Climatic variability and change can have profound impacts on human societies and wildlife habitats. Extreme events and natural hazards such as floods, droughts, and windstorms, can lead to loss of lives, economic damages, and disruption in livelihoods, infrastructure, and ecosystems. These impacts depend on the intensity and the magnitude of the hazard and the characteristics of the society hit by the disaster. Investigating and predicting adverse effects of frequent climatic hazards are essential for policy makers and resource managers to plan for the future and be prepared for the consequences of these types of natural disasters. Vulnerability assessments provide a framework to detect the potential threats by exploring the nature of the hazard as well as the political, economic, and social conditions that are expected to affect the capacity of communities to cope with or adapt to that hazard. This research involves the development of a framework for vulnerability assessment of flood and drought at the river basin, sub-catchment, and community scale. The vulnerability assessment method is composed of three major components of exposure, sensitivity, and adaptive capacity. Several indicators are identified to represent these major components of the vulnerability structure. The developed vulnerability assessment has then been implemented on the Upper Ottawa River Basin, Canada. A Geographic Information System-based methodology is used to manage a wide variety of data, to aggregate and integrate several indicators including socio-economic and biophysical indicators, and to visualize the final vulnerability map. The studied areas are categorized in three levels of the vulnerability, high, moderate, and low. North Bay is identified as highly vulnerable to both flood and drought risk. Noranda is also classified as a highly flood-vulnerable area. The vulnerability assessment will provide a valuable insight for mitigation planning as well as prioritizing resource allocation for decision makers. In this research, the location and adequacy of the hydrometric monitoring stations in the Upper Ottawa River Basin are evaluated using the vulnerability map for optimum design of monitoring network.

Water resources managers rely on information collected by hydrometric networks without a quantitative way to assess their efficiency, and most Canadian water monitoring networks still do not meet the minimum density requirements. There is also no established way to quantify the importance of each existing station in a hydrometric network. This research examines the properties of Combined Regionalization Dual Entropy Multi-Objective Optimization (CR-DEMO), a robust network design technique which combines the merits of information theory and multi-objective optimization. Another information theory based method called transinformation (TI) which can rank the contribution of unique information from each specific hydrometric station in the network is tested for use with CR-DEMO. When used in conjunction, these methods can not only provide an objective measure of network efficiency and the relative importance of each station, but also allow the user to make recommendations to improve existing hydrometric networks across Canada. The Ottawa River Basin, a major Canadian watershed in Ontario and Quebec, was selected for analysis. Various regionalization methods which could be used in CR-DEMO such as distance weighting and a rainfall runoff model were compared in a leave one out cross validation. The effect of removing stations with regulated and unnatural flow regimes from the regionalization process is also tested. The analysis is repeated on a smaller tributary of the Ottawa River Basin, the Madawaska Watershed, to examine scale effects in TI analysis and CR-DEMO application. In this study, tests were conducted to determine whether to include stations outside of the river basin in order to provide more context to the basin boundaries. It was found that the TI analysis complemented CR-DEMO well and it provided a detailed station ranking which was supported by CR-DEMO results. The inverse distance weighting drainage area ratio method was found to provide more accurate regionalization results compared to the rainfall-runoff model, and was thus chosen for CR-DEMO. Regionalization was shown to be more accurate when the regulated basins were omitted using leave one out cross validation. It was discovered that CR-DEMO is sensitive to scaling because some sub-basins which are relatively “well-equipped” compared to others in dire conditions may be penalized. The TI analysis was not as sensitive to scaling. Including stations outside of the Ottawa River Basin improved the information density and regionalization accuracy in the Madawaska Watershed because they provided context to sparse areas. Finally, Pareto optimal network solutions for both the Ottawa River Basin and the Madawaska Watershed were presented and analyzed. A number of optimal networks are proposed for each watershed along with “hot-spots” where new stations should be added whatever the end users’ choice of network. / Thesis / Master of Applied Science (MASc)