Improved hydrologic modelling strategies for reservoir management

安大略发电公司（OPG）和魁北克水电公司负责水库的管理，这些水库分流或储存了加拿大东部很大一部分年径流。他们利用这种资源每年产生高达242 TWh的可再生能源，占加拿大电力需求的37%。该研究项目将与滑铁卢大学的研究人员合作，提高他们负责任地管理这些资源的能力，并通过开发改进的水文建模策略，最大限度地有效利用这些资源进行水电生产。根据天气预报模型建立的水循环计算机模型经常用于提供水库流入量的短期和长期预测，可用于为水库的日常运作提供信息。从长远来看，它们也用于资本投资决策。然而，在流域尺度上如何最好地代表水文过程的不确定性，可以降低这些模型的预测技能。这里开发的方法将被用来提高这些模型的实用性和预测技能，通过智能参数化，改进识别的最佳模型结构，更好地整合结构的不确定性。这些改进的方法反过来将导致更好地管理这些水资源，使OPG和Hydro Québec能够满足生态法规并保护下游免受洪水和干旱影响，同时最大限度地提高水电生产的短期和长期收入。这些方法将传播给加拿大和国外的其他水库和洪水管理机构，以提高我们更明智地管理水资源的国家能力。

Ontario Power Generation (OPG) and Hydro Québec are responsible for the management of water reservoirs which divert or store a significant portion of Eastern Canada's annual runoff. They use this resource to generate up to 242 TWh of renewable energy annually, 37% of Canada's electricity demand. In collaboration with researchers at the University of Waterloo, this research project will improve their ability to responsibly manage such resources and maximize their efficient use for hydropower production via the development of improved hydrological modelling strategies. Computer models of the water cycle, informed by weather prediction models, are regularly used to provide short- and long-term forecasts of reservoir inflows that can be used to inform day-to-day operation of reservoirs. They are also used, in the long-term, for capital investment decisions. However, uncertainty in how best to represent hydrologic processes on at the watershed scale can reduce the predictive skill of these models. Methods developed here will be used to improve the utility and forecasting skill of these models through intelligent parameterization, improved identification of optimal model structure, and better integration of structural uncertainty. These improved methods, in turn, will lead to better management of these water resources, enabling OPG and Hydro Québec to satisfy ecological regulations and protect from downstream flood and drought impacts whilst maximizing revenue from hydroelectricity production in both the short- and long-term. Such methods will be disseminated to other reservoir and flood management agencies in Canada and abroad to improve our national capacity to more intelligently manage our water resources.