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

点击翻译按钮获取中文摘要

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.