An acoustic frequency-swept wave was investigated as a means for determining Snow Water Equivalent (SWE) in cold wind-swept prairie and sub-alpine environments. Building on previous research conducted by investigators who have examined the propagation of sound in snow, digital signal processing was used to determine acoustic pressure wave reflection coefficients at the interfaces between 'layers' indicative of changes in acoustic impedance. Using an iterative approach involving boundary conditions at the interfaces, the depth-integrated SWE was determined using the Berryman equation from porous media physics. Apparatuses used to send and receive sound waves were designed and deployed during the winter season at field sites situated near the city of Saskatoon, Saskatchewan, and in Yoho National Park, British Columbia. Data collected by gravimetric sampling was used as comparison for the SWE values determined by acoustic sounding. The results are encouraging and suggest that this procedure is similar in accuracy to SWE data collected using gravimetric sampling. Further research is required to determine the applicability of this technique for snow situated at other geographic locations.

An acoustic frequency-swept wave was investigated as a means for determining Snow Water Equivalent (SWE) in cold wind-swept prairie and sub-alpine environments. Building on previous research conducted by investigators who have examined the propagation of sound in snow, digital signal processing was used to determine acoustic pressure wave reflection coefficients at the interfaces between 'layers' indicative of changes in acoustic impedance. Using an iterative approach involving boundary conditions at the interfaces, the depth-integrated SWE was determined using the Berryman equation from porous media physics. Apparatuses used to send and receive sound waves were designed and deployed during the winter season at field sites situated near the city of Saskatoon, Saskatchewan, and in Yoho National Park, British Columbia. Data collected by gravimetric sampling was used as comparison for the SWE values determined by acoustic sounding. The results are encouraging and suggest that this procedure is similar in accuracy to SWE data collected using gravimetric sampling. Further research is required to determine the applicability of this technique for snow situated at other geographic locations.

No description available.

Le caribou de Peary est l’unité désignable du caribou la plus septentrionale ; sa population a chuté d’environ 70% au cours des trois dernières générations. Le Comité sur la situation des espèces en péril au Canada (COSEPAC) identifie les conditions difficiles d’accès à la nourriture à travers le couvert nival comme le facteur le plus influant contribuant à ce déclin. Cette étude se concentre sur l’établissement d’un outil spatial de caractérisation des conditions nivales pour l’accès à la nourriture du caribou de Peary dans le Nord canadien, utilisant des simulations du couvert nival générées avec le logiciel suisse SNOWPACK à partir des données du Modèle Régional Climatique Canadien. Le cycle de vie du caribou de Peary a été divisé en trois périodes critiques : la période de mise bas et de migration printanière (avril – juin), celle d’abondance de nourriture et de rut (juillet – octobre) et celle de migration automnale et de survie des jeunes caribous (novembre – mars). Les conditions nivales sont analysées et les simulations du couvert nival comparées aux comptes insulaires de caribous de Peary pour identifier un paramètre nival qui agirait comme prédicateur des conditions d’accès à la nourriture et expliquerait les fluctuations des comptes de caribous. Cette analyse conclue que ces comptes sont affectés par des densités de neige au-dessus de 300 kg/m³. Un outil logiciel cartographiant à une échelle régionale (dans l’archipel arctique canadien) les conditions d’accès à la nourriture possiblement favorables et non favorables basées sur la neige est proposé. Des exemples spécifiques de sorties sont données pour montrer l’utilité de l’outil, en cartographiant les pixels selon l’épaisseur cumulée de neige au-dessus de densités de 300 kg/m³, où des épaisseurs cumulées au-dessus de 7000 cm par hiver sont considérées comme non favorables pour le caribou de Peary.

This project investigates the near surface energy balance on the Murt`el-Corvatsch rock glacier in the Upper Engadine, Swiss Alps, using the 1D physical SNOWPACK model. A correct representation of the near surface energy balance is important to predict the long term evolution of permafrost below rock glaciers. This is of interest in the context of future water availability and management of water resources in a changing climate and also in the context of natural hazards. Some difficulties in modelling the thermal regime of rock glaciers are related to the large pore spaces between the blocks, which allow for different modes of heat transport. With this in mind, different modelling approaches were investigated: using the standard SNOWPACK (without advective heat flux, ventilation or canopy module), adding an advective heat flux, using the ventilation and canopy modules. The most promising results, i.e. the best match between measured and modelled temperatures, were obtained from the ventilation parameterisation. This parameterisation accounts for boundary-layer air penetrating into the blocky layer. Furthermore it was found that the most important input variables are the thickness of the the blocky layer, since this is where the additional modes of heat exchange take place, and the ice and void volume fraction together with the field capacity in the icy layer. The latter are particularly relevant for long term modelling as they determine the amount of ice melt and water transport in the icy layer. Measured and modelled temperatures at depths of 0.5 m, 2.5 m, 3.5 m and 7.5 m were compared. Generally good agreements between modelled and measured temperatures were obtained for the depths 0.5 m, 3.5 m and 7.5 m. The slight warming trend at the end of the modelled period (2012- 2016) that can be observed in the borehole data is also present in the modelled temperatures. The depth of 2.5 m shows the least agreement between modelled and measured temperatures with and overestimation during the snow free period and an underestimation during the snow covered period. However, agreement between modelled and measured temperatures improves for the snow covered period after a simulation period of about ten years.

In Pacific North America, a substantial amount of the streamflow available during the dry summer months originates from melting mountain snowpacks. Since the start of the twenty-first century, these mountain snowpacks have been declining due to the impacts of global climate change and could have severe implications for future water availability in many regions. To develop robust predictive models of future water availability derived from mountainous snowpacks, the longest possible data record is required. However, instrumental data for snow measurements, when available, are limited to a length of only five or six decades in most regions of Pacific North America. In this study, tree-rings from snow-depth sensitive tree species (mountain hemlock (Tsuga mertensiana (Bong.) Carrière) and subalpine fir (Abies lasiocarpa (Hook.) Nutt.)) were used as a proxy to develop a 258-year record of precipitation as snow (PAS) for the southern Coast Mountains of British Columbia. Four snow models were evaluated based on a suite of dendroclimatological model diagnostics. From these, one PAS reconstruction was carried out. The reconstruction was unable to properly validate using the leave-one-out cross validation method. This result is attributed to the combination of a short calibration period, a potentially weak climate signal, and the absence of signal enhancement. Despite this outcome the research resulted in number of inferences and recommendations useful for future research. / Graduate

La couverture neigeuse arctique peut atteindre jusqu'à un tiers de la surface terrestre. Cet environnement, chimiquement très dynamique, est en interaction avec tous les compartiments environnementaux : l’atmosphère, le sol, les aquifères, et ce influence la biosphère toute entière. Durant les dernières décennies, la neige a été reconnue comme étant un réservoir de microorganismes. Pourtant l’écologie des microbes du manteau neigeux reste mal comprise. L’objectif principal de cette thèse est donc de caractériser le manteau neigeux en tant qu’écosystème fonctionnel, par définition une communauté d’organismes vivants, en conjonction avec la composante non vivante de leur environnement et agissant comme un système. Pour cela, la composition taxonomique et fonctionnelle des communautés microbiennes a été analysée via la technologie de séquençage haut débit pour deux types de modèles de manteau neigeux : une neige saisonnière d’eau douce d’un manteau neigeux terrestre (Ny--‐Alesund, Svalbard) et une couverture neigeuse saline sur la glace de mer (Nuuk, Greenland). Le premier objectif est de caractériser l’hétérogénéité des communautés microbiennes en relation avec les fluctuations conditions environnementales. La composition des communautés microbiennes du manteau neigeux est très variable en fonction de l’avancement dans la saison du printemps vers l’été et en fonction de la profondeur. La corrélation entre les fonctions microbiennes et les conditions environnementales soutient l’hypothèse que les communautés microbiennes interagissent avec les fluctuations des conditions en abiotiques de leur habitat. Le second objectif concerne la spécificité des communautés microbiennes du manteau neigeux ; si le manteau neigeux est un écosystème fonctionnel alors les communautés microbiennes le composant devraient présenter des caractéristiques spécifiques liées à leur adaptation aux conditions de cet habitat, malgré la variabilité. La comparaison de la distribution fonctionnelle entre la neige et des environnements distants (polaires ou non) ainsi que des environnements en interaction proche permet de confirmer une spécificité des communautés microbiennes de la neige. Le troisième objectif se concentre sur la sélection environnementale ; étant donné que l’existence d’une communauté microbienne spécifique implique que des processus de sélection se réalisent au sein du manteau neigeux. La comparaison de la distribution de la structure (quels microorganismes sont présents) et la fonction (que sont-ils capables de faire ?) des communautés microbiennes en fonction de la source des microorganismes au sein d’un manteau neigeux couvrant la glace de mer révèle que la communauté est largement influencée mais diffère de leur source en réponse aux conditions environnementales spécifiques. Les résultats préliminaires des analyses metagénomiques et metatranscriptomiques ont révélé qu’il existe une grande variabilité entre les communautés présentes et potentiellement actives au sein du manteau neigeux. Bien que des limitations conceptuelles et techniques persistent, les méthodes de séquençages haut-débit basées sur les molécules d’ARN sont des outils prometteurs pour décrire les réponses à court terme des communautés microbiennes du manteau neigeux aux variations des conditions environnementales. Finalement, une approche mécanistique préliminaire basée sur la mise en place de microcosmes de neige artificielle et des microorganismes modèles a été développée afin de déterminer les processus de colonisation au sein du manteau neigeux. Alors que de nombreuses questions demeurent concernant l’activité microbienne et les interactions complexes de communautés, les études menées durant cette thèse ont permis de soutenir l’hypothèse que la neige est un écosystème fonctionnel. / The Arctic seasonal snowpack can extend at times over a third of the Earth’s land surface. This chemically dynamic environment interacts with different environmental compartments such as the atmosphere, soil and meltwater, and thus, strongly influences the entire biosphere. During the last decades, snow has been recognized as a microbial reservoir. The ecology of snow microorganisms however remains poorly understood. The main goal of this thesis was to investigate the snow as a functional ecosystem; i.e. a community of living organisms in conjunction with the non--‐living component of their environment and interacting as a system. In order to do so, microbial community taxonomic and functional composition of snow samples from two arctic snowpack models: seasonal snow from terrestrial fresh water snowpack (Ny--‐Alesund, Svalbard) and sea ice snow cover (Nuuk, Greenland) was analyzed using high throughput sequencing technologies. The first objective addressed microbial community heterogeneity in relation with fluctuating environmental conditions. Snow microbial community composition was highly variable during spring season and depth. The relationship between microbial functions and environmental conditions supports the hypothesis that the snow microbial community interacts with the abiotic variability characteristic of their habitat. The second objective addressed snow community specificity; if the snowpack is a functional ecosystem, then the microbial communities inhabiting it should have specific features related to their adaptation to the conditions of this environment, despite variability. The comparison of functional distribution between snow and both remote (polar and non polar) and closely interacting environments provided evidence of snowpack microbial community specificity. The third objective focused on environmental selection, given that the existence of a specific snow microbial community implies that one or more selective processes occur in the snowpack. Comparing the distribution of microbial community structure and function as related to the source of the microorganisms in a sea ice snow cover revealed that snow microbial communities were largely influenced by, yet differed from their seeding sources in response to specific environmental conditions. Mechanistic approaches with model microorganisms in snow microcosms were developed during this thesis and, based on preliminary results, will help to determine colonization processes within snowpack. Finally, preliminary results in the first section of Chapter 4 also showed that a high variability exists between the microorganisms present within the snowpack, and those that are active. Although technical and conceptual issues remain, RNA based high throughput sequencing was evaluated as an encouraging tool to evaluate short--‐term responses of microbial communities to environmental fluctuations. While numerous questions remain about microbial activity and complex community interactions, the results from this thesis support the hypothesis that snow is a functional ecosystem.

This thesis works through the design of a radar-based system for imaging snowpacks remotely and over large areas to assist in avalanche prediction. The key to such a system is the ability to image volumes of snow at shallow, spatially-varying angles of incidence. To achieve this prerequisite, the design calls for a ground-based Synthetic Aperture Radar (SAR) capable of generating three-dimensional, ultra-high-resolution images of a snowpack. To arrive at design parameters for this SAR, the thesis works through relevant principles in avalanche mechanics, alpine-snowpack geophysics, and electromagnetic scattering theory. The thesis also works through principles of radar, SAR, antenna, and image processing theory to this end. A preliminary system is implemented to test the feasibility of the overall design. The preliminary system demonstrates ultra-high-resolution, three-dimensional imaging capabilities and the ability to image the volume of multiple alpine snowpacks. Images of these snowpacks display the structural patterns indicative of different layers in the snowpacks. Possible attributions of the patterns to physical properties in the snowpack are explored, but conclusions are not arrived at. Finally, lessons from the implementation of this preliminary system are discussed in terms of opportunities to be capitalized upon and problems to be overcome in future systems that more faithfully realize the complete design set forth in the thesis.

Water managers throughout the world are challenged with managing scarce resources and therefore rely heavily on forecasts to allocate and meet various water demands. The need for improved streamflow and snowpack forecast models is of the utmost importance. In this research, the use of oceanic and atmospheric variables as predictors was investigated to improve the long lead-time (three to nine months) forecast of streamflow and snowpack. Singular Value Decomposition (SVD) analysis was used to identify a region of Pacific and Atlantic Ocean SSTs and a region of 500 mbar geopotential height (Z500mb) that were teleconnected with streamflow and snowpack. The resulting Pacific and Atlantic Ocean SSTs and Z500mb regions were used to create indices that were then used as predictors in a non-parametric forecasting model. The majority of forecasts resulted in positive statistical skill, which indicated an improvement of the forecast over the climatology or no-skill forecast. The results indicated that derived indices from SSTs were better suited for long lead-time (six to nine month) forecasts of streamflow and snowpack while the indices derived from Z500mb improved short lead-time (3 month) forecasts. In all, the results of the forecast model indicated that incorporating oceanic-atmospheric climatic variability in forecast models can lead to improved forecasts for both streamflow and snowpack.

The snow cover plays an important role not only for the whole climate system but also for tourism and economy in the Lapland winter (e.g. dog sledding, snow mobile, etc). Snow constitutes a shelter for animals and plants during the winter due to thermal isolation, but, on the range of this investigation, it can make grazing difficult for reindeers, if the conditions are not favorable. Different approaches to the study have been made; the first and most important part of the investigation was a campaign in Järämä, in Swedish Lapland. During 3 days (between the 3rd and 5th of March 2009), a series of snow pits were done, recording snow grain size, snow layers depth, snow hardness/compactness, density and temperature. The hardness in the snow was evaluated through ram penetration tests. It was additionally studied the correspondence between the snow layers found in situ and the Sámi terminology. Another approach of the study consisted of satellite observations during the winter season 2008/2009 with day light in the region. The type of imagery used was MODIS (The Moderate Resolution Imaging Spectroradiometer) daily snow albedo and 8-day surface reflectance products. Measurements of temperature, precipitation, snow depth were used to cover the polar night time when satellite images were missing. According to these weather observations some snow metamorphisms were also studied, and their influence on the snowpack conditions. Through the comparison between all these forms of data it was found that in the winter season 2008/2009 the conditions for reindeers grazing were not good due to the formation of ice encapsulating the lichens and grass. Additionally several hard snow layers have been found in the snowpack which increase the difficulty to dig in the snow and may cause problems to the reindeers’ digestion. Snow hardness measurements with a ram penetrometer, manual tests and visual grain size observation proved these discovers. Several periods of positive temperature may cause melting/refreezing cycles contributing to the formation of hard snow layers. These conclusions are supported by the snow albedo and surface reflectance satellite imagery. In these images is visible a period with snow albedo decreasing a lot in the beginning of autumn, after the first lasting snowfall. The weather conditions in early fall, when the first durable snow occurs, are of extreme importance for the reindeers’ grazing, and in the case of the studied winter season, the conditions were not favorable.