The majority of people in the world live in urban areas with high population densities, relying heavily on external sources of food, energy, and water, and producing disproportionately large amounts of waste. These phenomena, characteristic of many urban areas, result in serious and cumulative negative effects, such as increased energy consumption, greenhouse gas emissions and surface water pollution. The team will use simulation models and expert knowledge to guide assessment of current conditions for individual and combined systems in the urban food-energy-water (F-E-W) nexus, and to propose system improvements to increase local food production and simultaneously decrease environmental impacts. Investigators on this project will derive potential solutions by integrating social and biophysical models in a co-simulation approach to investigate these problems. The team will analyze current conditions and make future predictions focusing on local food production in urban and near-urban areas. The framework will include climate dynamics, land cover/land use changes, built forms, energy use, and environmental outcomes, with consideration of specific social, policy, crop management, technology, and market force scenarios. Project investigators will quantify environmental effects (energy use and water quality outcomes) for current food production systems. The team will then explore environmental effects and changes in local food supply under scenarios designed using data from producers and consumers in urban and near-urban areas. The Des Moines-West Des Moines Metropolitan Statistical Area will serve as a study area representative of cities in rain-fed agricultural regions. The team will collaborate with local stakeholders who are interested in improving local food systems as part of their sustainability strategies.
The team will develop an innovative approach to enable integration of social and biophysical models for urban and urban-adjacent food-energy-water (F-E-W) systems. The hypotheses guiding this research are: 1) data-driven co-simulation strategies will enable coupling of disparate F-E-W system simulation models across spatial and temporal scales; 2) the environmental footprint (energy use and water quality outcomes) for urban systems can be significantly reduced and food supply can be substantially increased through enhanced human food production in urban and urban-adjacent areas; and 3) the potential effects of changes (social, economic, and environmental) in urban areas and their adjacent landscapes will be synergistic. The team will create an empirical agent-based model that describes current actions and predicts the impacts of future decision-making by urban agricultural producers and consumers. This project will advance understanding of and enable projections to explore different scenarios for local food production to increase city resiliency and sustainability. The team will link parameterizations of single F-E-W system models that allow characterization of current and future conditions in individual systems as well as for the urban food-energy-water system-of-systems under predicted climate variability. The team will use co-simulations to explore the influence of individual drivers of system changes and allow analyses of critical system feedbacks, thresholds, and resiliency. This project will evaluate five specific drivers (related to policy, crop management, technology, social interactions and market forces) that influence human decisions leading to F-E-W systems changes. Local meteorological data for current conditions and predicted future conditions will drive modeling for building energy use, crop growth, and water dynamics. Changes in model outputs related to heat discharge from buildings and surfaces will be integrated as feedbacks for modeling future conditions and impacts on crop growth and urban water management. These analyses will provide critical new knowledge about how impacts of urban F-E-W systems can be reduced and local food supplies can be increased. The open-source coupling framework created in this project will be made available to ensure the broader research community can use it to analyze other F-E-W systems. This research will create scalable and transferable models that will support efforts to improve local food production, reduce energy use, and protect surface water quality in urban and urban-adjacent landscapes. This project is jointly funded by INFEWS Directorates (ENG, GEO and SBE and others) and the Established Program to Stimulate Competitive Research (EPSCoR); and managed by the GEOSCIENCES Directorate.
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.
