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CNH: The Dynamics of Multi-Scalar Adaptation in Megacities

CNH: The Dynamics of Multi-Scalar Adaptation in Megacities
CNH:特大城市多标量适应的动态
批准号:
1414052
负责人:
Hallie Eakin
金额:
$149.89万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-08-15 至 2020-03-31

项目摘要

项目成果

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中文摘要
翻译
特大城市(定义为人口超过1,000万的城市)极易受到环境变化的影响,但其规模和复杂性可能使成功的适应变得困难。 这个跨学科的研究项目将研究在世界上最大的大都市地区之一减少对洪水增加,长期缺水和相关健康问题的脆弱性的挑战。 特大城市的居民、企业和公共机构以不同且往往不协调的方式应对极端情况,这些行动(如土地使用和基础设施的变化)可能会无意中在城市的其他地区造成危险。 该项目将建立一个动态系统模型,以探讨特定人群对未来不同环境变化情景的个人反应如何相互影响并产生意想不到的后果。 作为一个决策支持工具,该模型将使决策者能够测试如何改变风险管理优先事项或调整旨在应对不断变化的气候条件的干预措施的地理重点,从而改善总体结果。该模型将开发用于研究地点,并将随时调整,以减少地球仪复杂城市环境中不断变化的环境条件所带来的风险,该项目将与墨西哥城国立自治大学和当地利益攸关方密切合作实施。 将对墨西哥城内的脆弱地区进行四项个案研究,以说明居民遭受灾害的经历及其应对战略的复杂性。 城市一级的分析将掌握决策的体制背景以及城市一级机构和行为者的任务、干预战略和优先事项。 将开发一个生物物理模型,以模拟土地使用变化和基础设施改造对城市水文气候系统的影响。 将开发基于代理的建模来模拟居民和城市一级机构对空间差异水文气候风险的响应,该模型的输出与水文气候系统相关联。 嵌入多标准决策分析框架的地理信息系统将允许以空间上明确的方式探索不同的灾害应对方案,从而便利关于适应不断变化的水文气候条件的决策。 该项目将支持美国和墨西哥的跨学科和跨文化教育和培训。
英文摘要
Megacities (defined as cities with populations over 10 million) are highly vulnerable to environmental change but their size and complexity can make successful adaptation difficult. This interdisciplinary research project will study the challenge of reducing vulnerability to increased flooding, chronic water scarcity, and associated health problems in one of the world's largest metropolitan areas. Residents, businesses and public agencies in megacities respond to extremes in disparate and often uncoordinated ways, and these actions (such as changes in land use and infrastructure) may unintentionally create hazardous conditions in other parts of the city. The project will produce a dynamic systems model to explore how the individual responses of specific populations to different future scenarios of environmental change may affect one another and produce unintended consequences. As a decision-support tool, the model will enable decision-makers to test how altering risk-management priorities or adjusting the geographic focus of interventions aimed at dealing with changing climatic conditions could lead to improved overall results. The model will be developed for use in the study locale and would be readily adaptable for reducing risks from changing environmental conditions in complex urban environments across the globe.The project will be implemented in close collaboration with the National Autonomous University of Mexico City and local stakeholders. Four case studies of vulnerable regions within Mexico City will be conducted to characterize the complexity of residents' experience of hazards and their response strategies. City-level analyses will capture the institutional context of decision -making and the mandates, intervention strategies, and priorities of city-level agencies and actors. A biophysical model will be developed to simulate the impact of land-use change and infrastructure modification on the city's hydro-climatic system. Agent-based modeling will be developed to simulate the responses of residents and city-level agencies to spatially differentiated hydro-climatic risk, with the outputs from this model linked to the hydro-climatic system. A geographic information system embedded in a multi-criteria decision-analysis framework will permit the exploration of different scenarios of hazard response in a spatially explicit manner, thereby facilitating decision making concerning adaptation to changing hydro-climatic conditions. The project will support the interdisciplinary and cross-cultural education and training of U.S. and Mexico.
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U.S.-Mexico Planning Workshop: International Collaboration to Advance Sustainability Science in the U.S. and Mexico
  • 批准号:
    1132687
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.99万
  • 财政年份:
    2011
  • 负责人:
    Hallie Eakin
  • 依托单位:
International Research Fellowship Program: Disaster Preparedness and Response in Central Mexico: Towards an Adaption Baseline
  • 批准号:
    0401939
  • 项目类别:
    Fellowship
  • 资助金额:
    $0.0万
  • 财政年份:
    2004
  • 负责人:
    Hallie Eakin
  • 依托单位:
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  • 批准号:
  • 项目类别:
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  • 资助金额:
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  • 批准年份:
    2023
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