INFEWS / T1: Social and biophysical models to integrate local food systems, climate dynamics, built forms, and environmental impacts in the urban FEWS nexus

INFEWS / T1：将当地粮食系统、气候动态、建筑形式和环境影响融入城市 FEWS 关系的社会和生物物理模型

• 批准号: 1855902
• 负责人: Janette Thompson
• 金额: $ 250万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855902&HistoricalAwards=false
• 关键词: INFEWS; T1; Social; biophysical; models

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.

世界上大多数人生活在人口密度高的城市地区，严重依赖外部的食物、能源和水资源，并产生不成比例的大量废物。 这些现象是许多城市地区的特点，造成严重和累积的负面影响，如能源消耗增加、温室气体排放和地表水污染。该团队将利用模拟模型和专家知识指导对城市粮食-能源-水（F-E-W）关系中单个和组合系统的当前状况进行评估，并提出系统改进建议，以增加当地粮食产量，同时减少环境影响。该项目的研究人员将通过将社会和生物物理模型整合到共同模拟方法中来研究这些问题，从而获得潜在的解决方案。 该小组将分析当前的情况，并对城市和近城市地区的当地粮食生产进行未来预测。该框架将包括气候动态、土地覆盖/土地利用变化、建筑形式、能源使用和环境结果，并考虑到具体的社会、政策、作物管理、技术和市场力量情景。项目调查人员将量化当前粮食生产系统的环境影响（能源使用和水质结果）。然后，研究小组将根据城市和近城市地区生产者和消费者的数据设计的情景，探索环境影响和当地粮食供应的变化。得梅因-西得梅因大都市统计区将作为代表雨水灌溉农业区城市的研究区。该团队将与有兴趣改善当地粮食系统作为其可持续发展战略的一部分的当地利益攸关方合作，开发一种创新方法，以整合城市和城市周边粮食-能源-水（F-E-W）系统的社会和生物物理模型。指导本研究的假设是：1）数据驱动的协同仿真策略将使不同的F-E-W系统仿真模型在空间和时间尺度上耦合; 2）环境足迹通过加强城市和城市周边地区的人类粮食生产，可以大幅减少城市系统的能源使用和水质结果，并大幅增加粮食供应; 3）城市地区及其邻近景观的变化（社会、经济和环境）的潜在影响将是协同的。该团队将创建一个基于代理人的经验模型，描述当前的行动，并预测城市农业生产者和消费者未来决策的影响。 该项目将促进对当地粮食生产不同情景的理解和预测，以提高城市的复原力和可持续性。该团队将把单一F-E-W系统模型的参数化联系起来，这些模型允许在预测的气候变化下表征单个系统以及城市粮食-能源-水系统的当前和未来条件。 该团队将使用联合模拟来探索系统变化的各个驱动因素的影响，并允许分析关键系统反馈，阈值和弹性。该项目将评估五个具体的驱动因素（与政策，作物管理，技术，社会互动和市场力量有关），这些因素影响人类决策，导致F-E-W系统的变化。当前条件和预测未来条件的当地气象数据将推动建筑能源使用，作物生长和水动力学的建模。与建筑物和表面的热量排放相关的模型输出的变化将被整合为反馈，用于模拟未来的条件和对作物生长和城市水管理的影响。这些分析将为如何减少城市F-E-W系统的影响和增加当地粮食供应提供重要的新知识。该项目中创建的开源耦合框架将被提供，以确保更广泛的研究社区可以使用它来分析其他F-E-W系统。这项研究将创建可扩展和可转移的模型，以支持改善当地粮食生产，减少能源使用，保护城市和城市周边景观的地表水质量。 该项目由INFEWS董事会（ENG，GEO和SBE等）和激励竞争研究的既定计划（EPSCoR）共同资助;并由GEOSCIENCES董事会管理。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Modeling a localized metropolitan food system in the Midwest USA: Life cycle impacts of scenarios for Des Moines, Iowa

对美国中西部本地化大都市粮食系统进行建模：爱荷华州得梅因情景的生命周期影响

• DOI: 10.1016/j.scitotenv.2022.161095
• 发表时间: 2023
• 期刊: Science of The Total Environment
• 影响因子: 9.8
• 作者: Stone, Tiffanie F.;Thompson, Janette R.;Rosentrater, Kurt A.;Liebman, Matt
• 通讯作者: Liebman, Matt

A Life Cycle Assessment Approach for Vegetables in Large-, Mid-, and Small-Scale Food Systems in the Midwest US

美国中西部大、中、小型食品系统中蔬菜的生命周期评估方法

• DOI: 10.3390/su132011368
• 发表时间: 2021
• 期刊: Sustainability
• 影响因子: 3.9
• 作者: Stone, Tiffanie F.;Thompson, Janette R.;Rosentrater, Kurt A.;Nair, Ajay
• 通讯作者: Nair, Ajay

Computational study of natural ventilation in a sustainable building complex geometry

可持续建筑综合体几何形状中自然通风的计算研究

• DOI: 10.1016/j.seta.2021.101153
• 发表时间: 2021
• 期刊: Sustainable energy technologies and assessments
• 影响因子: 8
• 作者: Passe, U;Ganapathysubramanian, B
• 通讯作者: Ganapathysubramanian, B

A systematic review of social equity in FEWS analyses

• DOI: 10.3389/fenvs.2023.1028306
• 发表时间: 2023-03
• 作者: Tiffanie F. Stone;L. C. Dickey;Haleigh Summers;Jan R. Thompson;C. Rehmann;E. Zimmerman;J. Tyndall

Evaluation of bias correction methods for current and future RCM projections in hydrological regional applications

水文区域应用中当前和未来 RCM 预测偏差校正方法的评估

• DOI: 10.5194/egusphere-egu22-10600
• 发表时间: 2022
• 期刊: European Union General Assembly 2022 Abstracts
• 作者: Brighenti, T.;Gassman, P.;Gutowski, W.;Thompson, J.
• 通讯作者: Thompson, J.