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ABSTRACT This Thesis investigates ionospheric plasma flows (commonly referred to as “convection”) at high latitudes with the objectives to assess seasonal and solar cycle variations in the shape of the flow patterns and the flow intensity in terms of external drivers of the flow, first of all the magnitude and orientation of the interplanetary magnetic field (IMF). Multi-year (2001-1011) line-of-sight Doppler velocity data collected by the Super Dual Auroral Network (SuperDARN) HF radars are considered. Two approaches are used: 1) analysis of monthly-averaged 2-dimentional patterns inferred from data of all SuperDARN radars operated and 2) analysis of near magnetic noon data from only two SuperDARN radars, Rankin Inlet and Inuvik monitoring meridional component of the flow in the near North Pole areas (polar cap). We show and discuss seasonal and solar cycle variations of three characteristics of the flows: magnetic latitudes of the region where plasma flow direction changes from toward the noon to away from the noon (convection reversal boundary), the magnetic local time location of the near noon region with stagnated flow (throat region) and, finally, the magnitude of the flow. All three parameters show trends, although not strong and consistent all the time, which agrees with previous publications where different analysis approaches and more limited data sets were used. For two specific points, one at the magnetic latitude of 72 degrees, representing the auroral oval latitudes (region where optical arcs occur most frequently) and the other one at 82 degrees, representing the polar cap latitudes we demonstrate that the average flow magnitude increases with the IMF intensity, and the effect is much stronger for the negative vertical component of the IMF Bz. In our second approach we demonstrate that the flow velocity increases almost linearly with an increase of the reconnection electric fields characterizing processes of interaction between the solar wind/IMF and the Earth`s magnetic dipole. Saturation effect is seen for strongest electric field. More clear seasonal effects are noticeable in these data; the velocity response to the reconnection electric field enhancement is stronger summer (winter) time for positive (negative) IMF Bz. The data are consistent with previous reports, where highly smoothed velocity data were considered.

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Concerns regarding the issue of climate change and, in particular, the rapid retreat of glaciers around the world, have placed great importance on glacial monitoring. Some of the methods most commonly used to observe glacial change--direct mass balance measurements and remote sensing--provide valuable information about glacier change. However, these methods do not address the englacial and subglacial environments. Surface meltwater that enters englacial and subglacial hydrological networks can contribute to acceleration of ice flow, increased calving on marine-terminating glaciers, surges or outburst floods, and greater overall ablation rates. Because subsurface drainage systems often freeze during the winter and re-form each summer, examining the seasonal evolution of these networks is crucial for assessing the impact that internal drainage may have on the behavior of a glacier each year. The goal of this study is to determine the role englacial and subglacial drainage system evolution plays in influencing summer ablation and discharge at the terminus of Exit Glacier, a small valley glacier located in South-central Alaska. During the summers of 2010 and 2011, we used ground-penetrating radar (GPR) to locate internal drainage features on the lower 100 meters of the glacier. GPR surveys were conducted in June and August of each year in an effort to observe the evolution of the drainage systems over the course of an ablation season. Three antenna frequencies--250, 500, and 800 MHz--were used on a dual frequency GPR so that various resolutions and depths in the ice could be viewed simultaneously. Stream monitoring was conducted to document discharge in the proglacial stream throughout the 2011 season. These data were compared with weather records to differentiate noticeable meltwater releases from precipitation events. Additionally, morphological changes in the glacier were observed through photographic documentation. Throughout the observation period, significant subglacial tunnels appeared, followed by the collapse of terminal ice above the tunnels. This phenomenon was most noticeable in 2011. These observations indicate that the internal drainage systems near the terminus of Exit Glacier became very well-developed each summer, and contributed approximately 75 meters of ice loss between June, 2010 and August, 2011.

The monitoring of continental and global scale net primary production remains a major focus of satellite-based remote sensing. Potential benefits which follow are diverse and include contributions to, and improved scientific understanding of, ecological systems, rangeland management, famine warning, agricultural commodity trading, and the study of global climate change.A NOAA-AVHRR data set containing monthly observations of green vegetation cover over a ten year period was acquired and analysed, to extract information on seasonal conditions. The data were supplied as a vegetation index, commonly known as the Normalised Difference Vegetation Index (NDVI), with a spatial resolution of approximately five km. The data set was acquired from three different satellites, and calibration problems were known to exist. A new technique was developed to estimate, and subsequently remove, the calibration bias present in the data.Monthly rainfall measurements were used as surrogate ground truth to validate the NDVI data. For regions of native vegetation, linear models relating NDVI to previous rainfall were derived, using transfer function techniques in common use in systems engineering. The models demonstrate that, in mid-latitude regions, the NDVI is a linear function of rainfall recorded over the preceding seven or eight months.Annual summaries of the image data were developed to highlight the amount and timing of plant growth. Three fundamental questions were posed as an aid to the development of the summary technique: where, when and how much? These summaries highlight the extraordinary spatial and temporal variations in plant growth, and hence rainfall, over much of Western Australia each year.Standard analysis techniques used in time series analysis, such as classical decomposition, were successfully applied to the analysis of NDVI time series. These techniques highlighted ++ / structural differences in the image data, due to land use, climatic factors and vegetation type.Overall, the results of the research undertaken in this study, using NOAA-AVHRR data in Western Australia, demonstrate that vegetation indices acquired from satellite platforms can be used to monitor continental scale seasonal conditions in an effective manner. As a consequence of these results, further research using this type of data is proposed in rangeland management and climate change modelling.

Approximately 60% of New Zealand's electricity is produced from hydro generation. The Waitaki River catchment is located in the centre of the South Island of New Zealand, and produces 35-40% of New Zealand's electricity. Low inflow years in 1992 and 2001 resulted in the threat of power blackouts, and a national demand for electricity that is currently growing at 2 to 5% a year gives strong justification for better management of the hydro resource. Improved seasonal rainfall and inflow forecasts will result in the better management of the water used in hydro generation on a seasonal basis. Seasonal rainfall forecasting has been the focus of much international research in recent years, but seasonal inflow forecasting is in its relative infancy. Researchers have stated that key directions for both fields are to decrease the spatial scale of forecast products, and to tailor forecast products to end user needs, so as to provide more relevant and targeted forecasts, which will hopefully decrease the enormous socio-economic costs of climate fluctuations. This study calibrated several season ahead lake inflow and rainfall forecast models for the Waitaki river catchment, using statistical techniques to quantify relationships between land-ocean-atmosphere state variables and seasonally lagged inflows and rainfall. Techniques included principal components analysis and multiple linear regression, with cross-validation techniques applied to estimate model error. Many of both the continuous and discrete format models calibrated in this study predict anomalously wet and dry seasons better than random chance, and better than the long term mean as a predictor. 95% confidence limits around most model predictions in this study offer significant skill when compared with the range of all probable inflows (based on the 80 year recording history in the catchment). Models predicting winter Lake Pukaki inflows are those with the strongest predictive relationships in this study. Spring and summer predictions were generally less skilful than those for winter and autumn. Inflows could be predicted with some skill in winter and summer, but not rainfall, and rainfall could be predicted with some skill in autumn and spring, but not inflows. Models predicting inflows and rainfall for different seasons in this study use very different sets of predictor variables to accomplish their seasonal predictability. This may be related to the significant seasonal snow storage in the catchment, so that other factors such as temperature and the number of north-westerly storms may have a large part to play in the magnitude of inflows. Similarly, predicting the same dependent variable but for different seasons led to different contributing variables, leading to the conclusion that different wider physical causative mechanisms are behind the predictability in different seasons, and that they too should be studied separately in any future research. SST5 (sea surface temperature to the north of New Zealand) was found to have more relevance than any other predictor in predicting Waitaki river inflows and rainfall in any season. The models calibrated with SOI and IPO included as predictor variables were almost invariably worse in their predictive skill than those without, and the list of the most important predictor variables in all models did not include equatorial sea surface temperatures, sea level pressures, or 700hpa geopotential height variables. The conclusion from these findings is that equatorial ocean-atmosphere state variables do not have significant relationships with season ahead inflows and rainfall in the South Island of New Zealand. Seasonal climate forecasting on single catchment scale, and focussed to end user needs, is possible with some skill, at least in the South Island of New Zealand.

The presence and seasonal variability of human and veterinary pharmaceuticals in surface water (raw water) and treated water samples from two drinking water facilities in Southern Ontario was investigated. Water samples were collected at monthly intervals for one year to characterize the seasonal variability of these contaminants. The presence of these compounds in raw water samples collected from groundwater wells, which were potentially under the influence of surface water, was also examined. All samples were extracted by solid phase extraction (SPE) techniques and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-ESI-MS/MS). The compounds detected represented different therapeutic classes, including antibiotics, lipid regulating agents and anti-inflammatory drugs. The concentrations detected for most compounds were in the low ng/L range, with one compound being detected close to 1 μg/L. In general, human pharmaceuticals (i.e. gemfibrozil, ibuprofen and carbamazepine) were detected in raw and treated water samples, while the antibiotics were not detected after treatment. Seasonal variability was observed in the concentrations and compounds detected, which could be partially explained by changes in surface water hydrology and sources of contamination. The results demonstrate that the application of conventional treatment technologies were not very effective in reducing some of these compounds from a drinking water facility. In contrast, a second drinking water facility using additional treatment technologies, including ozonation and granular activated carbon (GAC) filters, could reduce the concentrations of these contaminants. Although, the presence of these contaminants in surface water represents a potential risk, the results suggest that appropriate treatment can minimize exposure to at least some of these emerging contaminants.

The presence and seasonal variability of human and veterinary pharmaceuticals in surface water (raw water) and treated water samples from two drinking water facilities in Southern Ontario was investigated. Water samples were collected at monthly intervals for one year to characterize the seasonal variability of these contaminants. The presence of these compounds in raw water samples collected from groundwater wells, which were potentially under the influence of surface water, was also examined. All samples were extracted by solid phase extraction (SPE) techniques and analyzed by liquid chromatography coupled with tandem mass spectrometry (LC-ESI-MS/MS). The compounds detected represented different therapeutic classes, including antibiotics, lipid regulating agents and anti-inflammatory drugs. The concentrations detected for most compounds were in the low ng/L range, with one compound being detected close to 1 μg/L. In general, human pharmaceuticals (i.e. gemfibrozil, ibuprofen and carbamazepine) were detected in raw and treated water samples, while the antibiotics were not detected after treatment. Seasonal variability was observed in the concentrations and compounds detected, which could be partially explained by changes in surface water hydrology and sources of contamination. The results demonstrate that the application of conventional treatment technologies were not very effective in reducing some of these compounds from a drinking water facility. In contrast, a second drinking water facility using additional treatment technologies, including ozonation and granular activated carbon (GAC) filters, could reduce the concentrations of these contaminants. Although, the presence of these contaminants in surface water represents a potential risk, the results suggest that appropriate treatment can minimize exposure to at least some of these emerging contaminants.

In this paper, we briefly describe the automatic model selection which is provided by Autometrics in the PcGive program. The modeler only needs to specify the initial model and the significance level at which to reduce the model. Then, the algorithm does the rest. The properties of Autometrics are discussed. We also explain its background concepts and try to see whether the model selected by the Autometrics can perform well. For a given data set, we use Autometrics to find a “new” model, and then compare the “new” model with a previously selected one by another modeler. It is an interesting issue to see whether Autometrics can also find models which fit better to the given data. As an illustration, we choose three examples. It is true that Autometrics is labor saving and always gives us a parsimonious model. It is really an invaluable instrument for social science. But, we still need more examples to strongly support the idea that Autometrics can find a model which fits the data better, just a few examples in this paper is far from enough.