Flexible Model Compositions and Visual Representations for Planning and Policy Decisions for the Food-Energy-Water Nexus

The overarching goal of this research is to develop basic interdisciplinary scientific understanding of food, energy, and water system dynamics to inform an integrated modeling, visualization, and decision support infrastructure for comprehensive FEW systems. This will require the development of (i) a multi-resolution integrated modeling framework that explicitly captures the feedbacks among food, energy and water sectors; and (ii) a visualization infrastructure that enables model composition and reveals cascading effects across the three FEW areas as well as their multivariate spatiotemporal uncertainties. Such an infrastructure needs to be easy to use with a seamless integration of analytical and visual tools, adaptable to new algorithms, and should empower individuals to gain knowledge about their data and the associated uncertainty. To test the proposed framework, the focus of this project will be on a use case in Arizona, the Phoenix Active Management Area (AMA). The Phoenix AMA is a compelling case study for exploring the FEW nexus at sub-regional scale and is ideal to design and test an integrated modeling, visualization, and decision support framework to interactively explore the interconnections of the FEW systems and support effective resource management and human decision making. While the focus is on a specific study region, the long-term outcome of this proposal is to create a flexible and multiscale visualization and decision support infrastructure that could be easily adapted to other locations. Broader impacts of the research program include: 1) infrastructure for policy, research and education in the form of an anticipatory modeling framework; 2) expanding research in decision making under uncertainty for sustainability within the context of multi-directional linkages to FEW nexus, and; 3) enhanced partnerships between computer science, hydrology, agriculture, economics and sustainability to encourage the development of infrastructure that enables model coupling, anticipatory analysis and stakeholder engagement.