In June 2005, fieldwork was conducted during the spring snowmelt period at Cape Bounty, Melville Island, Nunavut to examine the relationships between snow accumulation, runoff, and nutrient fluxes in two High Arctic watersheds. The snowcover was quantified by means of eleven depth and three density measurements at 42 survey transects (100 m) distributed throughout the West and East watersheds. River discharge was monitored at the watershed outlets, where water samples were collected four times daily during the first ten days of melt and twice daily once flow receded. Water samples were also collected from headwater and tributary sites in the two watersheds, and samples were analyzed for DOC, DON and DIN (NH4+ and NO3-). An objective terrain classification weighted equally on slope, aspect and land surface curvature was applied to the two watersheds using an ISODATA unsupervised classification scheme to determine watershed SWE. The terrain model confirmed that topography likely explains greater SWE in the West watershed, and provides a method for reproducible estimates of watershed SWE in future years. However, improved methods for estimating SWE in channels and deep snowbanks are required to ensure accurate assessments of watershed SWE. The seasonal trends in DOC, DON, and DIN concentrations and specific fluxes are reported for both watersheds. The export of DON and DIN was strongly correlated with DOC in the West watershed, indicating that the flushing of terrestrial nutrients from surficial soils by snowmelt runoff governs nutrient export. Despite less watershed SWE (51%), the East watershed exported greater specific fluxes of DOC (33%) and DON (43%) during the melt season. This suggests that the East watershed had greater connectivity with OM sources early in the melt season. Furthermore, low DOC:DON ratios (< 15) in the East River indicate that a larger portion of DOM was likely derived from algal or microbial sources in the East watershed relative to the West watershed. The export of DIN was similar in the two watersheds, and results suggest that DIN export was likely controlled by watershed vegetation coverage and runoff volumes during snowmelt. / Thesis (Master, Geography) -- Queen's University, 2008-07-08 00:31:46.107

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.

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.

For the purpose of hydrological forecasting on mountains' and sub-mountains' rivers is important knowledge of distribution of snow water equivalent in the watershed. Submitted thesis therefore deals with comparison of 9 interpolation methods in terms of quality of their forecasting when predicting snow depth and snow water equivalent in watershed Bystřice (127,6 km2 ), which is situated in the northwest of Bohemia in the Ore mountains. Point data of snow depth and snow water equivalent used in interpolation were sampled during an off- road measuring in 17. 2. 2010 at the 14 snow sampling locations. The interpolation methods were: (1) Thiessen's polygons, (2) inverse distance weighting, (3) global polynomial (4) local polynomial (5) radial basis functions, (6) ordinary kriging, (7) cokriging, (8) residual kriging and (9) orographic interpolation. Independent variable-altitude used in the calculation of snow depth and snow water equivalent was used only in the last three listed methods. Predictive ability of interpolation methods was evaluated by using cross-validation and visual comparison of predicted maps. The best prediction ability was provided by residual kriging and orographic interpolation. The geostatistical methods were next in the order. The method of Thiessen's polygons and inverse distance...