| Summary: | Sweden’s power production is in the midst of a paradigm shift, where predictable carbon- and nuclear-based production is being replaced by renewable energy, such as wind and solar. Since both of these energy sources are intermittent, this introduces predictability issues to the energy production sector. Hydropower energy does not directly emit carbon dioxide in its power production, and it is to some extent a predictable source of energy. However, this predictability can be improved with enhanced estimation of the water stored in the form of snow over the catchment areas involved. The purpose of the project was to evaluate the impacts that topography and wind have on snow distribution over a catchment in northern Sweden and trying to model the snow conditions based on this, and how well snow and weather data-based and satellite-based snow data products could predict the snow water equivalent volumes within it. The analyses and evaluations were based on in situ data collected within the catchment area during one week of the snow season of 2018 to 2020 and consisted of data for, among other things, snow depth and density. Several topographical features were found to consistently correlate with SWE, up to 69%, over the catchment. Furthermore, models based on all investigated features could, at best, describe up to 51% of the snow variability. Even though the wind direction was quite evenly distributed among all cardinal directions, the accumulation of snow showed correlations with the net wind direction of the season, although with consistent signs of other factors influencing the direction of accumulation as well. Despite evaluating the satellite-based model with the highest resolution available, it displayed very low accuracy in describing the snow’s spatial distribution, as well as the aggregated volumes of SWE on the scale tested in this project. The snow and weather data-based data appeared, with slight adjustments, to be the only observational method successful in describing the snow’s spatial distribution, ...
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