EASM-3: Physics-Based Predictive Modeling for Integrated Agricultural and Urban Applications

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MahalovDMS-1419593 Challenges associated with a rapidly rising global population that is increasingly food-insecure and lacks fundamental awareness of how to build tomorrow's sustainable cities necessitate urgent study in light of a rapidly urbanizing planet. Unrelenting urban population growth -- an increase of more than 2.5 billion new urban inhabitants is projected by 2050, relative to 2011 -- requires considerable conversion of natural to agricultural (to meet increased food demand) and to urban (to meet increased commercial, housing, and transportation demand) landscapes. Strategic adaptation plans require development to increase production of agricultural commodities, maximize land-use efficiency, enhance community engagement, decrease reliance on outsourced food, reduce transportation costs while enhancing profitability, and mitigate adverse impacts such as the urban heat island effect. Localizing food strategies within urban areas can therefore concurrently address concerns associated with food insecurity, environmental degradation, citizen health, and socioeconomic well-being. Development and refinement of physics-based predictive modeling and assessment tools used at fine spatial resolution is necessary to effectively quantify co-benefits and reveal tradeoffs prior to any strategy deployment. Firmly grounded in mathematical sciences and high resolution data analytics, a collaborative and interdisciplinary team from Arizona State University and the National Center for Atmospheric Research jointly develops integrated agricultural and urban models necessary to examine hydroclimatic impacts and economic and social benefits/tradeoffs associated with agricultural and urban land use/cover changes accompanying localization of food production within cities. Students and postdocs are trained in the course of this project. In addition to university-based investigators, the team includes investigators at the National Center for Atmospheric Research who are an integral part of the project. The project is funded jointly by the National Science Foundation and by the US Department of Agriculture. The overarching goal of this team, consisting of computational and climate scientists, mathematicians, statisticians, geoscientists, and social scientists, is to develop high-resolution physics-based, coupled, dynamic, and predictive capabilities that not only characterize current multi-scale environmental and socio-economic impacts associated with agricultural productivity within cities but also enable the prediction of future impacts. Feedback loops and nonlinear interactions interconnect physical and human processes. Understanding of emergent regional climate modifiers (e.g., agriculture, urbanization, etc.) on decadal scales cannot be realized simply by studying these components in isolation. Novel computational methods to accelerate and improve accuracy of multi-scale nested models are developed by the team and integrated within an interactively coupled urban-climate-agricultural model utilizing high-resolution land use/land cover data to examine scale dependency of simulated outcomes. The team develops a conceptual framework to evaluate economic and social impacts of community gardens, quantifies socioeconomic benefits, and recommends geographically dependent strategies for sustainable integrated agri-urban development. The advanced mathematical and statistical modeling tools are utilized to conduct ensemble-based regional hydroclimate simulations, focusing on a set of rapidly urbanizing and diverse megapolitan areas across multiple US climate zones. Because the geographic focus spans emerging and expanding megapolitan areas across the US, techniques, strategies, and prioritization for sustainable integrated agri-urban development can be applied globally for comparable climate zones. These studies advance scientific knowledge and develop next-generation predictive modeling capabilities for linked agricultural and urban climate dynamics on regional and decadal scales.