04 Apr 2022
04 Apr 2022
Status: this preprint is currently under review for the journal NHESS.

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

Arefeh Safaei-Moghadam1, David Tarboton2, and Barbara Minsker1 Arefeh Safaei-Moghadam et al.
  • 1Department of Civil and Environmental Engineering, Southern Methodist University, Dallas, TX, USA
  • 2Department of Civil and Environmental Engineering, Utah Water Research Laboratory, Utah State University, Logan, Utah, USA

Abstract. Water ponding and pluvial flash flooding (PFF) on roadways can pose a significant risk to drivers. Furthermore, climate change, growing urbanization, increasing imperviousness, and aging stormwater infrastructure have increased the frequency of these events. Using physics-based models to predict pluvial flooding at the road segment scale requires notable terrain simplifications and detailed information that is often not available at fine scales (e.g., blockage of stormwater inlets). This brings uncertainty into the results, especially in highly urbanized areas where micro-topographic features typically govern the actual flow dynamics. This study evaluates the potential for flood observations collected from Waze–a community-based navigation app–to estimate the likelihood of PFF at the road segment scale. We investigated the correlation of the Waze flood reports with well-known flood observations and maps, including the National Flood Hazard Layer (NFHL), high watermarks, and low water crossings data inventories. In addition, highly-localized surface depressions and their catchments are derived from a 1-meter-resolution bare-earth digital elevation model (BE-DEM) to investigate the spatial association of Waze flood reports. This analysis showed that the highest correlation of Waze flood reports exists with local surface depressions rather than river flooding, indicating that they are potentially useful indicators of PFF. Accordingly, two data-driven models, Empirical Bayes (EB) and Random Forest (RF) regression, were developed to predict the frequency of flooding, a proxy for flood susceptibility, for three classes of historical storm events (light, moderate, and severe) in every road segment with surface depressions. Applying the models to Waze Data from 150 storms in the City of Dallas showed that depression catchment drainage area and imperviousness are the most important predictive features. The EB model performed with reasonable precision in estimating the number of PFF events out of 92 light, 41 moderate, and 17 severe storms with 0.84, 0.85 and 1.09 mean absolute errors, respectively. This study shows that Waze data provides useful information for highly localized PFF prediction. The superior performance of EB compared to the RF model shows that the historical observations included in the EB approach are important for more accurate PFF prediction.

Arefeh Safaei-Moghadam et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-77', Anonymous Referee #1, 26 Apr 2022
    • AC3: 'Reply on RC1', Arefeh Safaei-moghadam, 10 Jul 2022
  • RC2: 'Comment on nhess-2022-77', Anonymous Referee #2, 05 May 2022
    • AC4: 'Reply on RC2', Arefeh Safaei-moghadam, 10 Jul 2022

Arefeh Safaei-Moghadam et al.

Arefeh Safaei-Moghadam et al.


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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.