Title: Machine Learning advancements for design of water and energy policies in a changing climate and society

Abstract: Advances in environmental data monitoring and earth systems’ modeling have increasingly allowed us to accurately reproduce physical processes and their interactions at multiple scales, improving our ability to inform water and energy systems policy design. Yet, many process-based models are limited in predicting complex dynamics, which are the key to strategic planning, such as the impact of extreme weather and climate events or the mutual influence between humans and earth systems. Our work has shown that advanced data analytics and Machine Learning offer new opportunities to better characterize and model coupled human-earth system processes in a world in transition, ultimately supporting more equitable and efficient decision-making processes. In this talk, we provide an overview of recent advances in data-driven modeling and control of human-water-energy systems and showcase how Machine Learning techniques can help (i) infer natural and anthropogenic drivers of observed hydroclimatic patterns and improve their predictability in space and time, (ii) understand and conceptualize the mutual influences between human behaviors and water-energy systems; and (iii) design strategic planning and management policies optimizing multiple and conflicting objectives with different dynamics and informed by heterogeneous information. We recognize that this is a rapidly evolving research field, and we will also stimulate discussion around key challenges, including modeling decisions under uncertainty, model explain ability, data and computational requirements, model scalability, and transferability.