Effect of selected physical-geographical factors on the snow accumulation and snow melt Abstract: This master thesis analyzes the influence of physical-geographical factors on spatial distribution of snow water equivalent, and its evolution. In this work, emphasis is placed on describing the influence of vegetation, aspect and altitude. Measurement was carried out in experimental catchments Zlatý Brook and Bystřice River in western part of the Ore Mountains in winters 2008/2009, 2009/2010, 2010/2011 and 2011/2012. To evaluate the influence of these factors on value of snow water equivalent there was used one of the methods of multivariate statistical analysis - cluster analysis. The research shows that the greatest influence on the distribution and evolution of snow water equivalent in the experimental basins has vegetation and some dependency was proved also between the points of southern exposure. The measurement results demonstrate the suitability of cluster analysis for analyzing the data of point values of snow water equivalent. On the other hand the results showed the main limits of this method, especially the need for a large number of points with different characteristics. The results of measurements and statistical analysis are compared with results published in technical literature. Keywords: snow...

April 1st Snow Water Equivalent (SWE) reconstructions were generated using tree-ring chronologies for the Upper North Platte River Basin (UNPRB), located in north-central Colorado and south-eastern Wyoming. To regionalize April 1st snowpack data from 11 SNOw TELemetry stations (SNOTEL stations), Varimax Rotated Principal Components Analysis (PCA) was used. For the 11 station regionalization, the reconstruction explained 42% of the variance in the instrumental record and extended the record to 1378 (632 years). Retained tree-ring chronologies included those that were stable and positively correlated at 99% confidence levels or higher with the regional snowpack data for a 60–year overlapping period of record from 1940 to 1999. Stepwise Linear Regression was performed for the overlapping (calibration) period to develop regression models for the reconstructions. Eleven stations were individually reconstructed of which three stations (Dry Lake, Old Battle, and Lake Irene) explained variances greater than 40%. A contour plot of the R2 values for all 11 stations revealed that the more statistically skillful reconstructions were for stations spatially adjacent to the tree-ring chronologies used in the regression models. When the two individual stations with the lowest explained variance were removed from the 11 station snowpack regionalization, the new nine station regionalization reconstruction explained 45% of the variance over the same 632 year period.

Throughout the Holocene, bison have always been more abundant east of the Rocky Mountains with considerably fewer bison found west of the Rocky Mountains. It is likely that drought frequency and snowfall characteristics have influenced the pattern of historical bison occurrence across the northwest United States. Using monthly average snow and precipitation data from the past several decades, average April snow water equivalent (SWE) and summertime drought frequency were analyzed at sites across the northwest United States. A climatic stress index (CSI) was developed by combining average SWE and drought frequency for sites, as these are the climate factors that will most likely affect bison success. The results of the CSI revealed that locations west of the Rockies experience heavier snowfall and a greater frequency of droughts, thus presenting a “double whammy” of climate conditions that bison would have to endure. The locations of highest combined snow and drought frequencies coincide with locations of low bison occurrence.

The assimilation process to produce daily Snow Water Equivalent (SWE) maps is modified by using Helsinki University of Technology (HUT) snow emission model and AMSR-E passive microwave data. The characteristics of HUT emission model is analyzed in-depth and discussed with respects to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed for snow over mountainous areas. Performance of the modified model is checked against original and other modified cases against ground truth data covering 2003-2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation is successfully carried out by means of snow data measured at ground stations during 2008-2010 winter periods. Validation results were less satisfactory for SWE smaller than 75.0 mm and greater than 200.0 mm. Overestimation is especially observed for stations located below 1750.0 m elevation where SWE is less than 75.0 mm. Applied methodology is fine tuned to improve its performance for shallow snow depths observed below 1750 m elevation using a relationship that integrates 10.7 GHz channel data. But an underestimation for SWE greater than 150 mm could not beresolved due to microwave signal saturation that is expected in dense snowpack.

Both natural and anthropogenic climate change are driven by forcings that interact and result in hydroclimatic changes that alter ecosystems and natural resources at different temporal and spatial scales. Accordingly, changes within regions (i.e. individual points to large watersheds) may differ from patterns observed at sub-continental to global scales, thus necessitating the generation of point- to region-specific, cross-scale hydroclimatic data to elucidate important drivers of observed changes, and provide information at scales relevant to resource managers. Herein, we use the Northern U.S. Rocky Mountains as a study region to explore 1) the covariability between observed hydrologic and climatic changes, 2) the nature of changes occurring at the scale of days to decades, and 3) the ocean-atmosphere teleconnections operating at continental- to hemispheric-scales underlying the observed regional patterns of hydroclimatic variability. We then expand the scope of study to include the entire central North American Cordillera to investigate changes in winter precipitation (i.e. snowpack) spanning the last millennia+, with a focus on the spatial and temporal coherence of events from the medieval climatic anomaly to present. To accomplish this we utilize the full suite of hydroclimatic observational records in conjunction with proxy records of snowpack derived from a distributed network of tree-ring chronologies.Results from observational records in the Northern Rockies show important changes have occurred in the frequency and means of biophysically important temperature thresholds, and that recent changes appear greater in magnitude at the mid- to high-elevations. These changes, coupled with interannual- to interdecadal-scale moisture variability driven by ocean-atmosphere teleconnections, are shown to be strong controls on the timing and amount of regional snowpack and streamflow. Across the cordillera, tree-ring based records of snowpack show that before 1950, the region exhibited substantial inter-basin variability in snowpack, even during prolonged droughts and pluvials, marked by a predominant north-south dipole associated with Pacific variability. Snowpack was unusually low in the Northern Rocky Mountains for much of the 20th century and over the entire cordillera since the 1980s; heralding a new era of snowpack declines entrained across all major headwaters in western North America.

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.

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

Uncertainties were analyzed in three areas (remote sensing, dendroclimatology, and climate modeling) relevant to current water resources management. First, the research investigated the relationships between remotely sensed and in situ Snow Water Equivalent (SWE) datasets in three western U.S. basins. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Principal Components Analysis (PCA) revealed two distinct snow regions among the datasets and Singular Value Decomposition (SVD) was used to link both data products with regional streamflow. Remotely sensed SWE was found to be sufficient to use in statistically based forecast models in which magnitude did not affect results. Second, the research investigated the dendroclimatic potential of a critical flood control and hydropower region in the southeastern U.S. (Tennessee Valley) using climate division precipitation and regional tree-ring chronology datasets. Tennessee Valley May–July precipitation was reconstructed from 1692 to 1980 (289 years) using a stepwise linear regression model (R2 = 0.56). Weibull analysis illustrated that the Tennessee Valley reconstruction model developed generally underestimated extreme precipitation and overestimated average precipitation. The longest May–July drought occurred over 10 consecutive years (1827–1836). Instrumental records indicated that the two most recent droughts (1985–1988 and 2006–2008) ranked second and third in severity in the past three centuries. Third, past, present, and future patterns and extremes in streamflow within the North Platte River Basin were investigated. A streamflow reconstruction dating back to 1383 using tree rings was created to provide a proxy for the long-term variability in the region. Projected streamflow datasets from the Community Climate System Model (CCSM) were gathered to acquire future insight of the hydroclimatic variability within the North Platte River Basin (NRPB). Drought analysis revealed that 2002–2008 was one of the driest periods in the past 600 years. Multiple CCSM projections suggest that in the future, drier (5th percentile) years will become wetter relative to 1970–1999 CCSM hindcasts. Future average (50th percentile) and wet (95th percentile) years may yield statistically higher streamflow compared to those seen in the historical (1383–1999) record, suggesting potential anthropogenic influence beyond the historic natural variability.

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.