This work deals with the evaluation of snow cover and snow spatial distribution in experimental Zbytinský and Tetřivči stream basin. Snow mesaurements were focused on snow depth and a snow water equivalent. Interpolation metods and detailed monitoring of rainfall-flow process were applied in the period of the winter half-year 2009 - 2011. The effects of geografic factors on spatial distribution of snow cover in the accumulation period and in the snow melting period were analysed. The analysis of physical geografic factors effect was focused on vegetation, exposure, slope, wind flow and shading. Results confirmed the strong effect of vegetation in the accumulation and snow melting period. Other physical geografic effects on spatial distribution of snow cover were not so significant. The model HEC-HMS was applied to determine the runoff in both stream basins. Simulations result in the winter half-year period reached good agreement between observed and simulated hydrographs. Effects of snow cover contribution to runoff in the snow melting period in wooded Tetřívčí stream basin and also in antropogenic Zbytinský stream basin were proved based on simulations of outflow and snow water equivalent. Key words: snow depth, snow water equivalent, HEC-HMS, Blanice River basin, Zbytinský stream, Tetřívčí stream

Vattenkraft är en stor källa till energi i Sverige, främst i de norra delarna av landet. För att få ut maximal potential från vattenkraftverken behövs information om hur mycket vatten eller snö det finns uppströms från kraftverken. Genom att få fram tillförlitliga värden av snömängd är det möjligt att minska osäkerheten i uppskattningarna.Eftersom det är svårt att kartera större avrinningsområden via markbundna observationer, både praktiskt och ekonomiskt, har drönarobservationer utvecklats. För att använda sig av drönare krävs det vetskap om var de ska flygas i för område för att hela avrinningsområdet ska representeras. I projektet tas en modell fram i ArcGIS för att hitta mindre områden inom avrinningsområden som ska vara representativa inom utvalda parametrar. I projektet berörs parametrarna vegetation, höjd, lutningsgrad samt dess riktning.Arbetet för att ta fram en modell som ska underlätta framtida arbete inom och utanför forskningsprojektet DRONES är uppdelat i två delar. Den första delen är att ta fram och granska vilka parametrar som påverkar snödjupet i avrinningsområdet. Den andra delen innefattar arbetet med att skapa en modell i ArcGIS som ska analysera ett avrinningsområde med framtagna parametrar för att hitta mindre områden som representerar det hela.Resultatet från de framtagna modellerna kan tillämpas för att underlätta kartläggningen och snödjupsmätningar i avrinningsområden, vilket kan utnyttjas vid effektivisering av vattenreglering. / Hydropower is a major source of energy in Sweden mainly in the northern parts of the country. To get the maximum potential from the hydropower plants, information is required on how much water or snow there is upstream from the power plants. By obtaining reliable values of the amount of snow, it is possible to reduce the uncertainty in forecasts on spring flood.Due to difficulties in mapping larger catchment areas via ground-level observations, drone observations have been developed. In order to use drone observations, knowledge of where they are to be flown to represent the entire catchment area is required. In this project, a model was developed in ArcGIS to find smaller areas within catchments that are to be representative within selected parameters. The project touches upon the parameters vegetation, height, slope and aspect.The work to develop a model that will facilitate future work within and outside the DRONES research project is divided into two parts. The first part is to analyze which parameters affect the snow depth in the catchment area. The second part consists of creating a model in ArcGIS that will find a smaller area inside a catchment that represents the snow depth for the whole catchment.The results from the developed model can be applied to facilitate the mapping and snow depth measurements in catchment areas, which can be used to streamline water regulation.

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Rubber granulates, used at artificial grass pitches, pose a threat to the environment when leaking into the nature. As the granulates leak to the environment through rain water and snow clearances, they can be transported by rivers and later on end up in the marine life. Therefore, reducing the snow clearances to its minimum is of importance. If the snow clearance problem is minimized or even eliminated, this will have a positive impact on the surrounding nature. The object of this project is to propose a method for deciding when to remove snow and automate the information dispersing upon clearing or closing a pitch. This includes finding low powered sensors to measure snow depth, find a machine learning model to predict upcoming snow levels and create an application with a clear and easy-to-use interface to present weather information and disperse information to the responsible persons. Controlled experiments is used to find the models and sensors that are suitable to solve this problem. The sensors are tested on a single snow quality, where ultrasonic and infrared sensors are found suitable. However, fabricated tests for newly fallen snow questioned the possibility of measuring snow depth using the ultrasonic sensor in the general case. Random Forest is presented as the machine learning model that predicts future snow levels with the highest accuracy. From a survey, indications is found that the web application fulfills the intended functionalities, with some improvements suggested.