In a field trial during 1995/96, epidemics of <I>Pyrenophora teres</I> and <I>Rhynchosporium secalis</I> were studied in winter barley with concurrent records of weather data to identify key environmental parameters that affect epidemics. Temperature was identified as a key influence in the onset of <I>P. teres</I> epidemics. Disease symptoms were observed to progress when daytime temperatures consistently reached 10°C and minimum nightime temperatures for the same period remained above 5°C. Short leaf wetness periods and longer photoperiods also correlated with increased disease levels during the <I>P. teres </I>epidemic. In <I>R. secalis,</I> relationships between disease onset and individual environmental parameters were not consistent, however, high rainfall events and prolonged leaf wetness periods were recorded prior to greatest disease increase. Hypotheses based on individual and combined weather criteria, based on the results of the 1995/96 field trials, were tested in controlled conditions. The effect of temperature on <I>P. teres</I> was confirmed, with small differences between ascospores and conidiospores. Latent period of both <I>P. teres</I> and <I>R. secalis</I> was influenced by cultivar resistance, inoculum concentration and plant growth stage. In a second field trial in 1996/97 reduced dose fungicide programmes, using hypotheses of epidemic development based on environmental criteria, were tested and compared favourably to a standard programme with greater fungicide doses. Environmental criteria were combined within a decision model for timed reduced-dose fungicide programmes for each pathogen, where risk scores were allotted for each set of criteria and fungicide treatment decision was based on the cumulative risk score. Both the <I>P. teres</I> and <I>R. secalis</I> decision models were tested in a final field trial in 1997/98. Lower disease levels and greater yield response, with lower fungicide input, was achieved from both models compared to a standard fungicide programme. The potential for using the <I>P. teres</I> and <I>R. secalis</I> decision models in a decision support system for cereals is discussed.

Traffic crashes tend to occur at relatively greater frequencies at particular locations, at particular time periods, and for particular subsets of drivers and vehicles. It is well recognized among the road safety community that crash-risk is highly elevated when inclement weather conditions occur in the winter. To present, most of the road safety studies focus on event-based analysis or seasonal analysis and give little attention to explore high-risk conditions at the daily temporal scale. The purpose of the study is to advance our understanding of high-risk crash conditions at the daily level and their occurrences in Southern Ontario, Canada. The study explores different definitions of high-crash days, and quantifies the influences of weather conditions, risk exposure, months and timing of precipitation on the likelihood of a high-crash day occurring using binary logistic regression model. Additionally, an approach for estimating the relative risk exposure using available traffic count data has also been developed. The results of the study show a small proportion of high-crash days are responsible for a considerable amount of traffic crashes during the winter. The risk of traffic crash is twice as high on high-crash days in comparison to non-high-crash days. The modeling approach well-fits the data and shows that winter weather conditions have significant influence on high-crash days with results being mostly consistent across the four study areas, Toronto, the Area Surrounding Toronto, London and the Area Surrounding London. Low temperature, heavy snowfalls, high wind speeds, high traffic volumes, early winter months, occurrence of precipitation in both morning and evening increase the odds of high-crash days to a large extent. The results of study could help to pre-schedule traffic operation and enforcement, to effectively distribute road safety resources and personnel, and to create situational awareness among road users and other stakeholders.

Traffic crashes tend to occur at relatively greater frequencies at particular locations, at particular time periods, and for particular subsets of drivers and vehicles. It is well recognized among the road safety community that crash-risk is highly elevated when inclement weather conditions occur in the winter. To present, most of the road safety studies focus on event-based analysis or seasonal analysis and give little attention to explore high-risk conditions at the daily temporal scale. The purpose of the study is to advance our understanding of high-risk crash conditions at the daily level and their occurrences in Southern Ontario, Canada. The study explores different definitions of high-crash days, and quantifies the influences of weather conditions, risk exposure, months and timing of precipitation on the likelihood of a high-crash day occurring using binary logistic regression model. Additionally, an approach for estimating the relative risk exposure using available traffic count data has also been developed. The results of the study show a small proportion of high-crash days are responsible for a considerable amount of traffic crashes during the winter. The risk of traffic crash is twice as high on high-crash days in comparison to non-high-crash days. The modeling approach well-fits the data and shows that winter weather conditions have significant influence on high-crash days with results being mostly consistent across the four study areas, Toronto, the Area Surrounding Toronto, London and the Area Surrounding London. Low temperature, heavy snowfalls, high wind speeds, high traffic volumes, early winter months, occurrence of precipitation in both morning and evening increase the odds of high-crash days to a large extent. The results of study could help to pre-schedule traffic operation and enforcement, to effectively distribute road safety resources and personnel, and to create situational awareness among road users and other stakeholders.

The lee shores of the North American Great Lakes are subject to hazardous amounts of snowfall each winter as continental polar air masses are destabilized by the relatively warmer lakes which manifests as pronounced heat and moisture fluxes and subsequent convection and snow generation. This phenomenon, known as lake-effect snow (LES), has been studied by the atmospheric scientific community extensively as the local and mesoscale processes are becoming better understood through the implementation of in situ research projects and high-resolution numerical weather prediction models. However, considerably less research effort has inquired on what large-scale conditions are linked with lake-effect snow. The objective of this dissertation is to develop a more comprehensive understanding of the synoptic-scale conditions associated with lake-effect snowstorms and how they differentiate with non-LES winter storms. Chapter 1 provides a brief introduction to LES and reviews the basic dynamics of LES formation in the form of a comprehensive literature review. Chapter 2 consists of the first synoptic climatologies of lake-effect snowstorms off Lakes Michigan and Superior through statistical analysis of past lake-effect cases off those two lakes. Chapter 3 focuses on developing a synoptic climatology of wintertime cyclonic systems, specifically Alberta Clippers, that traversed the Great Lakes basin but did not result in lake-effect snow formation. Chapter 4 features the development of an objective classification model that differentiates between these two winter weather phenomena by using past LES and non-LES winter storm case repositories to train and test the model. This research effort will focus on wintertime Alberta Clipper systems and LES off Lakes Erie and Ontario. Finally, Chapter 5 reviews the primary results from this research and discusses their significance and implications regarding possible future research.

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