Frequency and risk analysis of extreme hydrological phenomena

准确估计洪水、河流流量低或暴雨等极端水文事件，对于管理水电设施、确定城市下水道网络的流量能力和规划水流水利工程等活动至关重要。这一估计依赖于多年来在加拿大收集的水文数据。根据水文记录，加拿大科学家和工程师对未来可能发生的罕见水文事件作出估计。估计中涉及的一个重要因素是将统计频率模型与观测数据拟合。有许多模型可供选择，以及许多将模型拟合到数据的方法。本研究的重点是寻找方法，帮助选择最好的频率模型来拟合观测数据和拟合这些模型的最合适的方法。一个重要的目标是达到未来罕见的水文事件的估计程序是稳定的，物理上合理的，不高估或低估的事件的真实价值。不正确的估计可能导致水力设计和水资源系统管理中的错误。我们希望，这项研究的结果将提供加拿大水文学家和工程师之间的频率分布和水文风险分析和水管理的拟合方法选择改进的指导方针。我们还希望这项研究有助于更好地量化罕见水文事件估计中固有的统计误差，因为迄今为止在加拿大这样一个地理辽阔的国家的各个地区收集的水文测量数据有限。对这些收集到的数据进行适当的分析有助于对今后必然会发生的罕见但严重的水文事件进行适当的估计。科学家、工程师、规划人员、社会工作者和决策者需要做好充分准备，以预测和管理未来的此类事件。

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.