Pushing back the boundaries to automating the operational hydrological forecasts used in hydropower reservoir management.

水文学的主要挑战之一是在多个时间尺度上对未来溪流和河流的流量做出准确、可靠和有价值的预测。高质量的预报使水资源管理者能够就其系统的运行作出重要决定。尽量减少洪水事件造成的损害和提高水力发电效率证明水文界在这方面所作的努力是合理的。******在过去的20年里，大量的出版物已经解决了这个问题，水文预报正迅速变得越来越可靠。尽管取得了这些进展，但仍有主要的障碍需要克服，特别是考虑到通常被视为最终产品的良好预测的产生与在业务环境中的使用之间存在的差距。此外，可操作的水文预报系统通常包括经验丰富的预报员的输入，他在预报之前根据自己的最佳判断调整水文模型的初始状态。这种人为干预在对过去系统性能的分析中产生不一致，从而模糊了水文预测的潜在统计特性。换句话说，绩效衡量因人为干预而变得混乱，正如俗话所说，“不衡量就无法改善”。******因此，在实际环境中评估水文预报的价值是一项艰巨的任务。因此，自动化预测方面将允许更深入的分析，并在与油藏系统模拟器配对时允许有针对性的改进。这项研究计划旨在填补这一空白，并最终允许在预测决策过程中有针对性的改进。主要目标是：******+实现水库运行水文预报决策链的自动化；***+提高我们对水文预报和水库管理决策支持系统之间相互作用的理解。******为推进研究计划，提出了以下具体目标：******-在分析传统方法的基础上，开发新的方法来改进流入预测方法；***-在多模型预测循环中协调模型状态初始化的集成；***-使用最先进的水释放优化算法实现和扩展水电系统模拟器；和***-探索和量化水文预测中包含的价值，以供模拟器的操作使用。******这项研究计划将提供深入了解水文预报在水力发电中的价值。考虑到加拿大每年生产超过380太瓦时的水力发电（160亿美元），任何对现状的改善都将带来巨大的经济和环境收益。

One of the main challenges in hydrology is producing accurate, reliable and valuable forecasts on multiple timescales regarding future discharge in streams and rivers. High-quality forecasts enable water resources managers to make important decisions regarding the operation of their systems. Minimizing the damage caused by flood events and increasing hydropower generation efficiency justify the effort deployed by the hydrological community in this regard. ******Over the past 20 years, a plethora of publications has tackled the issue and hydrological forecasts are quickly getting increasingly reliable. Despite this progress, there are still major roadblocks to overcome, especially when considering the gap that exists between the generation of good forecasts, which are often considered as the end product, and their use in an operational context. Furthermore, operational hydrologic forecasting systems usually include inputs from an experienced forecaster who adjusts the hydrological model's initial states according to his best judgement prior to the forecast. This human intervention generates inconsistencies in the analysis of past system performance and thus obfuscates the underlying statistical properties of hydrological forecasts. In other words, the performance measures are muddled by human intervention and, as the saying goes, “you can't improve what you don't measure”. ******Consequently, evaluating the value of hydrological forecasts in an operational setting is a daunting task. It follows, then, that automating the forecasting aspect would allow more in-depth analysis and permit targeted improvements when paired with a reservoir system simulator. This research program aims to fill that gap and eventually allow targeted improvements in the forecast-decision process. The main objectives are to:******+ Move towards automating the hydrological forecast-decision chain for reservoir operation; and***+ Improve our understanding of the interactions between hydrological forecasts and reservoir management decision-support systems.******The following specific objectives are proposed to advance the research program:******- Develop novel approaches to improve upon inflow forecasting methods based on an analysis of traditional methods in an operational context;***- Coordinate the integration of model state initialization within a multi-model forecasting loop;***- Implement and expand upon a hydropower system simulator using state-of-the-art water release optimization algorithms; and***- Explore and quantify the value contained in hydrological forecasts for operational use within the simulator.******This research program will provide insight into the value of hydrological forecasts in hydropower generation. Considering that over 380 TWh of hydroelectricity ($16 billion) are produced annually in Canada, any improvement to the status quo will result in substantial economic and environmental gains.