A Parametric Framework for Anticipatory Flashflood Warning: Integrating Landscape Vulnerability with Precipitation Forecasts

TL;DR

This paper introduces a lightweight, scalable framework that combines landscape vulnerability and precipitation forecasts to generate anticipatory flash flood threat levels at neighborhood scales, enhancing early warning capabilities.

Contribution

It presents a novel parametric, zone-aware threat assessment framework that integrates hazard likelihood and severity indices for proactive flood warning.

Findings

Framework accurately identified flood impact hotspots in Texas events.

Threat levels showed significant spatial correlation with impact proxies.

Supports 48-72 hour anticipatory decision-making for emergency management.

Abstract

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.