Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-1-2023
https://doi.org/10.5194/nhess-23-1-2023
Research article
 | 
05 Jan 2023
Research article |  | 05 Jan 2023

Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis

Arefeh Safaei-Moghadam, David Tarboton, and Barbara Minsker

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Latest update: 29 Jun 2024
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Short summary
Climate change, urbanization, and aging infrastructure contribute to flooding on roadways. This study evaluates the potential for flood reports collected from Waze – a community-based navigation app – to predict these events. Waze reports correlate primarily with low-lying depressions on roads. Therefore, we developed two data-driven models to determine whether roadways will flood. Analysis showed that in the city of Dallas, drainage area and imperviousness are the most significant contributors.
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