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      Iowa Urban FEWS: Integrating Social and Biophysical Models for Exploration of Urban Food, Energy, and Water Systems

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          Abstract

          Most people in the world live in urban areas, and their high population densities, heavy reliance on external sources of food, energy, and water, and disproportionately large waste production result in severe and cumulative negative environmental effects. Integrated study of urban areas requires a system-of-systems analytical framework that includes modeling with social and biophysical data. We describe preliminary work toward an integrated urban food-energy-water systems (FEWS) analysis using co-simulation for assessment of current and future conditions, with an emphasis on local (urban and urban-adjacent) food production. We create a framework to enable simultaneous analyses of climate dynamics, changes in land cover, built forms, energy use, and environmental outcomes associated with a set of drivers of system change related to policy, crop management, technology, social interaction, and market forces affecting food production. The ultimate goal of our research program is to enhance understanding of the urban FEWS nexus so as to improve system function and management, increase resilience, and enhance sustainability. Our approach involves data-driven co-simulation to enable coupling of disparate food, energy and water simulation models across a range of spatial and temporal scales. When complete, these models will quantify energy use and water quality outcomes for current systems, and determine if undesirable environmental effects are decreased and local food supply is increased with different configurations of socioeconomic and biophysical factors in urban and urban-adjacent areas. The effort emphasizes use of open-source simulation models and expert knowledge to guide modeling for individual and combined systems in the urban FEWS nexus.

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          Most cited references55

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          Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

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            Climate Change and Food Systems

            Food systems contribute 19%–29% of global anthropogenic greenhouse gas (GHG) emissions, releasing 9,800–16,900 megatonnes of carbon dioxide equivalent (MtCO2e) in 2008. Agricultural production, including indirect emissions associated with land-cover change, contributes 80%–86% of total food system emissions, with significant regional variation. The impacts of global climate change on food systems are expected to be widespread, complex, geographically and temporally variable, and profoundly influenced by socioeconomic conditions. Historical statistical studies and integrated assessment models provide evidence that climate change will affect agricultural yields and earnings, food prices, reliability of delivery, food quality, and, notably, food safety. Low-income producers and consumers of food will be more vulnerable to climate change owing to their comparatively limited ability to invest in adaptive institutions and technologies under increasing climatic risks. Some synergies among food security, adaptation, and mitigation are feasible. But promising interventions, such as agricultural intensification or reductions in waste, will require careful management to distribute costs and benefits effectively.
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              LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT

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                Author and article information

                Contributors
                Journal
                Front Big Data
                Front Big Data
                Front. Big Data
                Frontiers in Big Data
                Frontiers Media S.A.
                2624-909X
                05 May 2021
                2021
                : 4
                : 662186
                Affiliations
                [ 1 ]Natural Resource Ecology and Management, Iowa State University, Ames, IA, United States
                [ 2 ]Mechanical Engineering, Iowa State University, Ames, IA, United States
                [ 3 ]Department of Geological and Atmospheric Science, Iowa State University, Ames, IA, United States
                [ 4 ]Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, United States
                [ 5 ]Center for Agricultural and Rural Development, Iowa State University, Ames, IA, United States
                [ 6 ]Industrial, Manufacturing and Systems Engineering, University of Texas, Arlington, TX, United States
                [ 7 ]Department of Agronomy, Iowa State University, Ames, IA, United States
                [ 8 ]Department of Horticulture, Iowa State University, Ames, IA, United States
                [ 9 ]Department of Architecture, Iowa State University, Ames, IA, United States
                [ 10 ]Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, United States
                [ 11 ]Department of Psychology, University of Northern Iowa, Cedar Falls, IA, United States
                Author notes

                Edited by: Ahmed A. Mohamed, City College of New York (CUNY), United States

                Reviewed by: Zachary Langford, Oak Ridge National Laboratory (DOE), United States

                Bjorn J. Brooks, Cooperative Institute for Climate and Satellites, United States

                *Correspondence: Jan Thompson, jrrt@ 123456iastate.edu

                This article was submitted to Data-driven Climate Sciences, a section of the journal Frontiers in Big Data

                Article
                662186
                10.3389/fdata.2021.662186
                8132197
                ea23566a-51f3-401f-bf9e-0008637de121
                Copyright © 2021 Thompson, Ganapathysubramanian, Chen, Dorneich, Gassman, Krejci, Liebman, Nair, Passe, Schwab, Rosentrater, Stone, Wang, Zhou.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 January 2021
                : 21 April 2021
                Categories
                Big Data
                Original Research

                urban fews,agent-based model (abm),life cycle assessment (lca),soil and water assessment tool (swat),building energy use (energyplus),co-simulation

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