Collaborative Research: Novel Integration of Direct Measurements with Numerical Models for Real-time Estimation and Forecasting of Streamflow Response to Cyclical Processes

合作研究：直接测量与数值模型的新颖集成，用于实时估计和预测水流对循环过程的响应

• 批准号: 2139663
• 负责人: Ehab Meselhe
• 金额: $ 25.93万
• 依托单位: Tulane University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139663&HistoricalAwards=false
• 关键词: Collaborative; Research; Novel; Integration; Direct

Continuous monitoring of natural rivers supports socio-economic needs related to water resources management and forecasting and provides benchmark data for scientific investigations. The current protocols for continuously monitoring streamflows are based on approaches that fail to accurately capture the short-time effects of flood wave propagation and the seasonal impacts of changes to stream bank vegetation. While decades of substantial advancements in instrumentation technologies have dramatically transformed our in-situ measurement capabilities, this progress has not been mirrored by advancements in streamflow monitoring protocols. The outcomes of the proposed research will fill this gap by offering critical support for water science and management and by adding reliability to predictive models used to issue flood warnings required to protect communities and critical infrastructures. Increasing the accuracy of streamflow measurements in real time and improving the reliability of forecast models will have direct positive impacts on the wellbeing and safety of the public at a time when flooding continues to be a major threat to communities. The research outcomes will include rapid tools derived with artificial intelligence techniques that supplement existing forecasting capabilities. The proposed research combines inferences from experimental, data-driven (i.e., machine learning) and physically-based numerical investigations to enable adoption of a reach-scale monitoring method (rather than cross-sectional) and real time tracking of changes to all flow variables induced by unsteady flows and riparian vegetation growth. A heterogeneous routing approach complemented by extensive data analysis will allow the extraction of interdependencies among flow variables produced by subtle features of the hysteretic behavior associated with the above-mentioned cyclical processes. Generalization of the inferences for a wide range of flow and site conditions will cost-effectively improve the protocols for predictive streamflow relationships using only in-situ acquired data, without making recourse to modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

对天然河流的持续监测支持与水资源管理和预测有关的社会经济需求，并为科学调查提供基准数据。目前的协议，用于连续监测流量的方法，未能准确地捕捉洪水波传播的短期影响和季节性的影响，流银行植被的变化。虽然几十年来仪器技术的重大进步已经极大地改变了我们的现场测量能力，但流量监测协议的进步并没有反映出这一进步。拟议研究的成果将填补这一空白，为水科学和管理提供关键支持，并为用于发布保护社区和关键基础设施所需的洪水预警的预测模型增加可靠性。提高真实的流量测量的准确性和提高预测模型的可靠性，将对公众的福祉和安全产生直接的积极影响，因为洪水仍然是对社区的主要威胁。研究成果将包括人工智能技术衍生的快速工具，以补充现有的预测能力。 拟议的研究结合了实验，数据驱动（即，机器学习）和基于物理的数值研究，以便能够采用范围监测方法（而不是横截面）和真实的时间跟踪由非稳定流和河岸植被生长引起的所有流量变量的变化。一个异构路由的方法，辅以广泛的数据分析，将允许提取的流动变量之间的相互依存关系所产生的微妙功能的滞后行为与上述周期性过程。广泛的流量和现场条件的推论的推广将具有成本效益地提高预测流量关系的协议，只使用现场采集的数据，而不诉诸modeling.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准的评估支持。