In remote sensing, the application determines the type of platform and scale used during air or space –borne data collection as the pixel size of the collected data varies depending on the sensor or platform used. Applications involving some cryospheric environments require the use of the microwave band of the electromagnetic spectrum, with snow water equivalent (SWE) studies making use of passively emitted microwave radiation. A key issue in the use of passive microwave remotely sensed data is its spatial resolution, which ranges from 10 to 25 kilometres. The Climate Research Branch division of the Meteorological Service Canada is using passive microwave remote sensing as a means to monitor and obtain SWE values for Canada’s varying land-cover regions for use in climate change studies. Canada’s diverse landscape necessitated the creation of a snow water equivalent retrieval algorithm suite comprised of four different algorithms; all reflecting different vegetative covers. The spatial resolution of small scale remotely sensed data does provide a means for monitoring Canada’s large landmass, but it does, however, result in generalizations of land-cover, and in particular, vegetative structure, which is shown to influence both snow cover and algorithm performance. The Climate Research Branch is currently developing its SWE algorithm for Canada’s boreal forest region. This thesis presents a means of successfully and easily collecting in situ remotely sensed data in the form of hemispherical photographs for gathering vegetative structure data to ground-truth remotely sensed data. This thesis also demonstrates that the Gap Light Analyzer software suite used for analyzing hemispherical photographs of mainly deciduous environments during the spring-fall months can be successfully applied towards cryospheric studies of predominantly coniferous environments.

To date, remote sensing estimates of snow water equivalent (SWE) in mountainous areas are very uncertain. To test passive microwave algorithm estimations of SWE, a validation data set must exist for a broad geographic area. This study aims to build a data set through field measurements and statistical techniques, as part of the Canadian IPY observations theme to help develop an improved algorithm. Field measurements are performed at, GIS based, pre-selected sites in the Central Yukon. At each location a transect was taken, with sites measuring snow depth (SD), density, and structure. A mixed effects multiple regression was chosen to analyze and then predict these field measurements over the study area. This modelling strategy is best capable of handling the hierarchical structure of the field campaign. A regression model was developed to predict SD from elevation derived variables, and transformed Landsat data. The final model is: SD = horizontal curvature + cos( aspect) + log10(elevation range, 270m) + tassel cap: greenness, brightness (from Landsat imagery) + interaction of elevation and landcover.This model is used to predict over the study area. A second, simpler regression links SD with density giving the desired SWE measurements. The Root Mean Squared Error (RMSE) of this SD estimation is 25 cm over a domain of 200 x 200 km. This instantaneous end of season, peak accumulation, snow map will enable the vali- dation of satellite remote sensing observations, such as passive microwave (AMSR-E), in a generally inaccessible area.

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms.

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.

The accurate estimation of snow water equivalent (SWE) in the Canadian sub-arctic is integral to climate variability studies and water availability forecasts for economic considerations (drinking water, hydroelectric power generation). Common passive microwave (PM) snow water equivalent (SWE) algorithms that utilize the differences in brightness temperature (Tb) at 37 GHz – 19 GHz falter in lake-rich tundra environments because of the inclusion of lakes within PM pixels. The overarching goal of this research was to investigate the use of multiple platforms and methodologies to observe and quantify the effects of lake ice and sub-ice water on passive microwave emission for the purpose of improving snow water equivalent (SWE) retrieval algorithms. Using in situ snow and ice measurements as input, the Helsinki University of Technology (HUT) multi-layer snow emission model was modified to include an ice layer below the snow layer. Emission for 6.9, 19, 37 and 89 GHz were simulated at horizontal and vertical polarizations, and were validated by high resolution airborne passive microwave measurements coincident with in situ sampling sites over two lakes near Inuvik, Northwest Territories (NWT). Overall, the general magnitude of brightness temperatures were estimated by the HUT model for 6.9 and 19 GHz H/V, however the variability was not. Simulations produced at 37 GHz exhibited the best agreement relative to observed temperatures. However, emission at 37 GHz does not interact with the radiometrically cold water, indicating that ice properties controlling microwave emission are not fully captured by the HUT model. Alternatively, active microwave synthetic aperture radar (SAR) measurements can be used to identify ice properties that affect passive microwave emission. Dual polarized X-band SAR backscatter was utilized to identify ice types by the segmentation program MAGIC (MAp Guided Ice Classification). Airborne passive microwave transects were grouped by ice type classes and compared to backscatter measurements. In freshwater, where there were few areas of high bubble concentration at the ice/water interface Tbs exhibited positive correlations with cross-polarized backscatter, corresponding to ice types (from low to high emission/backscatter: clear ice, transition zone between clear and grey ice, grey ice and rafted ice). SWE algorithms were applied to emission within each ice type producing negative or near zero values in areas of low 19 GHz Tbs (clear ice, transition zone), but also produced positive values that were closer to the range of in situ measurements in areas of high 19 GHz Tbs (grey and rafted ice). Therefore, cross-polarized X-band SAR measurements can be used as a priori ice type information for spaceborne PM algorithms, providing information on ice types and ice characteristics (floating, frozen to bed), integral to future tundra-specific SWE retrieval algorithms.

This thesis analyses the impacts of winter snowpack and subsequent spring and summer liquid precipitation on low flows in the warm season. Meltwater is an important source of groundwater recharge. From groundwater storage streams are donated during summer months. Snow accumulation during cold season is reduced and snowmelt occurs earlier, which is a result of climate change and leads to lower groundwater recharge rates. That is the reason why change in snow cover dynamics affects summer low flows. Main goals of this thesis are to analyse how snow cover affects low flows I warm season and to compare it with impact of spring and summer precipitation. A conceptual runoff model HBV-light has been used to simulate the snow water equivalent (SWE) and streamflow from three mountain catchments. The integrated multi-variable model calibration procedure was used to calibrate the model. The model was used to simulate the snow and streamflow from 1981 to 2014. Besides the mentioned simulation, two hypothetical scenarios have been performed. These two scenarios accounted for reduced spring and summer liquid precipitation. In the first scenario, precipitation after maximum annual SWE was reduced to 75% of the real measured precipitation. In the second scenario, precipitation was reduced to 50% of the real...

Title: Snow water content modelling in small catchments This work deals with modeling of amount of snow cover, snow water equivalent, respectively, on an experimental catchment in the Jizerské hory Mts. Measuring and modelling of the snow cover is an important part of water management practice from the perspective of reservoir operation and flood management. The first part of this thesis describes physical-geographical characteristics of the Jizerské hory Mts.especially from the climatological and hydrological point of view but also other charakteristics and conditions that may affect the dynamic of snow accumulation and melting are described with detailed focus on the experimental catchment of Černá Desná river - Jezdecká. Two modelling approaches were applied to simulate snow water equivalent (SWE) based on observed precipitation and temperature. Beside the well knowen SNOW17 model a simple method based on heat index was developed in this work and its parameters were calibrated based on measured timeseries of daily average air temperature, daily precipitation and observed SWE for winter periods 2001 to 2009. Both methods provided reasonably accurate estimates of SWE over the tested period, however it was found that the results for winters with extreme conditions (very warm or very cold) are less...

<p dir="ltr">In Global Navigation Satellite Systems Reflectometry (GNSS-R) a critical theme is in un-<br>derstanding and delving into determining specular points, and how to optimize its solutions.<br>The implications are significant for soil moisture, Snow Water Equivalent (SWE), water stor-<br>age, and climate dynamics. For instance, the Signals of Opportunity in P-Band Investigation<br>(SNOOPI) will utilize observations in reference to the specular point to evaluate measurements<br>that could be used to determine water content, soil moisture, and SWE. The focus of this<br>study is presenting and evaluating two prominent methods for determining specular points:<br>the Minimum Path Delay (MPL) and the Unit Difference (UD). Specular point determin-<br>nation presents various challenges which include: surface roughness, temporal and spatial<br>variability, and multipath effects. All of these earth’s surface characteristics pose a challenge<br>for scientists and engineers who wish to collect terrestrial parameters. The analysis in this<br>study offers a comparative approach focusing on data from the simulator for the CubeSat<br>SNOOPI mission is to evaluate specular point determination accuracy as well as offer a real-<br>world application to determine the efficacy of the two methods. Through this evaluation,<br>the researcher aims to improve specular point determination techniques used in the GNSS-R<br>community and offer insights into future techniques and how they can support each other<br>for more precise results.<br></p>

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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...