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

数据驱动的水文预报与决策业务研究

• 批准号: 2152140
• 负责人: Steven Burian
• 金额: $ 299.89万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152140&HistoricalAwards=false
• 关键词: Research; Operations; Data; driven; Hydrologic

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.

与干旱、洪水和热带风暴相关的水灾害仍然是代价最高、最致命的自然灾害之一。对于美国的大部分地区来说，据预测，这些极端水文气候事件将增加和加剧。需要对这些危害进行精确、准确、早期和可操作的预测，以拯救生命、保护财产和维持商业需求。水文预报研究界正在以数据科学、人工智能（AI）和机器学习（ML）最新进展驱动的新思想、技术和工具做出回应。这些研究进展的快速和有效的翻译迫切需要在水系统的操作，以帮助改善水灾害的反应和规划。这些进步必须得到发展，并与业务天气/水预报界和依赖它们的企业，工业和公众相结合。同时，研究生的培训需要更多的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科学专业人员和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。这个NSF研究培训（NRT）项目将解决研究人员，预报员和预报用户的多方面和综合需求，通过启动一个独特的水文科学计划，重点是研究与操作的关键联系，或“R2 O”。“该研究计划将与水预测界的工作人员共同制作，以产生一个能够诊断水灾害预测需求的跨学科科学家和工程师的管道。这种合作生产的努力将导致使用人工智能，机器学习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。该项目预计将培养115名硕士和博士。学生，包括28名来自土木工程，地理和计算机科学的资助学员。学生招聘工作将侧重于传统上在学术界和水行业参与人数不足的群体。该项目将使学生接触到各种专业和模拟专业环境，并加强学生成为促进者，创新者和领导者的能力。该培训计划具有独特的模式，内容和交付，通过团队科学，基于挑战的学习，共同制作和迭代自我反思来建立能力。创新教育方面包括国内和国际学习图尔斯，模拟业务预测，实践实验室，圆桌讨论，混合辅导，体验式学习，实习，大规模和跨学科的团队建设和专业发展。该计划的毕业生将带来水文预报劳动力属性的独特组合。前两项是：（1）水文科学的深厚学科知识，加上（2）跨越人工智能和机器学习前沿的综合技能，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理解，以及（4）成为水文预报实践社区思想领袖的能力。学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，以表征水文过程的多尺度地球物理和气候驱动因素;（2）推进模型和预测工具，以减少水文预测的不确定性;（3）改善预测产品的沟通，以实现实际操作。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，将支持聚合研究的项目目标，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（NRT）计划和刺激竞争性研究的既定计划（EPSCoR）共同资助。NSF研究培训（NRT）计划旨在鼓励开发和实施大胆的，新的潜在变革模式，用于STEM研究生教育培训。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相一致的综合培训模式，在高优先级的跨学科或融合研究领域对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价值和更广泛的影响审查标准进行评估，被认为值得支持。