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有关的社会经济需求，并为科学研究提供了基准数据。当前用于连续监测流的协议是基于无法准确捕获洪水传播的短期影响以及变化对流河岸植被的季节性影响的方法。尽管仪器技术的数十年实质性进步已经显着改变了我们的原位测量能力，但该进步并未通过流量监控协议中的进步来反映出这一进步。拟议的研究的结果将通过对水科学和管理提供关键支持，并对用于发出保护社区和关键基础设施所需的洪水警告的预测模型的可靠性来填补这一空白。在洪水继续成为社区的主要威胁时，提高实时水流测量值的准确性并提高了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。研究结果将包括使用人工智能技术得出的快速工具，以补充现有的预测能力。 拟议的研究结合了从实验，数据驱动（即机器学习）和基于物理的数值研究中的推论，以实现接触尺度的监测方法（而不是横截面），并实时跟踪对不稳定流量和河流植被增长引起的所有流量变量的变化。通过广泛的数据分析补充的异质路由方法将允许在与上述周期性过程相关的滞后行为的微妙特征产生的流量变量之间提取相互依赖性。对广泛流量和现场条件的推断将概括提高仅使用原位获得的数据，而无需求助于建模，可以成本地改善预测水流关系的协议。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估来通过评估来获得支持的。

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.

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

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