Atmospheric particulate matter (PM) is linked to various adverse environmental and health impacts. PM in the atmosphere reduces visibility, alters precipitation patterns by acting as cloud condensation nuclei (CCN), and changes the Earth’s radiative balance by absorbing or scattering solar radiation in the atmosphere. The long-range transport of pollutants leads to increase in PM concentrations even in remote locations such as polar regions and mountain ranges. One significant effect of PM on the earth’s climate occurs while light absorbing PM, such as Black Carbon (BC), deposits over snow. In the Arctic, BC deposition on highly reflective surfaces (e.g. glaciers and sea ices) has very intense effects, causing snow to melt more quickly. Thus, characterizing PM sources, identifying long-range transport pathways, and quantifying the climate impacts of PM are crucial in order to inform emission abatement policies for reducing both health and environmental impacts of PM. Chemical transport models provide mathematical tools for better understanding atmospheric system including chemical and particle transport, pollution diffusion, and deposition. The technological and computational advances in the past decades allow higher resolution air quality and weather forecast simulations with more accurate representations of physical and chemical mechanisms of the atmosphere. Due to the significant role of air pollutants on public health and environment, several countries and cities perform air quality forecasts for warning the population about the future air pollution events and taking local preventive measures such as traffic regulations to minimize the impacts of the forecasted episode. However, the costs associated with the complex air quality forecast models especially for simulations with higher resolution simulations make “forecasting” a challenge. This dissertation also focuses on applications, performance analysis, and optimization of meteorology and air quality modeling forecasting models. This dissertation presents several modeling studies with various scales to better understand transport of aerosols from different geographical sources and economic sectors (i.e. transportation, residential, industry, biomass burning, and power) and quantify their climate impacts. The simulations are evaluated using various observations including ground site measurements, field campaigns, and satellite data. The sector-based modeling studies elucidated the importance of various economical sector and geographical regions on global air quality and the climatic impacts associated with BC. This dissertation provides the policy makers with some implications to inform emission mitigation policies in order to target source sectors and regions with highest impacts. Furthermore, advances were made to better understand the impacts of light absorbing particles on climate and surface albedo. Finally, for improving the modeling speed, the performances of the models are analyzed, and optimizations were proposed for improving the computational efficiencies of the models. Theses optimizations show a significant improvement in the performance of Weather Research and Forecasting (WRF) and WRF-Chem models. The modified codes were validated and incorporated back into the WRF source code to benefit all WRF users. Although weather and air quality models are shown to be an excellent means for forecasting applications both for local and hemispheric scale, further studies are needed to optimize the models and improve the performance of the simulations.

Rain-on-snow (ROS) occurs when warm, wet air moves into latitudes and/or elevations having vulnerable snowpacks, where it can alter water inputs to infiltration, runoff and erosion. The Pacific Northwest is particularly susceptible: winter storms off the Pacific cause locally heavy rain plus snowmelt almost annually, and disastrous flooding and landsliding intermittently. In maritime mountainous terrain, the effects seem more likely and hydrologically important where warm rains and seasonal snowpacks are liable to coincide, in middle elevations. Several questions arise: (1) In the PNW, does ROS affect the long-term frequency and magnitude of water delivery to the ground, versus total precipitation (liquid and solid), during big storms? Where and how much? (2) If so, can we determine which elevations experience maximum hydrologic effects, the peak ROS zone? Probabilistic characteristics of ROS are difficult to establish because of geographic variability and sporadic occurrence: scattered stations and short observational records make quantitative frequency analysis difficult. These problems dictate a modeling approach, combining semi-random selection of storm properties with physical rules governing snow and water behavior during events. I created a simple computer program to perform Monte Carlo simulation of large storms over 1000 "years", generating realizations of snowpack and storm-weather conditions; in each event precipitation falls, snow accumulates and/or melts, and water moves to the ground. Frequency distributions are based on data from the Washington Cascades, and the model can be applied to specific sites or generalized elevations. Many of the data sets were based on observations at Stampede Pass, where high-quality measurements of weather and snow at the Cascade crest have been made since the 1940s. These data were used to inform the model, and to test its reliability with respect to the governing data distributions. In addition, data from ROS events at Stampede, and at research sites in southwest Oregon, were used to confirm that the model's deterministic calculations of snow accumulation, snowmelt, and percolation (yielding water available for runoff) adequately simulate conditions observed in the field. The Monte Carlo model was run for elevations ranging from 200 to 1500 m, each over a hypothetical millennium. Results indicate that the presence of snow in some storms reduces the amount of water reaching the ground. This occurred more often in highlands but also at middle and lower elevations, affecting the long-term frequency-magnitude relations across the landscape. In these conditions, the rain-gauges overestimate the amount of liquid water actually reaching the ground. For many storms, however, ROS enhances water reaching the ground, most significantly at elevations between ~500-1100 m. At lower and higher elevations, the water available for runoff exceeds precipitation in ~2% of events, but this proportion rises to ~20-30% at ~800 m. Other metrics (e.g., series statistics, exponential regression coefficients, frequency-magnitude factors) also indicate that this middle-elevation band (around ~800 m) experiences ROS most often and with greatest water available for runoff. Of the west-central Washington Cascades study region, about one-third to one-half the landscape is susceptible to significant ROS influence. These results indicate areas where ROS currently has the greatest hydrologic consequence on ecosystems and human works, and possibly the greatest sensitivity to changes in land-use and climate.

We used demographic methods to address one of the main challenges facing ecological science: forecasting the effect of climate change on plant communities. Ecological forecasts will be crucial to inform long-term planning in wildland management and demographic methods are ideal to quantify changes in plant abundance. We carried out our research in the sagebrush steppe, one of the most extensive plant ecosystems of Western North America. Our research intended to inform ecological forecasts on an exotic invader, cheatgrass (Bromus tectorum). Moreover, we investigated the general question asking: to what degree competition among plants influences the outcome of ecological forecasts on the effect of climate change? We carried out two field experiments to test the hypothesis that warming will increase cheatgrass abundance in the sagebrush steppe. This hypothesis was strongly supported by both experiments. Warming increased cheatgrass abundance regardless of elevation, neighboring vegetation or cheatgrass genotype. Moreover, we found cheatgrass was hindered by snow cover. Therefore, warming increases cheatgrass growth directly by increasing temperature, and indirectly by decreasing or removing snow cover. In our last experiment, we tested whether forecasts of climate change effects on rare species can ignore competition from neighbors. This should occur because rare species should have little niche overlap with other species. The lower the niche overlap, the less competition with other species. To test this hypothesis, we used a long-term data set from an Idaho sagebrush steppe. We built population models that reproduced the dynamics of the system by simulating climate and competition. Model simulations supported our hypothesis: rare species have little niche overlap and little competitive interactions with neighbor species.

Regimes of snow depth, soil temperature, soil matric potential and quasi-friction velocity in a windward site and a lee shelter were examined. The differences were analyzed from a biological perspective to .characterize each location in terms of site favorability to plant growth. The chronology of wind and precipitation events was investigated. Snow depth was measured with a system of stakes arranged around and in the interior of a rectangular plot encompassing both a windward site and a lee shelter. Soil temperature, soil matric potential and water potential were measured along a transect which originated in the windward site and terminated in the lee shelter. Soil temperature and water potential were measured by thermocouple psychrometer. Mattie potentials was determined by the pressure-plate method. The regimes of quasi-friction velocity at both ends of the transect were determined by the logarithmic profile method, invoking similarity theory. Wind speed and temperature were measured at two heights in each site. A computer program was used to search the wind and precipitation records and ·categorize and sun the precipitation events by wind direction. The lee shelter exhibited tendencies toward theoretical optima of site favorability. The horizontal distribution of snow maxima was found. to be a function of wind direction at the time of each precipitation event as well as the interaction of wind and the topographical features. Snow was observed to accumulate to a greater depth in the lee shelter than in the windward site. Mean soil temperature over the study period was 8.5° C in the lee shelter while the windward site was 8.0° C. Soil temperature in the lee shelter was never observed to go below 0° C under a snowpack. The range of soil matric potential in the lee shelter was found to be about 14 atm at a depth of 20 cm and about 17 atm at a depth of 50 cm over the summer season. In the windward site the range of soil matric potential was approximately 30 atm at a depth of 20 cm and about 21 atm at a the 50 cm depth over the same period. The lee shelter exhibited lower (less negative) matric potentials than the windward site. These results were not corroborated by the measurement of water potential by thermocouple psychrometers. In the layer from 1.5 to 4.1 m, the mean quasi-friction velocity in the lee shelter was 39 cm s-1, favoring snow deposition there over the windward site where the mean friction velocity was 21 cm s-l. In the 0 m to 1. 5 m layer, mean friction velocity in the windward site was found to be 55 cm s-1.while the lee shelter mean was 48 cm s-1. These results indicate a distinct seperation of flow downwind of the windward site where the lee shelter resides in the turbulent wake of the windward site.

Le modèle de neige est souvent dépendant du modèle hydrologique avec lequel il est couplé, ce qui peut favoriser la représentation du débit au détriment de celle de la neige. L'objectif est de rendre le calage du modèle de neige plus indépendant de celui du modèle hydrologique en restant facilement utilisable en opérationnel. Dans cette optique, un modèle contraint sur des données d'observations de la neige permettrait d'améliorer d'une part la robustesse des paramètres du modèle de neige et d'autre part la simulation de l'état du manteau neigeux. Dans la première partie de cette thèse, nous avons étudié et modifié le modèle degrés-jour semi-distribué CemaNeige afin qu'il puisse simuler de manière plus réaliste la variable de surface d'enneigement du bassin versant. Cette modification, couplée au calage du modèle sur des données de surface enneigée et sur le débit, a permis d'améliorer la simulation de l'enneigement par le modèle sans détériorer significativement les performances en débits. Nous alors ensuite débuté le développement d'un nouveau modèle de neige à l'échelle ponctuelle. Celui-ci se compose d'un modèle de rayonnements, simulant les rayonnements incidents à partir de données d'amplitude de températures journalières, et d'un modèle de manteau neigeux. Le modèle de manteau neigeux résout les équations de la chaleur au sein du manteau neigeux à l'aide d'une représentation spectrale du profil de température. Cette représentation permet de simuler les profils et gradients de températures en utilisant moins de variables d'état qu'une discrétisation verticale par couches. Pour mieux prendre en compte les mesures ponctuelles de neige, ce modèle devra être distribué. / Snow models are often dependent on the hydrological model they are coupled with, which can promote higher performance on runoff simulation at the expense of snow state simulations performances. The objective of this thesis is to make the calibration of the snow model more independent from the calibration of the hydrological model, while remaining easily usable for runoff forecasting. Calibrating snow model on observed snow data would on one hand improve the robustness of the snow model parameters and on the other hand improve the snowpack modelling. In the first part of this manuscript, we modified the semi-distributed CemaNeige degree-day model so that it can explicitly simulate the watershed snow cover area. This modification coupled with the calibration of the model on snow cover area data and on river runoff data significantly improved the simulation of the snow cover area by the model without significantly deteriorating the runoff performances. Then we started the development of a new point scale snow model. It is based on a radiation model, which simulates incoming radiations from daily temperature range data, and a snowpack model. The snowpack model solves the heat equations within the snowpack by using a spectral representation of the temperature profile. This representation simulates the temperature profile and gradients using fewer state variables than a vertical discretization of the snowpack. In order to be able to use point scale snow observations in the model, it should be distributed on the watershed.

No description available.

<p>Sweden experiences heavy snowfall during the winter season and cost effective road maintenance is significantly affected by the routing of snowplows. The routing problem becomes more complex as the SwedishNational Road Administration (Vägverket) sets operational requirements such as satisfying a time window for each road segment. </p><p>This thesis focuses on route optimization for snowplows after snowfall; to develop and implement an algorithm for finding combinations of generated routes which minimize the total cost. The results are compared to those stated in the licentiate thesis by Doctoral student Nima Golbaharan (2001). </p><p>The algorithm calculates a lower bound to the problem using a Lagrangian master problem. A common subgradient approach is used to find near-optimal dual variables to be sent to a column-generation program which returns routes for the snowplows. A greedy heuristic chooses a feasible solution, which gives an upper bound to the problem. This entire process is repeated as needed. </p><p>This method for routing snowplows produces favorable results with a relatively small number of routes and are comparable to Golbaharan's results. An interesting observation involves the allocation of vehicles in which certain depots were regularly over- or under-utilized. This suggests that the quantity and/or distribution of available vehicles may not be optimal.</p>

Uncertainties were analyzed in three areas (remote sensing, dendroclimatology, and climate modeling) relevant to current water resources management. First, the research investigated the relationships between remotely sensed and in situ Snow Water Equivalent (SWE) datasets in three western U.S. basins. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Principal Components Analysis (PCA) revealed two distinct snow regions among the datasets and Singular Value Decomposition (SVD) was used to link both data products with regional streamflow. Remotely sensed SWE was found to be sufficient to use in statistically based forecast models in which magnitude did not affect results. Second, the research investigated the dendroclimatic potential of a critical flood control and hydropower region in the southeastern U.S. (Tennessee Valley) using climate division precipitation and regional tree-ring chronology datasets. Tennessee Valley May–July precipitation was reconstructed from 1692 to 1980 (289 years) using a stepwise linear regression model (R2 = 0.56). Weibull analysis illustrated that the Tennessee Valley reconstruction model developed generally underestimated extreme precipitation and overestimated average precipitation. The longest May–July drought occurred over 10 consecutive years (1827–1836). Instrumental records indicated that the two most recent droughts (1985–1988 and 2006–2008) ranked second and third in severity in the past three centuries. Third, past, present, and future patterns and extremes in streamflow within the North Platte River Basin were investigated. A streamflow reconstruction dating back to 1383 using tree rings was created to provide a proxy for the long-term variability in the region. Projected streamflow datasets from the Community Climate System Model (CCSM) were gathered to acquire future insight of the hydroclimatic variability within the North Platte River Basin (NRPB). Drought analysis revealed that 2002–2008 was one of the driest periods in the past 600 years. Multiple CCSM projections suggest that in the future, drier (5th percentile) years will become wetter relative to 1970–1999 CCSM hindcasts. Future average (50th percentile) and wet (95th percentile) years may yield statistically higher streamflow compared to those seen in the historical (1383–1999) record, suggesting potential anthropogenic influence beyond the historic natural variability.

The ongoing climate changes are predicted to accelerate fast in arctic regions with increases in both temperatures and precipitation. Although the duration of snow cover is generally expected to decrease in the future, snow depth may paradoxically increase in those areas where a large amount of the elevated precipitation will fall as snow. The annual distribution and duration of snow are important features in arctic ecosystems, influencing plant traits and species interactions in various ways. In this thesis, I investigated the effect of snow on plants and their interactions with herbivores by experimentally increasing the snow cover by snow fences in three different habitats along an environmental gradient in Abisko, northern Sweden. I found that the snow cover mattered for plant quality as food for herbivores and herbivore performance. An enhanced and prolonged snow cover increased the level of insect herbivory on dwarf birch leaves under field conditions. Autumnal moth larvae feeding on leaves that had experienced increased snow-lie grew faster and pupated earlier than larvae fed with leaves from control plots. These findings indicated that plants from snow-rich plots produced higher-quality food for herbivores. My studies showed that differences in snow-lie explained parts of the within-year spatial and seasonal variation in plant chemistry and patterns of herbivory in this arctic landscape. The relationship between leaf nitrogen concentration and plant phenology was consistent between treatments and habitats, indicating that snow per se, via a delayed phenology, was controlling the nitrogen concentration. The relationship between leaf age and level of herbivory was positive in the beginning of the growing season, but negative in the end of the growing season, indicating an increasing importance of plant palatability and a decreasing importance of exposure time in determining the level of herbivory throughout the growing season. The concentrations of phenolics varied among habitats, treatments and sampling occasions, suggesting that these plants were able to retain a mosaic of secondary chemical quality despite altered snow conditions. Furthermore, the nutrient limiting plant growth, according to N:P ratio thresholds, appeared to shift from nitrogen to phosphorus along the topographic gradient from snow-poor ridges to more snow-rich heathlands and fens. Snow addition had, however, no significant effect on other nutrient concentrations than nitrogen and no significant effect on the leaf N:P ratio, indicating that differences in snow cover could not explain the variation in plant nutrient concentrations among habitats. In a five-year study, I found opposing inter-annual effects of increased snow on plant chemistry. In contrast to earlier results, the effect of snow-lie on plant nitrogen concentration was predominantly negative. However, the effect of increased snow cover on the level of herbivory remained predominantly positive. The strong within-year relationship between snow-melt date (via plant phenology) and plant nitrogen concentration and level of herbivory could not predict inter-annual variation in the effect of snow manipulation. I did not find any conclusive evidence for a single factor causing the inter-annual opposing effect of snow addition, but the results indicated that interactions with summer and winter temperatures might be important. In conclusion, this thesis showed that climate-induced changes in snow conditions will have strong effects on plant traits and plant-herbivore interactions. However, alterations in snow cover do not influence all plant traits and the effect may vary in time and space.

Sweden experiences heavy snowfall during the winter season and cost effective road maintenance is significantly affected by the routing of snowplows. The routing problem becomes more complex as the SwedishNational Road Administration (Vägverket) sets operational requirements such as satisfying a time window for each road segment. This thesis focuses on route optimization for snowplows after snowfall; to develop and implement an algorithm for finding combinations of generated routes which minimize the total cost. The results are compared to those stated in the licentiate thesis by Doctoral student Nima Golbaharan (2001). The algorithm calculates a lower bound to the problem using a Lagrangian master problem. A common subgradient approach is used to find near-optimal dual variables to be sent to a column-generation program which returns routes for the snowplows. A greedy heuristic chooses a feasible solution, which gives an upper bound to the problem. This entire process is repeated as needed. This method for routing snowplows produces favorable results with a relatively small number of routes and are comparable to Golbaharan's results. An interesting observation involves the allocation of vehicles in which certain depots were regularly over- or under-utilized. This suggests that the quantity and/or distribution of available vehicles may not be optimal.