Collaborative Research: Novel integration of direct measurements with numerical models for real-time estimation and forecasting of streamflow response to cyclical processes

对天然河流的持续监测支持与水资源管理和预报有关的社会经济需要，并为科学调查提供基准数据。目前用于持续监测径流的协议基于未能准确捕捉洪水波传播的短期影响和河岸植被变化的季节性影响的方法。虽然几十年来仪器技术的重大进步极大地改变了我们的现场测量能力，但这种进步并没有反映在径流监测协议的进步上。拟议的研究成果将填补这一空白，为水科学和管理提供关键支持，并增加用于发布洪水警报的预测模型的可靠性，以保护社区和关键基础设施。在洪水仍然是社区的主要威胁之际，提高实时径流测量的准确性和改善预测模型的可靠性，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。拟议的研究结合了来自实验、数据驱动(即机器学习)和基于物理的数值调查的推论，以实现采用范围尺度的监测方法(而不是横断面)，并实时跟踪非恒定流和河岸植被生长引起的所有径流变量的变化。由大量数据分析补充的异质布线方法将允许提取由与上述循环过程相关的滞后行为的细微特征所产生的流动变量之间的相互依赖关系。对广泛的水流和场地条件的推论的泛化将以成本效益的方式改进仅使用现场获取的数据，而不求助于建模的预测径流关系的协议。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.