Frequency and risk analysis of extreme hydrological phenomena

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An accurate estimation of extreme hydrological events such as floods, low river flows or severe rainfall is essential for activities such as managing hydro-electrical facilities, establishing flow capacities of urban sewer networks and planning hydraulic works on water streams. This estimation relies on hydrological data gathered in Canada over many years. Based on hydrologic records, Canadian scientists and engineers make estimates of rare hydrologic events that would occur in the future. An important element involved in the estimation is the fitting of statistical frequency models to the observed data. There are many models to choose from, and many methods for fitting the models to the data. This research focuses on finding means that help in selecting the best possible frequency models to fit the observed data and the most appropriate methods of fitting these models. An important goal is to arrive at estimation procedures of future rare hydrological events that are stable, physically justifiable, and that do not overestimate or underestimate the true value of the events. An incorrect estimation may lead to errors in the hydraulic design and the management of water resources systems. We expect that the results of this research will provide Canadian hydrologists and engineers with improved guidelines for choosing between frequency distributions and fitting methods for hydrological risk analysis and water management. We also expect the research to help better quantify the statistical errors inherent in the estimation of rare hydrological events due to the limited amounts of hydrological measurements gathered up until now in various parts of a geographically vast country such as Canada. The proper analysis of this gathered data is instrumental to the proper estimation of rare but severe hydrological events that are bound to occur in the future. Scientists, engineers, planners, social workers and decision makers need to be well prepared to forecast and manage such future events.