- Ross Maciejewski (Principal Investigator)
- Hessam Sarjoughian (Co-Principal Investigator)
- Rimjhim Aggarwal (Co-Principal Investigator)
- Dave White (C0-Principal Investigator)
- Giuseppe Mascaro (Co-Principal Investigator)
- Arizona State University– Office of Research and Sponsored Projects
In recent years, there has been an increasing focus on the processes and interactions among food, energy, and water systems, or the so-called food-energy-water (FEW) nexus, and the resulting implications for sustainability, resilience, and security. Food represents agricultural trade and consumption and is a critical component of a region’s economy. Energy is required to supply and treat water for agriculture, municipal, and industrial uses, as well as to mechanize agricultural activities. Water is used for human and industrial consumption, crop irrigation, and energy production. While the multifaceted interactions between food, energy and water are often framed as threats or stresses of one system upon the others, basic understanding of the feedback dynamics is necessary for identifying synergies and potential efficiencies. Yet, despite known interrelationships at the FEW nexus, policy, planning, and management decisions for food, energy, and water are typically made in isolation from one another without full consideration of the tradeoffs between sectors. This is primarily because of the complexity of each isolated system, which makes understanding the interconnections between systems difficult to identify let alone assess in an integrated manner. Despite this challenge, knowledge of the linkages, synergies, and conflicts in the FEW nexus is desperately needed to provide evidence-based decision-making for policies in each sector that are most likely to produce positive effects in the other sectors. To achieve such integration, decision makers need to incorporate information about national, regional, and local scale impacts of food-energy-water interactions into the development of robust policy decisions across a range of future conditions. In this way, policies can be developed that can insure resilience of the FEW nexus under variable climate conditions and futures to help ensure public well-being and sustainable growth.
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. Additional information can be found at the project website (http://vader.lab.asu.edu/VA-INFEWS) including open source software, course learning modules and other material.
Articles produced by this research:
Middel, A., Lukasczyk, J., Maciejewski, R., Demuzere, M., & Roth, M. (2018). Sky View Factor footprints for urban climate modeling. Urban Climate, 25, 120–134. https://doi.org/10.1016/j.uclim.2018.05.004
White, D., Jones, J., Maciejewski, R., Aggarwal, R., & Mascaro, G. (2017). Stakeholder Analysis for the Food-Energy-Water Nexus in Phoenix, Arizona: Implications for Nexus Governance. Sustainability, 9(12), 2204. https://doi.org/10.3390/su9122204
Middel, A., Lukasczyk, J., Zakrzewski, S., Arnold, M., & Maciejewski, R. (2019). Urban form and composition of street canyons: A human-centric big data and deep learning approach. Landscape and Urban Planning, 183, 122–132. https://doi.org/10.1016/j.landurbplan.2018.12.001
Wang, H., Lu, Y., Shutters, S. T., Steptoe, M., Wang, F., Landis, S., & Maciejewski, R. (2019). A Visual Analytics Framework for Spatiotemporal Trade Network Analysis. IEEE Transactions on Visualization and Computer Graphics, 25(1), 331–341. https://doi.org/10.1109/tvcg.2018.2864844
Lu, Y., Wang, H., Landis, S., & Maciejewski, R. (2018). A Visual Analytics Framework for Identifying Topic Drivers in Media Events. IEEE Transactions on Visualization and Computer Graphics, 24(9), 2501–2515. https://doi.org/10.1109/tvcg.2017.2752166
Soni, U., Lu, Y., Hansen, B., Purchase, H. C., Kobourov, S., & Maciejewski, R. (2018). The Perception of Graph Properties in Graph Layouts. Computer Graphics Forum, 37(3), 169–181. https://doi.org/10.1111/cgf.13410
Sarjoughian, H. S. (2017). Restraining complexity and scale traits for component-based simulation models. In 2017 Winter Simulation Conference (WSC). IEEE. https://doi.org/10.1109/wsc.2017.8247824