Effective assessment of flood vulnerability and risk is essential for communities to manage flood hazards. This paper presents an empirical modeling methodology to predict flood vulnerability and risk, considering factors of hazard distribution, property exposure, built environment, and socio-demographic and economic characteristics of a community. Vulnerability is empirically modeled as the expected fraction of property loss that is uninsured within a community (i.e., census tract) given water depth. Risk is derived as the expected annual uninsured property loss and loss ratio. The proposed framework is applied to the state of North Carolina in the United States. For model calibration, modeled flood loss data from Hurricanes Matthew in 2016 and Florence in 2018 and insurance claims data from the Federal Insurance and Mitigation Administration's National Flood Insurance Program are used. The Federal Emergency Management Agency's National Flood Hazard Layer is adopted, along with empirical probability distribution of water depth given flood event, to characterize hazard distribution. Results demonstrate how the presented methodology can be used to predict annual loss in terms of currency and to highlight hotspots of flood vulnerability and risk. Future work is needed to reduce uncertainty associated with limited hazard information available to the public.

中文翻译：

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社区洪水脆弱性和风险评估：一种经验预测建模方法

有效评估洪水脆弱性和风险对于社区管理洪水灾害至关重要。本文提出了一种经验建模方法来预测洪水脆弱性和风险，考虑到灾害分布、财产暴露、建筑环境以及社区的社会人口和经济特征等因素。脆弱性根据经验建模为给定水深的社区（即人口普查区）内未投保的财产损失的预期比例。风险是根据预期的年度未投保财产损失和损失比率得出的。提议的框架适用于美国北卡罗来纳州。对于模型校准，来自 2016 年飓风马修和 2018 年佛罗伦萨的模拟洪水损失数据以及来自联邦保险和减灾管理局的保险索赔数据 使用国家洪水保险计划。采用联邦紧急事务管理局的国家洪水灾害层以及给定洪水事件的水深经验概率分布来表征灾害分布。结果展示了所提出的方法如何可用于预测货币方面的年度损失并突出洪水脆弱性和风险的热点。未来的工作需要减少与公众可获得的有限危害信息相关的不确定性。结果展示了所提出的方法如何可用于预测货币方面的年度损失并突出洪水脆弱性和风险的热点。未来的工作需要减少与公众可获得的有限危害信息相关的不确定性。结果展示了所提出的方法如何可用于预测货币方面的年度损失并突出洪水脆弱性和风险的热点。未来的工作需要减少与公众可获得的有限危害信息相关的不确定性。