NSF Convergence Accelerator - Track D: Hidden Water and Hydrologic Extremes: A Groundwater Data Platform for Machine Learning and Water Management

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。汇聚加速器第一阶段项目的更广泛影响和潜在社会效益是利用机器学习（ML）等人工智能方法实现更好的水管理成果，通过改进水文预报，培养更好地规划和管理极端事件的能力，从而直接造福社会。HydroFrame-ML是由水管理规划和决策的应用解决方案所驱动和构建的。干旱和洪水等极端事件具有深远的社会影响。它们很常见，代价高昂，而且未来可能会变得更糟。该项目团队与垦务局合作，垦务局是美国最大的批发供水机构，为3100多万人和1000万英亩农田提供水。垦务局将推动用例设计和用于评估第一阶段成功的度量，以及在第二阶段项目团队扩展中的合作伙伴。此外，项目团队将开发实践活动和挑战，旨在为本科生提供机器学习和数据科学方面的经验，以应对现实世界面临的紧迫挑战。在第二阶段计划增加的STEM指导项目合作伙伴的帮助下，该团队将构建内容，以帮助扩大代表性不足的学生的参与，远远超出该项目的时间框架。拟议的项目将通过HydroFrame开发的最严格的国家尺度地下水模拟与地球系统建模和水管理方面的国家领导者结合在一起。通过提供端到端的工作流程，将地下水科学状态与操作管理工具相结合，HydroFrame-ML将推进大规模水资源管理，以及我们对极端事件中人类操作和地下水相互作用的理解。他们的产品将提供改进预报的创新方法，并在此过程中扩展我们对以下方面的知识：(1)地下水对管理系统中极端事件的贡献；(2)当前风险评估方法存在偏差，未考虑地下水；(3)通过更积极地管理地下水和在预测中考虑地下水和地表水的相互作用来提高长期可持续性的潜力。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。

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. The broader impact and potential societal benefit of this Convergence Accelerator Phase I project is to utilize artificial intelligence methods such as machine learning (ML) to achieve better water management outcomes that directly benefit society by developing the ability to better plan for and manage extreme events through improved hydrologic forecasting. HydroFrame-ML is motivated by, and structured around, applied solutions for water management planning and decision making. Extreme events like drought and floods have far-reaching societal impacts. They are common, costly and likely to get worse in the future. The project team is partnered with the Bureau of Reclamation, which is the largest wholesale water provider in the country, providing water to more than 31 million people and 10 million acres of farmland. The Bureau of Reclamation will drive use case design and the metrics used to evaluate success in Phase 1, as well as partner in the expansion of the project team for Phase 2. Additionally, the project team will develop hands-on activities and challenges designed to give undergraduates experience in machine learning and data science, in the context of pressing real-world challenges. Aided by the planned addition of a STEM mentorship program partner in Phase 2, the team will build content with the vision of helping to broaden participation of underrepresented students well beyond the timeframe of this project.The proposed project brings together the most physically rigorous national scale groundwater simulations developed through HydroFrame with national leaders in Earth Systems Modeling and water management. By providing end-to-end workflows combining state of groundwater science with operational management tools, HydroFrame-ML will advance both large-scale water management as well as our understanding of how human operations and groundwater interact in extreme events. Their products will provide innovative ways to improve forecasts and in the process will expand our knowledge about the (1) contributions of groundwater to extreme events in managed systems; (2) biases in our current risk-assessment approaches which do not consider groundwater; and (3) potential to improve long-term sustainability by more actively managing groundwater and accounting for groundwater surface water interactions in projections.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.