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: 2139649
• 负责人: Marian Muste
• 金额: $ 40.37万
• 依托单位: University of Iowa
• 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=2139649&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相关的社会经济需求，并为科学研究提供基准数据。当前连续监测水流的协议所采用的方法无法准确捕获洪水波传播的短期影响以及河岸植被变化的季节性影响。虽然仪器技术几十年来的重大进步已经极大地改变了我们的原位测量能力，但这种进步并没有反映在水流监测协议的进步中。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，并增加用于发布保护社区和关键基础设施所需的洪水警报的预测模型的可靠性。在洪水继续成为社区的主要威胁之际，提高实时水流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。研究成果将包括利用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的推论，以实现采用范围尺度监测方法（而不是横截面）并实时跟踪由不稳定流和河岸植被生长引起的所有流量变量的变化。异构路由方法辅以广泛的数据分析，将允许提取由与上述循环过程相关的滞后行为的微妙特征所产生的流变量之间的相互依赖性。对各种流量和场地条件的推论的推广将仅使用现场获取的数据，经济高效地改进预测水流关系的协议，而不需要依赖建模。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A flood-crest forecast prototype for river floods using only in-stream measurements

• DOI: 10.1038/s43247-022-00402-z
• 发表时间: 2022-04-01
• 期刊: COMMUNICATIONS EARTH & ENVIRONMENT
• 影响因子: 7.9
• 作者: Muste, Marian;Kim, Dongsu;Kim, Kyungdong
• 通讯作者: Kim, Kyungdong

Monitoring the Manning's n Coefficient Variation over the Yearly Cycle

监测年度周期内的曼宁 n 系数变化

• DOI: 10.3850/978-90-833476-1-5_iahr40wc-p0541-cd
• 发表时间: 2013
• 期刊: IAHR
• 作者: Muste, M.;Cikmaz, B.;Lamoreux, A.;Yesilkoy, O. Baydaroglu;Demir, I.;Meselhe, E.;Bacotiu, C.
• 通讯作者: Bacotiu, C.

Monitoring Streamflow Pulses

监测水流脉冲

• 发表时间: 2022
• 期刊: Proceedings of the 39th IAHR World Congress
• 作者: Marian Muste, Dongsu Kim

Insights into Flood Wave Propagation in Natural Streams as Captured with Acoustic Profilers at an Index-Velocity Gaging Station

通过指数速度测量站的声学剖面仪捕获的洪水波在自然溪流中的传播的见解

• DOI: 10.3390/w14091380
• 发表时间: 2022
• 期刊: Water
• 影响因子: 3.4
• 作者: Muste, Marian;Kim, Dongsu;Kim, Kyungdong
• 通讯作者: Kim, Kyungdong