This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.

中文翻译：

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大型湖泊系统中程洪水预报的概率，地块水淹预测工具

这项研究为浴缸式淹没预测模型做出了贡献，该模型具有沿海过程（即风暴潮和波浪上升）的抽象概念，可用于在大湖沿岸的中期（每周至每月）时间范围内进行洪水预报。通过模型传播来自多个数据源的不确定性，以建立淹没的概率界限，从而提供了保守的风险度量。该模型是在对安大略湖纽约湖岸线的案例研究中开发的，该河网在三年中（2017-2019年）经历了两次创纪录的洪水。预测是在包裹级别上开发的，并使用在线调查中的淹没帐户和最近洪水事件期间拍摄的立交图像进行验证。将模型预测与基准进行比较，确定性模型，该模型说明了相同的过程，但不会传播前瞻性数据不确定性。结果表明，概率方法有助于捕获观察到的淹没实例，否则确定性淹没模型将无法捕获这些实例。但是，在概率预测中仍然存在向下偏差，尤其是对于受波浪上升影响的包裹而言。该工具的目的是为社区规划者和财产所有者提供关于洪水风险的保守的，包裹级的评估，以帮助告知短期应急响应并更好地为未来的洪水事件做好准备。概率预测中仍然存在向下偏差，尤其是受波浪加速影响的包裹。该工具的目的是为社区规划人员和财产所有者提供关于洪水风险的保守的，包裹级的评估，以帮助告知短期应急响应并为未来的洪水事件做好更好的准备。概率预测中仍然存在向下偏差，尤其是受波浪加速影响的包裹。该工具的目的是为社区规划者和财产所有者提供关于洪水风险的保守的，包裹级的评估，以帮助告知短期应急响应并更好地为未来的洪水事件做好准备。