A wide area assessment of forecast changes in wintertime synoptic conditions over western North America is combined with a meso-scale alpine hydrometeorology model to evaluate the joint impact(s) of forecast climate change on snowpack conditions in an alpine watershed in the southern Canadian Rockies. The synoptic analysis was used to generate long-term climate time series scenarios using the CCCma CGCM1. An alpine hydrometerology model is used to predict changes in wintertime precipitation at the watershed scale. A mass balance snow model is utilized to predict the overall snow accumulation throughout a watershed. A vapour transfer model has been incorporated in the snow model to estimate snow volumes more accurately. The synoptic analysis and GCM output forecasts a modest increase in both winter precipitation and temperatures in the study area, resulting in a decline of winter snow accumulations, and hence an expected decline in spring runoff. / ix, 87 leaves : ill. ; 28 cm.

Whitebark pine (Pinus albicaulis) is an endangered keystone tree species growing at the highest elevations in the mountain ranges of western North America. Across its range, whitebark pine is faced with a number of threats including fire suppression, mountain pine beetle, white pine blister rust, and climate change. Climate change is perhaps the greatest threat facing the species, yet it is the least understood. Most studies rely on model predictions and only look at the impacts on whitebark pine itself, not taking into consideration the other bird, mammal, and plant communities that are associated with it. In order to assess the potential effects of climate change on whitebark pine communities in the southern Coast Mountains of British Columbia, this thesis examined the vegetation associations and climate controls currently shaping the communities. My results showed that whitebark pine is growing in the open away from other subalpine tree species. This suggests that whitebark pine is not facilitating other subalpine tree species, contrary to what has been shown in the Rocky Mountains. Evidence of a distinct suite of understory vegetation associated with whitebark pine is weak and inconclusive. Differences in understory vegetation appear to be mainly due to site differences in climate, soils, and topography. Age distributions constructed from tree cores revealed that whitebark pine decline at lower elevation sites may be due to successional advancement to subalpine fir, and subalpine fir is currently encroaching into higher elevation sites. A dendrochronological assessment revealed that winter conditions, including snowpack, temperature, and the Aleutian Low Pressure Index (ALPI) were the most limiting to whitebark pine growth at high-elevation sites, but biotic factors including disease and competition appear to be more important than climate in determining annual ring growth at lower elevation sites. Bootstrapped correlations between annual ring widths and snowpack records showed that tree responses to fluctuating snowpack have changed over time. For most of the 20th century, low snowpack periods were associated with greater annual growth. Since around 1970, when the snowpack levels dropped below anything previously recorded for the area, annual tree growth has been reduced. It appears that these high elevation tree species require a balance between too much snow (shorter growing season) and too little snow (reduced protection from harsh winter conditions). Climate change models for the area predict drastically reduced snowpack in the coming decades. If snowpack continues to drop, as it has since 1970, it will likely lead to severe impacts on whitebark pine growth in the southern Coast Mountains. / Graduate / 0329 / carlsonkim@hotmail.com

No description available.

Snowpack energy balance in forest environment: spatial and temporal variability of shortwave and longwave radiation Assessment of the role of forest on snowmelt processes and snowpack attributes contributes to the accuracy of spring floods forecasting. An importance of the coniferous forest consists in change of the snowpack energy balance. Forest reduces the total amount of solar radiation, however trees cause emitting of longwave radiation, both factors are fundamentally reflected in time of snowmelt in forest environment. Master thesis focuses on temporal and spatial variability of shortwave and longwave radiation depending on the structures of vegetation cover. Individual site types were defined as an open area, a forest affected by the bark beetle (Ips typographus) and a healthy coniferous forest, based on the hemispheric images of vegetation and its Leaf Area Index (LAI). Moreover, repeated manual measurements of the snow depth and snow water equivalent (SWE) were done in plots during winter period 2016/2017 in the Ptačí Brook catchment in the Šumava Mountains, and an analysis of shortwave and longwave radiation data the radiometers in plots was performed. Radiation fluxes in different plots were described in daily and seasonal scale, including the calculation of total heat from shortwave and...

O propósito geral desta tese foi modelar a dinâmica do retroespalhamento SAR-X ao longo de um pacote superficial de neve seca através do uso de uma ferramenta computacional de modelagem de micro-ondas (RF Module®, PDETOOL®, MATLAB®), baseado na física da interação entre o feixe de micro–ondas e este pacote de neve, e executar a aplicação de métodos estatísticos para geração de relações entre variáveis estratigráficas desse pacote de neve e o respectivo retroespalhamento SAR-X observado. Para tanto, o presente trabalho buscou avançar na organização de um modelo analítico para o processo de interação entre um feixe de micro–ondas na banda X e o pacote de neve superficial, aplicando ferramentas computacionais para a resolução dos equacionamentos que compõem esse problema. Como área de estudo, delimitou–se a porção ocidental antártica, especificamente junto à área da geleira Union. O modelo de retroespalhamento utilizado pautou–se na consideração do Modelo de Transferência Radiativa (MTR), adotando como variáveis principais a profundidade da neve acumulada, a rugosidade da superfície (interface ar–neve e neve–sologelo), o tamanho dos cristais de neve (tamanho dos grãos), o perfil de densidade da neve acumulada e as características das camadas de neve que formam o pacote de neve superficial (espessura, forma da interface entre camadas, variação dielétrica entre camadas, dentre outros). Posteriormente, através da reversão modelagem estatística do modelo de retroespalhamento criado, foram obtidos dados estratigráficos indiretos modelados (número médio de camadas de neve, densidade média do pacote de neve superficial e tamanho médio dos grãos de neve), permitindo a inferência de variáveis da estratigrafia local a partir de dados de retroespalhamento SAR COSMO–SkyMed, banda X. Por fim, a comparação entre os valores modelados e aqueles observados em campo para a estratigrafia e para o retroespalhamento permitiram estimativas do desempenho da modelagem proposta. Para fins de validação desta modelagem, foram considerados dados comuns de entrada, constituídos de dados estratigráficos e de temperatura da neve em um perfil de 2 m de profundidade e dados SAR–X COSMO–SkyMed (modo de aquisição Stripmap/Himage com resolução espacial de 3x3 m) na banda X coletados na região da geleira Union no verão antártico de 2011–2012. Como resultados, foram obtidas equações analíticas para estimativa do tamanho médio dos grãos de neve, número médio de camadas espalhadoras e densidade média do pacote de neve superficial a partir de dados de retroespalhamento SAR– banda X, com consistência estatística mínima estimada de 86% (R² ≥ 86%). Já o modelo de retroespalhamento utilizado, tendo seus resultados comparados aos dados de retroespalhamento in situ COSMO–SkyMed exibiram estimativas com R² da ordem de 90% ou maior, o que é considerado estatísticamente adequado. Este trabalho traz como contribuição a implementação computacional via ferramenta de modelagem de um modelo de retroespalhamento SAR–X, voltado para massas de neve seca, e propõe a obtenção de dados estratigráficos a partir de dados de retroespalhamento SAR–X com o uso de equações determinadas por regressão estatística. Isto permitiu a espacialização de variáveis estratigráficas em zonas de neve seca a partir de dados SAR obtidos ao longo da banda X. Cabe ressaltar o fato de que devido ao limitado número de amostras de campo (7 amostras), a consistência estatística e a confiabilidade dos resultados deve ser tomada com ressalva, quando considerada a análise glaciológica da variação nos parâmetros do pacote de neve, cabendo melhores testes e análises em sua aplicação. / The present thesis proposes an analytical model for interaction between a beam of microwaves in the X band and surface snowpack. To this end, statistical analysis were performed with SAR-X backscattering data and reference data from snowpits focusing the interaction between the microwave beam and the snowpack in dry snow areas. Numerical methods were employed for solution of differential equations that make up this issue. The model was proposed for Union Glacier, located in the West Antarctic Ice Sheetregarding a study area including the Antarctic western portion, recognized as the Union Glacier. The backscattering model used was based under the assumption of the Radiative Transfer Model (RTM), considering as main variables the depth of accumulated snow, the surface roughness (air-snow interface and snow-ice interface), the size of snow crystals (grain size), the density profile of the accumulated snow and snow characteristics of the layers forming the surface snowpack (thickness, shape of the interface between layers variation between dielectric layers, among others). After that, reversal statistical modelling of backscatter was performed to estimate stratigraphic parameters of the snowpack usingdata allowing the local stratigraphy of estimated variables SAR backscatter data from COSMO-SkyMed satellite. To validate the proposed model, the same input data were considered for all experiments performed experiments. These data were made up of snow stratigraphic data and snow temperature data in a 2 m depth glaciological profiles (snowpits) 2m depth and data SAR-X COSMO-SkyMed X-band SAR data (acquisition mode Stripmap / Himage with 3x3 m spatial resolution 3x3 m) acquired atin Union Glacier snowpits and remote sensing SAR data during summer 2011-2012. The results showed average density of the snow pack surface from SAR-X backscatter data SAR-X with R² ≥ 86%. The main contribution of this work is the resulting model for SAR-X backscattering for dry snow masses, which was proved to be statistically consistent with the ground truth data. Even with limited reference data, this result indicates the soundness of the proposed approach, allowing the estimation of spatial distribution ofvariations in stratigraphic parameters of the snowpack variables in dry snow areas from SAR X-band SAR data over the X band. However, snowpack parameters estimated by the method should be used carefully, as the input data used for model development may underestimate all possible variations found at the snow surface of Union Glacier.

La neige est un matériau poreux dont la microstructure change en permanence. L'ensemble de ces transformations, qui prend le nom de ``métamorphisme", est susceptible d'affecter les propriétés thermiques, mécaniques et électromagnétiques de la neige au niveau macroscopique. En particulier, les échanges d'énergie et de matière à l'intérieur du manteau neigeux et entre la neige et l'atmosphère sont fortement influencés par l'évolution au cours du temps de la microstructure de la neige. Une représentation adéquate du métamorphisme dans les modèles de manteau neigeux s'avère donc cruciale. La microstructure d'un matériau poreux peut être raisonnablement décrite en se servant d'un nombre réduit de variables. En effet, la masse volumique, la surface spécifique (SSA) et la distribution de courbure permettent de caractériser la microstructure d'un matériau. Cependant, dans le cas de la neige cette approche n'en est qu'à ses débuts et n'a pas encore été appliquée de façon systématique. Des variables semi-empiriques, difficiles à mesurer et dépourvues de lien direct avec d'autres propriétés physiques, sont encore largement utilisées dans les modèles détaillés de manteau neigeux. Ce travail de thèse s'inscrit dans cette tentative de représenter la microstructure de la neige au cours du temps à l'aide de variables bien définies et mesurables sur le terrain. Parmi ces variables, nous nous sommes attachés notamment à la SSA, qui constitue une grandeur essentielle pour l'étude du manteau neigeux et de son évolution temporelle. Différentes lois d'évolution de la SSA ont été étudiées, à partir de relations empiriques basées sur des ajustements de données expérimentales jusqu'aux modèles physiques qui représentent le flux de la vapeur d'eau entre les grains de neige. Ces lois ont été dans un premier temps testées à l'aide d'un modèle simplifié de manteau neigeux et puis introduites directement dans le modèle SURFEX/ISBA-Crocus. Pour ce faire, la SSA dans Crocus a été transformée en variable prognostique, en remplaçant d'autres variables semi-empiriques préexistantes. Les différentes formulations de l'évolution temporelle de la SSA ont été comparées à des mesures de terrain, acquises lors de deux campagnes à Summit (Groenland) et au Col de Porte (France). Ces mesures ont été effectuées en utilisant de nouvelles techniques optiques et ont permis d'obtenir un riche jeu de données avec une grande résolution verticale. Les résultats montrent que les différentes formulations sont comparables et reproduisent bien les mesures, avec un écart quadratique moyen entre les valeurs de SSA simulées et observées inférieur à 10 m^2/kg. Enfin, nous avons contribué à faire le pont entre la microstructure de la neige et ses propriétés macroscopiques. En particulier, nous nous sommes intéressés au lien entre, d'une part, la SSA et, d'autre part, les propriétés mécaniques et optiques. Dans le premier cas, nous avons investigué la corrélation entre la SSA et la résistance à l'enfoncement mesurée avec un Snow Micro Pen (SMP). Les résultats encore préliminaires semblent indiquer que la SSA peut être dérivée de la masse volumique et de grandeurs micro-mécaniques estimées à partir du signal du SMP avec un modèle statistique. Dans le deuxième cas, nous avons simulé l'albédo de surface à Summit à partir des profils mesurés de masse volumique et de SSA et du contenu en impuretés. Les résultats de cette étude ont démontré que l'albédo spectral peut être correctement simulé à l'aide d'un modèle de transfert radiatif et l'énergie absorbée par le manteau neigeux peut être estimée avec une précision d'environ 1%. / Snow is a porous medium whose microstructure is constantly subjected to morphological transformations. These transformations, which take the name of ``metamorphism", are likely to affect the thermal, mechanical and electromagnetic properties of snow at the macroscopic level. Specifically, the exchange of energy and matter within the snowpack and between the snow and the atmosphere above are strongly impacted by the evolution over time of the snow microstructure. Therefore, an adequate representation of metamorphism in snowpack models is crucial. The microstructure of a porous medium can be reasonably described using a reduced number of variables. Indeed, the density, the specific surface area (SSA) and the curvature distribution are able to characterize the microstructure of such a material. However, in the case of snow this approach is still in its infancy and has not yet been systematically applied. Semi-empirical variables, difficult to measure and not directly linked to other relevant physical properties, are still widely used in so-called detailed snowpack models. This work contributes to the attempt to represent the state of the snow using well-defined and easily measurable microstructural variables. Among these variables, we focused particularly on the SSA, which is a key quantity for the study of snow and its temporal evolution. Different evolution laws of SSA were studied, starting from empirical relationships based on experimental data adjustments to physical models that represent the flow of water vapor between snow grains. These laws were initially tested using a simplified snowpack model and then introduced directly into the SURFEX/ISBA-Crocus snowpack model. To this end, the SSA in Crocus was turned into a prognostic variable, replacing other preexisting semi-empirical variables. The different formulations of the temporal evolution of the SSA were compared with field measurements, acquired during two campaigns at Summit (Greenland) and the Col de Porte (France). These measurements were carried out using new optical techniques and yielded a rich dataset with high vertical resolution. The results show that the different formulations are comparable and reproduce well the observations, with an average root-mean-square deviation value between simulated and measured SSA lower than 10 m^/kg. Finally, we contributed to bridge the gap between snow microstructure and macroscopic properties. In particular, we investigated the link between the SSA on the one hand and the mechanical and optical properties on the other hand. In the first case, we investigated the correlation between the SSA and the penetration resistance measured with a Snow Micro Pen (SMP). The preliminary results suggest that the SSA can be retrieved from the snow density and the micro-mechanical parameters estimated from the SMP signal using a statistical model. In the second case, we simulated the surface albedo at Summit from the measured profiles of density, SSA and impurities within the snowpack. The results of this study showed that the spectral albedo can be simulated successfully using a radiative transfer model and the energy absorbed by the snowpack can be estimated with a good accuracy (about 1%).

O propósito geral desta tese foi modelar a dinâmica do retroespalhamento SAR-X ao longo de um pacote superficial de neve seca através do uso de uma ferramenta computacional de modelagem de micro-ondas (RF Module®, PDETOOL®, MATLAB®), baseado na física da interação entre o feixe de micro–ondas e este pacote de neve, e executar a aplicação de métodos estatísticos para geração de relações entre variáveis estratigráficas desse pacote de neve e o respectivo retroespalhamento SAR-X observado. Para tanto, o presente trabalho buscou avançar na organização de um modelo analítico para o processo de interação entre um feixe de micro–ondas na banda X e o pacote de neve superficial, aplicando ferramentas computacionais para a resolução dos equacionamentos que compõem esse problema. Como área de estudo, delimitou–se a porção ocidental antártica, especificamente junto à área da geleira Union. O modelo de retroespalhamento utilizado pautou–se na consideração do Modelo de Transferência Radiativa (MTR), adotando como variáveis principais a profundidade da neve acumulada, a rugosidade da superfície (interface ar–neve e neve–sologelo), o tamanho dos cristais de neve (tamanho dos grãos), o perfil de densidade da neve acumulada e as características das camadas de neve que formam o pacote de neve superficial (espessura, forma da interface entre camadas, variação dielétrica entre camadas, dentre outros). Posteriormente, através da reversão modelagem estatística do modelo de retroespalhamento criado, foram obtidos dados estratigráficos indiretos modelados (número médio de camadas de neve, densidade média do pacote de neve superficial e tamanho médio dos grãos de neve), permitindo a inferência de variáveis da estratigrafia local a partir de dados de retroespalhamento SAR COSMO–SkyMed, banda X. Por fim, a comparação entre os valores modelados e aqueles observados em campo para a estratigrafia e para o retroespalhamento permitiram estimativas do desempenho da modelagem proposta. Para fins de validação desta modelagem, foram considerados dados comuns de entrada, constituídos de dados estratigráficos e de temperatura da neve em um perfil de 2 m de profundidade e dados SAR–X COSMO–SkyMed (modo de aquisição Stripmap/Himage com resolução espacial de 3x3 m) na banda X coletados na região da geleira Union no verão antártico de 2011–2012. Como resultados, foram obtidas equações analíticas para estimativa do tamanho médio dos grãos de neve, número médio de camadas espalhadoras e densidade média do pacote de neve superficial a partir de dados de retroespalhamento SAR– banda X, com consistência estatística mínima estimada de 86% (R² ≥ 86%). Já o modelo de retroespalhamento utilizado, tendo seus resultados comparados aos dados de retroespalhamento in situ COSMO–SkyMed exibiram estimativas com R² da ordem de 90% ou maior, o que é considerado estatísticamente adequado. Este trabalho traz como contribuição a implementação computacional via ferramenta de modelagem de um modelo de retroespalhamento SAR–X, voltado para massas de neve seca, e propõe a obtenção de dados estratigráficos a partir de dados de retroespalhamento SAR–X com o uso de equações determinadas por regressão estatística. Isto permitiu a espacialização de variáveis estratigráficas em zonas de neve seca a partir de dados SAR obtidos ao longo da banda X. Cabe ressaltar o fato de que devido ao limitado número de amostras de campo (7 amostras), a consistência estatística e a confiabilidade dos resultados deve ser tomada com ressalva, quando considerada a análise glaciológica da variação nos parâmetros do pacote de neve, cabendo melhores testes e análises em sua aplicação. / The present thesis proposes an analytical model for interaction between a beam of microwaves in the X band and surface snowpack. To this end, statistical analysis were performed with SAR-X backscattering data and reference data from snowpits focusing the interaction between the microwave beam and the snowpack in dry snow areas. Numerical methods were employed for solution of differential equations that make up this issue. The model was proposed for Union Glacier, located in the West Antarctic Ice Sheetregarding a study area including the Antarctic western portion, recognized as the Union Glacier. The backscattering model used was based under the assumption of the Radiative Transfer Model (RTM), considering as main variables the depth of accumulated snow, the surface roughness (air-snow interface and snow-ice interface), the size of snow crystals (grain size), the density profile of the accumulated snow and snow characteristics of the layers forming the surface snowpack (thickness, shape of the interface between layers variation between dielectric layers, among others). After that, reversal statistical modelling of backscatter was performed to estimate stratigraphic parameters of the snowpack usingdata allowing the local stratigraphy of estimated variables SAR backscatter data from COSMO-SkyMed satellite. To validate the proposed model, the same input data were considered for all experiments performed experiments. These data were made up of snow stratigraphic data and snow temperature data in a 2 m depth glaciological profiles (snowpits) 2m depth and data SAR-X COSMO-SkyMed X-band SAR data (acquisition mode Stripmap / Himage with 3x3 m spatial resolution 3x3 m) acquired atin Union Glacier snowpits and remote sensing SAR data during summer 2011-2012. The results showed average density of the snow pack surface from SAR-X backscatter data SAR-X with R² ≥ 86%. The main contribution of this work is the resulting model for SAR-X backscattering for dry snow masses, which was proved to be statistically consistent with the ground truth data. Even with limited reference data, this result indicates the soundness of the proposed approach, allowing the estimation of spatial distribution ofvariations in stratigraphic parameters of the snowpack variables in dry snow areas from SAR X-band SAR data over the X band. However, snowpack parameters estimated by the method should be used carefully, as the input data used for model development may underestimate all possible variations found at the snow surface of Union Glacier.

O propósito geral desta tese foi modelar a dinâmica do retroespalhamento SAR-X ao longo de um pacote superficial de neve seca através do uso de uma ferramenta computacional de modelagem de micro-ondas (RF Module®, PDETOOL®, MATLAB®), baseado na física da interação entre o feixe de micro–ondas e este pacote de neve, e executar a aplicação de métodos estatísticos para geração de relações entre variáveis estratigráficas desse pacote de neve e o respectivo retroespalhamento SAR-X observado. Para tanto, o presente trabalho buscou avançar na organização de um modelo analítico para o processo de interação entre um feixe de micro–ondas na banda X e o pacote de neve superficial, aplicando ferramentas computacionais para a resolução dos equacionamentos que compõem esse problema. Como área de estudo, delimitou–se a porção ocidental antártica, especificamente junto à área da geleira Union. O modelo de retroespalhamento utilizado pautou–se na consideração do Modelo de Transferência Radiativa (MTR), adotando como variáveis principais a profundidade da neve acumulada, a rugosidade da superfície (interface ar–neve e neve–sologelo), o tamanho dos cristais de neve (tamanho dos grãos), o perfil de densidade da neve acumulada e as características das camadas de neve que formam o pacote de neve superficial (espessura, forma da interface entre camadas, variação dielétrica entre camadas, dentre outros). Posteriormente, através da reversão modelagem estatística do modelo de retroespalhamento criado, foram obtidos dados estratigráficos indiretos modelados (número médio de camadas de neve, densidade média do pacote de neve superficial e tamanho médio dos grãos de neve), permitindo a inferência de variáveis da estratigrafia local a partir de dados de retroespalhamento SAR COSMO–SkyMed, banda X. Por fim, a comparação entre os valores modelados e aqueles observados em campo para a estratigrafia e para o retroespalhamento permitiram estimativas do desempenho da modelagem proposta. Para fins de validação desta modelagem, foram considerados dados comuns de entrada, constituídos de dados estratigráficos e de temperatura da neve em um perfil de 2 m de profundidade e dados SAR–X COSMO–SkyMed (modo de aquisição Stripmap/Himage com resolução espacial de 3x3 m) na banda X coletados na região da geleira Union no verão antártico de 2011–2012. Como resultados, foram obtidas equações analíticas para estimativa do tamanho médio dos grãos de neve, número médio de camadas espalhadoras e densidade média do pacote de neve superficial a partir de dados de retroespalhamento SAR– banda X, com consistência estatística mínima estimada de 86% (R² ≥ 86%). Já o modelo de retroespalhamento utilizado, tendo seus resultados comparados aos dados de retroespalhamento in situ COSMO–SkyMed exibiram estimativas com R² da ordem de 90% ou maior, o que é considerado estatísticamente adequado. Este trabalho traz como contribuição a implementação computacional via ferramenta de modelagem de um modelo de retroespalhamento SAR–X, voltado para massas de neve seca, e propõe a obtenção de dados estratigráficos a partir de dados de retroespalhamento SAR–X com o uso de equações determinadas por regressão estatística. Isto permitiu a espacialização de variáveis estratigráficas em zonas de neve seca a partir de dados SAR obtidos ao longo da banda X. Cabe ressaltar o fato de que devido ao limitado número de amostras de campo (7 amostras), a consistência estatística e a confiabilidade dos resultados deve ser tomada com ressalva, quando considerada a análise glaciológica da variação nos parâmetros do pacote de neve, cabendo melhores testes e análises em sua aplicação. / The present thesis proposes an analytical model for interaction between a beam of microwaves in the X band and surface snowpack. To this end, statistical analysis were performed with SAR-X backscattering data and reference data from snowpits focusing the interaction between the microwave beam and the snowpack in dry snow areas. Numerical methods were employed for solution of differential equations that make up this issue. The model was proposed for Union Glacier, located in the West Antarctic Ice Sheetregarding a study area including the Antarctic western portion, recognized as the Union Glacier. The backscattering model used was based under the assumption of the Radiative Transfer Model (RTM), considering as main variables the depth of accumulated snow, the surface roughness (air-snow interface and snow-ice interface), the size of snow crystals (grain size), the density profile of the accumulated snow and snow characteristics of the layers forming the surface snowpack (thickness, shape of the interface between layers variation between dielectric layers, among others). After that, reversal statistical modelling of backscatter was performed to estimate stratigraphic parameters of the snowpack usingdata allowing the local stratigraphy of estimated variables SAR backscatter data from COSMO-SkyMed satellite. To validate the proposed model, the same input data were considered for all experiments performed experiments. These data were made up of snow stratigraphic data and snow temperature data in a 2 m depth glaciological profiles (snowpits) 2m depth and data SAR-X COSMO-SkyMed X-band SAR data (acquisition mode Stripmap / Himage with 3x3 m spatial resolution 3x3 m) acquired atin Union Glacier snowpits and remote sensing SAR data during summer 2011-2012. The results showed average density of the snow pack surface from SAR-X backscatter data SAR-X with R² ≥ 86%. The main contribution of this work is the resulting model for SAR-X backscattering for dry snow masses, which was proved to be statistically consistent with the ground truth data. Even with limited reference data, this result indicates the soundness of the proposed approach, allowing the estimation of spatial distribution ofvariations in stratigraphic parameters of the snowpack variables in dry snow areas from SAR X-band SAR data over the X band. However, snowpack parameters estimated by the method should be used carefully, as the input data used for model development may underestimate all possible variations found at the snow surface of Union Glacier.

Le suivi de l’évolution du manteau neigeux est directement lié à des enjeux socio-économiques majeurs en zone de montagne. Parmi ces enjeux figure la prévision du risque d’avalanche qui s’appuie principalement sur des observations et sur la connaissance de l’état du manteau neigeux et de son évolution dans le temps. Dans cette thèse, co-financée par le CNES et par Météo- France, nous avons évalué l’apport d’observations de télédétection spatiale active micro-ondes issues du radar à synthèse d’ouverture (SAR) de Sentinel-1, pour suivre l’évolution de certaines propriétés du manteau neigeux. Dans un premier temps, nous avons évalué la chaîne de modélisation SAFRAN-ISBA/Crocus-MEMLS par rapport aux données Sentinel-1 pré-traitées sur 3 saisons hivernales de 2014 à 2017, sur une zone de 2310 km2 à 20 m de résolution dans les Alpes du Nord françaises. Nous avons montré que les données SAR étaient pertinentes pour suivre l’évolution du manteau neigeux et, avons démontré la capacité de la chaîne de modélisation à reproduire les variations du signal observé dans le temps malgré de forts biais négatifs en cas de neige humide. Nous nous sommes intéressés à la valeur ajoutée des observations SAR de Sentinel-1 pour cartographier la neige humide, c’est-à-dire, la neige avec un taux élevé d’eau liquide. Des comparaisons ont été effectuées entre les produits neige humide obtenus par Sentinel-1 et les produits neige de Sentinel-2 distribués par Theia. Cette étude a été menée sur la saison hivernale 2017-2018, qui a connu un enneigement exceptionnel. Ces travaux ouvrent la voie à l’assimilation de données de télédétection SAR dans le modèle de neige Crocus ainsi qu’à une plus grande exploitation de ces données dans le cadre du suivi de l’enneigement pour de multiples applications. / Monitoring snowpack properties in moutainous areas is directly related to major socio-economic issues. Among these issues, avalanche prediction works through a range of tools based on meteorological and snow observations and modeling. In this thesis, co-funded by the CNES and Météo-France, we evaluated the contribution of Sentinel-1 synthetic aperture radar (SAR) remote sensing observations to study the snowpack properties and the quality of the simulations for assimilation in a snowpack model. As a first step, we evaluated the SAFRAN-ISBA/Crocus- MEMLS modeling chain against pre-processed Sentinel-1 data for 3 winter seasons from 2014 to 2017 over an area of 2310 km2 in the Northern French Alps. We have shown that SAR data are relevant for monitoring snowpack evolution and demonstrated the ability of the modeling chain to reproduce observed signal variations despite strong negative bias in wet snow conditions. We focused on wet snow products derived from Sentinel-1 SAR observations in synergy with snow absence/presence products derived from visible Sentinel-2 observations. This study was conducted on the winter season 2017-2018, which was remarkable for its snow and avalanche conditions. Such combined products make it possible to follow the spatio-temporal variability of mountain wet snow and dry snow at high elevation. This work opens the way for the assimilation of SAR remote sensing data into the Crocus snowpack model as well as greater exploitation of this data in the context of avalanche snow monitoring and prediction for a variety of purposes.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20920号 / 理博第4372号 / 新制||理||1627(附属図書館) / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 松浦 純生, 教授 林 愛明, 准教授 松四 雄騎 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM