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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Cited articles

Agarwal, M., Maze, T. H., and Souleyrette, R. R.: Impacts of Weather on Urban Freeway Traffic Flow Characteristics and Facility Capacity, Proc. 2005 Mid-Continent Transp. Res. Symp., online, August 2005, pp. 18–19, https://www.researchgate.net/profile/Reginald-Souleyrette/publication/228720996 (last access: December 2022), 2005. a
Ahmadalipour, A. and Moradkhani, H.: A data-driven analysis of flash flood hazard, fatalities, and damages over the CONUS during 1996–2017, J. Hydrol., 578, 124106, https://doi.org/10.1016/j.jhydrol.2019.124106, 2019. a, b
Asquith, W. H., Roussel, M. C., Thompson, D. B., Cleveland, T. G., and Fang, X.: Summary of dimensionless Texas hyetographs and distribution of storm depth developed for Texas Department of Transportation Research Project, http://pubs.er.usgs.gov/publication/70176110 (last access: December 2022), 2005. a
Assumpção, T. H., Popescu, I., Jonoski, A., and Solomatine, D. P.: Citizen observations contributing to flood modelling: opportunities and challenges, Hydrol. Earth Syst. Sci., 22, 1473–1489, https://doi.org/10.5194/hess-22-1473-2018, 2018. a, b, c, d
Berndtsson, R., Becker, P., Persson, A., Aspegren, H., Haghighatafshar, S., Jönsson, K., Larsson, R., Mobini, S., Mottaghi, M., Nilsson, J., and Nordström, J.: Drivers of changing urban flood risk: A framework for action, J. Environ. Manag., 240, 47–56, https://doi.org/10.1016/j.jenvman.2019.03.094, 2019. a
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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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