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.

Snow is a common overlying surface during winter-time and the redistribution of snow by wind is a very important concept for any hydrological research project located within the cryosphere. Wind redistributes snow by eroding it from areas of high wind speed, such as ridge tops and windward slopes, and deposits it in areas of lower wind speeds, such as the lees of ridge tops, vegetation stands, and topographic depressions. The accurate modelling of blowing snow processes such as erosion, deposition, and sublimation have proven to be rather problematic. The largest issue that many modellers must deal with is the accurate collection of solid precipitation throughout the winter season. Without this, incorrect energy and mass balances can occur. This thesis makes use of a new method of acquiring solid precipitation values through the use of an SR50a ultrasonic snow depth sensor and then incorporates it into a version of the Cold Regions Hydrological Model (CRHM) which includes the Prairie Blowing Snow Model (PBSM) and the Minimal Snowmelt Model (MSM) modules. The model is used to simulate seasonal snow depth over an agricultural field in southern Ontario, Canada and is driven with half-hourly locally acquired meteorological data for 83 days during the 2008-2009 winter season. Semi-automated snow surveys are conducted throughout the winter season and the collected in situ snow depth values are compared to the simulated snow depth values at multiple scales. Two modelling approaches are taken to temporally and spatially test model performance. A lumped approach tests the model‟s ability to simulate snow depth from a small point scale and from a larger field scale. A distributed approach separates the entire field site into three hydrological response units (HRUs) and tests the model‟s ability to spatially discretize at the field scale. HRUs are differentiated by varying vegetation heights throughout the field site. Temporal analysis compares the simulated results to each day of snow survey and for the entire field season. Model performance is statistically analyzed through the use of a Root Mean Square Difference (RMSD), Nash-Sutcliffe coefficient (NS), and Model Bias (MB). Both the lumped and distributed modelling approaches fail to simulate the early on-set of snow but once the snow-holding capacities are reached within the field site the model does well to simulate the average snow depth during the latter few days of snow survey as well as throughout the entire field season. Several model limitations are present which prevent the model from incorporating the scaling effects of topography, vegetation, and man-made objects as well as the effects from certain energy fluxes. These limitations are discussed further.

Thesis (M.S.)--University of Delaware, 2009. / Principal faculty advisor: Daniel J. Leathers, Dept. of Geography. Includes bibliographical references.

Seasonal patterns of snow accumulation in the Northern Hemisphere are changing in response to variations in Arctic climate. These changes have the potential to influence global climate, regional hydrology, and sensitive ecosystems as they become more pronounced. To refine our understanding of the role of snow in the Earth system, improved methods to characterize global changes in snow extent and mass are needed. Current space-borne observations and ground-based measurement networks lack the spatial resolution to characterize changes in volumetric snow properties at the scale of ground observed variation. Recently, radar has emerged as a potential complement to existing observation methods with demonstrated sensitivity to snow volume at high spatial resolutions (< 200 m). In 2009, this potential was recognized by the proposed European Space Agency Earth Explorer mission, the Cold Regions High Resolution Hydrology Observatory (CoReH2O); a satellite based dual frequency (17.2 and 9.6 GHz) radar for observation of cryospheric variables including snow water equivalent (SWE). Despite increasing international attention, snow-radar interactions specific to many snow cover types remain unevaluated at 17.2 or 9.6 GHz, including those common to the Canadian tundra. This thesis aimed to use field-based experimentation to close gaps in knowledge regarding snow-microwave interaction and to improve our understanding of how these interactions could be exploited to retrieve snow properties in tundra environments. Between September 2009 and March 2011, a pair of multi-objective field campaigns were conducted in Churchill, Manitoba, Canada to collect snow, ice, and radar measurements in a number of unique sub-arctic environments. Three distinct experiments were undertaken to characterize and evaluate snow-radar response using novel seasonal, spatial, and destructive sampling methods in previously untested terrestrial tundra environments. Common to each experiment was the deployment of a sled-mounted dual-frequency (17.2 and 9.6 GHz) scatterometer system known as UW-Scat. This adaptable ground-based radar system was used to collect backscatter measurements across a range of representative tundra snow conditions at remote terrestrial sites. The assembled set of measurements provide an extensive database from which to evaluate the influence of seasonal processes of snow accumulation and metamorphosis on radar response. Several advancements to our understanding of snow-radar interaction were made in this thesis. First, proof-of-concept experiments were used to establish seasonal and spatial observation protocols for ground-based evaluation. These initial experiments identified the presence of frequency dependent sensitivity to evolving snow properties in terrestrial environments. Expanding upon the preliminary experiments, a seasonal observation protocol was used to demonstrate for the first time Ku-band and X-band sensitivity to evolving snow properties at a coastal tundra observation site. Over a 5 month period, 13 discrete scatterometer observations were collected at an undisturbed snow target where Ku-band measurements were shown to hold strong sensitivity to increasing snow depth and water equivalent. Analysis of longer wavelength X-band measurements was complicated by soil response not easily separable from the target snow signal. Definitive evidence of snow volume scattering was shown by removing the snowpack from the field of view which resulted in a significant reduction in backscatter at both frequencies. An additional set of distributed snow covered tundra targets were evaluated to increase knowledge of spatiotemporal Ku-band interactions. In this experiment strong sensitivities to increasing depth and SWE were again demonstrated. To further evaluate the influence of tundra snow variability, detailed characterization of snow stratigraphy was completed within the sensor field of view and compared against collocated backscatter response. These experiments demonstrated Ku-band sensitivity to changes in tundra snow properties observed over short distances. A contrasting homogeneous snowpack showed a reduction in variation of the radar signal in comparison to a highly variable open tundra site. Overall, the results of this thesis support the single frequency Ku-band (17.2 GHz) retrieval of shallow tundra snow properties and encourage further study of X-band interactions to aid in decomposition of the desired snow volume signal.

Snow is a common overlying surface during winter-time and the redistribution of snow by wind is a very important concept for any hydrological research project located within the cryosphere. Wind redistributes snow by eroding it from areas of high wind speed, such as ridge tops and windward slopes, and deposits it in areas of lower wind speeds, such as the lees of ridge tops, vegetation stands, and topographic depressions. The accurate modelling of blowing snow processes such as erosion, deposition, and sublimation have proven to be rather problematic. The largest issue that many modellers must deal with is the accurate collection of solid precipitation throughout the winter season. Without this, incorrect energy and mass balances can occur. This thesis makes use of a new method of acquiring solid precipitation values through the use of an SR50a ultrasonic snow depth sensor and then incorporates it into a version of the Cold Regions Hydrological Model (CRHM) which includes the Prairie Blowing Snow Model (PBSM) and the Minimal Snowmelt Model (MSM) modules. The model is used to simulate seasonal snow depth over an agricultural field in southern Ontario, Canada and is driven with half-hourly locally acquired meteorological data for 83 days during the 2008-2009 winter season. Semi-automated snow surveys are conducted throughout the winter season and the collected in situ snow depth values are compared to the simulated snow depth values at multiple scales. Two modelling approaches are taken to temporally and spatially test model performance. A lumped approach tests the model‟s ability to simulate snow depth from a small point scale and from a larger field scale. A distributed approach separates the entire field site into three hydrological response units (HRUs) and tests the model‟s ability to spatially discretize at the field scale. HRUs are differentiated by varying vegetation heights throughout the field site. Temporal analysis compares the simulated results to each day of snow survey and for the entire field season. Model performance is statistically analyzed through the use of a Root Mean Square Difference (RMSD), Nash-Sutcliffe coefficient (NS), and Model Bias (MB). Both the lumped and distributed modelling approaches fail to simulate the early on-set of snow but once the snow-holding capacities are reached within the field site the model does well to simulate the average snow depth during the latter few days of snow survey as well as throughout the entire field season. Several model limitations are present which prevent the model from incorporating the scaling effects of topography, vegetation, and man-made objects as well as the effects from certain energy fluxes. These limitations are discussed further.

Thesis (M.S.)--Montana State University--Bozeman, 2004. / Typescript. Chairperson, Graduate Committee: Edward E. Adams. Includes bibliographical references (leaves 176-181).

Thesis (M. SC.)--Carleton University, 2007. / Includes bibliographical references (p. 147-157). Also available in electronic format on the Internet.

The objective of this research is to develop a system capable of simulating snow depth and snow water equivalent in the Sherbrooke to Mount-Megantic area of Quebec's Eastern Townships using meteorological and digital terrain data as input.The working hypothesis is that meteorological data may drive a point energy and mass balance snow cover model.The model used was developed by the Hydrologic Research Lab (National Weather Service) which was calibrated for local conditions using field data collected during two winters at several sites on Mount-Megantic. Snow water equivalent and depth are used for calibration and validation of the model. Automated snow sensors were also used to obtain temperature calibration data.The snow surveys and correction of the air temperature for elevation improves the estimates of snow depth and water equivalent.The results suggest that data from the Sherbrooke meteorological stations can be used to estimate the snow cover over the area of Eastern Townships. Air temperature extrapolation across the field area is a challenge. However the simulated snow cover conforms generally well with data observed at several stations throughout the region.

The objective of this research is to explore the information content of repeat-pass cross-track Interferometric SAR (InSAR) with regard to snow, in particular Snow Water Equivalent (SWE) and snow depth. The study is an outgrowth of earlier snow cover modeling and radar interferometry experiments at Schefferville, Quebec, Canada and elsewhere which has shown that for reasons of loss of coherence repeat-pass InSAR is not useful for the purpose of snow cover mapping, even when used in differential InSAR mode. Repeat-pass cross-track InSAR would overcome this problem. As at radar wavelengths dry snow is transparent, the main reflection is at the snow/ground interface. The high refractive index of ice creates a phase delay which is linearly related to the water equivalent of the snow pack. When wet, the snow surface is the main reflector, and this enables measurement of snow depth. Algorithms are elaborated accordingly. Field experiments were conducted at two sites and employ two different types of digital elevation models (DEM) produced by means of cross track InSAR. One was from the Shuttle Radar Topography Mission digital elevation model (SRTM DEM), flown in February 2000. It was compared to the photogrammetrically produced Canadian Digital Elevation Model (CDEM) to examine snow-related effects at a site near Schefferville, where snow conditions are well known from half a century of snow and permafrost research. The second type of DEM was produced by means of airborne cross track InSAR (TOPSAR). Several missions were flown for this purpose in both summer and winter conditions during NASA's Cold Land Processes Experiment (CLPX) in Colorado, USA. Differences between these DEM's were compared to snow conditions that were well documented during the CLPX field campaigns. The results are not straightforward. As a result of automated correction routines employed in both SRTM and AIRSAR DEM extraction, the snow cover signal is contaminated. Fitting InSAR DEM's to known topography distorts the snow information, just as the snow cover distorts the topographic information. The analysis is therefore mostly qualitative, focusing on particular terrain situations. At Schefferville, where the SRTM was adjusted to known lake levels, the expected dry-snow signal is seen near such lakes. Mine pits and waste dumps not included in the CDEM are depicted and there is also a strong signal related to the spatial variations in SWE produced by wind redistribution of snow near lakes and on the alpine tundra. In Colorado, cross-sections across ploughed roads support the hypothesis that in dry snow the SWE is measurable by differential InSAR. They also support the hypothesis that snow depth may be measured when the snow cover is wet. Difference maps were also extracted for a 1 km2 Intensive Study Area (ISA) for which intensive ground truth was available. Initial comparison between estimated and observed snow properties yielded low correlations which improved after stratification of the data set.In conclusion, the study shows that snow-related signals are measurable. For operational applications satellite-borne cross-track InSAR would be necessary. The processing needs to be snow-specific with appropriate filtering routines to account for influences by terrain factors other than snow.

The purpose of this study was to develop a stochastic model of the snowfall, snow accumulation and ablation process. Snow storms occurring in a fixed interval were assumed to be a homogeneous Poisson process with intensity X. The snow storm magnitudes were assumed to be independent and identically distributed random variables. The magnitudes were independent of the number of storms and concentrated at the storm termination epochs. The snow water equivalent from all storms was a compound Poisson process. In the model, storms then occurred as positive jumps whose magnitudes equaled the storm amounts. Between storms, the snowpack ablated at a constant rate. Random variables characterizing this process were defined. The time to the occurrence of the first snowpack, generated by the first storm, was a random variable, the first snow-free period. The snowpack lasted for a random duration, the first snowpack duration. The alternating sequence of snow-free periods followed by snowpacks of random duration continued throughout the fixed interval. The snow-free periods were independent and identically distributed random variables as were the snowpack durations. The sum of each snow-free period and the immediately following snowpack duration formed another sequence of independent and identically distributed random variables, the snow-free, snow cycles. The snow-free, snow cycles represented the interarrival times between epochs of complete ablation, and thus defined a secondary renewal process. This process, called the snow renewal process, gave the number of times the snowpacks ablated in the interval. Distribution functions of the random variables were derived. The snow-free periods were exponentially distributed. The distribution function of the snowpack durations was obtained using some results from queueing theory. The distribution function of the first snow-free, snow cycle was derived by convoluting the density function of the first snowfree period and the first snowpack duration. The distribution of the sum of n snow-free, snow cycles was then the n-fold convolution of the first snow-free, snow cycle with itself. The probability mass function of the snow renewal process was evaluated numerically, from a known relationship with the sum of snow-free, snow cycles. The snowpack ablation rate was considered to be a random variable, constant within a season, but varying between seasons. The snowpack durations and snow-free, snow cycles were conditioned on the ablation rate, then unconditional distributions derived. An application of the model was made in the case where snow storm magnitudes were exponentially distributed. Specific expressions for the distribution functions of the random variables were obtained. These distributions were functions of the Poisson parameter X, the exponential parameter of storm magnitudes, Ne l, and the snowpack ablation rate. The snow model was compared with data from the climatological station at Flagstaff, Arizona. Snow storms were defined as sequences of days receiving 0.01 inch or more of snow water equivalent separated from other storms by one or more dry days. Snow storms occurred approximately as a homogeneous Poisson process. Storm magnitudes were exponentially distributed. Empirical distributions of snowpack ablation rates were obtained as the coefficients of a regression analysis of snowpack ablation. Two methods of estimating the Poisson parameter were used. The theoretical distribution functions were compared with the observed. The method of moments estimate generally gave more satisfactory results than the second estimate.