Reducing the Human Impacts of Flash Floods: Development of Microdata and Causal Model to Inform Mitigation and Preparedness

山洪在几乎没有预警时间的情况下袭击公众，其速度和力量使其成为最致命的自然灾害之一（以死亡人数与受影响人数的比例来衡量）。这个项目的目的是为了更好地理解为什么在山洪暴发期间存在不安全状况，以及如何减少或消除这些状况。前提是，解决问题的最好办法是纠正问题的根源，而不是对问题的症状做出反应。考虑到山洪暴发的局地性，这种灾害研究方法需要比现有的更精确的分辨率数据，而这个项目填补了这一空白。需要这些数据来了解导致死亡和伤害的完整情况，并设计有效的结构和非结构风险降低措施。本项目创建的新数据和模型原则可用于确定有效的结构性和非结构性干预措施，以便纳入减灾计划、应急计划和基本建设改善计划。这项研究将推动一个由土木工程、地理学、公共卫生和社会学组成的跨学科研究团队的学术势头，以改善公共安全和社区对山洪暴发的抵御能力。因此，该项目支持美国国家科学基金会的使命，即通过减少未来洪水造成的死亡人数，促进科学进步，促进国家的健康、繁荣和福利。本研究的目标是通过创建数据和框架，为山洪伤亡的因果路径建模，为预防提供信息，从而加强公共安全。指导本研究设计的研究问题是：(1)山洪伤亡的因果途径是什么？(2)易感地区的社区如何准备和减轻山洪暴发？该项目混合使用数据类型和研究方法来解决这些问题。利用创新的网络技术，将从网络上的结构化和非结构化数据源中获得过去10年来每次山洪事件和受害者的新精细数据。新数据将在公共领域提供，同时保护个人的匿名性并遵守原始来源设置的数据使用条款。该奖项反映了美国国家科学基金会的法定使命，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。

Flash floods hit with little lead time to warn the public and are of such velocity and force so as to make them one of the most lethal natural hazards (measured by the ratio of fatalities to people affected). The purpose of this project is to better understand why unsafe conditions exist during flash flood events, and how to reduce or eliminate these conditions. The premise is that problems are best solved by correcting their root causes, rather than reacting to their symptoms. Given the locality of flash floods, this approach to disaster research requires finer resolution data than currently exists, a gap this project fills. Such data are needed to understand the complete circumstances leading up to fatalities and injuries and to design effective structural and non-structural risk reduction measures. The new data and model principles created by this project can be used to identify effective structural and non-structural interventions for inclusion in hazard mitigation plans, emergency response plans, and capital improvement plans. This research will advance the scholarly momentum of an interdisciplinary team of investigators from civil engineering, geography, public health, and sociology to improve public safety and community resilience to flash flooding. Hence, the project supports NSF's mission to promote the progress of science and to advance the nation's health, prosperity, and welfare by reducing future fatalities from flash flooding. The goal of this research is to enhance public safety by creating the data and framework for modeling the causal pathways of flash flood fatalities and injuries to inform prevention. The research questions that guide the design of this study are: (1) What are the causal pathways to flash flood fatalities and injuries? and (2) How are communities in susceptible areas preparing for and mitigating against flash floods? The project uses a mixture of data types and research methods to address these questions. Using innovative web technologies, new fine-scale data will be obtained from structured and unstructured data sources on the web on each flash flood event and victim from the past 10 years. The new data will be made available in the public domain, while protecting the anonymity of individual persons and adhering to the terms of data usage set by the original sources.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.