Frequency and risk analysis of extreme hydrological phenomena

极端水文现象发生频率及风险分析

• 批准号: RGPIN-2018-05214
• 负责人: Ashkar, Fahim
• 金额: $ 1.89万
• 依托单位: Université de Moncton
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713545
• 关键词: Frequency; risk; analysis; extreme; hydrological

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.

准确估计极端水文事件，如洪水、低河水流量或强降雨，对于管理水电设施、建立城市污水管网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进行了估计。估计中涉及的一个重要因素是统计频率模型对观测数据的拟合。有许多模型可供选择，也有许多方法可以将模型拟合到数据中。本研究的重点是寻找有助于选择最佳频率模型来拟合观测数据的方法和最合适的拟合这些模型的方法。一个重要的目标是得出未来罕见水文事件的估计程序，这些事件是稳定的，物理上合理的，并且不会高估或低估事件的真实价值。不正确的估计可能导致水力设计和水资源系统管理的错误。我们期望这项研究的结果将为加拿大的水文学家和工程师提供改进的指导方针，以便在水文风险分析和水管理的频率分布和拟合方法之间进行选择。我们还希望这项研究能够帮助更好地量化罕见水文事件估计中固有的统计误差，因为到目前为止，在加拿大等地理广阔的国家的各个地区收集的水文测量量有限。对这些收集到的数据进行适当的分析，有助于正确估计将来一定会发生的罕见但严重的水文事件。科学家、工程师、规划师、社会工作者和决策者需要做好充分的准备来预测和管理这些未来事件。