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

马哈洛夫-DMS-1419593与快速增长的全球人口相关的挑战，这些人口越来越缺乏粮食保障，并且缺乏对如何建设明天的可持续城市的基本认识，鉴于地球迅速城市化，迫切需要进行研究。持续不断的城市人口增长--预计到2050年将比2011年增加超过25亿城市居民--需要大量将自然景观转变为农业景观(以满足日益增长的粮食需求)和城市景观(以满足日益增长的商业、住房和交通需求)。战略适应计划需要发展以增加农业初级商品的生产，最大限度地提高土地利用效率，加强社区参与，减少对外包粮食的依赖，降低运输成本，同时提高盈利能力，并减轻城市热岛效应等不利影响。因此，在城市地区将粮食战略本地化可以同时解决与粮食不安全、环境退化、公民健康和社会经济福祉有关的关切。在部署任何战略之前，有必要开发和完善在精细空间分辨率下使用的基于物理的预测建模和评估工具，以便有效地量化协同效益并揭示权衡取舍。一个来自亚利桑那州立大学和国家大气研究中心的合作和跨学科团队以数学科学和高分辨率数据分析为坚实基础，共同开发了必要的农业和城市综合模型，以检查与农业和城市土地利用/覆盖变化相关的水文气候影响以及与城市内食品生产本地化相关的经济和社会效益/权衡。学生和博士后在这个项目的过程中接受培训。除了大学的研究人员外，该团队还包括国家大气研究中心的研究人员，他们是该项目不可或缺的一部分。该项目由美国国家科学基金会和美国农业部联合资助。这个由计算和气候科学家、数学家、统计学家、地球科学家和社会科学家组成的团队的首要目标是开发高分辨率的、基于物理的、耦合的、动态的和预测的能力，这些能力不仅表征当前与城市农业生产力相关的多尺度环境和社会经济影响，而且能够预测未来的影响。反馈回路和非线性相互作用将物理过程和人类过程相互联系起来。了解新出现的区域气候变化因素(如农业、城市化等)在十年尺度上，不能简单地孤立地研究这些成分。该小组开发了新的计算方法来加速和提高多尺度嵌套模型的精度，并将其集成到一个互动耦合的城市-气候-农业模型中，利用高分辨率的土地利用/土地覆盖数据来检查模拟结果的尺度相关性。该小组开发了一个概念框架，以评估社区花园的经济和社会影响，量化社会经济效益，并为可持续的农业-城市综合发展建议地理上的战略。利用先进的数学和统计建模工具进行基于整体的区域水文气候模拟，重点关注美国多个气候区的一组快速城市化和多样化的大都市地区。由于地理焦点横跨美国各地新兴和不断扩大的特大城市地区，可持续农业-城市一体化发展的技术、战略和优先顺序可以在全球范围内应用于可比较的气候地区。这些研究促进了科学知识的发展，并开发了区域和十年尺度上农业和城市气候动态相互关联的下一代预测建模能力。

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.