RII Track-4:@NASA: Combining Physics and Deep Learning for Accurate River Discharge and Bathymetry Estimation from the Surface Water and Ocean Topography Mission

RII Track-4：@NASA：结合物理学和深度学习，通过地表水和海洋地形任务进行准确的河流流量和水深测量估计

• 批准号: 2327502
• 负责人: Jonghyun Lee
• 金额: $ 24.77万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327502&HistoricalAwards=false
• 关键词: RII; Track; NASA; Combining; Physics

Estimation of river bottom elevation and discharge plays an essential role in many practical applications such as safe and efficient maritime transportation, flood risk management, and water management planning. However, in-situ measurement of river depth and discharge is logistically challenging, expensive, and time-consuming. Recently launched Surface Water and Ocean Topography (SWOT) satellite mission opens a new possibility to estimate river discharge and depth measurements by providing the river water elevation above sea level, river width, and slope over the watershed to continental scales. This proposed research will enable the development and application of large-scale accurate river depth measurements and discharge estimation using the recently acquired SWOT data sets. The research will also focus on the effect of surface-groundwater interaction on river discharge estimation. The fellowship program will train the PI and a graduate student from the University of Hawaii at Manoa in both technical aspects of SWOT data processing and analysis as well as computational approaches for real-time water depth estimation. The collaborative research will ultimately improve the research capacity of the home institution and benefit the State of Hawaii, which is the state that resorts to groundwater resources and is subject to both water management and balance.This EPSCoR Research Infrastructure Improvement (RII) Track-4: EPSCoR Research Fellows (RII Track-4:@NASA) will provide a fellowship to an Associate Professor and training for a graduate student at the University of Hawaii at Manoa. This work would be conducted in collaboration with researchers at NASA Jet Propulsion Laboratory (JPL). The PI and one graduate student will visit the Terrestrial Hydrology group at NASA JPL to learn the SWOT mission data acquisition and associated river discharge estimation from experts in the field. The PI has actively developed river dynamics-based machine-learning techniques to estimate river bathymetry and discharge and their corresponding uncertainties in a computationally efficient unified framework. The main objectives of this research study are to advance the currently used methods to account for the surface-groundwater interaction, improve the accuracy of the discharge estimation, and develop a new data assimilation method to estimate spatiotemporal river bathymetry and discharge using the SWOT data. For close-to-real-time bathymetry estimation, the shallow water equations will be approximated through physics-informed neural networks with a controlled accuracy for real-time simulation. The proposed methods will be packaged in a Python library, with the intent of releasing them in a public repository once properly tested and quality checked. Expertise gained during this fellowship will enable the PI to train and educate students at the University of Hawaii at Manoa in cutting-edge remote sensing data analysis and machine learning-based assimilation techniques.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.

河底高程和流量的估算在许多实际应用中发挥着重要作用，例如安全高效的海上运输、洪水风险管理和水管理规划。然而，河流深度和流量的现场测量在后勤方面具有挑战性、昂贵且耗时。最近发射的地表水和海洋地形 (SWOT) 卫星任务通过提供大陆尺度的河流水位高于海平面的高度、河流宽度和流域坡度，为估计河流流量和深度测量提供了新的可能性。这项研究将利用最近获得的 SWOT 数据集来开发和应用大规模精确的河流深度测量和流量估计。该研究还将重点关注地表水与地下水相互作用对河流流量估算的影响。该奖学金计划将对 PI 和夏威夷大学马诺阿分校的一名研究生进行 SWOT 数据处理和分析技术方面以及实时水深估计的计算方法方面的培训。这项合作研究最终将提高母机构的研究能力，并使夏威夷州受益，夏威夷州是一个利用地下水资源并受到水管理和平衡影响的州。EPSCoR 研究基础设施改进 (RII) Track-4 ：EPSCoR 研究员（RII Track-4：@NASA）将为夏威夷大学马诺阿分校的副教授提供奖学金并为研究生提供培训。这项工作将与 NASA 喷气推进实验室 (JPL) 的研究人员合作进行。 PI 和一名研究生将访问 NASA JPL 的陆地水文小组，向该领域的专家学习 SWOT 任务数据采集和相关河流流量估算。 PI 积极开发基于河流动力学的机器学习技术，以在计算高效的统一框架中估计河流测深和流量及其相应的不确定性。本研究的主要目标是改进目前用于解释地表水与地下水相互作用的方法，提高流量估算的准确性，并开发一种新的数据同化方法，利用 SWOT 数据估算时空河流测深和流量。为了接近实时的测深估计，浅水方程将通过物理信息神经网络进行近似，并具有实时模拟的受控精度。所提出的方法将打包在 Python 库中，目的是在经过适当测试和质量检查后将其发布到公共存储库中。在此奖学金期间获得的专业知识将使 PI 能够对夏威夷大学马诺阿分校的学生进行尖端遥感数据分析和基于机器学习的同化技术的培训和教育。该奖项反映了 NSF 的法定使命，并被认为值得支持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。