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性的策略，这些策略在未来的气候和环境变化中对深度不确定性具有牢固的态度。为了管理这种不确定性，最近的决策框架促进了灵活的适应途径，这些途径对攀登的发展轨迹动态反应。该项目将探讨以下假设：柔性适应途径的最佳设计（包括适应性动作的序列，时机和永久性）取决于动态和热力学气候变化的机制影响水系统，以及自然气候变化的程度和跨时间范围的预测性。为了检验这一假设，这项工作将开发物理知识的机器学习中的创新，这些创新将使过程引导的气候模拟，水文预测以及季节性到季节的预测，以支持基于风险的适应计划。这些方法将应用于安大略湖生态流行系统来检查三个基本问题：1）大湖区动态和热力学气候变化的主要模式是什么？如何将它们整合到基于风险的模拟框架中？ 2）这些气候机制如何影响安大略湖系统的水文和生态反应以及利益相关者的不同利益，以及这些对十年时间表的亚季节的影响有多可预测？ 3）如何设计适应湖泊水平管理和沿海弹性的适应途径来应对这些气候变化机制？ 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, spatial temporal modeling, emulation, functional dependence, and synthetic data generation, with the ability to propagate physically meaningful features through sequentially linked systems while考虑不确定性。目的是为基于风险的模拟和开发强大的气候适应途径所需的计算高效和概率建模框架开发一个计算高效和概率的建模框架。预期的结果包括六个主要的科学进步：1）对与生态流行系统相关的历史和预计未来热力学和动态气候过程的诊断理解； 2）随机模型的发展，这些模型可以揭示这些物理过程如何影响未来的水基础设施气候风险； 3）增强了生态流行病学响应对跨季节至十年时间尺度的攀爬的可预测性； 4）可靠的模拟系统目标，以支持适应计划中的不确定性传播； 5）内源性学习策略，以检测项目和噪声观察的气候变化机制； 6）鉴定如何在暴露于多尺度气候变化的水系统中发展适应途径的一般原则和气候变化的不同机制。该项目将通过社区参与的教学和科学模型来整合研究，教学和服务任务，该模型促进知识的共同生产和翻译，学者，扩展和教育专家，大湖社区和国际水管理人员委员会。本科和研究生教育：通过这项工作建立的模型和合作伙伴关系将增强在水文工程，气候变化和机器学习课程中的本科和研究生社区参与的项目经验。主动学习模块将数据科学素养直接纳入这些教育经验中。学生培训：该项目将为1位博士生提供跨学科的培训经验，并且还将招募本科生通过基于课程的研究参与。公共论坛和K-12教育：通过与纽约州伊萨卡大学和纽约海赠款的科学者的合作，这项工作将开发公共论坛，以向安大略湖社区（尤其是在粗糙，低收入领域，关于水位水平的可变性，管理，管理，管理，管理和对社区弹性的影响）的教育和学习。这些合作还将支持中学课程的开发，并在大湖海岸线上广泛传播。现实世界的决策：通过与国际联合委员会合作，这项工作将增强世界上最大的管理，淡水湖泊之一的自适应管理计划，该计划是北美最大的湿地恢复工作之一。这项奖项反映了NSF的法定任务，并通过评估了基金会的智力效果，并诚实地对其进行了评估。