Multi-objective automatic data assimilation of a hydrological model based on classification of initial hydrologic states

基于初始水文状态分类的水文模型多目标自动数据同化

• 批准号: 522813-2018
• 负责人: Tolson, Bryan
• 金额: $ 0.91万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=666938
• 关键词: Multi; objective; automatic; data; assimilation

The Power Operations division of Rio Tinto Aluminium (RTA) has the mandate to manage two large water**resources systems, namely the Lac-Saint-Jean system in Québec and the Nechako system in British-Columbia.**To manage the water resources systems efficiently and safely, multiple scenarios of inflow predictions are**produced and are fed into water resources management optimization models. The quality of the forecasted**inflows is crucial as it allows adequately mitigating flooding risks as well as maximizing hydropower**generation for a given volume of water.**Hydrologic models are used to produce inflow predictions and these are first trained on past data and then used**to produce inflow forecasts by driving the model with forecasted inputs like precipitation and temperature. The**initial states of the model are key parameters required to obtain an accurate forecast. To provide the best**possible initial states before starting the forecast simulations, an expert analyst compares the model states with**current observations and corrects these states manually if necessary. This procedure yields reliable short-term**forecasts. The skill of long-term forecasts, however, is poor due to the unclear propagation of the manual**changes through the complex system over a long horizon. This research is intended to automatize the process**of initial state updating using advanced sensitivity analysis and classification algorithms. The automatic**procedure will search for the optimal corrections to achieve both reliable short-term and long-term performance**of the forecasts based on the initial conditions of the catchment. This will help RTA to optimize their**operations using sustainable hydroelectricity.

Rio Tinto铝（RTA）的电力运营部门具有管理两个大水**资源系统的任务，即魁北克的Lac-Saint-Jean系统和不列颠哥伦比亚省的Nechako System。预测**流入的质量至关重要，因为它允许适当缓解洪水风险，并最大化水力发电**在给定的水量中生成**。模型的**初始状态是获得准确预测所需的关键参数。为了在启动预测模拟之前提供最佳**可能的初始状态，专家分析师将模型状态与**当前的观察结果进行比较，并在必要时手动纠正这些状态。该程序可获得可靠的短期**预测。然而，由于手册**在漫长的地平线上通过复杂系统的变化，长期森林人的技巧差。这项研究旨在使用高级灵敏度分析和分类算法自动化初始状态更新的过程**。自动**程序将根据流域的初始条件来搜索最佳校正，以实现森林的可靠短期和长期绩效**。这将有助于RTA使用可持续水电性优化其**操作。