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

河底高程和流量估算在安全高效的海上运输、洪水风险管理和水资源管理规划等许多实际应用中起着至关重要的作用。然而，原位测量河流深度和流量在物流上具有挑战性、昂贵且耗时。最近发射的地表水和海洋地形（SWOT）卫星任务为估计河流流量和深度测量提供了一种新的可能性，它提供了海平面以上的河水高度、河流宽度和分水岭上的坡度到大陆尺度。本研究将利用最近获得的SWOT数据集开发和应用大规模精确的河流深度测量和流量估算。研究还将重点关注地表-地下水相互作用对河流流量估算的影响。该奖学金项目将培训PI和夏威夷大学马诺阿分校的一名研究生，学习SWOT数据处理和分析的技术方面，以及实时水深估计的计算方法。这项合作研究最终将提高本国机构的研究能力，并使夏威夷州受益，夏威夷州利用地下水资源，并受到水资源管理和平衡的约束。EPSCoR研究基础设施改进（RII） Track-4: EPSCoR研究研究员（RII Track-4:@NASA）将为夏威夷大学马诺阿分校的一名副教授提供奖学金，并为一名研究生提供培训。这项工作将与美国宇航局喷气推进实验室（JPL）的研究人员合作进行。PI和一名研究生将访问美国宇航局喷气推进实验室的陆地水文学小组，向该领域的专家学习SWOT任务数据采集和相关的河流流量估算。PI积极开发基于河流动力学的机器学习技术，以在计算效率高的统一框架中估计河流水深和流量及其相应的不确定性。本研究的主要目标是改进现有的地表-地下水相互作用估算方法，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数据估算河流水深和流量的时空。对于接近实时的水深估计，浅水方程将通过具有控制精度的物理信息神经网络进行近似模拟，以进行实时模拟。建议的方法将打包在Python库中，目的是在经过适当测试和质量检查后将它们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能够在夏威夷大学马诺阿分校培训和教育尖端遥感数据分析和基于机器学习的同化技术的学生。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.