CIVIC-PG Track B: Creating the West Virginia Flood Resilience Framework for comprehensive disaster response and long-term community recovery

CIVIC-PG 轨道 B:创建西弗吉尼亚州防洪框架,以实现全面灾难响应和长期社区恢复

基本信息

项目摘要

Community resilience is the ability of communities to withstand and mitigate the stress of a disaster and is a key component of long-term recovery from natural disasters such as floods. Community resilience requires effective pre-disaster planning and coordination of the complex network of actors involved in disaster response. Every county in West Virginia (WV) has a high risk of flooding and these risks are compounded by increasing frequency and intensity of precipitation, aging infrastructure, and high levels of socioeconomic vulnerability. This project is a collaboration between academic researchers and civic actors and will identify lessons learned during response and recovery efforts to a major flood in 2016 in WV. This flood devastated several small towns, killed 23 people, and caused over $1 billion in damage across the state. In 2022, some communities still have not made a full recovery and lessons from the 2016 event have yet to be clearly identified or incorporated into local or statewide planning. Project findings from this Civic Innovation Challenge (CIVIC) project will be reported back to communities and policymakers to be used in future planning efforts, as well as to procure funding to increase resilience. The team will build on insights from the planning grant to develop a Stage 2 project to create the West Virginia Flood Resilience Framework (WVFRF), which will be a comprehensive online toolkit to empower communities with the knowledge they need for coordination and capacity building to prepare for future floods, which will have broad application to flood-prone communities across the US.The goals of this project are to determine the gaps in organizational capacity, cross-organization coordination, and flood risk knowledge that need to be filled for more comprehensive flood response and long-term flood recovery in WV. The team will integrate findings from a county-wide survey, Participatory GIS (PGIS), and focus groups in two case study sites with different levels of socioeconomic vulnerability that impacted success of long-term recovery to provide a comprehensive understanding of gaps in flood response and recovery. As a result of a novel integration of quantitative, qualitative, and PGIS methods, the project will have insights for scholarship on building resilience to natural disaster in socioeconomically vulnerable communities while also presenting a unique transdisciplinary model for community-engaged research that centers co-production of knowledge and creates a comprehensive approach to disaster response and recovery.This project is in response to the Civic Innovation Challenge program—Track B. Bridging the gap between essential resources and services & community needs—and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy.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.
社区复原力是社区抵御和减轻灾害压力的能力,是洪水等自然灾害长期恢复的关键组成部分。社区复原力需要有效的灾前规划和复杂的参与者网络的协调。西弗吉尼亚州 (WV) 的每个县都面临着很高的洪水风险,而且降水频率和强度不断增加、基础设施老化以及社会经济脆弱性较高,这些风险变得更加复杂。公民行动者并将确定在应对和恢复过程中吸取的经验教训2016 年西弗吉尼亚州发生的一场大洪水摧毁了多个小镇,造成 23 人死亡,并在全州造成了超过 10 亿美元的损失。2022 年,一些社区仍未完全恢复,并从 2016 年的事件中吸取了教训。尚未明确确定或纳入地方或全州规划。该公民创新挑战 (CIVIC) 项目的项目结果将报告给社区和政策制定者,用于未来的规划工作以及采购。该团队将根据规划赠款的见解开发第二阶段项目,以创建西弗吉尼亚州洪水抵御能力框架(WVFRF),该框架将是一个全面的在线工具包,为社区提供协调所需的知识。和能力建设,为未来的洪水做好准备,这将广泛应用于美国各地的洪水易发社区。该项目的目标是确定需要解决的组织能力、跨组织协调和洪水风险知识方面的差距。为更全面的洪水响应和该团队将整合全县范围的调查、参与式 GIS (PGIS) 和两个案例研究地点的焦点小组的调查结果,这些案例研究地点具有不同程度的社会经济脆弱性,影响了长期恢复的成功。由于定量、定性和 PGIS 方法的新颖整合,该项目将为经济脆弱社区建立抵御自然灾害的能力提供学术见解,同时也提出独特的跨学科研究。以社区参与研究为中心的模型该项目是为了响应公民创新挑战计划(Track B)。弥合基本资源和服务与社区需求之间的差距,是 NSF、该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优点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

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Aaron Maxwell其他文献

Aaron Maxwell的其他文献

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{{ truncateString('Aaron Maxwell', 18)}}的其他基金

CAREER: Mapping Anthropocene Geomorphology with Deep Learning, Big Data Spatial Analytics, and LiDAR
职业:利用深度学习、大数据空间分析和激光雷达绘制人类世地貌图
  • 批准号:
    2046059
  • 财政年份:
    2021
  • 资助金额:
    $ 4.98万
  • 项目类别:
    Continuing Grant

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  • 批准号:
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