Improved hydrologic modelling strategies for reservoir management

安大略省发电公司(OPG)和魁北克水电公司负责管理水库，这些水库分流或储存了加拿大东部年径流的很大一部分。他们使用这种资源每年生产高达242太瓦时的可再生能源，占加拿大电力需求的37%。该研究项目与滑铁卢大学的研究人员合作，将通过制定改进的水文模型战略，提高他们负责任地管理这些资源的能力，并最大限度地有效利用这些资源进行水电生产。由天气预报模型提供信息的水循环计算机模型经常用于提供水库流入的短期和长期预测，这些预测可用于告知水库的日常运行情况。从长远来看，它们也被用于资本投资决策。然而，在如何最好地表示流域尺度上的水文过程方面的不确定性可能会降低这些模型的预测技能。本文开发的方法将用于通过智能参数化、改进最优模型结构的识别以及更好地整合结构不确定性来提高这些模型的实用性和预测技巧。反过来，这些改进的方法将导致对这些水资源的更好管理，使OPG和魁北克水电公司能够满足生态法规并保护下游免受洪水和干旱的影响，同时实现短期和长期水电生产收入的最大化。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，以提高我国更智能地管理水资源的能力。

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.