För att förutsäga snö är en av de viktigaste redskapen en snömodell som beskriver hur snö ackumuleras och avsmälter. En viktig aspekt i snömodellering är variationmed höjden. Höjden påverkar temperatur och nederbörd och därigenom också mönstret för avsmältning och ackumulering.En grad-dag snömodell över området anslutande till Kultsjöns avrinningsområde utfördes med hänsyn till höjdfördelningen. Modellens snötäcke kalibrerades med hjälp av klassificerade satellitfoton över området under perioden mars till juni 2014. Jämförelsen gjordes med hjälp av Cohens Kappa.Resultatet av simuleringen påvisade en påtaglig överrensstämmelse mellan modellen och den observerade data. De simulerade värdena för snödjup jämfördes med observerade data för att utföra en enkel validering. Igen erhölls till stor del överrensstämmelse.Det finns säkert ett behov av tillägg till modellen som tar hänsyn till strålning och vind, då båda dessa faktorer uteblev i modellen. / To predict snow, one of the most important tools is a snow model that describes how snow accumulates and melts. An important aspect in snow modeling is variation with elevation. Elevation influences temperature and precipitation, and therefore also the patterns of snow melt and accumulation.A degree-day snow model over the area around Kultsjön’s catchment area was made with respect to elevation distribution. The modeled snow cover was calibrated using classified satellite photo over the area during the period March to June 2014. The comparison was done using Cohen’s Kappa.The results of the simulation show a large portion of agreement between the model and observed data. The simulated values for snow depth were then compared to the observed data to perform a basic validation. Again there was a large portion of agreement.There is certainly a need for supplementary adjustments to the model that take into account radiation and wind, as both factors were left out of the model.

Les avalanches de plaque représentent un risque naturel majeur dont la prévision demeure très difficile. Le manque de lois constitutives fiables à l’échelle du matériau rend difficiles les tentatives de modélisation de ce phénomène. Plus spécifiquement, la réponse mécanique de la neige durant et après la rupture, dans des régimes de chargements rapides , demeure relativement méconnue. La nature particulièrement fragile du matériau au sein de ce régime de déformation rend ardue la réalisation d’expériences et complique l’observation à l’échelle microstructurale.Dans ce travail de thèse, un modèle numérique de neige fondé sur la Méthode des Éléments Discrets a été développé en tant qu’alternative aux expériences. Le modèle nous permet de simuler la réponse de la neige à des chargements mécaniques en tenant compte de la microstructure réelle du matériau grâce à l’intégration d’images acquises par microtomographie à rayons X en entrée du modèle. La neige est considérée comme un matériau granulaire cohesif, et une méthode originale a été développée afin de modéliser la forme de chaque grain. Les grains individuels sont ensuite assemblés pour reconstituer la matrice de la neige grâce à la prise en compte de lois de contact cohésives.Le modèle a été utilisé afin d’explorer la réponse mécanique macroscopique de différent échantillons de neige à un chargement mixte normal-cisaillant. Trois modes de rupture ont été observés dans tous les échantillons de neige testés, en fonction du niveau de contrainte normale appliquée : une rupture en cisaillement localisée pour des niveaux de contrainte normale faibles (mode A), un effondrement normal induit par rupture en cisaillement à des niveaux intermédiaires de contrainte normale (mode B) et un effondrement normal pour des valeurs de contrainte normale élevées (mode C). Ces différents modes de rupture produisent une enveloppe de rupture fermée dans l’espace des contraintes, ce pour les différents types de neige étudiés.Les mécanismes internes conduisant à l’effondrement normal des échantillons ont été étudiés plus en détail à l’échelle microscopique. Il a été montré que ce mode de rupture était associé à un mécanisme de flambement des chaînes de force. En outre, la stabilité de ces chaînes de force semble être contrôlée par les contacts entre les éléments des chaînes et les grains environnants. La rupture de ces contacts, observée dans les modes B et C, autorise le développement du flambement des chaînes de force et aboutit à l’effondrement volumique. / Dry slab snow avalanches represent a major natural hazard that is extremely difficult to manage. Attempts to model this phenomenon are hindered by the lack of a constitutive law that would describe the mechanical behaviour of snow on a material scale. In particular, relatively little is known on the failure and post-failure response of snow at high loading-rates. The highly fragile nature of the material in this deformation regimerenders experimental investigation difficult and complicates observation at the microstructural level.As an alternative to experiments, a Discrete Element Method-based numerical model of snow is developed in this thesis. The model enables us to simulate the response of snow to mechanical loading, while accounting for actual snow microstructure by using X-ray attenuation images of snow microstructure as input. Snow is considered as a cohesive granular material and an original methodology is developed in order to model the shape of each grain. Individual grains are bound into the snow matrix by modelling cohesion between neighbouring grains.The model is then used to explore the macroscopic mechanical response of different snow samples to mixed-mode loading. Three typical modes of failure are observed in all tested snow samples, depending on the level of applied normal stress: a localized shear failure at low normal stress (mode A), a shear failure-induced volumetric collapse at intermediate levels of normal stress (mode B), and a normal failure and collapse for high values of normal stress (mode C). The observed failure modes result in closed failure envelopes and no qualitative difference is observed between the mechanical responses of different snow types.The internal mechanisms that lead to volumetric collapse are further examined on the microscale. Force chain buckling is identified as a trigger of this material instability. Additionally, force chain stability appears to be controlled by the contacts between the force chain members and the surrounding grains. The failure in these contacts, which is evidenced in modes B and C, allows force chain buckling to develop and results in subsequent volumetric collapse.

Les ressources en eau du Liban sont soumises à une pression croissante due au développement économique, à la croissance démographique, à la gestion non-durable des ressources en eau et au changement climatique. Les montagnes du Mont et Anti-Liban sont des châteaux d'eau naturels pour le Liban car elles augmentent les précipitations par le soulèvement orographique des masses d'air. En raison de l'influence du climat méditerranéen, la plupart des précipitations au-dessus de 1200 m a.s.l. tombe sous la forme de neige en hiver. Par conséquent, la fonte des neiges contribue de façon importante au bilan hydrique national. En particulier, la fonte des neiges du Mont-Liban alimente les réseaux d'eau souterraine karstiques, qui fournissent des ressources en eau essentielle pour la région côtière. Malgré l'importance du manteau neigeux au Liban, sa variabilité spatiale et temporelle est insuffisament observée si bien que sa contribution au débit des fleuve et des sources reste méconnue. L'objectif de ce travail est de réduire ce manque de connaissance en utilisant des mesures in situ, des observations satellite et de la modélisation du manteau neigeux. 1. Nous présentons d'abord une revue de la littérature sur les processus nivo- hydrologiques dans les régions montagneuses méditerranéennes. De nombreuses études - principalement aux Etats-Unis de l'Ouest et dans les montagnes au sud de l'Europe - soulignent l'impact fort de la variabilité interannuelle du climat méditerranéen sur la dynamique du manteau neigeux. Le rayonnement solaire élevé est un facteur important du bilan énergétique du manteau neigeux, mais la contribution des flux de chaleur est plus forte à la fin de la saison nivale. La sublimation de la neige et la densification rapide sont des processus importants dans ce contexte. Les approches hybrides combinant des données de stations météorologiques et la télédétection optique de la surface enneigée à travers la modélisation sont recommandées pour compenser l'absence d'observations spatialisées du forçage météorologique. 2. Ensuite, nous présentons un ensemble original de données sur le manteau neigeux au Mont-Liban pour la période 2013-2016. Nous avons recueilli des observations sur le terrain de la hauteur de neige (HS), de l'équivalent en eau de neige (SWE) et de la densité de neige entre 1300 et 2900 m d'altitude sur le flanc occidental du Mont-Liban. De plus, des données météorologiques continues ont été acquises par trois stations météorologiques automatiques situées dans la partie enneigée du Mont-Liban. Le produit MODIS a été utilisé pour calculer la superficie couverte par la neige dans trois bassins hydrographiques couverts par les observations in situ. Nous remarquons la grande variabilité de HS et SWE et une densité élevée du manteau neigeux. Nous trouvons une corrélation significative entre HS et SWE qui peut être utile pour réduire la quantité de travail de terrain en vue d'un suivi opérationnel futur. 3. Grâce à ces données, nous avons mis en place un modèle distribué du manteau neigeux sur le Mont-Liban à une résolution de 100 m. Le modèle est validé à différentes échelles en utilisant les observations de SWE, densité, HS et SCA. Une simulation avec des modifications très limitées du paramétrage par défaut permet de capturer correctement la plupart des observations. Cette simulation permet donc d'estimer l'évolution du SWE et la fonte dans les trois bassins étudiés entre 2013 et 2016. Cette recherche a mis en évidence l'importance de réaliser simultanément des mesures sur le terrain et des observations météorologiques continues pour mieux appréhender les processus physiques qui contrôlent l'évolution du manteau neigeux sur le Mont-Liban. Enfin, l'influence du transport de la neige par le vent et des dépôts de poussière sur la fonte des neiges reste à évaluer en perspective de ce travail. / Lebanon's water resources are under increasing pressure due to economic development, demographic growth, unsustainable water resource management, and climate change. The Mount- and Anti-Lebanon Mountains are natural water towers for Lebanon as they play an important role in enhancing orographic precipitation. Due to the influence of the Mediterranean climate, most precipitation above 1200 m a.s.l. falls as snow during winter season. As a result, snowmelt is an important contributor to the national water balance. In particular, snowmelt from Mount-Lebanon feeds the karst groundwater systems, which provide key water resources to the coastal region. Despite the importance of the snow cover in the Lebanese mountains, the actual snowpack spatial and temporal variability and its contribution to the spring and river discharges in Lebanon remains poorly constrained. The objective of this work is to reduce this lack of knowledge using a combination of in situ measurements, remote sensing observations and modelling of the snowpack in Mount-Lebanon. 1. We first present an extensive review of the literature about the snow hydrological processes in Mediterranean-like mountain regions. Many studies - mainly from Western USA and Southern Europe mountains - emphasize the strong impact of the interannual Mediterranean climate variability on the snowpack dynamics. The high incoming solar radiation is an important driver of the snowpack energy balance, but the contribution of heat fluxes is stronger at the end of the snow season. Snow sublimation and rapid densification are important processes to consider. Hybrid approaches combining weather station data with optical remote sensing of the snow extent through modelling are recommended to tackle the lack of spatially-distributed observations of the meteorological forcing. 2. Then, we introduce an original dataset on the snow cover in Mount-Lebanon for the period 2013-2016. We collected field observations of the snow height (HS), snow water equivalent (SWE), and snow density between 1300 and 2900 m a.s.l. in the western slope of Mount-Lebanon. In addition, continuous meteorological data were acquired by three automatic weather stations located in the snow dominated region of Mount-Lebanon. The MODIS snow product was used to compute the daily snow cover area in three snow dominated basins. We find that HS and SWE have large variances and that snow density is high. The strong correlation between HS and SWE may be useful to reduce the amount of field work for future operational monitoring. 3. Using these data we set up a distributed snowpack energy balance in the Mount- Lebanon at 100 m resolution. The model is validated at different scales using the observed SWE, snow density, HS and SCA. A simulation with very limited adjustments to the default parameterization is found to correctly capture most of the observations. This simulation allows the estimation of the SWE evolution and snow melt in the three study basins between 2013 and 2016. This research highlighted the importance of conducting simultaneous field surveys and meteorological observations to gain insights into the physical processes driving snowpack evolution in Mount-Lebanon. Finally, the influence of snow erosion by wind and the influence of dust deposits on snowmelt, remains less known, and are warrant for future research.

No description available.

Snow interception is one of the most important process of the hydrological balance of river basins. Measuring of snow interception is a very complex activity, therefore, models are frequently used to calculated snow interception from the vegetation structure and measured meteorological variables. A field research has been carried out in the Ptačí Brook basin in the Sumava Mts. to describe the canopy structure of the spruce forest using hemispherical images taken in the winter season 2020/21. The vegetation characteristics are essential for modelling of the snow interception. The mean Leaf area index calculated from the hemispherical images at the study plot reached 2.34 with the respective canopy closure equal to 86.16%. These values were further used as input values for the calculation of seasonal cumulative snow interception at the study plot for the winter season 2020/21. The original, more complex model was compared with two, less complex equations. Consequently, the model was applied to four consecutive winter seasons 2017-2021. The efficiency of the snow interception (a proportion of the intercepted snow to total snowfall water equivalent) ranged from 36.85% to 45.81% depending on the study season. The snow interception efficiency was considerably higher in the last winter season compared to...

This dissertation presents the design, development and validation of new data integration methods for mapping the extent of snow cover based on open access ground station measurements, remote sensing images, volunteer observer snow reports, and cross country ski track recordings from location-enabled mobile devices. The first step of the data integration procedure includes data discovery, data retrieval, and data quality control of snow observations at ground stations. The WaterML R package developed in this work enables hydrologists to retrieve and analyze data from multiple organizations that are listed in the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI) Water Data Center catalog directly within the R statistical software environment. Using the WaterML R package is demonstrated by running an energy balance snowpack model in R with data inputs from CUAHSI, and by automating uploads of real time sensor observations to CUAHSI HydroServer. The second step of the procedure requires efficient access to multi-temporal remote sensing snow images. The Snow Inspector web application developed in this research enables the users to retrieve a time series of fractional snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) for any point on Earth. The time series retrieval method is based on automated data extraction from tile images provided by a Web Map Tile Service (WMTS). The average required time for retrieving 100 days of data using this technique is 5.4 seconds, which is significantly faster than other methods that require the download of large satellite image files. The presented data extraction technique and space-time visualization user interface can be used as a model for working with other multi-temporal hydrologic or climate data WMTS services. The third, final step of the data integration procedure is generating continuous daily snow cover maps. A custom inverse distance weighting method has been developed to combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the MODIS snow cover product. The method is demonstrated by producing a continuous daily time step snow presence probability map dataset for the Czech Republic region. The ability of the presented methodology to reconstruct MODIS snow cover under cloud is validated by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. Using crowdsourcing data (volunteer snow reports and ski tracks) improves the map accuracy by 0.7 – 1.2 %. The output snow probability map data sets are published online using web applications and web services.

Snow removal is an important optimization problem in countries with snowfall. Bus stops can only be cleared after the adjacent street is cleared. The problem of optimizing snow removal for bus stops in an urban area is a special case of the Travelling Salesman Problem with Time Windows, where each stop only can be cleared after a certain time has passed. The solver Gurobi is used to solve the mathematical model of this problem to optimality. A local search and a tabu search is implemented. The results of the mathematical model are compared to the results of the implemented tabu search method. The results show that if a solution needs to be produced quickly, the tabu search provides better solutions than Gurobi. / Snöröjning är ett viktigt optimeringsproblem i länder med snöfall. Busshållplatsen kan bara röjas efter att den angränsande vägen är röjd. Problemet att optimera snöröjning av busshållplatser i en stad är ett Handelsresandeproblem med tidsfönster, där varje hållplats bara kan röjas efter att en tid har gått. I arbetet har vi implementerat en tabusökning för att hitta snabbt hitta bra tillåtna lösningar till problemet. För att utvärdera prestandan hos tabusökningen har vi också implementerat en matematisk modell och använt Gurobi som lösare. Resultaten visar att tabusökningen är snabbast på att hitta tillåtna lösningar av god kvalité.

Knowledge about a species’ reproductive parameters such as breeding behaviours is a vital building block for essential conservation actions, especially for endangered species. Despite this, there is a considerable knowledge gap about the snow leopard (Panthera uncia) maternal behaviours, as well as the timing of den independence for the cubs. It has been assumed that female snow leopards change their behaviours post-denning and that the cubs leave their den together with their mothers around two to three months of age. However, until this day no quantitative data has been used to analyse female behaviours post-denning and when the cubs leave their den. I analysed pre- and post-denning activity for seven GPS-collared snow leopard females in Tost Mountains of southern Mongolia during the years 2010 to 2019. With linear mixed models and generalized linear mixed models, I found that female snow leopards with small cubs changed their behavioural patterns and space use by decreasing their monthly home range size, compared to females with older or no cubs. When the cubs became six months old, there were no detectable differences in these behaviours which suggests that the cubs started to travel continuously with their mother at the age of 5-6 months. The rate at which the collars successfully acquired positions decreased considerably during the early phase of denning when the female spent considerable time at the den sites where the collars could not communicate with the satellites. The age of the cubs when the female left the den ranged from 21-61 days (mean =44 days), suggesting that snow leopard females use their dens for 1.5 ± 0.5 months. This study provides the first estimate of the extent of den use by snow leopards, as well as the first estimates of post-denning behavioural patterns for snow leopard females and their cubs.

The aim of this project was to examine if a machine learning model could be used to predict snow density from six different weather parameters. These were artificially generated snow density, air temperature, ground temperature, relative humidity, windspeed and the snow depth change. The questions asked were what parameters correlates to the snow density, what model will perform best and could this approach be a better alternative to measure snow density manually. The research was performed in the application Regression Learner in MATLAB by testing five different premade machine learning models on a dataset. The premade models were, Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide Neural Network and Trilayered Neural Network. Also, the project includes data collection, data cleaning, data modification, data generation, training, testing, and evaluating the models. The results show that air temperature and windspeed overall are the most important parameters and the GPR Matern 5/2 and the Wide Neural Network had the highest performance. Lastly, it was concluded that the machine learning model could be a better alternative to measuring snow density with a real sensor. / Målet med detta arbete var att undersöka om en maskininlärningsmodell kunde användas för att förutse snödensitet utifrån sex olika väderparametrar. Dessa var artificiell genererad snödensitet, lufttemperatur, marktemperatur, relativ luftfuktighet, vindhastighet och snödjupsförändring. Frågeställningarna som skulle besvaras var vilka väderparametrar som korrelerar med snödensiteten, vilken eller vilka modeller som presterade bäst samt om maskininlärningsmodellen skulle kunna vara att bättre alternativ till att mäta snödensitet manuellt. Undersökningen utfördes i applikationen Regression Learner i MATLAB genom att testa fem olika förhandsgjorda modeller vilka var Linear Regression, GPR Matern 5/2, SVM Medium Gaussian, Wide neural network och Trilayered neural network. Projektet inkluderar även datainsamling, städning av data, datamodifiering, datagenerering, träning, testning och evaluering av modellerna. Resultaten visar att lufttemperaturen och vindhastigheten över lag är viktigast för modellerna och att GPR Matern 5/2 samt Wide neural network presterade bäst. Slutligen kunde man argumentera för att maskininlärningsmodellen är ett bättre alternativ till att mäta snödensitet manuellt.

This thesis aims to analyze how object detectors perform under winter weather conditions, specifically in areas with varying degrees of snow cover. The investigation will evaluate the effectiveness of commonly used object detection methods in identifying vehicles in snowy environments, including YOLO v8, Yolo v5, and Faster R-CNN. Additionally, the study explores the method of labeling vehicle objects within a set of image frames for the purpose of high-quality annotations in terms of correctness, details, and consistency. Training data is the cornerstone upon which the development of machine learning is built. Inaccurate or inconsistent annotations can mislead the model, causing it to learn incorrect patterns and features. Data augmentation techniques like rotation, scaling, or color alteration have been applied to enhance some robustness to recognize objects under different alterations. The study aims to contribute to the field of deep learning by providing valuable insights into the challenges of detecting vehicles in snowy conditions and offering suggestions for improving the accuracy and reliability of object detection systems. Furthermore, the investigation will examine edge devices' real-time tracking and detection capabilities when applied to aerial images under these weather conditions. What drives this research is the need to delve deeper into the research gap concerning vehicle detection using drones, especially in adverse weather conditions. It highlights the scarcity of substantial datasets before Mokayed et al. published the Nordic Vehicle Dataset. Using unmanned aerial vehicles(UAVs) or drones to capture real images in different settings and under various snow cover conditions in the Nordic region contributes to expanding the existing dataset, which has previously been restricted to non-snowy weather conditions. In recent years, the leverage of drones to capture real-time data to optimize intelligent transport systems has seen a surge. The potential of drones in providing an aerial perspective efficiently collecting data over large areas to precisely and timely monitor vehicular movement is an area that is imperative to address. To a greater extent, snowy weather conditions can create an environment of limited visibility, significantly complicating data interpretation and object detection. The emphasis is set on edge devices' real-time tracking and detection capabilities, which in this study introduces the integration of edge computing in drone technologies to explore the speed and efficiency of data processing in such systems.