Atmospheric nitrous acid (HONO) is a significant and sometimes dominant OH source at polar region. An improved method of detecting HONO is developed using photo-fragmentation and laser-induced fluorescence (LIF). The detection limit of this method is 2-3 pptv for ten-minute integration time with 35% uncertainty. The abundance of laser-induced fluorescence (LIF) HONO measurements during ANTCI (Antarctic troposphere chemistry investigation) 2003 exceeds the pure gas phase model predictions by a factor of 1.92±0.67, which implies snow emission of HONO. A 1D air-snowpack model of HONO was developed and constrained by observed chemistry and meteology data. The 1D model includes pure gas phase chemical mechanisms, molecular diffusion and mechanical dispersion, windpumping in snow, gas phase to quasi-liquid layer phase HONO transfer and quasi-liquid layer nitrate photolysis. Based on the air-snowpack model, snow emission of HONO is highly likely and will be transported to place of the measurements. The pH, thickness of quasi liquid layer and contineous nitrite measurement are key factors to calibrate and validate the air snowpack model.

Une modélisation précise du manteau neigeux saisonnier est indispensable pour comprendre son évolution et améliorer la prévention d risque d’avalanche. Le Centre d’Études de la Neige (CEN) développe depuis plus de 20 ans un modèle de manteau neigeux nommé Crocus dédié à simuler son évolution et ses propriétés physiques uniquement à partir de variables météorologiques de surface. Les erreurs du modèle et l’imprécision des forçages météorologiques sont des sources inévitables d’incertitudes dans les prévisions de Crocus.Contraindre le modèle avec des observations peut être un moyen de minimiser l’impact de ces incertitudes dans les simulations. En raison de la faible densité des réseaux de mesures in situ et de la forte variabilité spatiale du manteau neigeux, il est vraisemblable que seule l’imagerie satellitaire puisse permettre une contrainte efficace du modèle. Le spectroradiomètre MODIS,fournissant quotidiennement des informations sur la surface terrestre à une résolution de 250m, est bien adapté pour l’observation du couvert nival. Ce capteur opère dans les domaines du visible et de l’infrarouge où les réflectances mesurées (rapport du flux solaire réfléchi surincident selon les longueurs d’onde) sont sensibles à certaines propriétés du manteau neigeux.Le nouveau schéma radiatif TARTES de Crocus est en mesure de simuler l’évolution de ces réflectances, ce qui ouvre la voie à l’assimilation des réflectances MODIS.L’objectif de la thèse est d’explorer l’assimilation des réflectances MODIS dans le modèle de manteau neigeux Crocus, dans une perspective opérationnelle à moyen terme. Ce projet s’appuie fortement sur l’expertise en modélisation physique et radiative du manteau neigeux et en assimilation de données présente au Centre d’Études de la Neige et au Laboratoire de Glaciologie et Géophysique de l’Environnement à Grenoble.Le projet s’est déroulé en deux étapes pour répondre aux questions suivantes :Les réflectances optiques satellitaires possèdent-elles un contenu informatif capable de contraindre efficacement le modèle Crocus ?Quels sont les obstacles à surmonter pour parvenir à l’assimilation effective des réflectances optiques mesurées par satellites ?Un filtre particulaire est utilisé comme méthode d’assimilation pour évaluer l’apport des réflectances sur les estimations du manteau neigeux en termes de hauteur de neige et son équivalent en eau liquide. Le choix de ce filtre, permis par la petite dimension du problème,est conforté par sa facilité d’implémentation au vu des contraintes fortes du modèle Crocus.Les expériences conduites dans cette étude sont réalisées au niveau du Col du Lautaret et du Col de Porte (Alpes françaises). Des expériences d’assimilation d’observations virtuelles démontrent le potentiel des réflectances spectrales pour guider Crocus dans ses estimations du manteau neigeux. L’erreur quadratique moyenne (RMSE) des variables intégrées de la hauteur de neige et de son équivalent en eau est réduite de près de moitié par l’assimilation des observations. L’efficacité de l’assimilation est cependant fortement dépendante de la distribution temporelle des observations.Des expériences d’assimilation de réflectances réelles mettent en évidence une grande sensibilité des résultats de l’assimilation à la qualité des observations. La conversion et le traitement des données MODIS au sommet de l’atmosphère (TOA) en réflectances de surface sont la cause de fortes incertitudes dans ces données. Les biais occasionnés et une mauvaise caractérisation de ces erreurs détériorent les estimations du manteau neigeux. Le contrôle qualité et la sélection des données satellitaires sont à ce titre une priorité dans la perspective d’assimilation des données satellitaires.Ce travail démontre ainsi le potentiel des données spatiales pour le suivi et la prévision du manteau neigeux, potentiel qu’il conviendra d’exploiter dans un futur proche. / An accurate seasonal snowpack modeling is needed to study its evolution and to improvethe avalanche hazard forecast. For 20 years, the snow study center (CEN) has developed asnowpack model named Crocus to simulate the snow cover and its physical properties drivenby near-surface meteorological conditions. Model and meteorological forcing errors are themain uncertainties in the Crocus forecasts. Constraining the model with observations canminimize the impacts of these uncertainties on simulations. Because of the low density ofground-based measurement networks combined to the high spatial variability of the snowcover, satellite observations should be the best way to constrain the model. The MODISspectroradiometer which provides daily surface information at 250 m spatial resolution isappropriated to study the snow cover. The visible and near-infrared reflectances (definedas the fraction of incident solar flux that is reflected by the surface) measured by MODISare strongly sensitive to physical properties of the snowpack. The radiative transfer modelTARTES, recently implemented into Crocus, calculates the same spectral reflectances and so,opens routes to data assimilation of MODIS reflectances.The aim of this thesis is to investigates the assimilation of the MODIS reflectances into thesnowpack model Crocus in an operational perspective. This work benefits from the expertisein physical and radiative snowpack modeling as well as data assimilation from two laboratoriesof Grenoble, the snow study center and the Laboratory of Glaciology and Geophysics of theEnvironment.The project took place in two steps to answer the following questions:Do MODIS reflectances offer an informative content allowing an efficient constraint ofthe Crocus snowpack model?What are the challenges associated to the assimilation of remotely-based optical reflectances?A particle filter is used as data assimilation scheme to evaluate the ability of opticalreflectance data assimilation to improve snow depth and snow water equivalent simulations.The choice of this filter, allowed by the small size of the problem, is based on its ease ofimplementation regarding the severe constraints of the Crocus model. The experiments wereconducted at the Col du Lautaret and the Col de Porte in the French Alps.The assimilation of synthetic observations demonstrates the potential of spectral reflectancesto constraint the Crocus snowpack model simulations. The root-mean square errors(RMSE) of bulk variables like snow depth and snow water equivalent are reduced by a factorof roughly 2 after assimilation. However, the performance of assimilation is highly dependenton the temporal distribution of the observations.The assimilation of real reflectances shows a high sensitivity to the quality of the assimilatedobservations. Converting MODIS top of atmosphere reflectances into surface reflectancesintroduces uncertainties in these data. Resulting biases and a poor characterization of errorsdeteriorate the estimation of the snowpack. Screening methods prior assimilation are thereforea priority in the prospect of satellite data assimilation.This work demonstrates the potential of remotely-based data assimilation to monitor and forecast the snow cover, potential which should be used in the near future.

Le suivi et la représentation de la variabilité du manteau neigeux en montagne sont des enjeux écologiques et sociétaux majeurs. Le récent développement de modèles météorologiques à échelle kilométrique offre un potentiel nouveau pour améliorer les simulations d'enneigement en montagne. Dans cette thèse, nous avons évalué l'apport des prévisions météorologiques du modèle de prévision numérique du temps AROME à 2.5 km de résolution horizontale pour alimenter le modèle détaillé de manteau neigeux Crocus. Les simulations AROME-Crocus distribuées ont d'abord été évaluées sur les Pyrénées de 2010 à 2014, montrant un apport en termes de représentation de la variabilité spatio-temporelle du manteau neigeux par rapport à l'approche par massif du système opérationnel actuel SAFRAN-Crocus, malgré une surestimation des hauteurs de neige. Par la suite, la valeur ajoutée de produits satellitaires de rayonnements incidents a été étudiée pour des simulations d'enneigement dans les massifs alpins et pyrénéens, soulignant leur bonne qualité en montagne mais un impact mitigé sur le couvert neigeux simulé. Enfin, on a montré comment le schéma de microphysique nuageuse d'AROME associé à Crocus permet de mieux prévoir la formation de glace en surface du manteau neigeux par précipitations verglaçantes dans les Pyrénées. Ces travaux ouvrent la voie à une prévision nivologique distribuée à haute résolution en montagne. / Monitoring and representing the snowpack variability in mountains are crucial ecological and societal issues. The recent development of meteorological models at kilometric scale offers a new potential to improve snowpack simulations in mountains. In this thesis, we assessed the potential of forecasts from the numerical weather prediction model AROME at 2.5 km horizontal resolution to drive the detailed snowpack model Crocus. AROME-Crocus distributed simulations were first evaluated over the Pyrenees from 2010 to 2014. They showed benefits in representing the snowpack spatio-temporal variability as compared to the massif-based approach of the current operational system SAFRAN-Crocus, despite an overestimation of snow depth. Then, we studied the potential added value of satellite-derived products of incoming radiations for simulating the snow cover in the French Alps and Pyrenees. These products were found of good quality in mountains but their impact on the simulated snow cover is questionable. Finally, we showed how the cloud microphysics scheme of AROME associated with Crocus enables to better predict ice formation on top of the snowpack due to freezing precipitation in the Pyrenees. These works pave the way for high-resolution distributed snowpack forecasting in mountains.

The objective of this study is to assess the uncultured bacterial diversity in the snowpack of the Mont Blanc (MtBl) glacier containing Saharan dust deposited during four dust events during the period 2006 – 2009 by means of molecular phylogenetics. The final goal is to discover the bacteria that could be involved in the establishment of snow microbiota. Bacterial diversity was evaluated using rybotyping and subsequent sequencing of partial (V3-V5) and full-length 16S rRNA genes. For comparison purpose we also studied following samples: “clean” MtBl snow containing no Saharan dust; Saharan sand collected in Tunisia; Saharan dust collected in Grenoble (200 m a.s.l.) and recovered later on MtBl (4250 m a.s.l.). In order to verify possible microbial activity in situ, both rDNA and rRNA approaches were implemented for the “clean” snow sample. To evaluate the survival/colonization abilities of bacterial phylotypes recovered in snow samples with Saharan dust, we analyzed their closest strain physiology as well as sources of environmental clones using a threshold of ≥98% sequence similarity. For the result interpretation, we also used data on dust elemental composition and dust particles size distribution. As a result 8 clone libraries (including rRNA-based one) were constructed using V3-V5 16S rRNA gene sequences for 5 snow samples (4 with Saharan dust and one “clean”), sample of Saharan dust collected in Grenoble and Saharan sand sample. Furthermore, 4 clone libraries were generated using full-length 16S rRNA gene amplicons obtained from 4 of the above snow samples (three with Saharan dust and one ‘clean'). Species content and dominant phylotypes and their assigning to major divisions varied significantly in alpine snow on a Mont Blanc glacier associated with four depositions of Saharan dust over a 3-year. Dominant phylotypes revealed are belonged to Actinobacteria, Proteobactreia, Firmicutes, Deinococcus-Thermus, Bacteroidetes and Cyanobacteria. Such variability was detected by both partial and full-length 16S rRNA gene sequencing and seems to be caused more by conditions of dust transport than bacterial load from the original dust source. Also the preservation period of dust in snowpack could affect the species composition. Thirteen icy phylotypes as candidates into snow microbiota establishing were recognized in snow containing Saharan dust and only two in “clean” snow sample. Of them, both dominant and minor phylotypes of Cyanobacteria, Proteobacteria, Actinobacteria и Firmicutes were revealed. Data on the closest strain physiology of recognized icy phylotypes suggests that representatives of genera Massilia (Betaproteobacteria), Tumebacillus (Firmicutes), Phormidium and Stigonema (both Cyanobacteria) are most relevant findings in terms of propagation in snow. By analyzing 16S rRNA from the “clean” snow containing no Saharan dust and comparing the data with those obtained for 16S rDNA library, it has been shown that Stigonema-like cyanobacterium identified could be propagating in snow at subzero temperature. Among all identified phylotypes, 10% were categorized as HA-phylotypes based on their con-specificity (≥98% similarity) with normal (non-pathogenic) human microbiome representatives. Furthermore, 11% out of all phylotypes showed less than 90% similarity with known taxa, thus, presenting novel taxa. Sequencing of both partial (V3-V5) and full-length 16S rRNA genes permitted to describe microbial diversity more fully and get more detailed picture. / The objective of this study is to assess the uncultured bacterial diversity in the snowpack of the Mont Blanc (MtBl) glacier containing Saharan dust deposited during four dust events during the period 2006 – 2009 by means of molecular phylogenetics. The final goal is to discover the bacteria that could be involved in the establishment of snow microbiota. Bacterial diversity was evaluated using rybotyping and subsequent sequencing of partial (V3-V5) and full-length 16S rRNA genes. For comparison purpose we also studied following samples: “clean” MtBl snow containing no Saharan dust; Saharan sand collected in Tunisia; Saharan dust collected in Grenoble (200 m a.s.l.) and recovered later on MtBl (4250 m a.s.l.). In order to verify possible microbial activity in situ, both rDNA and rRNA approaches were implemented for the “clean” snow sample. To evaluate the survival/colonization abilities of bacterial phylotypes recovered in snow samples with Saharan dust, we analyzed their closest strain physiology as well as sources of environmental clones using a threshold of ≥98% sequence similarity. For the result interpretation, we also used data on dust elemental composition and dust particles size distribution. As a result 8 clone libraries (including rRNA-based one) were constructed using V3-V5 16S rRNA gene sequences for 5 snow samples (4 with Saharan dust and one “clean”), sample of Saharan dust collected in Grenoble and Saharan sand sample. Furthermore, 4 clone libraries were generated using full-length 16S rRNA gene amplicons obtained from 4 of the above snow samples (three with Saharan dust and one ‘clean'). Species content and dominant phylotypes and their assigning to major divisions varied significantly in alpine snow on a Mont Blanc glacier associated with four depositions of Saharan dust over a 3-year. Dominant phylotypes revealed are belonged to Actinobacteria, Proteobactreia, Firmicutes, Deinococcus-Thermus, Bacteroidetes and Cyanobacteria. Such variability was detected by both partial and full-length 16S rRNA gene sequencing and seems to be caused more by conditions of dust transport than bacterial load from the original dust source. Also the preservation period of dust in snowpack could affect the species composition. Thirteen icy phylotypes as candidates into snow microbiota establishing were recognized in snow containing Saharan dust and only two in “clean” snow sample. Of them, both dominant and minor phylotypes of Cyanobacteria, Proteobacteria, Actinobacteria и Firmicutes were revealed. Data on the closest strain physiology of recognized icy phylotypes suggests that representatives of genera Massilia (Betaproteobacteria), Tumebacillus (Firmicutes), Phormidium and Stigonema (both Cyanobacteria) are most relevant findings in terms of propagation in snow. By analyzing 16S rRNA from the “clean” snow containing no Saharan dust and comparing the data with those obtained for 16S rDNA library, it has been shown that Stigonema-like cyanobacterium identified could be propagating in snow at subzero temperature. Among all identified phylotypes, 10% were categorized as HA-phylotypes based on their con-specificity (≥98% similarity) with normal (non-pathogenic) human microbiome representatives. Furthermore, 11% out of all phylotypes showed less than 90% similarity with known taxa, thus, presenting novel taxa. Sequencing of both partial (V3-V5) and full-length 16S rRNA genes permitted to describe

La cryosphère désigne les milieux où l’eau est présente sous sa forme solide, comme la neige ou les glaciers. La sensibilité des composantes de la cryosphère aux fluctuations climatiques, notamment température et précipitation, permet de construire des indicateurs de première importance dans le suivi de la ressource en eau et de l’évolution du climat. Les structures sociétales sont directement impactées par les altérations de ces réservoirs naturels de montagne : irrigation, potentiel hydro-électrique, tourisme, patrimoine paysager. Dans le cadre du projet de thèse-CRYOPYR, un important travail méthodologique a été effectué pour estimer les variations de surface et de volume de neige et de glace à partir de l’imagerie optique très haute résolution. Le massif des Pyrénées (3 404 m) abrite les glaciers les plus méridionaux d’Europe, et présente un important manteau neigeux saisonnier. Nous avons pu estimer la hauteur de neige en fin de période d’accumulation dans le bassin versant de la centrale hydro-électrique de Bassiès (Ariège). Dans le cas du glacier d’Ossoue (Hautes-Pyrénées), nous avons pu cartographier les variations pluriannuelles d’altitude du glacier. Complétée par une recherche de données historiques, cette démarche a permis de reconstruire les fluctuations du glacier depuis la fin du petit âge glaciaire (1850), et de caractériser les variations du climat régional à haute altitude dans les Pyrénées. L’association de ces méthodes quantitatives et de ces sites d’études permet de fournir des éléments de réponses à la problématique hydro-climatique pyrénéenne. / The Pyrenees mountain range hosts the southernmost glaciers in Europe (south of 43 _N), and are covered by a large seasonal snowpack. Glacier and snowpack are both components of the cryosphere, the water in its frozen state, and present high sensitivity to climatic influences. In the Pyrenees, water availability from snowmelt is an important issue concerning hydropower generation, irrigation in lowlands and are potentially linked to conflict usage. Pyrenees ski resorts are highly vulnerable to a potential declining snowpack. Pyrenean glaciers are strongly out of balance with regional climate and are in jeopardy in this new century. Natural patrimony and the visual perception of the high mountain landscape could be irrevocably affected by this lost. Snow depth cartography may provide valuable information to manage human activities in link with snow presence. To date, there is no direct approach to map snow depth in mountainous areas from spaceborne sensors. Here, we examined the potential of very-high-resolution stereo satellites to map snow depth in a mountain catchment. The results showed a decimetric accuracy and precision in the Pléiades derived snow depths, and realistic snow patterns. We also validated Pléiades data to estimate the annual glacier mass balance of a Pyrenean glacier. Thanks to this new approach and a deep sounding of archives data, we reconstructed the evolution of the second largest glacier of the Pyrenees (Ossoue glacier, 42.46 _N, 0.45 km2). Ossoue glacier has retreated since the end of the little ice age (LIA) with some stable phases, and would probably disappear by the half of the 21th century. Based on a new complete inventory, we maped the outline of the Pyrenean glaciers at the end of the little ice age (1850 approximately) and in 2011. It appears clear that the Pyrenees mountain range is in its last stage of deglaciation. / El término criósfera designa al conjunto de lugares donde el agua está presente en forma sólida, como la nieve o los glaciares. La sensibilidad de los componentes de la criósfera a las fluctuaciones climáticas, en particular la temperatura y las precipitaciones, permite construir indicadores de primera magnitud en el seguimiento del recurso hídrico y de la evolución del clima. Las alteraciones de esos depósitos naturales de montaña afectan de forma directa a estructuras de tipo social, tales como la: irrigación, la energía hidroeléctrica, el turismo o el patrimonio paisajístico. En el marco del proyecto de tesis CRYOPYR, llevamos a cabo un importante trabajo metodológico a partir de imágenes ópticas por satélite de muy alta resolución con el fin de evaluar las variaciones de superficie y de volumen de las capas de nieve y de hielo. Los Pirineos (3 404 m) cobijan los glaciares más meridionales de Europa, así como un importante manto de nieve estacional. Pudimos determinar la altura de la capa de nieve al final del periodo de acumulación en la cuenca de la central hidroeléctrica de Bassiès (Ariège, Francia). En el caso del glaciar de Ossoue (Hautes-Pyrénées, Francia), cartografiamos las variaciones plurianuales de altitud del glaciar. Completamos este trabajo basado en adquisiciones de imágenes por satélite con una búsqueda de datos históricos. De este modo, reconstruimos las fluctuaciones del glaciar de Ossoue desde el final de la pequeña edad de hielo (1850), y caracterizamos las variaciones del clima regional a alta altitud en los Pirineos. La combinación de estos métodos cuantitativos con medidas en sitios representativos de los cambios en curso ofrece elementos de respuesta a la problemática hidroclimática pirenaica.

This dissertation presents the design, development and validation of new data integration methods for mapping the extent of snow cover based on open access ground station measurements, remote sensing images, volunteer observer snow reports, and cross country ski track recordings from location-enabled mobile devices. The first step of the data integration procedure includes data discovery, data retrieval, and data quality control of snow observations at ground stations. The WaterML R package developed in this work enables hydrologists to retrieve and analyze data from multiple organizations that are listed in the Consortium of Universities for the Advancement of Hydrologic Sciences Inc (CUAHSI) Water Data Center catalog directly within the R statistical software environment. Using the WaterML R package is demonstrated by running an energy balance snowpack model in R with data inputs from CUAHSI, and by automating uploads of real time sensor observations to CUAHSI HydroServer. The second step of the procedure requires efficient access to multi-temporal remote sensing snow images. The Snow Inspector web application developed in this research enables the users to retrieve a time series of fractional snow cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) for any point on Earth. The time series retrieval method is based on automated data extraction from tile images provided by a Web Map Tile Service (WMTS). The average required time for retrieving 100 days of data using this technique is 5.4 seconds, which is significantly faster than other methods that require the download of large satellite image files. The presented data extraction technique and space-time visualization user interface can be used as a model for working with other multi-temporal hydrologic or climate data WMTS services. The third, final step of the data integration procedure is generating continuous daily snow cover maps. A custom inverse distance weighting method has been developed to combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the MODIS snow cover product. The method is demonstrated by producing a continuous daily time step snow presence probability map dataset for the Czech Republic region. The ability of the presented methodology to reconstruct MODIS snow cover under cloud is validated by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. Using crowdsourcing data (volunteer snow reports and ski tracks) improves the map accuracy by 0.7 – 1.2 %. The output snow probability map data sets are published online using web applications and web services.

Throughout many of the world’s mountain ranges snowpack accumulates during the winter and into the spring, providing a natural reservoir for water. As this reservoir melts, it fills streams and recharges groundwater for over 1 billion people globally. Despite its importance to water resources, our understanding of the storage capacity of mountain snowpack is incomplete. This partial knowledge limits our abilities to assess the impact that projected climate conditions will have on mountain snowpack and water resources. While understanding the effect of projected climate on mountain snowpack is a global question, it can be best understood at the basin scale. It is at this level that decision makers and water resource managers base their decisions and require a clarified understanding of basin's mountain snowpack. The McKenzie River Basin located in the central-western Cascades of Oregon exhibits characteristics typical of many mountain river systems globally and in the Pacific Northwestern United States. Here snowmelt provides critical water supply for hydropower, agriculture, ecosystems, recreation, and municipalities. While there is a surplus of water in winter, the summer months see flows reach a minimum and the same groups have to compete for a limited supply. Throughout the Pacific Northwestern United States, current analyses and those of projected future climate change impacts show rising temperatures, diminished snowpacks, and declining summertime streamflow. The impacts of climate change on water resources presents new challenges and requires fresh approaches to understanding problems that are only beginning to be recognized. Climate change also presents challenges to decision makers who need new kinds of climate and water information, and will need the scientific research community to help provide improved means of knowledge transfer. This dissertation quantified the basin-wide distribution of snowpack across multiple decades in present and in projected climate conditions, describing a 56% decrease in mountain snowpack with regional projected temperature increases. These results were used to develop a probabilistic understanding of snowpack in projected climates. This section described a significant shift in statistical relations of snowpack. One that would be statistically likely to accumulate every 3 out of 4 years would accumulate in 1 out of 20 years. Finally this research identifies methods to improved knowledge transfer from the research community to water resource professionals. Implementation of these recommendations would enable a more effective means of dissemination to stakeholders and policy makers. While this research focused only on the McKenzie River Basin, it has regional applications. Processes affecting snowpack in the McKenzie River Basin are similar to those in many other maritime, forested Pacific Northwest watersheds. The framework of this research could also be applied to regions outside of the Pacific Northwestern United States to gain a similar level of understanding of climate impacts on mountain snowpack. / Graduation date: 2012

From the Proceedings of the 1973 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 4-5, 1973, Tucson, Arizona / A preliminary assessment and ranking of the relative significance of five processes that may contribute to snow removal from ponderosa pine forest canopies was made, including wind erosion of canopy snow, snowslide from the canopy, stemflow, vapor transport from melt water, and vapor transport of canopy snow. The first three represent delayed delivery rather than net water loss. A snow load index was obtained through use of time lapse photography of the study site canopy, while incoming solar radiation and atmospheric processes were monitored. The snow load index was expressed as a ratio of forest canopy area covered with snow to the total canopy area. Results obtained over a 4-day period following a six-hour snowstorm showed that snow removal by snowslide and wind erosion was of significant importance, while vapor transport of melt water and canopy snow, stemflow, and dripping of melt water was of comparatively minor importance.

Climate change poses significant threats to mountain ecosystems in North America (Barnett et al., 2005) and will subsequently impact water supply for human and ecosystem use. To assess these threats, we must have an understanding of the local variability in hydrometeorological conditions over the mountains. This thesis describes the continued development and application of a fine scale spatial hydrometeorological model, GENESYS (GENerate Earth SYstems Science input). The GENESYS model successfully simulated daily snowpack values for a 10 year trial period and annual runoff volumes for a thirty year period. Based on the results of these simulations the model was applied to estimate potential changes in snowpack over the St. Mary River watershed, Montana. GCM derived future climate scenarios were applied, representing a range of emissions controls and applied to perturb the 1961-90 climate record using the “delta” downscaling technique. The effects of these changes in climate were assessed for thirty year time slices centered on 2020s, 2050s, and 2080s. The GENESYS simulations of future climate showed that mountain snowpack was highly vulnerable to changes in temperature and to a lesser degree precipitation. A seasonal shift to an earlier onset of spring melt and an increase in the ratio of rain to snow occurred under all climate change scenarios. Results of mean and maximum snowpack were more variable and appeared to be highly dependent on scenario selection. The results demonstrated that although annual volume of available water from snowpack may increase, the seasonal distribution of available water may be significantly altered. / viii, 93 leaves ; 29 cm

A modelling approach focused on snow hydrology was developed and applied to project future changes in spring streamflow volumes in the St. Mary River headwaters basin, Montana. A spatially distributed, physically-based, hydrometeorological and snow mass balance model was refined and used to produce snow water equivalent (SWE) and rainfall surfaces for the study watershed. Snowmelt runoff (SR) and effective rainfall runoff (RR) volumes were compiled for the 1961-2004 historical period. A statistical regression model was developed linking spring streamflow volume (QS) at Babb, Montana to the SR and RR modelled data. The modelling results indicated that SR explained 70% of the variability in QS while RR explained another 9%. The model was applied to climate change scenarios representing the expected range of future change to produce annual QS for the period 2010-2099. Compared to the base period (1961-1990), average QS change ranged from -3% to -12% for the 2020s period. Percent changes increased to between -25% and -32% for the 2050s, and -38% and -55% for the 2080s. Decreases in QS also accompanied substantial advances in the onset of spring snowmelt. Whereas the spring pulse onset on average occurred on April 8 for the base period, it occurred 36 to 50 days earlier during the 2080s. The findings suggest that increasing precipitation will not compensate for the effects of increasing temperature in watershed SWE and associated spring runoff generation. There are implications for stakeholder interests related to ecosystems, the irrigation industry, and recreation. / xii, 136 leaves : ill. ; 28 cm. --