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 Aluminium（RTA）的电力运营部门负责管理两个大型水资源系统，即魁北克省的Lac-Saint-Jean系统和不列颠哥伦比亚省的Nechako系统。为了有效和安全地管理水资源系统，生成了多种流入预测情景，并将其输入水资源管理优化模型。预测的 ** 流入量的质量至关重要，因为它可以充分减轻洪水风险，并最大限度地提高给定水量的水力发电量。水文模型用于生成流入预测，这些模型首先在过去的数据上进行训练，然后通过使用预测输入（如降水和温度）驱动模型来生成流入预测。模型的初始状态是获得准确预测所需的关键参数。为了在开始预测模拟之前提供最佳的初始状态，专家分析师将模型状态与当前观测值进行比较，并在必要时手动校正这些状态。这一程序产生可靠的短期 ** 预测。然而，长期预测的技能很差，因为在很长一段时间内，人工 ** 变化在复杂系统中的传播不清楚。本研究的目的是自动化的过程 ** 的初始状态更新使用先进的灵敏度分析和分类算法。自动 ** 程序将根据流域的初始条件搜索最佳校正，以实现可靠的短期和长期预报性能 **。这将有助于RTA利用可持续的水电优化其 ** 运营。