Towards a Road Weather Impacts Prediction System:Phase 1 - Flash Flood-Related Impacts

This work presents a prototype of a rapidly-updating decision-support system (DSS) based on potentially impactful weather information, coupled with a routing decision support tool. This effort, developed in partnership with the Missouri Department of Transportation (MODOT), aims to aid in effectively responding to road-weather hazards and impacts. While the main focus of this project's initial stage is on road impacts from flash flooding, potential for implementing similar models for additional and distinct road hazards (such as winter weather) is the ultimate goal. The presented work will showcase the foundation for developing the weather impact prediction capabilities for the aforementioned DSS.

Based on a comprehensive data set of road closures due to flooding in the state of Missouri between 2017 and 2023, current efforts have produced an event-based data set, which incorporates diverse data sources such as MRMS precipitation estimates and accumulations, as well as FLASH rainfall average recurrence intervals and streamflow estimates. A data-driven approach was used to define event scales (spatial and temporal), for matching road closure reports to model and product outputs. A pilot study demonstrating the feasibility of implementing machine learning-based approaches was performed, which will provide state-wide probabilistic flash flood impacts on road networks. Results on both data set preparation and model implementation will be shown, with an emphasis towards prospective multi-hazard, real-time implementation of a scalable road weather impacts prediction system (RWIPS).