Research to Operations in Data-driven Hydrologic Forecasting and Decision-making

与干旱、洪水和热带风暴有关的水害仍然是代价最高、最致命的自然灾害之一。对于美国的大片地区，预计这些极端水文气候事件将会增加和加剧。为了挽救生命、保护财产和维持商业需求，需要对这些危险进行准确、准确、早期和可操作的预测。在数据科学、人工智能(AI)和机器学习(ML)最新进展的推动下，水文预报研究界正在用新的想法、技术和工具做出回应。在水系统的运作中，迫切需要将这些研究进展迅速有效地转化，以帮助改进水灾的应对和规划。这些进展必须与业务天气/水预报社区以及依赖它们的企业、行业和公众一起开发和整合。同时，研究生的培养需要更多跨学科和跨学科的课程。培训的这一变化将使这些未来的水专业人员和水科学领域的领导者能够理解研究进展的实际意义，并发展技能，将其有效地转化为业务环境中使用的预测和决策框架。该NSF研究培训计划(NRT)将通过推出一项独特的水文科学计划来满足研究人员、预报员和预报用户的多方面和综合需求，该计划侧重于研究与业务的关键联系，或称“R2O”。该研究计划将与水预报社区的工作人员共同制作，以产生一条能够诊断水灾害预报需求的跨学科科学家和工程师的管道。这一联合制作工作将导致使用人工智能、最大限度和数据科学的最新进展来设计预测工具和技术，并以可操作的形式为广泛的决策者传播预测产品。该项目预计将培训115名不同的硕士和博士生，其中包括28名来自土木工程、地理和计算机科学的资助学员。招生工作将侧重于传统上在学术界和水务行业参与人数较少的群体。该项目将使学生接触到各种专业和模拟的专业环境，并加强学生成为促进者、创新者和领导者的能力。该培训计划以独特的形式、内容和交付为特色，通过团队科学、基于挑战的学习、联合制作和反复自我反省来建立能力。创新教育方面包括国内和国际考察旅行、模拟业务预测、实践实验室、圆桌讨论、混合指导、体验式学习、实习、广泛和跨学科的团队建设以及专业发展。该项目的毕业生将为水文预报工作人员带来独特的属性组合。前两项是：(1)深厚的水文科学学科知识；(2)跨越人工智能和最大限度前沿的综合技能，以及先进的行业标准软件的使用。第三和第四是：(3)对复杂的水文预报和决策系统的全面了解；(4)在水文预报实践中成为思想领袖的能力。受训人员将加快三个重点领域的研究进展：(1)创建新的数据科学工作流程，以确定水文过程的多尺度地球物理和气候驱动因素；(2)改进模型和预测工具，以减少水文预测的不确定性；以及(3)改进预报产品的交流，以供实际业务使用。来自多个学科的协调的内部和外部项目评估、开源软件开发、数据管理和新的教学方法，以在计算机科学、工程和地球科学的界面上进行培训，将支持融合研究的项目目标，并在学术界、政府和私营部门产生更广泛的影响。该项目由NSF研究培训计划(NRT)和既定的激励竞争性研究计划(EPSCoR)联合资助。NSF研究培训计划(NRT)旨在鼓励STEM研究生教育培训的大胆、新的潜在变革性模式的开发和实施。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合实习生模式，在高度优先的跨学科或趋同研究领域对STEM研究生进行有效培训。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。

Water hazards related to droughts, floods, and tropical storms remain amongst the costliest and deadliest natural hazards. For large sections of the U.S., an increase and intensification in these extreme hydroclimate events are predicted. Precise, accurate, early, and actionable forecasts of these hazards are needed to save lives, protect property, and sustain commerce needs. The hydrologic forecasting research community is responding with new ideas, techniques, and tools driven by recent advances in data science, artificial intelligence (AI), and machine learning (ML). Rapid and effective translation of these research advances are urgently needed in water system operations to help improve water hazard responses and planning. These advances must be developed and incorporated with the operational weather/water forecasting community and the businesses, industry, and the public that depend on them. Simultaneously, graduate students' training requires greater inter- and transdisciplinary curriculum inclusion. This change in training will allow these future water professionals and leaders in water science to comprehend the practical significance of research advances and develop skills to effectively translate them into forecast and decision-making frameworks used in operational settings. This NSF Research Traineeship (NRT) project will address the multifaceted and integrated needs of researchers, forecasters, and users of forecasts, by launching a unique hydrologic science program focusing on the critical linkage of research to operations, or "R2O." The program of study will be co-produced with those working in the water prediction community to generate a pipeline of interdisciplinary scientists and engineers capable of diagnosing water-hazard forecasting needs. This co-production effort will result in the design of prediction tools and techniques using the latest advances in AI, ML, and data science, and the dissemination of the forecast products in actionable forms for a wide array of decision-makers. The project anticipates training a diverse set of 115 master's and Ph.D. students, including 28 funded trainees from civil engineering, geography, and computer science. Student recruitment efforts will focus on groups traditionally underrepresented in their participation in academia and water industries. This project will expose students to a variety of professional and simulated professional contexts and strengthen student competencies to be facilitators, innovators, and leaders. The training program features unique modalities, content, and delivery to build competency through team science, challenge-based learning, co-production, and iterative self-reflection. Innovative educational aspects include domestic and international study tours, mock operational forecasting, practical labs, roundtable discussions, mixed mentoring, experiential learning, internships, broad-scale and interdisciplinary team building, and professional development. Graduates of the program will bring to the hydrologic forecasting workforce a unique combination of attributes. The first two are: (1) deep disciplinary knowledge in hydrologic science coupled with (2) comprehensive skills spanning the cutting edge of AI and ML, and the use of advanced industry-standard software. Third and fourth are: (3) a holistic understanding of the complex hydrologic forecasting and decision-making system paired with (4) the competencies to be thought leaders in the hydrologic forecasting community of practice. Trainees will accelerate research advances in three focal areas: (1) creating new data science workflows to characterize multi-scale geophysical and climate drivers of hydrologic processes; (2) advancing models and predictive tools to reduce the uncertainty of hydrologic prediction; and (3) improving the communication of forecast products for practical operations. Coordinated internal and external project evaluation from multiple disciplines, open-source software development, data curation, and new pedagogical approaches to train at the interface of computer science, engineering, and geoscience will support the project goals of convergent research and the delivery of broader impacts in academia, government, and the private sector. This project is jointly funded by the NSF Research Traineeship (NRT) program and the Established Program to Stimulate Competitive Research (EPSCoR).The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.