CAREER: CAS- Climate: Climate Adaptation Pathways in Eco-Hydrologic Systems with Physics-Informed Machine Learning

职业：CAS-气候：基于物理的机器学习在生态水文系统中的气候适应途径

• 批准号: 2144332
• 负责人: Scott Steinschneider
• 金额: $ 50.66万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144332&HistoricalAwards=false
• 关键词: CAREER; CAS; Climate; Adaptation; Pathways

Sustainable water resources planning and management in the 21st century requires adaptation strategies that are robust to deep uncertainties in future climate and environmental change. To manage this uncertainty, recent decision-making frameworks have promoted flexible adaptation pathways that respond dynamically to the trajectory of climate as it unfolds. This project will explore the hypothesis that the optimal design of flexible adaptation pathways – including the sequence, timing, and permanence of adaptation actions - depends on the mechanisms of dynamic and thermodynamic climate change influencing the water system, and the degree of natural climate variability and predictability across time scales. To test this hypothesis, this work will develop innovations in physics-informed machine learning that will enable process-guided climate simulation, hydrologic prediction, and sub-seasonal-to-seasonal forecasting that support risk-based adaptation planning. These approaches will be applied in the Lake Ontario eco-hydrologic system to examine three fundamental questions: 1) What are the primary patterns of dynamic and thermodynamic climate change over the Great Lakes, and how can they be integrated into risk-based simulation frameworks? 2) How do these climate mechanisms influence the hydrologic and ecological response of the Lake Ontario system and the diverse interests of stakeholders, and how predictable are these impacts at sub-seasonal to decadal timescales? and 3) How should adaptation pathways for lake level management and coastal resilience be designed to cope with these mechanisms of climate change? These questions will be addressed alongside a co-production model of community engagement and knowledge sharing with Great Lakes communities, students, and other stakeholders.This work will impact hydroclimatic modeling for eco-hydrologic systems by developing physics-informed machine learning techniques for feature identification, spatiotemporal modeling, emulation, functional dependence, and synthetic data generation, with the ability to propagate physically meaningful features through sequentially linked systems while accounting for uncertainty. The goal is to develop a computationally efficient and probabilistic modelling framework for risk-based simulation and forecasting needed to develop robust climate adaptation pathways. Expected outcomes include six major scientific advancements: 1) a diagnostic understanding of historical and projected future thermodynamic and dynamic climate processes relevant to eco-hydrologic systems; 2) the development of stochastic models that can reveal how those physical processes shape future climate risk to water infrastructure; 3) enhanced predictability of eco-hydrologic response to climate across sub-seasonal to decadal time scales; 4) credible emulation of system objectives to support uncertainty propagation in adaptation planning; 5) endogenous learning strategies to detect mechanisms of climate change from projections and noisy observations; and 6) the identification of general principles for how to develop adaptation pathways in water systems exposed to multi-scale climate variability and different mechanisms of climate change. This project will integrate research, teaching, and service missions through a pedagogic and scholarly model of community engagement that promotes knowledge co-production and translation between students, academics, extension and education specialists, Great Lakes communities, and an international board of water managers. Undergraduate and Graduate Education: Models and partnerships developed through this work will enhance community-engaged project experiences for undergraduate and graduate students in courses on hydrologic engineering, climate change, and machine learning. Active learning modules will embed data science literacy directly into these educational experiences. Student Training: This project will provide an interdisciplinary training experience for 1 PhD student, and undergraduates will also be recruited to participate through course-based research. Public Forums and K-12 Education: Through collaborations with the Sciencenter of Ithaca NY and New York Sea Grant, this work will develop public forums to educate and learn from Lake Ontario communities, particularly those in rural, low-income areas, about water level variability, management, and impacts on community resilience. These collaborations will also support middle school curriculum development, disseminated widely across the Great Lakes shoreline. Real-World Decision-Making: By collaborating with the International Joint Commission, this work will enhance the adaptive management plan of one of the largest managed, freshwater lakes in the world that is undertaking one of the largest wetland restoration efforts in North America.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.

21世纪的可持续水资源规划和管理需要适应未来气候和环境变化的深刻不确定性的战略，为了管理这种不确定性，最近的决策框架具有灵活的适应路径，可以动态响应气候变化的轨迹。该项目将探讨这样一个假设：灵活适应路径的优化设计（包括适应行动的顺序、时间安排和持久性）取决于影响水系统的动态和热力学气候变化机制以及自然气候的程度。随时间的变化和可预测性为了检验这一假设，这项工作将开发基于物理的机器学习创新，以实现过程引导的气候模拟、水文预测和次季节到季节的预测，从而支持基于风险的适应规划。将其应用于安大略湖生态水文系统，以研究三个基本问题：1）五大湖动态和热力学气候变化的主要模式是什么，以及如何将它们整合到基于风险的模拟框架中？这些气候机制影响安大略湖系统的水文和生态响应以及利益相关者的不同利益，这些影响在次季节到十年时间尺度上的可预测性如何？3）应如何设计湖泊水位管理和沿海恢复力的适应路径？应对气候变化的这些机制？这些问题将与五大湖社区、学生和其他利益相关者的社区参与和知识共享的共同生产模型一起得到解决。这项工作将通过发展物理学来影响生态水文系统的水文气候模型。 - 知情的机器学习特征识别、时空建模、仿真、函数依赖和合成数据生成技术，能够通过顺序链接的系统传播物理上有意义的特征，同时考虑不确定性。开发稳健的气候适应途径所需的基于模拟和预测的预期成果包括六项重大科学进展：1）对与生态水文系统相关的历史和预测的未来热力学和动态气候过程的诊断性了解；随机模型可以揭示这些物理过程如何影响水基础设施的未来气候风险；3）增强跨季节到十年时间尺度的气候生态水文响应的可预测性；4）可靠的系统目标模拟，以支持适应过程中的不确定性传播规划；5）从预测和噪声观测中检测气候变化机制的内生学习策略；6）确定如何在暴露于多尺度气候变化和不同气候机制的水系统中制定适应途径的一般原则；该项目将通过社区参与的教学和学术模式整合研究、教学和服务任务，促进学生、学者、推广和教育专家、五大湖社区和国际水务委员会之间的知识共同生产和翻译。本科生和研究生教育：通过这项工作开发的模型和合作伙伴关系将增强本科生和研究生在水文工程、气候变化和机器学习课程中的社区参与项目经验，并将数据科学素养直接嵌入这些课程中。教育经历。培训：该项目将为 1 名博士生提供跨学科培训体验，还将招募本科生通过基于课程的公共论坛和 K-12 教育参与：通过与纽约伊萨卡科学家和纽约海洋基金合作。这项工作将建立公共论坛，向安大略湖社区，特别是农村低收入地区的社区进行教育和学习，了解水位变化、管理和对社区复原力的影响。这些合作还将支持中学课程的开发和传播。广泛分布于五大湖海岸线的现实决策：通过与国际联合委员会合作，这项工作将加强世界上最大的管理淡水湖之一的适应性管理计划，该湖正在进行世界上最大的湿地恢复工作之一。北美。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。