The thesis deals with ecophysiology of overwintering larvae of two butterfly genera, Colias and Erebia. It focuses on identification of supercooling point, survival of various low temperature regimes and composition of cryoprotective substances. Results are presented in the context of distributional limits of individual species, their habitat requirements and their potential endangerment due to environmental and habitat changes.

As a result of climate change, the snowfall amounts may be reduced and hence the snow accumulation, which recharges the groundwater in spring. Groundwater significantly influences summer low flows and its deficiency may negatively affect the streamflow and reduce the water supply in snow- dominated regions. This thesis aimes to describe the influence of changes in snow water equivalent on the inter-annual variability of minimal discharges in warm season (April to September) in eleven mountain catchments of the Czech Republic. The aimes were to determine 1) the duration of snow effects on the minimum discharges after the snowmelt onset, 2) the effect of inter-annual changes of snow water equivalent on minimal discharges in the warm season and 3) the date of the summer lowflows and the trend of its shift within the year. The results are based on hydrological and climatological station data collected by Czech Hydrometeorological Institute between the years 1980 and 2014. Snow affected the summer low flows until June and in exceptional cases even until July in higher elevation catchments. The most significant change was recorded in Úpa catchment, which belongs to higher elevation catchments, the 10 % decrease of maximum snow water equivalent caused reductions in minimal discharge by 8.8 % and 6.8 % in...

Currently there is no suitable and commonly used device for volumetric quantification of snowmelt erosion in the Czech Republic (CR). The determination of erosion rate in the catchment is a essential prerequisite for the correct design of conservation measures. The thesis tries to offer the possible ways of monitoring the snowmelt erosion, compares individual methods and defines their optimal use. In the first year of the research, a runoff plot was developed to capture sheet erosion. The thesis compares also the methods using mobile devices - erosion bridge method and UAV photogrammetry - as effective instrument for snowmelt erosion monitoring. So far, no attention has been paid to snowmelt erosion in CR. Therefore, the thesis focuses mainly on the analysis of causal factors specific to this type of erosion - the erosion potential of snow cover and the possible soil erodibility changes due to freeze-thaw cycles. The thesis assesses the rate of snowmelt erosion risk in selected climatically different catchments and its changes in recent years. shows the timeliness of the problem. In the end, the thesis presents possibilities for solving the problem. The thesis claims the problem of snowmelt erosion actual and offers its possible solution.

Modelling of the rainfall-runoff process is one of the basic scientific skills in hydrology. Rainfall-runoff modelling can help to improve water management, handling of the reservoir's storage volume, or also to facilitate adaptation to current climatic conditions. The aim of the diploma thesis is to create a functional rainfall-runoff model on the basis of water balance equations based on the lumped water balance principle of the hydrological model. Several modifications of the general rainfall-runoff model are approached in the diploma thesis. Four types of the daily evapotranspiration determination are used in the calculations. The rainfall-runoff model is compiled from temperature data and precipitation totals in a daily step. The practical application is carried out on a sub-basin of the river Dyje, which is located above Vranov water reservoir. The main output is a series of daily flow rates that were obtained from calibrated rainfall-runoff models. The best rainfall-runoff model takes into account the water from snow cover melting, the value of the Nash Sutcliffe calibration criterion of this model is 0.608. Finally, the hydrological simulation for the period 2021-2060 is performed in the diploma thesis.

Observed climatic change may result in modification of the ground thermal regime.The causes of shallow ground temperature variability, however, are not well documented.This thesis reports ground temperatures from Absiko Scientific Research Station, measured ata site currently not underlain by permafrost to illustrate the response of shallow groundtemperatures to changes in climatic parameters. Both air temperature and precipitationincreased at Abisko from 1985-2010. The strongest increase in air temperature occurred inwinter, whereas the precipitation increased mainly during the summer months. There was asignificant trend towards later onset of permanent snow cover, as well as a steadily earlierdisappearance of permanent snow cover in spring, resulting in reduced snow cover duration.Also the snow thickness decreased at Abisko during the study period. The ground experiencedapproximately five months of frost at 5 and 20 cm depth and approx. four, respectively two,months at 50 and 100 cm depth. Annual ground temperatures were found to be increasingfrom 1985-2010 with approx. 0.31 °C, 0.64 °C, 0.82 °C and 0.94 °C at 5, 20, 50, respectively100 cm depth from the surface. The duration and intensity of the seasonal frost cycles weredecreasing, which would reflect the increasing ground temperatures. Changes in short-termfrost cycles were not found to be significant. The changes in mean annual and winter groundtemperature were significantly correlated to the changes in mean annual and winter airtemperature, but surprisingly not to the changes in snow cover. However, seasonally theincreasing trend of ground temperatures was found in autumn and winter, whereas thesummer ground temperatures were decreasing. The cooling of ground temperature in summerat increasing air temperatures may be explained by increased precipitation totals and henceincreased soil moisture due to the so called soil-moisture feedback. From this fact, it can bededuced that the changes in air temperature alone cannot explain all variances in groundtemperatures. However, the results of the study may suggest that in sub-Arctic Swedenchanges in air temperatures may be used as indicator for changes in shallow groundtemperatures. / perioden 1985-2010 ökade både lufttemperatur och nederbörd i Abiskoområdet. Denstörsta ökningen av lufttemperatur skedde under vinterhalvåret medan nederbörden ökademest under sommarhalvåret. En signifikant förkortning i längden av vintersnötäckets existensunder året observerades under studieperioden. Reduceringen av vintesnötäcket skedde genomatt den första snön kom senare och bortsmältningen på våren skedde tidigare. Snötäcketstjocklek minskade också under studieperioden. Marktemperaturmätningarna visar frysgraderpå 5 och 20 cm djup fem månader och fyra respektive två månader på 50 och 100cm djup.Den årliga medeltemperaturen i marken ökade under perioden med 0.31 °C, 0.64 °C, 0.82 °Coch 0.94 °C vid 5, 20, 50 och 100 cm djup. Den årliga längden och intensiteten avfrysförhållandena i marken minskade vilket förmodligen är en konsekvens av de ökandemarktemperaturerna. Ingen trend i förekomsten av kortare svängningar i frysförhållandenakunde observeras. Förändringarna i årsmedetemperaturen i marken är signifikant korrelerademed förändringen i den årliga medeltemperaturen och vintertemperaturen i luften, men ingenkorrelation mellan marktemperaturen och förändringar i snötäckets tjocklek och längdobserverades. Studien avslöjade också att temperaturen i marken ökade under vinternhalvåretmedan den sjönk under sommaren. Avkylningen av marken under sommaren kan förklaras avökad nederbörd under sommaren som ger högre markfuktighet som ger en kylande effektgenom den så kallade jord-fuktighets återkopplingsmekanismen (soil-moisture feedback).Från detta kan vi dra slutsatsen att förändringar i enbart lufttemperatur inte kan förklara denhela observerade variansen av marktemperatur men att lufttemperaturen har en domineranderoll. Resultaten från denna studie indikerar således att förändringar lufttemperatur kananvändas som en indikator på marktemperaturförändringar i Abisko området.

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.

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.

Water stored in snow is a critical contribution to the world’s available freshwater supply and is fundamental to the sustenance of natural ecosystems, agriculture and human societies. The importance of snow for the natural environment and for many socio-economic sectors in several mid‐ to high‐latitude mountain regions around the world, leads scientists to continuously develop new approaches to monitor and study snow and its properties. The need to develop new monitoring methods arises from the limitations of in situ measurements, which are pointwise, only possible in accessible and safe locations and do not allow for a continuous monitoring of the evolution of the snowpack and its characteristics. These limitations have been overcome by the increasingly used methods of remote monitoring with space-borne sensors that allow monitoring the wide spatial and temporal variability of the snowpack. Snow models, based on modeling the physical processes that occur in the snowpack, are an alternative to remote sensing for studying snow characteristics. However, from literature it is evident that both remote sensing and snow models suffer from limitations as well as have significant strengths that it would be worth jointly exploiting to achieve improved snow products. Accordingly, the main objective of this thesis is the development of novel methods for the estimation of snow parameters by exploiting the different properties of remote sensing and snow model data. In particular, the following specific novel contributions are presented in this thesis: i. A novel data fusion technique for improving the snow cover mapping. The proposed method is based on the exploitation of the snow cover maps derived from the AMUNDSEN snow model and the MODIS product together with their quality layer in a decision level fusion approach by mean of a machine learning technique, namely the Support Vector Machine (SVM). ii. A new approach has been developed for improving the snow water equivalent (SWE) product obtained from AMUNDSEN model simulations. The proposed method exploits some auxiliary information from optical remote sensing and from topographic characteristics of the study area in a new approach that differs from the classical data assimilation approaches and is based on the estimation of AMUNDSEN error with respect to the ground data through a k-NN algorithm. The new product has been validated with ground measurement data and by a comparison with MODIS snow cover maps. In a second step, the contribution of information derived from X-band SAR imagery acquired by COSMO-SkyMed constellation has been evaluated, by exploiting simulations from a theoretical model to enlarge the dataset.

Chaque jour, des décisions doivent être prises quant à la quantité d'hydroélectricité produite au Québec. Ces décisions reposent sur la prévision des apports en eau dans les bassins versants produite à l'aide de modèles hydrologiques. Ces modèles prennent en compte plusieurs facteurs, dont notamment la présence ou l'absence de neige au sol. Cette information est primordiale durant la fonte printanière pour anticiper les apports à venir, puisqu'entre 30 et 40% du volume de crue peut provenir de la fonte du couvert nival. Il est donc nécessaire pour les prévisionnistes de pouvoir suivre l'évolution du couvert de neige de façon quotidienne afin d'ajuster leurs prévisions selon le phénomène de fonte. Des méthodes pour cartographier la neige au sol sont actuellement utilisées à l'Institut de recherche d'Hydro-Québec (IREQ), mais elles présentent quelques lacunes. Ce mémoire a pour objectif d'utiliser des données de télédétection en micro-ondes passives (le gradient de températures de brillance en position verticale (GTV)) à l'aide d'une approche statistique afin de produire des cartes neige/non-neige et d'en quantifier l'incertitude de classification. Pour ce faire, le GTV a été utilisé afin de calculer une probabilité de neige quotidienne via les mélanges de lois normales selon la statistique bayésienne. Par la suite, ces probabilités ont été modélisées à l'aide de la régression linéaire sur les logits et des cartographies du couvert nival ont été produites. Les résultats des modèles ont été validés qualitativement et quantitativement, puis leur intégration à Hydro-Québec a été discutée. / Every day, decisions must be made about the amount of hydroelectricity produced in Quebec. These decisions are based on the prediction of water inflow in watersheds based on hydrological models. These models take into account several factors, including the presence or absence of snow. This information is critical during the spring melt to anticipate future flows, since between 30 and 40 % of the flood volume may come from the melting of the snow cover. It is therefore necessary for forecasters to be able to monitor on a daily basis the snow cover to adjust their expectations about the melting phenomenon. Some methods to map snow on the ground are currently used at the Institut de recherche d'Hydro-Québec (IREQ), but they have some shortcomings. This master thesis's main goal is to use remote sensing passive microwave data (the vertically polarized brightness temperature gradient ratio (GTV)) with a statistical approach to produce snow maps and to quantify the classification uncertainty. In order to do this, the GTV has been used to calculate a daily probability of snow via a Gaussian mixture model using Bayesian statistics. Subsequently, these probabilities were modeled using linear regression models on logits and snow cover maps were produced. The models results were validated qualitatively and quantitatively, and their integration at Hydro-Québec was discussed.

In dieser Dissertation wird erstmalig ein theoretisches Fundament zur Beschneiung virtueller Szenen entwickelt. Das theoretische Fundament wird als analytisches Modell in Form einer Diffusionsgleichung formuliert. Aus dem analytischen Modell lässt sich eine Gruppe von Algorithmen zur Beschneiung virtueller Szenen ableiten. Eingehende Voruntersuchungen zur allgemeinen Modellierung natürlicher Phänomene in der Computergraphik sowie eine Klassifikation der bestehenden Literatur über mathematische Schneemodellierung bilden den Anfang der Arbeit. Aus der umfassenden Darstellung der Eigenschaften von Schnee, wie er in der Natur vorkommt, ergeben sich die Grundlagen für die Modellbildung. Die Modellbildung fußt auf den grundlegenden Ansätzen der klassischen Mechanik und der statistischen Physik. Für die Beschneiung auf visueller Skala erweist sich der Diffusionsprozess als geeignete Beschreibung. Mit der Beschreibung lassen sich diffusiv Schneeoberflächen erzeugen. Der konkrete computergraphische Wert des theoretischen Fundaments wird anhand zweier Implementierungen exemplarisch dargestellt, und zwar in der Distanzfeldmethode und der Diffusionskernmethode. Die Ergebnisse werden mithilfe dreidimensionaler Rauschtexturen und Alpha-Masken an den Rändern fotorealistisch visualisiert. / In this dissertation for the first time a theoretical foundation is developed for snow accumulation in virtual scenes. The theoretical foundation is formulated in an analytical model as diffusion equation. The analytical model leads to a group of algorithms for virtual snow accumulation. Comprehensive investigations for the modelling of natural phenomena in computer graphics in general are used to develop a method classification scheme. Another classification is given for an overview over the aspects of snow in the real world. This allows an efficient presentation of related literature on snow modelling. A new approach of snow modelling is then drawn from first principles of classical mechanics and statistical physics. Diffusion processes provide an efficient theoretical framework for snow accumulation. The mathematical structure of diffusion equations is discussed and demonstrated to be adequate to snow modelling in visual scales. The value of the theoretical foundation for computer graphics is demonstrated with two exemplary implementations, a distance field method and the diffusion kernel method. Results are visualized with 3D noise textures and alpha masks near borders delivering photorealistic snow pictures.