The advent of detailed hydrodynamic model simulations of urban flooding has not been matched by improved capabilities in flood exposure analysis which rely on validation against observed data. This work introduces a generic, building-level flood exposure analysis tool applying high resolution flood data and building geometries derived from hydrodynamic simulations performed with the 2D hydrodynamic flood modelling software CityCAT. Validation data were obtained from a survey of affected residents following a large pluvial flood event in Newcastle upon Tyne, UK. Sensitivity testing was carried out for different hydrodynamic model and exposure tool settings and between 68% and 75% of the surveyed buildings were correctly modelled as either flooded or not flooded. The tool tends to underrepresent flooding with a better performance in identifying true negatives (i.e. no flooding observed with no flooding modelled) compared to true positives. As higher true positive rates were accompanied by higher false positive rates, no single scenario could be identified as the optimal solution. However, the results suggest a greater sensitivity of the results to the classification scheme than to the buffer distance applied. Overall, if applied to high resolution flood depth maps, the method is efficient and suitable for application to large urban areas for flood risk management and insurance analysis purposes.

城市洪水的详细水动力模型模拟的出现并没有与洪水暴露分析能力的提高相匹配，而洪水暴露分析依赖于观察数据的验证。这项工作介绍了一种通用的建筑物级洪水暴露分析工具，该工具应用高分辨率洪水数据和建筑物几何形状，这些几何图形源自使用 2D 水动力洪水建模软件 CityCAT 执行的水动力模拟。验证数据来自于英国泰恩河畔纽卡斯尔发生大型雨洪事件后对受影响居民的调查。对不同的水动力模型和暴露工具设置进行了敏感性测试，68% 到 75% 的被调查建筑物被正确建模为被淹或未被淹。与真阳性相比，该工具倾向于低估洪水，在识别真阴性（即未观察到洪水而未建模洪水）方面具有更好的性能。由于较高的真阳性率伴随着较高的假阳性率，因此无法将单一方案确定为最佳解决方案。然而，结果表明结果对分类方案的敏感性高于对应用的缓冲距离的敏感性。总体而言，如果应用于高分辨率洪水深度图，该方法是有效的，适用于大城市地区的洪水风险管理和保险分析目的。没有一种方案可以被确定为最佳解决方案。然而，结果表明结果对分类方案的敏感性高于对应用的缓冲距离的敏感性。总体而言，如果应用于高分辨率洪水深度图，该方法是有效的，适用于大城市地区的洪水风险管理和保险分析目的。没有一种方案可以被确定为最佳解决方案。然而，结果表明结果对分类方案的敏感性高于对应用的缓冲距离的敏感性。总体而言，如果应用于高分辨率洪水深度图，该方法是有效的，适用于大城市地区的洪水风险管理和保险分析目的。