Flash flood warnings are largely reactive, providing limited advance notice for evacuation planning and resource prepositioning. This study presents and validates an anticipatory, parametric framework that converts landscape vulnerability and precipitation into transparent, zone-aware threat levels at neighborhood scales. We first derive an inherent hazard likelihood (IHL) surface using pluvial flood depth, height above nearest drainage, and distance to streams. Next, we compute a hazard severity index (HSI) by normalizing 24-hour rainfall against local Atlas-14 100-year, 24-hour depths. We then integrate IHL and HSI within a localized threat severity (LTS) matrix using 20 class-specific triggers, requiring lower exceedance in high-risk terrain and higher exceedance in uplands. Applied to two Texas flood events, the LTS exhibits statistically significant spatial association with independent crowdsourced impact proxies, capturing observed disruption hotspots. The framework is computationally lightweight, scalable, and extends actionable situational awareness into a 48-72 hour anticipatory window, supporting pre-event decision-making by emergency managers.

翻译：山洪预警多为被动响应式，难以为疏散规划和资源预置提供充足提前量。本研究提出并验证了一种前瞻性参数化框架，该框架将景观脆弱性与降水数据转化为邻里尺度下透明且分区感知的威胁等级。我们首先利用雨洪淹没深度、相对最近排水点高程及距河道距离，推导出固有灾害概率曲面。继而通过将24小时降雨量归一化至当地Atlas-14百年一遇24小时雨深基准，计算灾害严重度指数。随后采用包含20个类别特定阈值的本地化威胁严重度矩阵，将固有灾害概率与灾害严重度指数进行融合集成——高风险地形区要求较低的超标阈值，而高海拔区域则需较高阈值。在得克萨斯州两次洪水事件中的应用表明，该框架与独立众包影响代理指标具有统计显著的空间关联性，能有效捕捉观测到的灾害热点区域。本框架计算轻量、可扩展性强，可将可操作态势感知延伸至48-72小时的前瞻窗口，为应急管理人员的灾前决策提供支持。