The by SMHI (Swedish Meteorological and Hydrological Institute) developed snow cover product, that analyses snow cover from satellite images, needs to be validated. A reliable validation method should be developed and concentrated to verify the snow cover analyses from images emerged from the recently operative European MSG-satellite. The validation is done for Europe, and this first validation test to evaluate the validation method is only done for a couple of clear days in March 2004. The snow cover analyses from the MSG images, computed by the snow cover product, are compared to synoptic snow observations and to a similar snow cover product from the NOAA project NESDIS. Every grid point of the MSG analysis area and the reference NESDIS area has been given a snow classification, describing the local status of the snow cover. The synoptic classification is derived from snow depth reports, stored in SMHI database. The product (MSG) classification and the reference classification in every grid point has then been added to a table and presented for manual evaluation. The most exacting work is to prepare the validation data to be comparable. The preparation quality affects the results, especially at the comparison to the synoptic source where the snow cover classification is a delicate problem. The synoptic reference data has shown up to be far too sparse to be used for a serious validation. There are also problems with the interpretation of the snow reports. Using the NESDIS source as reference the result looks better and the validation method is probably reliable. Images of the snow cover from MSG and NESDIS sources have also been sketched and compared. This comparison shows that the snow cover differences might originate from the snow cover product. The temperature of the ground might affect the snow detection; the snow is not detected sufficiently when ground is cold. On the other hand high altitude clouds seems possibly generate false snow detection. From the image comparison could also be presumed that forest might hide the snow cover. A more complete validation is now needed to draw any definitive conclusions if the existing snow cover differences originate from the snow cover product or from the validation method. But the method seems to work. Synoptic source is not recommended to use as validation reference, but the snow cover scenes from NESDIS seems to be a reliable reference source and works well for the validation method. / En produkt för beräkning av snötäckningsgrad har utvecklats av SMHI (Sveriges meteorologiska och hydrologiska institut). Produkten analyserar snötäcke utifrån satellitbilder och en tillförlitlig metod att validera produkten ska utvecklas. Valideringen som sedan ska göras, koncentreras till att verifiera snötäckesanalyser utifrån den nyligen operativa Europeiska MSG-satellitens bilder. Valideringen görs för Europa, och denna första testvalidering för att utvärdera valideringsmetoden görs för ett fåtal dagar med klart väder under mars 2004. Produktens snötäckesanalyser från MSG-satellitens bilder jämförs med synoptiska snöobservationer tillika analyser från en liknande produkt från amerikanska NOAAs projekt NESDIS. MSG- och NESDIS-analysernas snötäckesinformation finns lagrat i ett snöklassificeringsfält motsvarande den geografiska arean (Europa), där alla gridpunkter har tilldelats en klassificering vilket beskriver den lokala statusen på snötäcket i punkten. Snötäckesklassificeringen för de synoptiska observationerna görs utifrån snödjupsrapporter lagrade i SMHIs databas. De olika värdena på MSG-klassificeringen och referensklassificeringen i varje punkt summeras och presenteras i en tabell för utvärdering. Det mest krävande jobbet är att förbehandla indatat från de olika källorna för att få det jämförbart. Kvalitéten på förarbetet påverkar resultatet, speciellt vid jämförelsen mot synoptiska data där snötäckesklassificeringen är komplicerad. Resultattabellen tenderar att visa på ett bra resultat, men produkten för snötäckesanalys verkar ha svårt att detektera snö tillfredställande. Den synoptiska referenskällan har visat sig innehålla alldeles för lite data för att kunna användas i en seriös validering. Det finns även vissa problem med tolkningen av snörapporterna från databasen. Med NESDIS-produktens analys som referens ser resultatet bättre ut och valideringsmetoden kan sannolikt betraktas som tillförlitlig. En jämförelse mellan kartbilder över de två källornas klassificeringar har visat att det är möjligt att avvikelserna i beskrivningen av snötäcket beror på produkten för snötäckesanalys. Produktens snödetektering ser ut att kunna påverkas av marktemperaturen, snön upptäcks inte tillräckligt bra då marken är kall. Även höga moln ser ut att kunna påverka snödetekteringen och ger i så fall falskt klassificeringen snö där det enligt referenskällan är barmark. Utifrån bildjämförelsen kan också antas att skog kan gömma snötäcket. En mer komplett validering krävs för att dra några definitiva slutsatser om skillnaderna i snötäckningsgrad beror på valideringsmetoden eller på produkten för snötäckesanalys. Men metoden ser ut att kunna fungera. Synoptiska observationer rekommenderas inte att använda som referens, men snötäckesanalyser från NESDIS-projektets produkt verkar vara en tillförlitlig referens och fungerar väl för valideringsmetoden.

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.

Snow represents an important resource in mountainous regions. Monitoring its extent and amount is relevant for several applications, such as hydrology, ecology, avalanche monitoring, or hydropower production. However, a correct understanding of the high spatial and temporal variability of snow accumulation, redistribution and ablation processes requires its monitoring in a spatialized and detailed way. Recently, the launch of the Sentinel missions has opened the doors to new approaches that mainly exploit high resolution (HR) data having a spatial detail of few dozens of m. In this thesis, we aimed at exploiting these new sources of information to retrieve important parameters related to the snowmelt processes. In detail, we i) investigated the use of Sentinel-1 Synthetic Aperture Radar (SAR) observations to evaluate snowmelt dynamics in alpine regions, ii) developed a novel approach based on a hierarchical multi-resolution analysis of optical time-series to reconstruct the daily HR snow cover area (SCA), and iii) explored the combination of HR SCA time-series, SAR snowmelt information and other multi-source data to reconstruct a daily HR snow water equivalent (SWE) time-series. In detail, in the first work we analyzed the relationship between the snowmelt phases of a snowpack and the multi-temporal SAR backscattering. We found that the SAR is able to provide useful information about the moistening, ripening and runoff phases. In the second work, we exploited the snow pattern repetition on an inter-annual basis driven by the geomorphological features of a study area to carry out historical analyses. Thus, we took advantage of these repeated patterns to fuse low resolution and HR satellite optical data and set up a gap filling to derive daily HR snow cover area (SCA) time-series. These two research works are the pillars for the last contribution, which aims at combining all these information sources together with both in-situ data and a simple yet robust degree day model that provides an estimate of the potential melting to derive daily HR SWE time-series. These final results have an unprecedented spatial detail, that allows to sample the phenomena linked to the complex snow accumulation, redistribution and ablation processes with the required spatial and temporal resolution. The methodology and the results of each experimental work are illustrated and discussed in detail in the chapters of this thesis, with a look on further research and potential applications.

Master thesis is concerned with the subject of a snow cover dynamics (focused on snow melting) and of the detailed analysis of each physical-geographic factors effect on its character. Knowledges published in the domestic and foreign scientific literature are confronted with the data acquired within a number of expeditionary snow monitoring campaignes carried out during winter periods 2011/2012 a 2012/2013 in headwaters of rivers of Šumava (Šumava Mts., southwestern Czechia). Mobile field survey was done in a number of time horizons within the broadly conceived research in the upper Otava River basin concentrated on the assessment of the retention potential in headstream areas.

Use of remote sensing for snow water content determination Abstract The aim of this diploma thesis is an integration of remote sensing to snow water equivalent measurement in Czech Republic conditions. The summary of present information of snow parameters retrieval is presented. For snow water equivalent obtaining, radar differential interferometry technique was chosen. The technique was carried out with seven ERS-2 radar images. The result of processing was finished after coherence images creation because of low coherence value at all interferometric pairs. The low coherence values did not enable next processing. Terms of the negative result are discussed. In the second part of the thesis, connection between snow characteristics and radar backscattering is searched. Dependence between snow moisture and backscattering is demonstrated. Factors, which impact values of backscattering and correlation with snow parameters, are discussed. In order to obtain snow water equivalent, the processing of remote sensed data was carried out for the first time in Czech Republic region. Therefore the negative result is still valuable information. Keywords: snow cover, snow water equivalent, remote sensing, radar interferometry

A suitable climate, varied scenic terrain, and proximity of communities along Ontario’s system of 39,742 km of snowmobile trails have provided for domestic and international snowmobile tourism. Outdoor winter tourism in many parts of the world has been identified to be at risk to changes in global climate. The Intergovernmental Panel on Climate Change in its Fourth Assessment Report (AR4) reported a global increase of temperature of 0.74 degrees Celsius for the period 1906 to 2005 and estimates that by the end of the 21st century the global mean temperature will increase between 1.8 degrees Celsius to 4.0 degrees Celsius. Temperature increases of only a few degrees may contribute to variances in snow-based tourism reliant on the reliability of natural snow cover. This study examines the spatial and temporal impacts of climate change scenarios upon snowmobile season length and operations within the snowmobile industry in the Province of Ontario Canada to six climate change scenarios for the 21st century. Snowmobile trail operations in Ontario are reliant upon a minimum natural snow cover of 15 cm for smooth terrain trails and 30 cm to 60 cm for rough terrain trails, temperatures less than 0 degrees Celsius and, human and financial capital. Three or more consecutive snowmobile seasons with ≤ 28 days have been identified as having serious implications for human and financial capital necessary to develop and maintain the snowmobile trail system. As early as the 2020s, north eastern snowmobile districts are projected to be least vulnerable to changes in climate with the longest snowmobile seasons > 28 days, while south central snowmobile districts are projected to be the most vulnerable to changes in climate with the shortest snowmobile seasons of < 28 days. Snowmobile trail managers identified possible strategies to adapt to a changing climate (2020s to 2080s) including: pre-season preparation of the terrain including early season packing of snow cover, re-location of the most vulnerable snowmobile trails, and strengthening inter-district alliances.

A suitable climate, varied scenic terrain, and proximity of communities along Ontario’s system of 39,742 km of snowmobile trails have provided for domestic and international snowmobile tourism. Outdoor winter tourism in many parts of the world has been identified to be at risk to changes in global climate. The Intergovernmental Panel on Climate Change in its Fourth Assessment Report (AR4) reported a global increase of temperature of 0.74 degrees Celsius for the period 1906 to 2005 and estimates that by the end of the 21st century the global mean temperature will increase between 1.8 degrees Celsius to 4.0 degrees Celsius. Temperature increases of only a few degrees may contribute to variances in snow-based tourism reliant on the reliability of natural snow cover. This study examines the spatial and temporal impacts of climate change scenarios upon snowmobile season length and operations within the snowmobile industry in the Province of Ontario Canada to six climate change scenarios for the 21st century. Snowmobile trail operations in Ontario are reliant upon a minimum natural snow cover of 15 cm for smooth terrain trails and 30 cm to 60 cm for rough terrain trails, temperatures less than 0 degrees Celsius and, human and financial capital. Three or more consecutive snowmobile seasons with ≤ 28 days have been identified as having serious implications for human and financial capital necessary to develop and maintain the snowmobile trail system. As early as the 2020s, north eastern snowmobile districts are projected to be least vulnerable to changes in climate with the longest snowmobile seasons > 28 days, while south central snowmobile districts are projected to be the most vulnerable to changes in climate with the shortest snowmobile seasons of < 28 days. Snowmobile trail managers identified possible strategies to adapt to a changing climate (2020s to 2080s) including: pre-season preparation of the terrain including early season packing of snow cover, re-location of the most vulnerable snowmobile trails, and strengthening inter-district alliances.

While significant research has been performed in predicting winter snowpack behavior, maximums and extent, no efforts focused on predicting large-scale spring snowpack behavior have produced successful results. Increasing sensitivity to snowpack changes in the areas of water supply, energy production, agriculture, transportation, tourism and safety are making seasonal prediction of snowpack particularly important. The known breakdown of the wintertime relationship between tropospheric dynamics and snow characteristics indicates the need to explore new approaches to seasonal snowpack forecasts for the spring melt season. To examine possible new methods, Northern Hemisphere snow water equivalent and snow cover data from 1980-2004 are used in correlation analysis with traditional climate indices as well as newly defined sea surface temperature and sea ice regions. Additionally, large scale continental and latitude divisions are applied to the snow variables and the impact of ENSO is incorporated into the analysis. Results suggest the following: 1) Both sea ice and sea surface temperatures show promise as seasonal predictors for snowpack; 2) ENSO plays a critical role even though it is represented through indirect relationships; 3) Predicting spring snowpack behavior is feasible.

TEMPORAL EVALUATION OF SNOW DEPLETION CURVES DERIVED FOR UPPER EUPHRATES BASIN AND APPLICATIONS OF SNOWMELT RUNOFF MODEL Marim, G&ouml / khan M.S., Department of Geodetic and Geographic Information Technologies Supervisor: Prof.Dr.A.&Uuml / nal Sorman September 2008, 112 pages Water is becoming very important issue day by day with descending usable water and energy resources. In the aspect of water resources management, especially for the optimum reservoir management, predicting runoff for large reservoirs by applying hydrologic model is a recent and crucial topic. The most important model input and predictor parameters to estimate runoff for the mountainous regions are to be distribution of rainfall / temperature and snow cover area, (SCA). It is seen that many predictor variables should be integrated with Geographic Information Systems (GIS) and Remote Sensing Techniques especially for hydrologic model variable preparation. Satellite products have the potential for obtaining those kinds of data in near real time. In this study, the changes of SDC are generated by the analysis of optical satellite and by using SDC as an input to hydrological models runoff is simulated for Upper Euphrates Basin (10215.7 km2) which is a sub basin of Euphrates Basin. Largest dams of Turkey / Keban, Karakaya and Atat&uuml / rk are located on Euphrates River. Optimum operations of these dams depend on forecasting incoming water in early summer season. Euphrates River is fed mainly from snowmelts in spring or early summer time.65-70 % of the annual flow is contributed from snowmelt in that region. Main objective of this study is to obtain the spatially and temporally distributed SCA percentages from optical satellite, which are required as one of the main input variables of the hydrological model used in the application. SCA percentages and SDC are obtained for snowmelt years 2004-2007 by using high temporal resolution optical remote sensing data: Terra Moderate Resolution Imaging Spectroradiometer (MODIS). In this study, Terra MODIS snow cover map product, MOD10A1 which has a spatial resolution of 500 m is used. As a hydrological model Snowmelt Runoff Model (SRM) was applied. SRM was built up on the well-known degree day approach. In this study SRM is simulated for two years 2006 and 2007.The simulation results are compared and resultant model parameters are obtained for future runoff forecast studies. In this study, beside recommendations, discussions on the variables and SRM parameters are also provided.

Snowmelt runoff in the mountainous eastern part of Turkey is of great importance as it constitutes 60-70% in volume of the total yearly runoff during spring and early summer months. Therefore, forecasting the amount and timing of snowmelt runoff especially in the Euphrates Basin, where large dams are located, is an important task in order to use the water resources of the country in an optimum manner. The HBV model, being one of the well-known conceptual hydrological models used more than 45 countries over the world, is applied for the first time in Turkey to a small basin of 242 km2 on the headwaters of Euphrates River for 2002-2004 water years. The input data are provided from the automatic snow-meteorological stations installed at various locations and altitudes in Upper Euphrates Basin operating in real-time. Since ground based observations can only represent a small part of the region of interest, spatially and temporally distributed snow cover data are acquired through the use of MODIS optical satellite. Automatic model parameter estimation methods, GML and SCE_UA, are utilized to calibrate the HBV model parameters with a multi-objective criteria using runoff as well as snow covered area to ensure the internal validity of the model and to generate a Pareto front. Model simulations show that the choice of study years and timing of satellite images affect the results and further suggest that more study catchments and years should be included to achieve more comprehensible conclusions. In the second part of the study, the calibrated HBV model is applied to forecast runoff with a 1-day lead time using gridded input data from numerical weather prediction models of ECMWF and MM5 for the 2004 snowmelt period. Promising results indicate the possible operational use of runoff forecasting using numerical weather prediction models in order to prevent or at least take precautions before flooding ahead of time.