all regions of Svalbard except a few sites where the duration of the snow free-period has decreased most likely due to local climatic factors. These factors can be better understood by incorporating meteorological elements like precipitation, air temperature and wind speed. Overall, the CARRA reanalysis dataset is very good in determining snow cover trends in non- glaciated regions of Svalbard and with some updates and modifications, it might be able to determine snowcover for the glaciated regions in future.

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